Evidence for a change in the rate of aging of osteological indicators in

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Evidence for a change in the rate of aging of osteological indicators in American documented skeletal samples Wendy Elizabeth Potter

Follow this and additional works at: https://digitalrepository.unm.edu/anth_etds Part of the Anthropology Commons Recommended Citation Potter, Wendy Elizabeth. "Evidence for a change in the rate of aging of osteological indicators in American documented skeletal samples." (2010). https://digitalrepository.unm.edu/anth_etds/54

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EVIDENCE FOR A CHANGE IN THE RATE OF AGING OF OSTEOLOGICAL INDICATORS IN AMERICAN DOCUMENTED SKELETAL SAMPLES

BY

WENDY ELIZABETH POTTER B.A. ANTHROPOLOGY, ARIZONA STATE UNIVERSITY, 1998 M.S. BIOLOGICAL ANTHROPOLOGY, UNIVERSITY OF NEW MEXICO, 2001

DISSERTATION Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Anthropology The University of New Mexico Albuquerque, New Mexico August 2010

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DEDICATION To my family, with love.

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ACKNOWLEDGEMENTS I would like to thank all of the members of my committee for their encouragement, support, and feedback throughout the course of my graduate studies and dissertation research. I am indebted to my committee chairs, Dr. Osbjorn Pearson and Dr. Jane Buikstra, for their invaluable comments and suggestions to better this project. Many thanks also to Dr. Keith Hunley and Dr. Heather Edgar, who provided constructive criticism of the project design and the manuscript. Finally, thanks to Dr. Edward Bedrick, who was instrumental in helping me properly interpret the outcome of the statistics used in the project. Thanks also to Dr. George Milner and Dr. Lyle Konigsberg. Dr. Milner kindly provided instructional materials for the Transition Analysis aging standard and a version of the ADBOU Age Estimator program. In addition, he provided helpful feedback on a poster I presented at the 78th annual meeting of the American Association of Physical Anthropologists, which reported preliminary results from this research. Dr. Konigsberg graciously provided assistance with the statistical package R (R Core Development Team, 2008), including basic code that was adapted for this project. This project could not have been completed without the assistance of collection curators at the following institutions: Dr. David Hunt, National Museum of Natural History at the Smithsonian Institution; Dr. Laura Fulginiti, Maricopa County Forensic Science Center; Dr. Heather Edgar, Maxwell Museum; Dr. Lee Meadows Jantz, University of Tennessee; and Lyman Jellema, Cleveland Museum of Natural History. My thanks to these individuals for providing both documentation of and access to the

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remains. Many thanks go to the staff of these institutions for their support, including Lara Noldner, Carmen Mosley, and Rebecca Wilson. Thanks also to Dr. Kristin Hartnett for her assistance with arranging access to the collection of pubes and ribs she procured at the Maricopa County Forensic Science as part of her dissertation project. I would like to thank the following agency for grant money supporting this research: the Student Research Allocations Committee of the Graduate and Professional Student Association at the University of New Mexico. I and deeply appreciative of the contributions my friends and colleagues have made toward bettering my dissertation, including scholarly discourse, statistical help, formatting, and editing/feedback. Thanks particularly to Andrew McQuade, Megan Rhoads, Shamsi Daneshvari, and Angie Evans. I am especially grateful for and indebted to my family, whose continued love and support throughout my graduate education has been an immense source of strength for me. I could not have completed this without my parents, Jean and Glen Potter, my siblings, Dr. Karen Cadman and Steve Potter, and my “Albuquerque parents,” Colonel and Dru Rhoads. Thanks to my computer support staff, Andrew McQuade, Steve Potter, and my brother-in-law, Caleb Cadman; no doubt this process would have been delayed without your help. Thanks also to Jessica Sullivan, who has supported me throughout this endeavor and is like a sister to me. Thanks to Skylar, who kept me company during the writing process and helped me keep my perspective. Finally, I want to express my gratitude to Andrew McQuade, who has provided unwavering emotional support and endured the hardships of living with a doctoral candidate.

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EVIDENCE FOR A CHANGE IN THE RATE OF AGING OF OSTEOLOGICAL INDICATORS IN AMERICAN DOCUMENTED SKELETAL SAMPLES

BY

WENDY ELIZABETH POTTER

ABSTRACT OF DISSERTATION Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Anthropology The University of New Mexico Albuquerque, New Mexico

August 2010

EVIDENCE FOR A CHANGE IN THE RATE OF AGING OF OSTEOLOGICAL INDICATORS IN AMERICAN DOCUMENTED SKELETAL SAMPLES

by

Wendy Elizabeth Potter B.A. IN ANTHROPOLOGY, ARIZONA STATE UNIVERSITY, 1998 M.S. IN BIOLOGICAL ANTHROPOLOGY, UNIVERSITY OF NEW MEXICO, 2001 DOCTORATE OF PHILOSOPHY IN ANTHROPOLOGY, UNIVERSITY OF NEW MEXICO, 2010

ABSTRACT

The question of uniformity in skeletal age changes across populations is fundamental to all comparative work in skeletal biology. Whether an aging standard will work on target groups that differ in time, space, and background from the reference sample is essential for reliable, accurate age estimation. This research addressed whether a difference in skeletal senescence exists between older American documented collections and more recent ones. The pubic symphysis, auricular surface, sternal rib end, and suture obliteration were scored for a sample of American Blacks and Whites drawn from the Terry, Hamann-Todd, Bass Documented, Maxwell Museum, and Maricopa County Forensic Science Center collections. The samples were divided into two groups: the Reference group included the Terry and Hamann-Todd samples, and the

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Recent group included the remaining three series. Differences between Reference and Recent groups were tested using proportional odds probit regression analysis and an analysis of deviance. Results indicated a significant difference in pubic symphyseal senescence between older Reference and more Recent American skeletal samples. No difference was found for cranial suture closure or the sternal rib end. Statistical problems and noteworthy critiques of auricular surface aging methods precluded an assessment of whether a difference between groups was present for this indicator. For the pubic symphysis, a slight deceleration of the rate of metamorphosis was reported for the Recent group, particularly for males and Whites. However, the broad age ranges associated with phases defined by most pubic symphyseal aging methods appear to mitigate this problem for forensic assessments of age at death. In contrast, paleodemographic and bioarchaeological analyses may be more greatly affected, as broad age ranges are often not desirable for such investigations. These results advance anthropologists’ current knowledge and understanding of the applicability and reliability of aging standards when used on skeletal samples differing in time, space, and composition from the reference sample, and impact how skeletal age estimations are interpreted in forensic anthropological, paleodemographic, and bioarchaeological investigations. Uncertainty as to the possible causes for the differences observed, whether due to secular change, sampling issues, observer bias, or environmental factors, provides an abundance of future research opportunities.

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Table of Contents DEDICATION ............................................................................................................. iii ACKNOWLEDGEMENTS..........................................................................................iv ABSTRACT .................................................................................................................vii LIST OF FIGURES....................................................................................................xiii LIST OF TABLES.......................................................................................................xv CHAPTER 1 INTRODUCTION...................................................................................1 INTELLECTUAL MERIT ...................................................................................................6 CHAPTER 2 HISTORICAL SETTING.......................................................................8 HISTORICAL CONTEXT OF THE DEVELOPMENT OF HUMAN SKELETAL COLLECTIONS IN THE UNITED STATES ............................................................................................................8 Summary................................................................................................................37 CHAPTER 3 THEORETICAL BACKGROUND AND LITERATURE REVIEW.39 ESTIMATION OF AGE AT DEATH ...................................................................................40 Development of osteological aging standards ........................................................41 Osteological aging standards ..............................................................................42 Cranial suture closure.....................................................................................45 Pubic symphysis.............................................................................................49 Sternal extremity of the fourth rib...................................................................54 Iliac auricular surface .....................................................................................55 Multiple-trait approaches................................................................................57 Transition analysis......................................................................................58 APPLICATION OF AMERICAN AGING STANDARDS TO TARGET GROUPS ...........................61 Cranial sutures ......................................................................................................61 Strengths and weaknesses ..................................................................................62 Pubic symphysis.....................................................................................................64 Strengths and weaknesses ..................................................................................68 Auricular surface ...................................................................................................76 Strengths and weaknesses ..................................................................................77 Fourth rib ..............................................................................................................79 Strengths and weaknesses ..................................................................................81 Transition Analysis ................................................................................................82 Strengths and weaknesses ..................................................................................83 CRITIQUES OF ESTIMATING AGE FROM THE ADULT SKELETON .......................................85 Inherent variation in the aging process ..................................................................85 Methodological problems.......................................................................................87 Statistical problems................................................................................................89 Regression toward the mean...............................................................................89 Age structure mimicry........................................................................................90 SUMMARY ..................................................................................................................96 ix

CHAPTER 4 RESEARCH DESIGN ..........................................................................99 MATERIALS ..............................................................................................................100 Reference Collections .......................................................................................... 101 Anatomical Collections ....................................................................................101 Hamann-Todd Collection (HTH)..................................................................102 Terry Collection (TC)...................................................................................103 Recent Collections ............................................................................................... 105 Documented Collections ..................................................................................105 Bass Collection (UTK) .................................................................................105 Maxwell Museum Collection (MMA) .......................................................... 106 Autopsy Collections......................................................................................... 107 Maricopa County Forensic Science Center autopsy sample (MCFSC) ..........107 DATA COLLECTION METHODS ..................................................................................108 Sample Selection Protocol ...................................................................................108 Dataset ................................................................................................................109 Data collection ....................................................................................................116 Pubic Symphysis.............................................................................................. 117 Phase/Stage Based Standards .......................................................................117 Transition Analysis ...................................................................................... 118 Auricular Surface ............................................................................................. 118 Phase/Stage Based Standard .........................................................................118 Transition Analysis ...................................................................................... 119 Sternal End of the Fourth Rib...........................................................................120 Phase/Stage Based Standard .........................................................................120 Ectocranial Suture Closure ...............................................................................120 Phase/Stage Based Standard .........................................................................120 Transition Analysis ...................................................................................... 121 Other data ........................................................................................................121 DATA ANALYSIS METHODS ...................................................................................... 121 Data preparation .................................................................................................121 Analytical methods............................................................................................... 124 Right versus left side morphology ....................................................................124 Intraobserver agreement ...................................................................................124 Descriptive statistics ........................................................................................ 125 RESEARCH QUESTIONS AND HYPOTHESIS TESTING ....................................................127 ASSUMPTIONS ..........................................................................................................134 Documented Skeletal Collections .........................................................................134 Age at Death ....................................................................................................135 Racial Designation ........................................................................................... 135 Osteological Aging Standards..............................................................................136 Reliability of Aging Standards .........................................................................136 Validity of Aging Standards .............................................................................137 LIMITATIONS ............................................................................................................138 Availability of Information ...................................................................................139 Issues of Sample Bias and Sample Representativeness .........................................139 SUMMARY ................................................................................................................141 x

CHAPTER 5 RESULTS............................................................................................ 143 PRELIMINARY DATA ANALYSIS .................................................................................143 Right versus left side morphology.........................................................................143 Intraobserver agreement ...................................................................................... 144 DATA ANALYSIS .......................................................................................................146 Descriptive statistics ............................................................................................ 146 Spearman’s Correlations ..................................................................................151 Spearman’s correlations between variables scored and age at death..............151 Spearman’s correlations between variables scored........................................152 Plots of stage versus age at death......................................................................153 Comparison of observed and expected values...................................................155 Agreement between observed and expected values...........................................156 Plots of differences between observed and expected values .............................. 158 Plots Highlighting Differences for the Entire Dataset ...................................158 Plots Highlighting Differences Among Skeletal Series .................................160 Plots Highlighting Differences Among Age Cohorts ....................................164 Identification of the best predictors of the difference between observed and expected phases................................................................................................ 167 Calculation of bias and inaccuracy ......................................................................170 Hypothesis Testing ............................................................................................... 172 Question 1........................................................................................................172 Reference versus Recent, sexes and races pooled .........................................179 Question 2........................................................................................................187 Method type .................................................................................................188 Indicator used............................................................................................... 188 Anatomical region of the indicator ............................................................... 190 Sex...............................................................................................................191 Race.............................................................................................................194 Sex-race category......................................................................................... 198 Time, by ten-year birth cohorts.....................................................................200 Adult Stature................................................................................................ 202 Synopsis.......................................................................................................204 Question 3........................................................................................................205 Standards supported by the anthropological literature...................................206 Pubic symphysis....................................................................................... 206 Auricular surface ...................................................................................... 208 Fourth Rib ................................................................................................ 209 Cranial sutures.......................................................................................... 209 Multiple indicators: pubic symphysis, auricular surface, and cranial sutures210 Standards selected for inclusion in the stepwise regression model ................211 Pubic symphysis....................................................................................... 213 Auricular surface ...................................................................................... 214 Fourth Rib ................................................................................................ 214 Cranial sutures.......................................................................................... 214 Multiple indicators: pubic symphysis, auricular surface, and cranial sutures214 Standards supported by other data ................................................................ 215 xi

Pubic symphysis....................................................................................... 215 Auricular surface ...................................................................................... 217 Fourth Rib ................................................................................................ 219 Cranial sutures.......................................................................................... 219 Multiple indicators: pubic symphysis, auricular surface, and cranial sutures221 Evaluation of Hypothesis 3: combining multiple lines of evidence ...............221 SUMMARY ................................................................................................................223 CHAPTER 6 DISCUSSION...................................................................................... 225 HYPOTHESES REVISITED............................................................................................ 225 DIFFERENCES IN AGING BETWEEN AMERICAN SKELETAL SAMPLES ............................. 226 INTERPRETATION ......................................................................................................227 Changes in the American political, social, cultural, and technological landscape 229 IMPLICATIONS ..........................................................................................................233 SUMMARY ................................................................................................................235 CHAPTER 7 CONCLUSIONS .................................................................................237 RECOMMENDATIONS.................................................................................................240 Forensic investigations ........................................................................................ 241 Bioarchaeological and paleodemographic analyses .............................................243 FUTURE RESEARCH ...................................................................................................244 SUMMARY ................................................................................................................245 REFERENCES CITED ............................................................................................. 250 APPENDICES ...........................................................................................................299 APPENDIX A: SPEARMAN’S CORRELATIONS ............................................................... 300 APPENDIX B: PLOTS OF DOCUMENTED AGE BY PHASE ................................................308 APPENDIX C: PLOTS OF THE DIFFERENCE BETWEEN OBSERVED AND EXPECTED PHASES BY YEAR OF BIRTH .........................................................................................................326 APPENDIX D: PLOTS OF THE DIFFERENCE BETWEEN OBSERVED AND EXPECTED PHASES BY SKELETAL SERIES ......................................................................................................332 APPENDIX E: PLOTS OF THE DIFFERENCE BETWEEN THE OBSERVED AND EXPECTED PHASE BY 10-YEAR BIRTH COHORT....................................................................................... 338 APPENDIX F: REGRESSION OUTPUT FOR IDENTIFICATION OF THE BEST DESCRIPTIVEVARIABLE PREDICTORS OF THE DIFFERENCE BETWEEN OBSERVED AND EXPECTED PHASES344 APPENDIX G: DESCRIPTIVE DATA FOR PHASES BY GROUP, SEX, AND RACE ..................356 APPENDIX H: AGE AT TRANSITION DATA BY GROUP ...................................................382 APPENDIX I: GRAPHS OF THE NUMBER OF INDIVIDUALS PER OBSERVED PHASE, BY REFERENCE AND RECENT GROUPS .............................................................................408 APPENDIX J: AGE AT TRANSITION DATA BY SEX ......................................................... 419 APPENDIX K: AGE AT TRANSITION DATA BY RACE ..................................................... 435 APPENDIX L: AGE AT TRANSITION DATA BY SEX-RACE CATEGORY ............................. 451

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List of Figures Figure 1: Number of skeletal remains in the dataset by series.......................................110 Figure 2: Number of Males and Females by Series ......................................................111 Figure 3: Number of Blacks and Whites by Series .......................................................112 Figure 4: Average Age by Collection...........................................................................112 Figure 5: Average Age at Death by Sex-Race Category ...............................................114 Figure 6: Average Year of Birth by Collection.............................................................115 Figure 7: Box plot comparing age at death by skeletal series........................................149 Figure 8: Box plot comparing year of birth by skeletal series .......................................150 Figure 9: Box plot comparing stature by skeletal series................................................150 Figure 10: Plot of observed Suchey-Brooks phase by age for Recent and Reference populations ..................................................................................................................154 Figure 11: Plot of observed Boldsen and colleagues’ symphyseal texture component score by age for Recent and Reference populations......................................................154 Figure 12: Plot of observed Boldsen and colleagues coronal-pterica suture closure component score by age for Recent and Reference populations....................................155 Figure 13: Plot exemplifying a tendency of the method to underestimate chronological age...............................................................................................................................159 Figure 14: Plot exemplifying a tendency of the method to overestimate chronological age ....................................................................................................................................159 Figure 15: Plot illustrating overestimation of age in the Terry and Hamann-Todd series (Reference)..................................................................................................................161 Figure 16: Plot illustrating overestimation of age in the Maricopa County sample (Recent).......................................................................................................................161 Figure 17: Example of an aging method that tended to underestimate age in the Maxwell sample .........................................................................................................................162 Figure 18: Plot of İşcan’s method, which tended to underage the Bass and Maricopa County series...............................................................................................................162 Figure 19: The Transition Analysis (COR) method tended to underestimate the age for all series ...........................................................................................................................163 Figure 20: Example of steeper trend line slopes were observed for all skeletal series ...163 Figure 21: Example of an aging standard that overestimates age in younger cohorts ....164 Figure 22: Example of an aging standard that underestimates age in older cohorts .......165 Figure 23: Example of an aging method that illustrates a shift from overaging to a value closer to zero (see specifically the 20-29 year age cohort)............................................165 Figure 24: Example of an aging method that illustrates a shift from underaging to a value closer to zero (see specifically the 70-79 and 80+ year age cohorts).............................166 Figure 25: Example of a difference in the rate of change, illustrating the Recent group’s slower rate of progression through stages.....................................................................174 Figure 26: Example of a difference in the rate of change, illustrating the Recent group’s faster rate of progression through stages ......................................................................175

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Figure 27: Example of no difference in the rate of change, but different ages at transition between the Reference and Recent groups ...................................................................176 Figure 28: Example of no difference in the rate of change or ages at transition between Reference and Recent groups.......................................................................................177 Figure 29: Example of methods with the majority of individuals classified into earlier phase scores.................................................................................................................183 Figure 30: Example of methods with the majority of individuals classified into later phase scores ..........................................................................................................................183 Figure 31: Closest approximation to a normal distribution of individuals classified into phases..........................................................................................................................184

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List of Tables Table 1: Number of skeletal remains in the dataset, by series and sex-race category ....110 Table 2: Sample Sizes by Age Cohort..........................................................................113 Table 3: Age at Death Statistics for Series in the Dataset .............................................113 Table 4: Average Birth Year for Sex-Race Groups by Series .......................................115 Table 5: Average Stature by Sex-Race Groups.............................................................115 Table 6: Correlation coefficients between age indicator and age at death .....................138 Table 7: The estimated concordance correlation coefficient, with 95% confidence limits ....................................................................................................................................143 Table 8: Agreement between first and second observation scores using the weighted Kappa statistic .............................................................................................................144 Table 9: Intraobserver agreement values across all aging methods, by skeletal series ...146 Table 4.10: Summary table of descriptive statistics for all variables in the dataset........147 Table 11: Descriptive statistics for continuous variables ..............................................148 Table 12: Agreement between observed and expected values using the weighted kappa statistic ........................................................................................................................157 Table 13: Summary of significant (α=0.05) predictors of calculated differences between observed and expected values (PE) by aging standard..................................................168 Table 14: Bias and inaccuracy values for traditional phase-based aging methods .........171 Table 15: Bias and inaccuracy values for Boldsen and colleagues’ Transition Analysis methods.......................................................................................................................171 Table 16: Analysis of deviance and improvement chi-square output: Total .................181 Table 17: Analysis of deviance output: Total ..............................................................182 Table 18: Recent sample’s rate of progression through morphological stages, compared to the Reference sample...................................................................................................185 Table 19: Summary of the significant and non-significant differences between groups for the tested osteological aging standards.........................................................................189 Table 20: Analysis of deviance and improvement chi-square output: Females.............191 Table 21: Analysis of deviance output: Females .........................................................192 Table 22: Analysis of deviance and improvement chi-square output: Males ................193 Table 23: Analysis of deviance output: Males .............................................................193 Table 24: Recent males rate of progression through morphological stages, compared to Reference males ..........................................................................................................194 Table 25: Analysis of deviance and improvement chi-square output: Blacks...............195 Table 26: Analysis of deviance output: Blacks ............................................................195 Table 27: Analysis of deviance and improvement chi-square output: Whites ..............196 Table 28: Analysis of deviance output: Whites ...........................................................197 Table 29: Recent Whites rate of progression through morphological stages, compared to Reference Whites ........................................................................................................197 Table 30: Analysis of deviance and improvement chi-square output: Sex-race categories ....................................................................................................................................198 Table 31: Analysis of deviance output: Sex-race categories ........................................199 Table 32: Recent White males rate of progression through morphological stages, compared to Reference White males ............................................................................199

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Table 33: Recent Black males rate of progression through morphological stages, compared to Reference Black males ............................................................................199 Table 34: Analysis of deviance and improvement chi-square output: 10-year birth cohorts.........................................................................................................................201 Table 35: Analysis of deviance output: 10-year birth cohorts ......................................202 Table 36: Analysis of deviance and improvement chi-square output: Adult stature .....203 Table 37: Analysis of deviance output: Adult stature ..................................................204 Table 38: Summary of strengths and weaknesses for each aging indicator from the literature ......................................................................................................................207 Table 39: Summary of aging standards and indicator components selected for inclusion in the regression model....................................................................................................211 Table 40: Summary of aging standards selected for inclusion in the regression model .213 Table 41: Summary of other data used to assess the reliability of age estimation methods ....................................................................................................................................216 Table 42: Summary of aging methods supported by the lines of evidence presented in this research .......................................................................................................................222

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Chapter 1 Introduction Biological anthropologists study human variation. Through the study of variation, norms are identified, sources of variability are assessed, and adaptations to change are revealed (Brant & Pearson 1994). In order to understand the evolutionary processes that have molded variation in our species, it is important that we be able to assess the age at death in past populations. Our ability to assess age at death from the skeleton is hampered by our lack of understanding of the aging process and how it varies and changes in space and time (Brant & Pearson 1994). So little is understood about human aging that Schmitt and colleagues (2002) consider the age at death assessment of adult skeletons one of the most difficult problems in forensic and physical anthropology. Since the turn of the 20th Century, life expectancy at birth of the total United States population has increased significantly from 49 to 77.5 years, (Shrestha 2006). In 1900, the median age in the United States was 24 years and the average life expectancy was 47 years; nearly a century later, the median age was 31.5 years and the average life expectancy had passed 75 (Spirduso 1995). Causes for this trend likely include lower activity levels, improved diet and living conditions, improved health care, and improved control of infectious diseases (Flegal et al. 1998; Armstrong et al. 1999; Jantz 2001; Shrestha 2006). However, these improvements in environmental conditions do not have a linear relationship with time; in their study of cranial asymmetry in American skeletal samples over the last 200 years, Kimmerle and Jantz (2005), suggest that fluctuations in environmental and material conditions as a consequence of slavery, the American Civil

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War and Reconstruction Period, and the Great Depression, likely account for this nonlinear relationship. The human lifespan is generally thought to have been shorter in the past than it is now (Aykroyd et al. 1999). Paleodemographic analyses of past populations suggest that young adult mortality was high and that few individuals lived past 50 years (Goldstein 1953; Brooks 1955; Weiss 1973; Ruff 1981; Mensforth & Lovejoy 1985). For example, Goldstein’s (1953) estimate for the average age at death at Pecos Pueblo, New Mexico is 43 years, Brooks (1955) reports mean ages at death for native California Indians at less than 30 years, and Lovejoy and colleagues (1977) estimate the mean life expectancy at the Libben site to be 20 years, with only a few individuals living beyond 40. Howell (1982) argues that the young average age at death estimated by Lovejoy and colleagues 1977) are not realistic, because many children would be orphaned and few adults would attain a lifespan long enough to become grandparents. In addition, according to Masset (1989), it is a myth that cemeteries in antiquity lack older individuals. In instances where historical documents accompany archaeological cemetery samples, parish records always include older individuals (Masset 1989). Aykroyd and colleagues (1999) agree, stating that historical evidence does not support the conclusion that individuals did not live past 50 years of age. One explanation for these discrepancies might be that a genuine difference exists between chronological age and skeletal age1, and these studies are using biological indicators to assess chronological age. Chronological age is the number of years lived from birth, and biological age is measured by the senescence of functions of the

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See Chapter 3.

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organism, which can be affected by lifestyle, environment, and genes (du Noüy 19372; Laugier 1955; Acsádi & Nemeskéri 1970). The morphology of skeletal indicators is a manifestation of biological age, and it is important to acknowledge that biological and chronological ages do not necessarily correspond (Laugier 1955). Another reason why historical records do not support the short lifespan suggested by skeletal analyses might be that older individuals are systematically under-aged (Mensforth & Lovejoy 1985) as a result of methodological biases and regression-based age estimation techniques employed by many anthropologists (Konigsberg & Frankenberg 1992; Skytthe & Boldsen 1993; Paine & Harpending 1998; Aykroyd et al. 1999). Alternatively, the age structure of the aging-standard reference population might be mirrored in the target population (Bocquet-Appel & Masset 1982). Such age mimicry3 might create the illusion that adult mortality rates are high and that the age at death estimates are low (Boldsen et al. 2002). Yet another possibility might be that the features used to estimate age vary in space and time as a result of genetic, socio-cultural, or ecological variation. With respect to this last issue, American reference sample-based skeletal ageestimation standards assume that the underlying biological basis of the age-indicator relationship is constant across populations, but this assumption has little empirical basis. Although researchers routinely apply American osteological aging standards to archaeological samples in the United States and abroad, recent studies have shown that

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Pierre Lecomte du Noüy, a French biologist and head of the division of biophysics at the Insitut Pasteur in Paris from 1922 until 1947, made contributions of mathematics to modern problems of biology and introduced the concept of a “biological time interval” specific to living. 3 See Chapter 3 for a more thorough discussion of this problem, as well as a brief summary of the ensuing debate in paleodemography.

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this approach leads to systematic error in age-at-death estimation (Todd 1921; Singer 1953; Brooks 1955; Gilbert & McKern 1973; Gilbert 1973; Pal & Tamankar 1983; Mensforth & Lovejoy 1985; İşcan et al. 1987; İşcan 1988; Katz & Suchey 1989; Brooks & Suchey 1990; Klepinger et al. 1992; Russell et al. 1993; Sinha & Gupta 1995; Galera et al. 1998; Nawrocki 1998; Baccino et al. 1999; Hoppa 2000; Oettle & Steyn 2000; Schmitt et al. 2002; Schmitt 2004; Kimmerle et al. 2008a; Sharma et al. 2008), often in the direction of overestimation of age for younger individuals and an underestimation of age for older individuals (Murray & Murray 1991, Russell et al. 1993, Osborne et al. 2004, Djuric et al. 2007, Hartnett 2007, Martrille et al. 2007, Berg 2008). The age-indicator relationship also no doubt varies as a function of biological factors like sex and ancestry. In addition, as a result of the tremendous socio-cultural change in our species over the past 10,000 years, it is likely that this age-indicator relationship has changed over time. Several studies have reported temporal changes in the osteological indicators used to assess age (Masset 1989; Bocquet-Appel & Masset 1995; Hoppa 2000), though support for such temporal change is not universal (Osborne et al. 2004). Spatially patterned socio-cultural and ecological variation may also contribute to substantial variation in this age-indicator relationship (Bocquet-Appel & Masset 1982; Angel et al. 1986; Murray & Murray 1991; Kemkes-Grottenthaler 1996); WittwerBackofen et al. 2008; Legoux 1966; Eveleth & Tanner 1976). These studies emphasize the limitations of using a spatially and temporally isolated American reference sample to estimate the ages of skeletal remains from different places and times. The impact of such temporal and spatial variation on age estimation is the focus of this thesis. The specific goal is to determine whether older American skeletal series

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progress through morphological age-related changes at a different rate than more recent ones, specifically for four commonly used osteological indicators of age: the pubic symphysis, auricular surface, sternal end of the fourth rib, and cranial sutures. This goal will be accomplished by testing multiple skeletal age estimation standards to address three specific questions: 1) is the observed morphology of the aging indicator associated with the same chronological ages for both older reference and more recent American skeletal populations; 2) is there a pattern that explains why some aging standards produce significant differences in the aging process of skeletal indicators between groups while others do not; and 3) which standard is the true gauge of whether a change in the indicator’s rate of aging has occurred if results from different age estimation standards for a single osteological indicator contrast? Skeletal remains used in this study are drawn from four documented American skeletal collections and a modern autopsy sample of pubic symphyses and rib ends; these collections are an ideal data source for this research because, with some caveats, the remains have known sex, age4, race5, and date of birth and/or death information. American Blacks and Whites of both sexes were drawn from the Terry Anatomical, Hamann-Todd Osteological, Bass Donated, Maxwell Museum Documented, and the

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A discussion of the issues regarding the reliability of the documented ages for the Terry and HamannTodd collections is presented in Chapter 3. 5 At this point, I must clarify the term “race” as it is used in this manuscript. The current view of many physical anthropologists, including myself, is that human variation is best described as global geographic variation in physical features that follow gradations or clines for a given trait. Race, as defined here by such terms as Black and White, represent social constructs, not a biological reality. These categories are often strongly correlated with social and environmental influences like class, diet, living conditions, health, activity level, and other factors. The terms Black and White were chosen to describe these social categories because this is the vocabulary used in each skeletal series’ database to describe individuals within the collection. It is understood that these terms may carry undertones that are traditionally associated with the popular conception of races as distinct biological groups, but this is not the context in which I am using them. It is also understood that the terms Black and White are not applied in the same manner through time or by different individuals. See additional discussion in Chapter 4.

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Maricopa County Forensic Science Center autopsy collections. These collections comprise skeletal remains collected from autopsy, cadaver, unclaimed, and donated bodies. Analyses are limited to Blacks and Whites because these documented collections have relatively few individuals from other groups. Data collection for this study includes demographic information consisting of age, sex, race, adult stature, year of birth, and year of death, as well as phase/stage or component data for four skeletal morphological indicators of age: the pubic symphysis, auricular surface, sternal end of the fourth rib, and cranial sutures. Established American osteological aging methods, specifically Todd (1920), Suchey and Brooks (1990), Hartnett and Fulginiti (2007), Lovejoy and colleagues (1985b), İşcan and colleagues (1984, 1985, 1986), Meindl and Lovejoy (1985), and Boldsen and colleagues (2002), standardize the scoring of the morphological data.

Intellectual merit Estimating age is a critical part of the study of skeletons from archaeological and forensic contexts, but despite an abundance of research on the estimation of age from the adult skeleton, improvement is still needed, specifically regarding the disclosure of the accuracy and precision of estimates. Many current aging standards are based on older reference skeletal samples, and some authors have argued that these standards and reference collections are outdated due to secular changes in overall body size, health, activity, and nutritional status. Whether an aging standard will work on target groups that differ in time, space, and background from the reference sample is essential for reliable, accurate age estimation. This dissertation will address whether a difference in the skeletal aging process exists between older American documented collections and more recent ones. This issue 6

is particularly important for disciplines requiring accurate and reliable estimations of age based on skeletal remains, whether medicolegally significant or archaeological. It is important to note, however, that the implications of this research differ for bioarchaeological and forensic anthropological applications. Although these two fields both rely upon estimates of age, bioarchaeological studies operate at the population level, exploring the impact of foreign pathogens on aboriginal/native populations, the demography of past populations, comparative research on human life span and life expectancy, among other endeavors. If an age estimation method is unbiased6 for that population, the impact of inaccuracy7 in age estimates is less of a concern than it would be for any one individual. In contrast, forensic examinations are concerned with the estimation of age on an individual level; these age estimates need to be both precise8 and accurate.9 These conditions must be satisfied to meet the Daubert Criteria for admission as scientific evidence in court. In addition, recent critiques of the forensic sciences outlined by the National Academy of Sciences are driving changes in forensic anthropology, including the need to identify and report the accuracy and error rates in age estimation, as well as sources of potential bias and human error (Committee on Identifying the Needs of the Forensic Sciences Community, National Research Council 2009).

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Bias = Σ(estimated age-chronological age)/number of individuals. Bias is directional, so the sign is important; bias is informative for the identification of systematic over- or under-estimation of age at death. 7 Inaccuracy = Σ|(estimated age-chronological age)|/number of individuals. 8 For age estimation, precision is defined as close to the actual chronological age. 9 Here, accuracy is defined as obtaining similar age estimates for a set of remains over many trials.

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Chapter 2 Historical Setting Ubelaker and Grant (1989) state that skeletal collections are essential for teaching anatomy and human variation, as well as learning about the medical and biological aspects of human history. The development of human skeletal collections in the United States is deeply rooted within the framework of the birth and progress of American physical anthropology, bioarchaeology, and forensic anthropology. With respect to these disciplines, prevailing cultural belief systems and physical anthropology’s “long peripheral relationship to medicine” (Stewart 1979, p. xi) strongly influenced their courses. A synopsis of the development of formal, well-documented human osteological collections in the United States follows.

Historical context of the development of human skeletal collections in the United States According to Walker (2000), the practice of collecting human skeletal remains as war trophies and for religious purposes has deep roots. In the past, Native American and Melanesian groups took heads, scalps, and other body parts during warfare (Driver 1969; Olsen & Shipman 1994; Owsley et al. 1994; White & Toth 1991; Willey & Emerson 1993), and this practice has taken the form of trophy skull collection by soldiers in more recent societies (Sledzik & Ousley 1991; McCarthy 1994; Walker 2000). Prior to the collection of human skeletal remains for scientific research, skulls and bones were often placed on display in cabinets of curiosities and at local historical societies (Quigley 2001). Oddities, pathological bones, and other skeletal remains continue to draw the

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attention of the public, as evidenced by the success in the 20th Century of Robert Ripley’s Believe it or not!, which displays trophy and trepanned skulls. In the United States, historical associations such as the Library Company of Philadelphia (1731), established by Benjamin Franklin (1706-1790) and others, began to maintain collections that included anatomical specimens; a contemporary collection of anatomical models and human skeletons was also established in Philadelphia at the Pennsylvania Hospital (Orosz 1990; Walker 2000). The German anatomist Johann Friedrich Blumenbach (1752-1840) is considered to be the founder of modern physical anthropology (Jarcho 1966; Cook 2006). Blumenbach was interested in the typological characterization of American Indian, and he tested models of human variation using craniology, or observations of crania (Cook 2006). Based on the crania he had collected and studied, Blumenbach wrote De generis humani varietate nativa (On the Natural Varieties of Mankind), which was first published in 1775. This volume presented a five-race schema: Caucasian, Mongolian, Ethiopian, American, and Malayan. He believed that these groups were different due to degeneration, a concept positing that a single original type accommodated to varying local conditions (Cook 2006). Blumenbach's work also included his description of over sixty human crania, published in Decas prima collectionis sua craniorum diversarum gentium illustrata (1790) and Nova penta collectionis sua craniorum diversarum gentium (1828). Several of the cranial descriptions presented were those of American Indian crania; as a result of his studies, Blumenbach recognized the Asian affinities of Eskimo and Aleut groups (Harper & Laughlin 1982).

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In the United States, Thomas Jefferson (1743-1826) conducted the first systematic study and collection of human remains. Jefferson examined ancient American Indian skeletal remains excavated from burial mounds on his property in Virginia (Jefferson 1853); prior to this, skeletal remains were collected without documentation as to provenience, and they were kept as curiosities (Quigley 2001). In contrast, Jefferson’s work has been commended for its scientific methodology, as well as its consideration of the archaeological context of the remains in Jefferson’s interpretations (LehmannHartleben 1943; Willey & Sabloff 1980; Buikstra 2006). Population numbers and age distributions were also important in Jefferson’s investigations (Buikstra 2006). Scientists in the 19th Century also showed an interest in ancient American Indian crania (Buikstra 2006). During this time, the study of human remains was being integrated into the investigations of living populations. Much of early work on skeletal remains in the United States focused on the diversity and origins of indigenous groups of the Americas. As in Europe, there was a fixation on cranial variation, and this affected what skeletal elements researchers collected from the field; as a result, postcranial remains were ignored for some time. John Collins Warren (1778-1856), a surgeon and anatomist in Boston, was inspired by Blumenbach’s works (Hrdlička 1918; Jarcho 1966). Warren had an interest in comparative anatomy of the brain and its relation to race, which lead to the collection of crania and his focus on craniology (Warren 1822; Hrdlička 1918; Jarcho 1966). In 1822, Warren published his book Natural History of the Nervous System, which included an appendix on the crania of American Indians (Warren 1822). In this text, he implied that Old World immigrants were the builders of sophisticated structures in the New

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World, not American Indians (Warren 1822; Cook 2006). Later, he published his conclusion that occipital flattening was the result of cultural modification (Warren 1837). In 1847, he established the Warren Anatomical Museum at the Harvard Medical School (Jarcho 1966); the museum included crania Warren had personally collected, as well as crania collected by the Boston Phrenological Society between 1832 and 1842 (Bowles 1976; Cook 2006). Samuel Morton (1799-1851), a physician and anatomy professor in Philadelphia, also sought to examine and classify the variation present among populations through craniometry (Quigley 2001). Morton was strongly influenced by his education at the University of Edinburgh, where he was immersed in the theories in vogue at the time: polygenism and the hereditarian views of phrenologists (Spencer 1983; Walker 2000; Quigley 2001). As a result, Morton began collecting human skeletal remains, which consisted solely of crania (Buikstra & Gordon 1981). Morton’s work followed Blumenbach’s craniological tradition but took on an impartial view of phrenology (Morton 1839; Hrdlička 1918; Cook 2006; Buikstra 2009). As Buikstra (2009) underscored, phrenologists collected skulls with the goal of investigating character, while Morton focused on context and culture: a decidedly ethnographic goal. To test the theory of a hierarchical ranking of humans, Morton began collecting skulls from all over world in the 1820s. During the course of his work, Morton amassed a large collection of approximately 900 human skulls from archaeological contexts (Morton 1849; Hrdlička 1914; Thomas 2000; Buikstra 2006), which is now curated at the University of Pennsylvania. However, unlike Thomas Jefferson, Morton did not have provenience data for the remains; the crania he studied lacked spatial and temporal

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contextual information, which undermined his ability to support his conclusions (Buikstra 2006). Other limitations of Morton’s collection included incomplete sampling at archaeological sites and acquisition bias, as only adult crania were collected (Wilson 1876; Buikstra 2006). Morton’s research on crania of aboriginal groups in the Americas culminated in the publication of Crania Americana in 1839 (Morton 1839). Crania Americana included detailed illustrations of crania, defined cranial measurements, presented Morton’s descriptions of Indian skulls, and discussed ethnohistoric, archaeological, and cultural contexts (Morton 1839; Buikstra 2009). The work also outlined his conclusions that all American Indians were descended from a common stock and that the Moundbuilders were indeed Indian, not European; this last point was a response directed at Warren’s claims. Although Morton’s work has been criticized as racist (Gould 1978a, 1978b, 1996), it actually presented data supporting the capability of Native Americans to build complex structures that were at the time assumed to be the work of Old World groups (Morton 1839; Stanton 1960; Silverberg 1968; Buikstra 1979; Buikstra 2006). Additionally, Buikstra (2009) has stressed that Morton’s classification of humans was based on both ethnographic data and the measurement of crania, a combination that was novel at the time. In fact, his research was so influential that Hrdlička (1918) stated physical anthropology in the United States began with Morton, and Wissler (1942-1943) named him the father of American physical anthropology (Jarcho 1966); however, most physical anthropologists today reserve that distinction for Aleš Hrdlička. The collection of human crania for the pursuit of exploring the origins of extant populations was not limited to North America. Paul Broca (1824-1880), the French

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founder of physical anthropology, built upon Morton’s concept of anthropology and professionalized the discipline (Cook 2006). Broca, like other scientists of the time, had an interest in exploring the origins of races and collected skulls from world travelers in order to further explore his interest in the origin of races. Broca’s work advanced craniometry by the science of cranial anthropometry by developing new measurement tools and measurement indices. During the mid-19th Century, the social, cultural, and political climate changed drastically in the United States. By the start of the third decade, the Industrial Revolution had begun, bringing with it an expedited exploration and settling of the American West, the development of railroads, the expansion of mining, the rise of philanthropy, the increased governmental support of science and exploration, and the establishment of new museums (Jarcho 1966). These changes had an enormous impact on the collection and curation of human skeletal remains, which fostered the development of physical anthropology in the United States. Walker (2000) notes that, at this time, large public natural history museums were established with the dual goals of popular education and scholarly research (Orosz 1990). Recognizing that skeletal collections were a valuable resource for providing information about the past, these newly founded museums provided an institutional framework within which large skeletal collections could be consolidated from smaller private collections. These museums had enough resources to support a staff of research scientists, purchase skeletal remains from private collectors, and sponsor archaeological expeditions (Buikstra 2006). From the perspective of collections of human skeletal remains, many important natural history museums were established in the United States during this time, including

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the Smithsonian Institution (1846), the Army Medical Museum (1862: now the National Museum of Health and Medicine), the Harvard Peabody Museum of Archaeology and Ethnology (1866), the American Museum of Natural History (1869), the Columbian Museum of Chicago (1893: now the Chicago Field Museum), the Lowie Museum of Anthropology (1901: now the Phoebe Hearst Museum), and the San Diego Museum of Man (1915) (Walker 2000). The Smithsonian Institution’s National Museum of Natural History, established in 1846, became the storehouse of skeletal remains from most federally funded excavations and eventually acquired large collections, like the Huntington and Terry collections, from other institutions when their curation of the remains were terminated (Quigley 2001). The Smithsonian Institution’s history is intertwined with other museums and numerous researchers in the 19th and 20th Centuries; as a result, its development will be dispersed throughout the remainder of this section of the dissertation. The 1860s alone saw the establishment of three museums associated with the collection, study, and curation of human skeletal remains in the United States: the Army Medical Museum, the Harvard Peabody Museum of Archaeology and Ethnology, the American Museum of Natural History. The Surgeon General William Hammond founded the Army Medical Museum in 1862, with J.S. Billings serving as its first curator (Quigley 2001; Buikstra 2006). The museum began as a repository for thousands of medical records and skeletal and soft tissue remains obtained during the treatment and autopsy of military casualties during the American Civil War (Barnes et al. 1870; Otis & Woodward 1865; Walker 2000; Buikstra 2006). At the close of the Civil War, army doctors shifted their focus toward collecting activities toward American Indian crania, as

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requested by George Otis (Bill 1862; Parker 1883; Wilson 1901; Lamb 1915) to further anthropological research (Henry 1964). As a result, over 2200 American Indian crania were collected. In 1869, as per an agreement between Otis and Joseph Henry of the Smithsonian, the Army Medical Museum transferred its ethnological and archaeological holdings to the Smithsonian, and in return received human skeletal remains possessed by the Smithsonian’s Division of Mammals (Hrdlička 1914; Henry 1964; Walker 2000; Buikstra 2006). Later, nearly all of the human skeletal remains curated by the Army Medical Museum would be transferred to the Smithsonian Institution’s new Division of Physical Anthropology. Between 1898 and 1904, about 3500 skeletal remains were transferred; the Army Medical Museum retained only those remains of pathological significance (Lamb 1915: Sledzick & Barbian 2001). The Harvard Peabody Museum of Archaeology and Ethnology was established in 1866 (Quigley 2001); Jeffries Wyman was the museum’s first curator. Wyman was a physician and anatomist (Jarcho 1966) who reported on disease observed in bones exhumed from mounds and caves in the southeastern United States (Wyman 1871). As curator, Wyman implemented a protocol for the systematic curation of human skeletal remains, which included important provenience data and the temporal origin of the bones (Wyman 1868; Buikstra 2006). Wyman studied postcranial remains (El-Najjar & McWilliams 1978), initiating a paradigm shift in physical anthropology, such that the emphasis of study extended beyond the cranium (Wyman 1869). In addition, he questioned race-based craniology, another prevailing thought of the time (Wyman 1871). Frederick Ward Putnam (1839-1915) was Wyman’s successor (Jarcho 1966), and the

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founder of the department of physical anthropology at Harvard University. Collections at the Peabody Museum grew quickly due to acquisition of donations from many smaller museums, local societies, and medical schools, as well as the Smithsonian’s holdings of American Indian remains from burial mounds in Florida, Kansas, and Ohio (Jarcho 1966). In the 1920s, the Peabody acquired over 2000 remains from Pecos Pueblos excavated by Alfred Kidder. The museum also curates over 3100 remains from northern Mexico and another 525 from Egypt. Dr. Albert Bickmore founded the American Museum of Natural History in New York in 1869 (Orosz 1990; Quigley 2001). The museum includes collections of human skeletons from around the globe, including the southwestern United States. Large collections curated there include a morphology collection from dissected cadavers, ancient western and eastern Inuits, and the von Luschan Collection, which is comprised of 277 black African skulls (Krogman & Iscan 1986; Schwartz 1998). Both the Army Medical Museum and the Harvard Peabody Museum of Archaeology and Ethnology held human remains from the famed Hemenway Southwestern Archaeological Expedition (1887-1888). Frank Hamilton Cushing (18571900), an ethnologist at the Smithsonian Institution, spent years living among the Zuni of New Mexico (Cushing 1890; Hinsley & Wilcox 1996, 2002). His experience there sparked his interest in Zuni ancestors, leading him to propose an archaeological investigation. The Hemenway Expedition was named after Mary Hemenway, the sponsor of Cushing’s excavations at Los Muertos in Arizona (Cushing 1890; Matthews et al. 1893; Hinsley & Wilcox 1996, 2002; Merbs 2002). During his investigation, Cushing applied his ethnographic knowledge to interpret the past (Cushing 1890; Hinsley &

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Wilcox 1996, 2002). Washington Matthews (1843-1905), the surgeon heading the United States Army Medical Museum in Washington, DC, was sent to visit the excavations in August of 1887 due to Cushing’s poor health (Matthews 1900; Haury 1945; Buikstra 2006). In November of the same year, Dr. Herman ten Kate, hired by Cushing earlier that spring, arrived to oversee the excavations, preserve the skeletal material, and analyze the human remains (ten Kate 1892; ten Kate & Hovens 1995). As a result of Matthew’s month long visit, he sent his colleague, anatomist Dr. Jacob Wortman, to assist ten Kate with the conservation of the friable skeletal remains (ten Kate & Hovens 1995; Buikstra 2006). Approximately 5000 skeletal remains were recovered from Los Muertos throughout the course of the expedition (Matthews et al. 1893; Merbs 2002; Buikstra 2006). Wortman returned to the Army Medical Museum with the Hemenway Expedition skeletons in 1888 (Matthews et al. 1893; Lamb 1915; Merbs 2002), and the final report, published in 1893, included a discussion of age using a scheme following Broca’s six-stage periods of life (Broca 1875; Matthews et al. 1893; NAS 1893). The cremains recovered during the Hemenway Expedition excavations, however, were originally sent to the Peabody Museum of Salem, before being transferred to the Peabody Museum of Harvard (Haury 1945). While at Harvard University, Emil Haury (1904-1992) studied the cremains for his dissertation research (Haury 1945); Earnest Hooton encouraged Haury’s research on this topic, because he saw the potential value in analyzing burned and fragmentary skeletal remains (Haury 1945). Another medical doctor, Joseph Jones (1833-1896), collected and studied ancient skeletal remains in the United States during the latter half of the 19th Century. Jones excavated remains from stone box graves, earthworks, and mounds from Tennessee and

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Kentucky, and he played a pivotal role in how syphilis was diagnosed from skeletal remains (Jones 1869, 1876); according to Jarcho (1966), Jones is the first to study preColumbian skeletal remains from the viewpoint of disease. Jones’ continued interest in archaeology led him to investigate shell mounds of the Deep South (Jones 1878). His work emphasized the importance of the archaeological context and temporal place of skeletal remains, allowing for conclusions to be made about the presence of non-venereal syphilis in the pre-Columbian New World (Jones 1878). A decade after his death, Jones’ collection of skulls and artifacts was bought by the Heye Foundation/Museum of the American Indian, but has since been deaccessioned and dispersed (Williams 1932; Buikstra 2006). In addition to the continued excavation and collection of human skeletal remains from archaeological contexts, the second half of the 19th Century saw the first systematic collection of skeletons from medical school cadavers. From 1886 to 1924, George Huntington (1861-1927) was an anatomy professor at the College of Physicians and Surgeons in New York (Quigley 2001). In 1893, Huntington began preserving skeletons from cadavers dissected at the medical school rather than disposing of them (Hrdlička 1937). The cadavers were primarily European immigrants and residents of New York, whose bodies were unclaimed and became the property of the state; cadavers were also acquired from sanitariums, poorhouses, and morgues (Hrdlička 1937; Quigley 2001). This collection, which contains over 3800 skeletons and includes examples of trauma and pathology, is now housed at the Smithsonian Institution. Huntington’s work strongly influenced Robert Terry, T. Wingate Todd, and Aleš Hrdlička.

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The late 19th Century also gave rise to pioneers in forensic anthropology (Stewart 1979; Klepinger 2006). Thomas Dwight (1843-1911) is considered to be the father of American forensic anthropology (Stewart 1979). Dwight, who was primarily concerned with human variability, was the first American to make major contributions to the field, participating in an unknown number of forensic cases (Warren 1911; Stewart 1979). The history of forensic anthropology was marked by Dwight’s 1878 medicolegal essay; while other contemporary anatomists were studying human skeletons, only Dwight did so with the intention of applying that knowledge to forensic matters (Stewart 1979). Dwight held the Parkman Professorship of Anatomy at Harvard, succeeding Oliver Wendell Holmes (1809-1894) (Warren 1911; Stewart 1979; Klepinger 2006) who had, along with Jeffries Wyman, presented skeletal evidence during their testimony at John Webster’s 1850 trial for murdering Harvard benefactor George Parkman. Another pioneer of American forensic anthropology was George Dorsey (18691931); Dorsey studied anthropology at Harvard and was influenced by the teachings of Dwight (Stewart 1979). Dorsey succeeded William Holmes as the curator of the Field Museum in Chicago (Cole 1931; Stewart 1979). At the trial of Luetgert, the German immigrant who was accused of killing his wife and disposing of her remains in a vat, Dorsey testified as prosecution’s star witness, identifying the bones recovered as those of a human female (Giles & Klepinger 1999; Loerzel 2003: Klepinger 2006). Because he was the first anthropologist to testify in an American criminal trial, Klepinger (2006) argues that Dorsey was the first “full-fledged” forensic anthropologist. After the Luetgert murder trial, Dorsey gave a lecture on “The skeleton in medico-legal anatomy” to the

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Medico-Legal Society of Chicago (Wigmore 1898; Dorsey 1899), further establishing the discipline. At the turn of the 20th Century, American scientists continued to be fueled by an interest in the origins of aboriginal Americans and human skeletal variation. Franz Boas (1858-1942) was a founder of American anthropology, and his research in physical anthropology focused on anthropometrics, osteometrics, race, racial origins, and race equality, environmental influences and plasticity of the body, human growth, and the development of children (Rankin-Hill & Blakey 1994; Walker 2000; Little & Sussman 2010). Boas collected anthropometric data and skeletal remains from American Indians between 1888 and 1903 (Jantz et al.1992; Quigley 2001), and amassed approximately 200 crania and 100 skeletons that were curated at the American Museum of Natural History in New York until he moved to Columbia University in 1899. The collection was transferred to the university where these and other skeletal remains were used for teaching and research. Boas used the collection to stress the biological equality of races by showing the cranial index varied widely within groups (Gould 1996; Thomas 2000; Quigley 2001). A strong challenger of hereditarian explanations of human variation, Boas illustrated the plasticity of the human cranium in response to environmental change (Boas 1912). Aleš Hrdlička (1869-1943) immigrated from Humpolec, Bohemia, to the United States with his father shortly after finishing high school at the age of 12 (Shultz 1945). Hrdlička eventually earned two medical degrees, the first from the Eclectic Medical College of the City of New York and the second from the New York Homeopathic Medical College. He then accepted an internship at the State Homeopathic Hospital for

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the Insane in Middletown, New York, where he began his research in anthropometry (Hrdlička 1895; Shultz 1945). He then became an Associate in Anthropology at the Pathological Institute of the New York State Hospitals, a position that permitted him to travel to Europe where he studied anthropology, physiology, and medico-legal topics under Manouvrier, Bouchard, and Brouardel, respectively (Schultz 1945). Hrdlička’s early work focused on “abnormal” individuals from inmates of state institutions and hospitals for the mentally ill; through this experience, he recognized the lack of adequate comparable data on “normal” persons and the need for that information (Schultz 1945). He intensively studied Huntington’s collection before leaving the Pathological Institute to join expeditions studying medical and physical anthropology sponsored by the American Museum of Natural History. In 1897, the United States National Museum at the Smithsonian Institution underwent a reorganization process, and archaeologist William Henry Holmes became head curator of the Department of Anthropology; Holmes created a Division of Physical Anthropology in 1903, which was headed by Hrdlička (Schultz 1945; Jarcho 1966; Quigley 2001; Buikstra 2006). According to Armelagos and van Gerven (2003), a lack of funding prevented Hrdlička from attaining his primary goal, which was to establish an institute of biological anthropology similar to that of Broca’s in France. As a result, Hrdlička was motivated to establish the Smithsonian's National Museum of Natural History as a major research institution (Armelagos & van Gerven 2003). During his appointment at the Smithsonian Institution, Hrdlička’s research and goals focused on the determination of the range of normal variation for humans, the collection and

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preservation of skeletal remains, and the investigation of the origins of ancient American Indians in the New World (Stewart 1940; Schultz 1945; Buikstra 2006). Hrdlička brought Huntington’s collection to the Smithsonian Institution; this collection was the source of Hrdlička’s inspiration for his earliest anthropological investigations (Jones-Kern 1997). Approximately 1200 of these skeletons arrived at the Division of Physical Anthropology, sorted by skeletal element, not by individual; Hrdlička had to first organize the remains, then catalogue them. This experience influenced Hrdlička, who advocated better accession procedures for record keeping (Jones-Kern 1997). Hrdlička was also instrumental in the recovery of archaeological remains and the creation of osteological research collections. In 1910, Hrdlička collected over 3000 skulls of Peruvian Indians, and in 1926, he began his expeditions to Alaska, which resulted in the collection of a large number of Aleutian, Indian, and Eskimo skeletal remains. Hrdlička also collected anthropometric data and skeletal remains from the North American desert west (Hrdlička 1908b, 1909d, 1935b; Rakita 2006). In his studies of early New World sites inhabited by American Indians, he carefully documented skeletal remains in situ, using stratigraphical evidence to support temporal antiquity (Hrdlička 1907b; Buikstra 2006). Hrdlička traveled the world in search of skulls (Quigley 2001), which he measured and cataloged; in addition, he procured skeletal remains from other travelers (Hrdlička 1904). Unfortunately, his goal was to amass a very large collection, rather than one composed of fewer remains with contextual information. During his forty-year tenure as curator, Hrdlička built up the collection from 3,000 skeletal remains acquired from the Army Medical Museum to over 15,000 skulls (Lamb 1915; Schultz 1945); in

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addition he recovered skeletal remains that now form part of the collections at the San Diego Museum of Man (Quigley 2001). The founding of the San Diego Museum of Man was the result of the Panama-California Exposition in 1915, which featured the story of man through the ages. Hrdlička coordinated expeditions to Africa, Alaska, Philippines, Siberia, and personally went to Mongolia and Peru to gather remains for the Exposition (Merbs 1980). Today the museum has grown to include southern California archaeological material (La Jolla), the Stanford-Meyer osteopathology collection, and the Hrdlička paleopathology collection (Quigley 2001). Hrdlička is considered to be the father of American physical anthropology (Krogman 1976; Quigley 2001). In 1918, he founded and became the editor of the American Journal of Physical Anthropology; twelve years later, he founded the American Association of Physical Anthropologists. His research marked the transition from the old dogma of single-specimen description toward a study of entire societies and samples (Jarcho 1966). Other significant contributions included the recognition of the importance of all bones, ages, races, and sexes to improving the science of physical anthropology (Hrdlička 1904); his focus was on the description of normal variation, rather than explaining how or why (Schultz 1945). However, the field of physical anthropology benefited significantly from his prolific research and publications. In addition to the origins of American Indians (Hrdlička 1907b, 1912a, 1912b, 1917, 1926, 1931b, 1941), Hrdlička also published on collections management (Hrdlička 1900), excavated skeletal remains (Hrdlička 1908c, 1909a, 1909b, 1909c, 1910, 1912c, 1913), standards for data collection (Hrdlička 1904, 1907a, 1920, 1939), variation in the skeleton and dentition

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(Hrdlička 1908d, 1934, 1935a), physical anthropology (Hrdlička 1908a, 1914, 1918), and catalogues of NMNH collections (Hrdlička 1924, 1927, 1928, 1931a, 1940, 1942, 1944). Like Hrdlička, Ernest Hooton (1887-1954) was influenced by the mainstream evolutionary approach, which emphasized a typological, craniometrically oriented framework that focused on biological determinism and taxonomic description rather than functional interpretation (Walker 2000; Quigley 2001). However, Hooton was a proponent of integrating physical anthropology, archaeology, and ethnology (Hooton 1935). He was classically trained, not a doctor of medicine, and marked the first generation of anthropologically trained biological anthropologists in the United States (Quigley 2001; Cook 2006). In 1913, Hooton founded the Harvard program in anthropology, which was the first major physical anthropology training program in the United States. Hooton also participated in the recovery of archaeological remains, including those at Alfred Kidder’s Pecos Pueblo site (Kidder 1924; Hooton 1925), and later the Pecos skeletal remains, classifying the crania into one of eight morphological types (Hooton 1930; Thomas 2000). Hooton was asked by law enforcement agencies to identify skeletal remains, but he was not a significant contributor to the development of forensic anthropology (Stewart 1979); in fact, Hooton (1943) did not believe that physical anthropology had contributed much to the improvement of methods of individual identification. Hooton was instrumental in establishing academic anthropology at Harvard University; as a result, he mentored dozens of prominent mid-20th Century physical anthropologists, including Harry Shapiro, J. N. Spuhler, J. Lawrence Angel, Alice Brues, Sherwood Washburn, C. Wesley Dupertuis, Stanley Garn, W.W. Howells, and Frederick

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Hulse, among others (Giles 1977; Shapiro 1981; Garn & Giles 1995; Ubelaker 2006a). Hooton’s influence in this realm was so extensive that, for decades, his academic progeny staffed most of the programs in physical anthropology at other American universities (Thomas 2000); he also trained seven of the eight presidents of the American Association of Physical Anthropologists serving from 1961-77 (Armelagos & van Gerven 2003). In

contrast, Hrdlička only formally trained one student, T. Dale Stewart; this was primarily because he was not affiliated with a university (Stewart 1979); instead, Hrdlička’s focus was on building the Division of Physical Anthropology in the National Museum of Natural History. As discussed previously, Hrdlička had a profound influence on physical anthropology in the United States, both promoting and professionalizing the discipline. Boas, Hrdlička, and Hooton were all instrumental in the founding of American physical anthropology. At the same time other scientists, medical doctors, and anatomists were also contributing to the discipline, specifically through the collection of large numbers of entire human skeletons. Following in the tradition of Huntington’s collection of skeletons, Carl August Hamann, Thomas Wingate Todd, and Robert James Terry amassed large collections during the early 20th Century from anatomy school dissecting rooms and institutional morgues (Thompson 1982). Carl August Hamann (1868-1930) was a surgeon in Philadelphia prior to becoming a professor of anatomy in 1893 at Western Reserve University in Cleveland, Ohio (Quigley 2001). Upon his arrival, Hamann began collecting remains in order to assemble an anatomical teaching museum. He collected both human and non-human skeletal remains. By 1912, when he was appointed the dean of the medical school, Hamann had amassed over 100 human skeletons originating from dissected medical

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school cadavers (Thompson 1982; Moore-Jansen 1989; Quigley 2001; Kern 2006). Thomas Wingate Todd (1885-1938) left his position at the anatomical department at the University of Manchester, succeeded Hamann, and assumed responsibility of the collection (Thompson 1982; Moore-Jansen 1989). Todd continued to collect human skeletons, cataloguing the unclaimed bodies obtained from the Cuyahoga County Morgue or city hospitals in Cleveland, Ohio (Lovejoy et al. 1985a; Quigley 2001; Kern 2006). The cadavers were measured, weighed, and photographed; after being dissected, the cadavers were macerated for inclusion into the skeletal collection (Quigley 2001). The collection grew significantly during Todd’s tenure at Western Reserve Medical School (Jones-Kern 1997); after his death in 1938, the collection eventually fell into disarray. In 1951, the medical school transferred the collection to the Cleveland Museum of Natural history, where it remains on permanent loan. At the start of the 20th Century, Robert James Terry (1871-1966), a student of the leading British anatomist Sir William Turner and American anatomist George Huntington, also began a skeletal collection derived from cadavers. Turner and Huntington had both amassed skeletal collections: Turner’s from churchyard exhumations and Huntington’s from medical school cadavers (Quigley 2001). It is apparent that both men had influenced Terry academically. Terry, a professor of anatomy and head of the Anatomy Department at Washington University Medical School in St. Louis, Missouri, (Thompson 1982; Moore-Jansen 1989; Quigley 2001; Hunt 2009) had research interests that centered on normal and pathological variants of the human skeleton (Quigley 2001). To facilitate his research, he had begun collecting skeletons from cadavers used in the medical school’s gross anatomy classes in 1910 (Tobias 1991);

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he instituted a uniform protocol for the collection, cataloging, maceration, and curation of remains. After Terry retired in 1941, Mildred Trotter (1899-1991) assumed the responsibilities for maintaining and adding to the collection (Cobb 1952); five years later, she became the first woman to hold a full professorship at the Washington University School of Medicine (Missouri Women in the Health Sciences 2004-2009). While managing

the Terry Collection, Trotter sought to counteract the existing biases inherent in the collection by making a concerted effort to collect young, White females; this action added hundreds of remains to the collection (Quigley 2001; Hunt 2009). Prior to her retirement in 1967, Trotter transferred the skeletons to T. Dale Stewart at the Smithsonian Institution’s Department of Anthropology, preserving the remains and their documentation for future researchers (Quigley 2001; Hunt 2009). The tradition of amassing large skeletal collections from archaeological excavations and medical school cadavers continued in the 1930s and beyond with archaeological projects sponsored by the Works Progress Administration and Civilian Conservation Corps, and W. Montague Cobb (Jacobi 2002; Milner & Jacobi 2006; Ubelaker 2006a). Roosevelt’s New Deal projects resulted in the excavation of significant archaeological sites, such as Indian Knoll. W. Montague Cobb (1904-1990), a medical doctor, was in graduate school when the prevailing view of the inequality of races was supported by craniology (Rankin-Hill & Blakey 1994; Quigley 2001; Lear 2002). He assisted Todd in acquiring skeletal remains in Cleveland and completed his dissertation on a survey of available anthropological materials, as well as methods of documentation, processing, and preservation of skeletons (Rankin-Hill & Blakey 1994). Cobb (1936)

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noticed that very little of the skeletal material in American museums was from Black individuals; he was inspired to change this by collecting Black American skeletal remains. In 1932, he moved to Howard University in Washington, DC, where he established its laboratory of anatomy and physical anthropology, collecting skeletons from dissection room cadavers. Like others of his time, Cobb retained anatomical, demographic, and medical records for each individual (Cobb 1936); the collection contains over 700 individuals from Washington, who died between 1932 and 1969 (Rankin-Hill & Blakey 1994). Over the years, Cobb continued to study the HamannTodd remains and also examined Terry’s collection of skulls in St. Louis. As mentioned previously in this chapter, the anatomical sciences served as the foundation of forensic anthropology (Grisbaum & Ubelaker 2001). Before 1939, anatomy departments contributed data on human skeletal variation using collections of cadavers of known age, ancestry, and sex. Law enforcement agencies consulted both anthropologists and anatomists in academic and museum settings regarding human skeletal remains; at varying times, those consulted included Thomas Dwight and Earnest Hooton of Harvard University, H.H. Wilder (1864-1928) of Smith College in Massachusetts, George Dorsey (1869-1931) of the Field Museum in Chicago, and Aleš Hrdlička of the Smithsonian Institution (Stewart 1979; Grisbaum & Ubelaker 2001). The modern period in American forensic anthropology began in 1939 with Wilton Marion Krogman’s (1903-1987) Guide to the Identification of Human Skeletal Material for the Federal Bureau of Investigation (Stewart 1979; Haviland 1994; Klepinger 2006). This text brought the skills of anthropologists to the attention of law enforcement and was the earliest work of its kind: written by an anthropologist who applied anthropological

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methods to the identification of individuals and published in a forensic themed periodical (Stewart 1979; Klepinger 2006). Krogman conducted his graduate studies of at the University of Chicago, during which Todd invited him to take a fellowship at Western Reserve University (Haviland 1994). Two years after completing his dissertation, Krogman was hired as an associate professor of anatomy and physical anthropology at Western Reserve University, where his interactions with Todd and access to the HamannTodd skeletal collection helped him to learn cranial and skeletal variation with respect to age, sex, and ancestry. Krogman’s understanding of variation as it related to individual identification culminated in his 1939 Guide and his 1962 text The Human Skeleton in Forensic Medicine (Krogman 1939, 1962). In 1938, he returned to the University of Chicago as an associate professor of both anatomy and physical anthropology, and nearly a decade later, Krogman took a professorship of physical anthropology at the University of Pennsylvania; he had a profound and lasting influence on his students and physical anthropology. T. Dale Stewart (1901-1997) began working as an assistant to Hrdlička at the Smithsonian's Division of Physical Anthropology in 1924 (Ubelaker 2000, 2006a, 2006b). After earning his medical degree, he returned to the Smithsonian as an assistant curator to Hrdlička; after Hrdlička’s death in 1943, Stewart became curator and remained at the Smithsonian for the rest of his career, eventually becoming the museum director in 1962 (Ubelaker 2006b). Stewart’s work ethic, problem-oriented research approach, and

extensive publication history was likely influenced by his mentor (Ubelaker 2000b). Shortly after becoming the Curator of Physical Anthropology in the National Museum of Natural History in 1942, Stewart began conducting forensic work at the Federal Bureau of Investigation’s request. Although he was a medical doctor, Stewart had a strong 29

interest in skeletal variation and anthropology (Stewart 1930, 1931b); he followed in is mentor’s footsteps, pursuing interests in anthropometry, early man, and forensic anthropology (Ubelaker 2006a). Stewart played an essential role in the identification of American soldiers from the Korean War, which is described in more detail below. After retiring, J. Lawrence Angel (1915-1986) took over as curator, freeing Stewart from his administrative responsibilities. Undisturbed, Stewart continued to conduct research and publish for two more decades, issuing his fundamental text Essentials of Forensic Anthropology in 1979 (Stewart 1979; Ubelaker 2006a, 2006b). The Second World War significantly influenced the discipline of forensic anthropology, and Krogman’s Guide (1939) was used extensively to assist in the United States Army’s task of identifying the war dead. For those military personnel who died in the European theater, European anthropologists actually conducted the identification work in consultation with Harry Shapiro, the Curator of the American Museum of Natural History in New York (Simonin 1948 cited in Stewart 1979; Snow 1948; Vandervael 1952 and 1953 cited in Stewart 1979; Klepinger 2006). In contrast, American anthropologists selected by Francis Randall of the Anthropology Unit, Research and Development Branch, Office of the Quartermaster General were called upon to identify those involved in the Pacific theater (Stewart 1979); this task resulted in the temporary establishment of the Central Identification Laboratory (CIL) in Hawaii in 1947 (Klepinger 2006). Charles Snow, professor of anthropology at the University of Kentucky, was the first physical anthropologist to serve there and the first director of the lab (Bass 1968; Stewart 1979; Klepinger 2006). After Snow returned to the mainland in 1948, Mildred Trotter, an anatomist at Washington University, replaced him at the CIL.

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There she identified American war dead and eventually collected the long bone measurements on which she and Gleser’s stature formulae are based (Trotter & Gleser 1952; Stewart 1979). Her work at the CIL facilitated future research on military skeletal remains from Korea. In 1976, the United States Army Central Identification Laboratory was permanently established in Honolulu; the mission continues today as the Joint POW/MIA Accounting Command (JPAC), after merging with the Joint Task Force-Full Accounting (Klepinger 2006). Like World War II, the Korean War necessitated large-scale individual identification. It was at this point that Stewart (1953) indicated the need to improve adult age estimation; Stewart recognized that existing standards were based on predominantly lower income individuals from city morgues; in addition, he recognized the problems with the accuracy of the documented chronological ages in the Hamann-Todd (see section on the strengths and weaknesses of pubic symphyseal aging methods in Chapter 3) and the unhealthy lifestyles of many of the individuals (Stewart 1953). Stewart (1953) pushed for new age-estimation methods based on healthy Americans, and he found that deceased American military personnel from the Korean War, who were killed traumatically, would be a sufficient sample. Stewart temporarily transferred from the Smithsonian Institution to an identification laboratory in Japan to conduct research on age estimation with the help of Ellis Kerley and others (Stewart 1979); upon Stewart’s return, Thomas McKern (1920-1974) in 1955 went to Washington, DC, to work with him on his collected data. Results of their collaboration included a better understanding of age changes in young males and a new method for aging the pubic symphysis (McKern & Stewart 1957; Stewart 1979; Klepinger 2006).

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By the 1960s several anthropologists from the Smithsonian Institution and Federal Aviation Administration had worked on forensic investigations, and academic researchers were also contributing to anthropology and forensic obligations (Klepinger 2006). As a result, Ellis Kerley (1924-1998) found critical mass for the establishment of a separate Physical Anthropology section of the American Academy of Forensic Sciences in 1972 (Stewart 1979; Snow 1982; Klepinger 2006). This action also prepared for the establishment of the American Board of Forensic Anthropology, which was incorporated in 1977 as an organization to provide a program of examination and certification in forensic anthropology (Stewart 1979; Klepinger 2006). Kerley began his anthropological career by studying teeth from Indian Knoll, and he had spent several years identifying war dead in Japan after World War II (Ubelaker 2001). His dissertation was on the microscopic study of cortical bone. During his tenure at the Armed Forces Institute of Pathology, he developed an osteon counting method for estimating age from cortical bone and completed his doctorate from the University of Michigan in 1962 (Kerley 1962; Sledzick 2001; Ubelaker 2001). Three years later, Kerley took a teaching position at the University of Kentucky, before returning to his work with the identification of military remains in 1987. He identified war dead at the United States Central Identification Laboratory in Hawaii, first as a consultant then as the director (Ubelaker 2001). Recognizing the need for contemporary osteological collections that were not as biased as military samples, two autopsy samples were collected and two documented skeletal series were established in the late 1970s and early 1980s. Sheilagh Brooks (b 1923) was among the founders of the American Academy of Forensic Sciences Physical

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Anthropology section and the American Board of Forensic Anthropology. In addition, Brooks, along with her husband, Richard, and colleagues, Stanley Rhine and Walter Birkby, founded and sustained the Mountain Desert and Coastal regional group of forensic anthropologists. Brooks’ research has explored topics such as the differentiation between human and nonhuman bone, recovery protocol, and stature estimation from incomplete long bones (Powell et al. 2006), but skeletal aging criteria has been of particular interest to her (Brooks 1955; Brooks & Suchey 1990). She, along with colleague Judy Suchey, has left her mark on the study of skeletal aging, as manifested by the development of the Suchey-Brooks method for estimating age from the changes of the pubic symphyseal face. The Suchey-Brooks method is based on a very large and well-documented contemporary reference sample of autopsied individuals from Los Angeles, California. The pubic symphyses of over 1,000 individuals were removed during autopsies performed between 1977-1979 (Brooks & Suchey 1990), resulting in the formation of the largest collection established specifically to address age-related changes of the pubis as they vary by sex and ancestry. A smaller autopsy sample of the sternal ends of the fourth rib was subsequently collected in the early 1980’s. Mehmet Yasar İşcan, Susan Loth, and colleagues (1984, 1985) conducted research on improving the skeletal criteria for age estimation (Brooks 1951, 1955; Brooks & Suchey 1990; Suchey et al. 1988; Powell et al. 2006) that resulted in sex-specific age estimation standards based on the sternal rib end. İşcan has made other significant contributions to the disciplines of human osteology and forensic anthropology, including several prominent texts (Krogman & İşcan 1986; İşcan 1989a; İşcan & Kennedy 1989).

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The Suchey-Brooks and İşcan samples consist only of osteological elements retained after autopsy, based on a specific research goal. In contrast, the two documented skeletal series established in the 1980s are composed of complete, or nearly complete, skeletons that are collected to facilitate the investigation of many potential research questions. These documented collections are, to some degree, associated with teaching and research universities. William Bass III (b1928) founded the Forensic Anthropology Center at the University of Tennessee in Knoxville. He has been active in forensic anthropological casework throughout his career and was a founding member of the Physical Anthropology section of the American Academy Forensic Sciences and the American Board of Forensic Anthropology. In 1971, Bass created the University of Tennessee’s Anthropology Research Facility (ARF), an area where human cadavers are studied for taphonomic processes and postmortem changes. In 1981, he established the William M. Bass Donated Skeletal Collection (Ubelaker & Hunt 1995); many of the skeletons were once part of the cadaver research at the ARF, and as a result, most have decomposed naturally as opposed to being rendered in a lab setting (Bassett et al. 2003). To this day, the Bass Donated collection remains an active donation program. In 1984, Stanley Rhine established the Maxwell Museum of Anthropology’s Documented Skeletal Collection, which is located in the Department of Anthropology at the University of New Mexico. Like the Bass Donated series, the Maxwell Museum collection is one of the few active donation programs for skeletal remains. The collection contains remains that are donated, unclaimed, or medico-legal cases. The museum works in conjunction with the State of New Mexico Office of the Medical Investigator to render

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the fleshed remains down to bone. The Maxwell Museum was originally founded in 1932, under the name Museum of Anthropology of the University of New Mexico; forty years later, it was renamed the Maxwell Museum of Anthropology after a donation made by Dorothy and Gilbert Maxwell supported a significant expansion (UNM Maxwell Museum of Anthropology 2001-2010). Its collections also include over 3000 archaeological remains from the American Southwest and forensic skeletal remains from the State of New Mexico. The 1980s ushered in a new era, one that initiated investigations into human rights abuses (Klepinger 2006). The first such investigation was in Argentina, after the end of the military dictatorship that was suspected of being responsible for the disappearance of thousands of people (Klepinger 2006). Clyde Snow (b 1928) developed and honed his skills in forensic anthropology while working at the Civil Aeromedical Institute (CAMI). Like Kerley, Snow was one of the architects of the Physical Anthropology section of the American Academy of Forensic Sciences (Schick 1997). Beginning in the 1980s, he traveled to Argentina and consulted in the recovery and identification of individuals exhumed from unmarked graves (Joyce & Stover 1991; Klepinger 2006). Snow trained students in the proper excavation of the graves to ensure the recovery of the maximum amount of forensic evidence, thus forming the Argentine Forensic Anthropology Team in 1984. Later he served as the training director for the Guatemalan Forensic Anthropology Foundation (Schick 1997) and assisted with the extensive forensic excavations in the former Yugoslavia (Stover 1997). With a continuing need for large contemporary collections, the next generation of documented skeletal collections in the United States is virtual. The Forensic

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Anthropology Data Bank was created in 1986 with a grant from the National Institute of Justice (Jantz and Moore-Jansen 1988). The Data Bank contains demographic and osteological data from the Bass Donated collection and forensic cases submitted by anthropologists from around the United States (Jantz & Jantz 1999; Dirkmaat et al. 2008). The collection contains data for over 2,600 individuals, including approximately 1100 forensic cases with known sex and ancestry information. While the Data Bank is the most current collection (Dirkmaat et al. 2008), many different anthropologists have contributed the data; these observers have varying—and unknown—experience levels, a potential source of error that should not be overlooked. While virtual collections like the Forensic Data Bank exist, physical skeletal collections continue to proliferate. Most recently, between 2005 and 2006 Kristen Hartnett collected the pubic symphyses and sternal ends of the fourth ribs from subset of individuals autopsied at the Maricopa County Forensic Science Center in Phoenix, Arizona. As with the Suchey-Brooks and İşcan samples, Hartnett’s collection consists of certain skeletal elements that were gathered for a specific research goal, which in this case is the modification of pubic symphyseal and rib age estimation methods (Hartnett 2007). The importance of the maintenance and development of contemporary skeletal collections that encompass a wide range of human variation cannot be understated. Human skeletal collections are essential for teaching and training, method development and testing, and research on anatomy and human variation.

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Summary At one time, isolated human skeletal remains were displayed in cabinets of curiosities. However, during the 19th Century, medical doctors, biologists, and anatomists with an interest in human cranial variation began collecting and studying larger numbers of human skeletal remains (Cook 2006). Many were particularly interested in the origins of American Indians, and this focus resulted in the collection of archaeological remains. During this time, some scientists thought intelligence was related to the anatomical features of the brain; cranial measurements were defined and calipers were developed to quantify them (Cook 2006). In the late 1800s, the seeds of forensic anthropology were planted and Huntington initiated the collection of large numbers of skeletons derived from medical school dissecting rooms. During the early 20th Century, American physical anthropology emerged as a profession due to the efforts of Hrdlička; the collection of archaeological remains continued and two additional large skeletal collections originating from anatomy schools and institutional morgues were established. The latter half of the 20th Century was marked by the acquisition of human skeletal remains that were donated, medico-legal, and/or military in origin. Anthropological goals included the description of human skeletal variability, the identification of war dead and medico-legal remains, and the modernization of collections. In addition, an innovative approach to the collection of data resulted in the creation of an electronic database in the mid-1980s; this data bank allows for the continual addition of demographic and anthropological information for current forensic

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casework and represents a new type of resource available for research in physical anthropology.

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Chapter 3 Theoretical Background and Literature Review Exploring and interpreting differences among human populations is a cornerstone of biological anthropology. According to Moore-Jansen and colleagues (1994), the skeletal biology of Americans has changed, and continues to change, due to secular modifications, migration, and gene flow. The literature also documents changes in the timing of physiological maturation, which are associated with nutritional differences related to socioeconomic status and/or cultural diversity (Ito 1942; Wingerd et al. 1974; Moore-Jansen 1989). If changes to skeletal biology and rates of physiological aging are present, regardless of cause, have modifications occurred in the rate of senescent change of skeletal indicators? At present, little is known about this issue, although this question is of great importance to the applicability and reliability of current aging methods (WittwerBackofen et al. 2008). Skeletal biologists and anthropologists make a fundamental assumption that both the pattern and rate of age-related morphological changes do not vary significantly through time. However, Schranz (1959) questioned the use of current standards on ancient remains, and Hoppa’s (2000) study suggests that significant differences in the timing of age-progressive change of the pubic symphysis may exist between reference and target samples. Paleodemographers have recognized this problem with respect to applying age assessment standards derived from modern reference populations to archaeological samples (Bocquet-Appel & Masset 1982), as these researchers must assume that a reference sample of 19th or 20th Century skeletons will

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provide valid information for estimating the age at death of historic groups10 (Usher 2002). This assumption must also be made for estimating the age at death of late 20th and early 21st Century forensic cases when using standards developed based on late 19th and early 20th Century American skeletal reference series. Despite this concern, no one has attempted a large-scale evaluation of how well current aging standards perform on documented American skeletal samples of varying genetic backgrounds, living conditions, health statuses, and time periods until now. This is an important contribution to the literature seeking to identify the validity and reliability of applying reference standards to target samples, since age estimation standards are tested against other known-age samples. Usher (2002) emphasizes that by using various documented skeletal collections, it is possible to test assumptions about the uniformity of patterns of biological aging of the human skeleton. This research addresses the importance of determining whether any aging methods can be uniformly applied to all individuals and which skeletal traits are the most reliable indicators of age.

Estimation of age at death As mentioned in the Introduction, a genuine difference exists between chronological age and skeletal, or biological, age (du Noüy 1937; Laugier 1955; Acsádi & Nemeskéri 1970; Angel et al. 1986; Borkan 1986; Cox 2000; Kemkes-Grottenthaler 2002). Skeletal age markers and biological variables are an estimate of physiological status, not a representation of chronological age (Arking 1998; Kemkes-Grottenthaler 2002; Rösing et al. 2007). Unlike chronological time, the rate of biological aging can be

10

The reader is referred to this chapter’s section on critiques of estimating age from the adult skeleton for a more detailed discussion of this issue.

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affected by lifeways, health and disease states, living and working conditions, climate, nutrition, endocrine function, and other environmental and genetic factors (Acsádi & Nemeskéri 1970; Angel 1984; Borkan 1986; İşcan 1989a; Loth & İşcan 1994). As a result, it is essential to recognize that physiological and chronological ages do not necessarily correspond (Laugier 1955; Acsádi & Nemeskéri 1970) and that chronological age estimates drawn from bone involves inherent risk of error (Acsádi & Nemeskéri 1970; Maples 1989; Arking 1998; Rösing et al. 2007).

Development of osteological aging standards In gerontological research, Spirduso (1995) outlined criteria for reliable agerelated biomarkers. Primary criteria include the following: strong correlation between the indicator and age, an indicator that is not altered by pathological conditions, ageprogressive changes that are not affected by metabolic or nutritional changes, a sequential and unambiguously identifiable aging pattern, and continuous remodeling throughout the organism’s lifespan (Spirduso 1995). Spirduso’s (1995) lesser criteria include wide applicability/generalization and reliable changes within a short time interval. In skeletal research, however, Kemkes-Grottenthaler (2002) notes that Spirduso’s standards cannot be applied to skeletal indicators of age because osteological changes are too complex and confounding factors abound. While not all of Spirduso’s primary criteria for age-related biomarkers are applicable to skeletal indicators of age, I do not believe that these criteria should be abandoned in the pursuit of age estimation in anthropological settings. Skeletal indicators of age do have a sequential aging pattern, and many features have a moderately strong correlation with chronological age; the task for anthropologists is to clearly 41

identify which indicators are not altered significantly by pathological, metabolic or nutritional changes, quantify the error in age estimates and consider other variables that may contribute to a more refined estimate particularly in older individuals, and define an unambiguously identifiable aging pattern that does not have a distinct end-point prior to the death of the individual. The paramount prerequisite for developing osteological aging standards in anthropology is an extensive knowledge of the skeletal system and its variation over time (İşcan and Loth 1989; Meindl & Russell 1998). In forensic anthropological contexts, specific requirements exist for age estimation methods: 1) the method must be transparent and replicable, with underlying data presented to the scientific community via publication in a peer-reviewed journal; 2) the accuracy of the method must be tested statistically; and 3) the method must be accurate enough to estimate age (Ritz-Timme et al. 2000). However, in general the construction of age estimation standards relies on identifying the divisible, unidirectional developmental/degenerative course of an osteological indicator (Boldsen et al. 2002) and subsequently corresponding these stages of skeletal morphology to chronological age based on known-age reference populations (Todd & Lyon 1924; Ferenbach et al. 1980; Usher 2002).

Osteological aging standards Bone is a living tissue that remodels throughout the body’s lifespan, responding to hormones, trauma, and pathological conditions. Bone remodeling was reported subsequent to tooth loss in the latter half of the 18th Century (Hunter 1771), and research in skeletal remodeling has expanded since to include studies on growth and development, pathological conditions and endocrine disorders, repair mechanisms subsequent to 42

trauma, isotope analyses, and degenerative changes/aging, to list a few. It is because of the dynamic nature of bone that age-related changes can be investigated. Changes in bone and cartilage occur during all stages of life (Plato et al. 1994). During childhood and adolescence, these changes are related to growth. Accordingly, age estimation of skeletally immature individuals is based on skeletal and dental growth and development (McKern & Stewart 1957; Moorees, Fanning, & Hunt 1963a; Moorees, Fanning, & Hunt 1963b; Redfield 1970; Suchey et al. 1984; Krogman & İşcan 1986; Ubelaker 1987; Ubelaker 1989a; Ubelaker 1989b; Meindl & Russell 1998; Scheuer & Black 2000), which follow a predictable order across all human populations (Brooks 1955; İşcan 1989a; Buikstra & Ubelaker 1994). As a result, sub-adult age estimation is fairly accurate (Ritz-Timme et al. 2000), though environmental stress can slow down epiphyseal union by several years (Johnston & Zimmer 1989). In contrast, changes in the adult skeleton are related to remodeling and degeneration; these changes are less uniform and are associated with decreased adaptability and performance (Plato et al. 1994). Because adult age estimation methods are essentially based on “wear and tear” indicators (Schmitt et al. 2002), the accuracy of most adult age estimation standards is poor in comparison to methods based on developmental changes. In addition, the rate of senescence is affected by genetics, life experiences, culture, and environment (Acsádi & Nemeskéri 1970; İşcan 1989a; Meindl & Russell 1998). As a result, there is a decrease in the accuracy of estimates with increasing chronological age. Simply stated, it is more difficult to estimate age in the adult due to the unpredictable irregularity of the aging process (Acsádi & Nemeskéri

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1970; Ferenbach et al. 1980; Bocquet-Appel & Masset 1982; İşcan 1989a; Maples 1989; Stini 1994; Lovejoy et al. 1997; Hoppa 2000; Schmitt et al. 2002). Though other age estimation methods exist (Gustafson 1950; Gustafson & Simpson 1953; Leopold & von Jagow 1960; Miles 1963; Gustafson 1966; Lengeyel 1968; Acsádi & Nemeskéri 1970; Kerley 1970; Burns & Maples 1976; Lovejoy & Burstein 1977; Thompson 1979; Lovejoy & Barton 1980; Lovejoy 1985; Condon et al. 1986; Stout & Paine 1992; Ritz et al. 1994; Kim et al. 2000; Ritz-Timme et al. 2000; Rösing et al. 2007; Griffin et al. 2009), most American anthropologists choose to utilize gross morphological aging standards for several reasons: these methods are easy to apply, do not require specialized equipment, and are non-destructive. These standards score the morphological changes of bony surfaces including the pubic symphysis, auricular surface of the ilium, sternal end of the fourth rib, and defined locales along the cranial sutures. Of course, the observer must have a solid understanding of normal variation in agerelated features and an ability to identify pathological conditions and postmortem damage (Meindl & Russell 1998) to properly apply these aging methods. Another disadvantage of gross morphological aging standards is that they are considered less accurate than some other methods, such as aspartic acid racemization (Ritz-Timme et al. 2000); however, aspartic acid racemization results can be influenced by fluctuations in temperature, humidity, and pH, as well as differences in laboratory methods (Waite et al. 1999; Alkass et al. 2010). Interestingly, the preferred adult aging methods utilized by European and American physical anthropologists differ (Brooks & Suchey 1990; Wittwer-Backofen et al. 2008). Europeans follow recommendations compiled during a symposium in Prague

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in 1972, which were subsequently revised at a paleodemographic conference in 1978 (Ferenbach et al. 1980); the recommendations for the skeletal aging of adults are based on the four criteria of the complex method (Acsádi & Nemeskéri 1970), specifically the pubic symphyseal face, the spongiosa structure of the humeral and femoral heads, and the obliteration of endocranial sutures. In contrast, physical anthropologists in the United States and the United Kingdom tend to follow guidelines printed in Buikstra and Ubelaker (1994) (Wittwer-Backofen et al. 2008). Because of their popularity and ubiquity in the anthropological literature, American gross-morphological age estimation standards are the focus of this dissertation. All adult skeletal age estimation standards are developed from the analysis of human remains drawn from archaeological and/or willed body/cadaver reference samples (see Chapter 2). It is important to consider that many reference samples have skewed age distributions and disproportionate representations of the sexes and races. These limitations, as they pertain to the reliability of specific aging methods, will be discussed later in the chapter. Cranial suture closure Cranial suture closure predates all other age indicators in the literature, originating with the first recognition of a relationship between age and cranial suture synostosis by Vesale in 1542 (Masset 1989). Beginning in the 1860s, the first studies on estimating age-at-death examined cranial sutures, coinciding with the collection and study of human crania (Masset 1989; Kemkes-Grottenthaler 2002). The estimation of age based on the obliteration of cranial sutures gained favor, as it was assumed that suture closure was a manifestation of a normal, progressive age-related physiological process (Masset 1971; 45

Hershkovitz et al. 1997; Dorandeu et al. 2008). However, the value of suture closure as an estimator of age at death has been questioned (Singer 1953; Hershkovitz et al. 1997; Galera et al. 1998), particularly because synostosis is affected by factors other than age, such as mechanical stress and genetic contributions (Cohen Jr. 1993), and is often asymmetrical within individuals11 (Zivanović 1983). Louis Gratiolet (1856) first proposed a sequence of ectocranial suture closure, which progressed from the obliteration of the sagittal to the lambdoid suture, with the coronal suture closing last. Five years later, Broca (1861) developed a five-point scoring system for sutures and noted that males aged 50 years and older still presented with many open sutures. In 1869, Pommerol determined from a single skull that that suture obliteration began around 40 years, though even by early 20th Century standards (Todd & Lyon 1924), his sample was lacking. Nearly two decades later, Ribbe (1885) examined 50 skulls and determined that the initiation of sutural union ranged from 21 to 55 years; as a result, Ribbe argued that it was not possible to estimate age any closer than fifteen to twenty years. Similarly, Paul Topinard (1885) noted that the age of union of sutures varied greatly; however, he determined that the sequence of closure was the same. The sagittal suture began closing first at around the age of forty, followed by the coronal suture at fifty years; the temporal suture was closing by the mid-60s. In 1890, T. Dwight examined the skulls of the poor from a sample of individuals that Todd and Lyon (1924) argue were of unverified age. Though it was unclear if Dwight examined ectocranial or 11

Zivanović examined a sample of East African Bantu from the Galloway Skeletal collection at Makerere University Medical School, Department of Anatomy, Kampala, Uganda, and a sample of European skulls from the following departments: Department of Anatomy and Department of Pathology in Belgrade; Department of Anatomy, Department of Pathology, and Department of Forensic Medicine in Novi Sad; Department of Anatomy in Vienna; Department of Anatomy, Medical College of St. Bartholomew’s Hospital in London. Both Bantu and European skulls showed evidence of asymmetrical closure at several landmarks.

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endocranial sutures, his conclusions were that the sequence of and age at closure were too irregular to be of use in age estimation. At the turn of the century, Frédéric (1906) examined 255 European and 119 non-European skulls using a modified version of Broca’s stages12, focusing primarily on the ectocranial suture closure; like other before him, he determined that suture closure did not result in precise age estimation. Todd and Lyon (1924) sought to address the question of estimating age from cranial suture closure by improving upon these past studies, particularly in the area of examining a large sample of documented age with both Black and White individuals. Todd and Lyon (1924) endorsed endocranial suture obliteration over that of ectocranial sutures for aging and followed Frédéric’s version of Broca’s scoring scheme. Although they determined that a definite pattern in suture closure was present, Todd and Lyon (1924) noted the large degree of individual variability and did not recommend using the method as the sole indicator of age. In 1925, Todd and Lyon examined the progression of ectocranial suture obliteration and developed a system for the determination of age at death based on the degree of closure. Their standards were developed using crania from the Hamann-Todd collection (Todd & Lyon 1925a; Todd & Lyon 1925b). They identified a modal pattern of closure (Meindl & Lovejoy 1985), but made a critical error when they eliminated many crania because they did not fit their idea of normal. The inaccuracy of the method was significant. As a result, this method was deemed inappropriate for age estimation for forensic purposes (Singer 1953; Brooks 1955; Thompson 1982; Suchey et al. 1986).

12

Frédéric inverted Broca’s scoring scheme, such that 0 indicated patent sutures, 1-3 indicated the degree of partial closure, and 4 indicated complete obliteration.

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Masset (1989) deemed the correlation of lateral and temporal sutures with age poor; accordingly, he scored three coronal, four sagittal, and three lambdoidal sites on a scale of 0 to 4, with zero denoting a segment that is completely open and four denoting a segment that is completely obliterated. Though the scoring procedure replicates that of Acsádi and Nemeskéri13 (1970), the age estimate is not based on the sum of the ten segment scores, as it is with Acsádi and Nemeskéri (1970). Instead, Masset calculated an obliteration coefficient (S), which is the average score for all suture sites. In addition, Masset (1989) attempted to correct for systematic statistical errors including those resulting from sex differences, the reference sample’s age structure, and regression analysis. In 1985, Meindl and Lovejoy published their newly developed method for estimating age at death based on the degree of ectocranial suture closure. Their method is the one most commonly employed by physical anthropologists in the United States, thanks in part to its inclusion in Buikstra and Ubelaker’s (1994) Standards for Data Collection from Human Skeletal Remains. The Meindl and Lovejoy method was developed using a sample of 236 crania from the Hamann-Todd anatomical collection. Ten sutural landmarks, divided into vault and lateral-anterior systems, are scored on a scale from 0 (open) to 3 (completely obliterated). Vault sites include midlambdoid, lambda, obelion, anterior sagittal, bregma, midcoronal, and pterion; lateral-anterior sites include midcoronal, pterion, sphenofrontal, inferior sphenotemporal and superior

13

Acsádi and Nemeskéri (1970) devised standard criteria for ectocranial aging, based on four stages created by the sum of segments of the sutures: three coronal, four sagittal, and three lambdoidal segments. The maximum score was 40. Stages progressed as follows: stage 1 correlated to a sum of 0-9; stage 2 correlated to a sum of 10-19; stage 3 correlated to a sum of 20-29; and stage 4 correlated to a sum of 30-40.

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sphenotemporal. Composite scores are calculated by summing the scores for sites within the vault and lateral-anterior systems; these composite scores correlate to mean ages and age ranges that are not sex-specific. Pubic symphysis In 1858, Aeby observed age variation in the pubic symphysis, but did not speculate as to which specific ages were associated with these changes. Aeby also made note of changes that were associated with the formation of the ventral margin, which were previously described by Bonn (1777). Over a decade later, Henle (1872) reported that the pubic symphyseal face underwent variation in texture with age. In the early 1920s, Todd was first to systematically address the age-progressive morphological changes in the pubic symphysis (Todd 1920; Todd 1921). His method was the first to be developed based on a large collection of skeletal material with documented age, sex, and ancestry (Kemkes-Grottenthaler 2002). The method focused on five major aspects of the pubic symphysis: the surface of the symphyseal face, the ventral border, the dorsal border, and the superior and inferior extremities (Todd 1920). Todd (1920) originally developed a ten-phase system to score morphological patterns of the pubic symphysis for White males based on 306 individuals from the Western Reserve University anatomical collection, now the Hamann-Todd Osteological Collection (Todd 1920; Brooks & Suchey 1990). While the collection is generally considered “documented,” the source of age information varies. Some ages were recorded from hospital death certificates or provided by relatives, but the coroner or anatomist estimated many after death (Brooks & Suchey 1990). Both Todd (1920) and Cobb (1952) noticed that the graphed mortality chart showed high peaks at five-year intervals and explained 49

that this display may be the result of age estimation by coroners and/or the tendency of people to round off their ages. Other problems emerged as the result of sample bias, specifically that the collection consists of a significant number of transients and lacks adequate representation of younger ages (Angel et al. 1986; Katz & Suchey 1986; Gillett 1991). As with Todd and Lyons’ cranial suture closure standard, certain skeletons were eliminated if they did not fit the expected standards for skeletal development existing at the time (Gillett 1991); this selectivity may have affected the overall variability in the sample and influenced downstream age estimates produced by the Todd method (Brooks 1955). Todd’s 10-phase method has been critiqued, revised, and refined many times since its inception, primarily for the development of component methods, adjustment of age ranges, and condensation of morphological descriptions into fewer phases. Brooks (1955), McKern and Stewart (1957), Gilbert and McKern (1973), Meindl and colleagues (1985), Brooks and Suchey (1990), and Hartnett (2007) have all made notable contributions. Brooks first modified Todd’s method in 1955, which shifted the age ranges in an attempt to correct for the overaging that resulted from the original phase age limits. Brooks (1955) also critiqued Todd for excluding pubic symphyses that didn’t fit the expected morphological patterns; instead she argued that all variants should be recorded. Shortly thereafter, McKern and Stewart (1957) published a new component aging system for pubic symphyses based on reference sample of 349 Korean War deaths: as a result, their sample over-represented young males and lacked females altogether. This system is based on the idea of maximizing the information gleaned from the pubic

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symphysis, and it contrasts with the phase-based method of Todd, which does not account for variability in the pattern of morphological features present. The authors divided the pubic symphysis into three separate components: the dorsal plateau, ventral rampart, and the symphyseal rim. Each component was then scored separately according to each one’s individual set of progressive changes; then the summed scores were used to determine the mean age, standard deviation, and observed age range from a table (Kemkes-Grottenthaler 2002). According to Hanihara & Suzuki (1978), the combination of these scores was highly correlated with chronological age in the Korean War sample. The component method was meant to be an improvement on Todd’s method by eliminating the subjective bias in the interpretation of skeletal changes and better describing individual variability. The major drawbacks to the McKern-Stewart method include its inapplicability to females, its bias toward young males, and its difficultly in application by inexperienced users, particularly as a result of the complexity of the scoring system (Katz & Suchey 1986). Fifteen years later, Gilbert and McKern (1973) addressed one of these problems by creating a standard for females. Following McKern and Stewart, Gilbert and McKern (1973) developed standards for a female-specific component method. The authors based their system on a reference sample of 103 autopsied American females14 with known age at death.

14

Gilbert and McKern (1973) do not state explicitly the source of their sample of female pubic symphyses. However, a list of individuals and institutions assisting with the “building of the necessary research population” (p.38) in the acknowledgements provides the information: University of Kansas, Department of Pathology and Department of Anatomy; University of Tennessee Institute of Pathology; University of Missouri-Columbia Department of Anatomy; Washington University Department of Anatomy; and Dr. T. Dale Stewart’s “own collection” (p.38).

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Within a few years, Hanihara and Suzuki (1978) published their research on the estimation of age from the pubic symphysis using a regression analysis of single traits on a sample of 70 Japanese males and females. The major critique of this method was that it was only reliable between the ages of 18-38 years, as the skeletal changes were highly variable for the pubic symphysis after age 40 (Hanihara & Suzuki 1978). Meindl and colleagues (1985) also modified Todd’s system using a set of 96 males and females drawn from the Hamann-Todd collection. Their research found that the original Todd method performed better than the Gilbert-McKern, McKern-Stewart, and Hanihara-Suzuki component methods and that no significant differences existed among Black females, Black males, White females, and White males. Meindl and colleagues’ (1985) revised method had defined the pubic symphyseal morphological transition using the following five stages: pre-epiphyseal, active epiphyseal, immediate post-epiphyseal, maturing/predegenerative, and degenerative, which corresponded to Todd stages I-V, VI, VII, VIII, and IX-X, respectively. In 1990, Brooks and Suchey generated a skeletal aging system with standards for males and females using linear regression analysis, which stemmed from an earlier investigation of age determination in the pubic symphysis of males (Katz & Suchey 1986). The Suchey-Brooks standard is a modification of the Todd method using six phases instead of ten. Katz and Suchey (1986) tested interobserver error for Todd’s method and found that certain phases needed to be condensed because the observers could not consistently discriminate between them. As such, Brooks and Suchey (1990) combined Todd phases I-III, IV-V, and VII-VIII; in addition, the authors also included all individuals so as not to lose the variability observed in the sample. The Suchey-Brooks

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method was based on a very large and well-documented reference sample of 20th Century individuals from Los Angeles, California, autopsied between 1977-1979; the sample included 739 males and 273 females. This is by far the largest sample to be studied for age-related changes in the pubis, and all individuals had legal documentation of their age at death. The Los Angeles County coroner sample also has better age representation than other reference samples, ranging 14-92 years, with most under 60. The sample is considered fairly representative of a 20th Century population in terms of race, socioeconomic class, and geographic origin (de Arenosa & Suchey 1987, cited in Brooks & Suchey 1990). Along with phase descriptions, Brooks and Suchey (1990) contracted France Casting to produce a set of reference casts, illustrating the early and late patterns for each stage. Currently, the Suchey-Brooks standard is one of the most trusted and frequently used pubic symphyseal aging techniques, which may be due in part to its endorsement by Klepinger and colleagues (1992) and its inclusion in Buikstra and Ubelaker’s (1994) Standards for Data Collection from Human Skeletal Remains. Recognizing the limitations of existing methods, particularly with aging older individuals, several researchers have added a seventh phase to the Suchey-Brooks pubic symphysis standard (Hartnett 2007; Berg 2008). Berg redefined age ranges for phases 5 and 6 using transition analysis, and added a phase 7 based on a reference sample of Balkans and Americans from Tennessee, focusing specifically on changes in the older female pubis. Berg (2008) argues to incorporate physiological changes associated with osteoporosis/osteopenia into phase descriptions; Hartnett includes physiological measures in her revision of the Suchey-Brooks phase/stage based pubic symphyseal aging technique for her dissertation. Hartnett’s revisions, hereafter denoted as the Hartnett-

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Fulginiti standard, are based on an autopsy sample drawn from the Maricopa County Forensic Science Center in Phoenix, Arizona, and account for better age estimates for those individuals over the age of 50-60 years. Sternal extremity of the fourth rib Kerley (1970) was the first to note age-related changes of the sternal end of the ribs, but methods for estimating age from this indicator did not emerge until the 1980’s. İşcan and Loth (1986) and İşcan and colleagues (1984, 1985) set standards for the age progressive morphological changes of the sternal end of the right fourth rib. Collectively, they developed a phase analysis system composed of nine phases (0-8) that describe changes in shape, form, texture, pit depth, and bone density of the sternal end. Their method was based on a reference sample of 277 rib ends collected at autopsy from medical examiner’s cases in Broward County, Florida (Loth & İşcan 1989). Originally, İşcan and his colleagues only tested the right side fourth rib, assuming that the left fourth rib would not be statistically significantly different from its antimere. Yoder and colleagues (2001) tested this hypothesis and found no significant difference between sides. Other variations of age estimation from ribs include methods developed by Kunos and colleagues (1999) and DiGangi and colleagues (2009). For each of these modifications, three aspects of the first rib were evaluated for their morphology: the costal face, rib head, and tubercle facet. The first rib was chosen because it was easily identified, was not influenced by mechanical stress, and exhibited remodeling into the eighth decade. These were then seriated by the degree of morphological changes for the first rib by age, and then a target age was assigned based on similar morphology. 54

DiGangi and colleagues (2009) take Kunos and colleagues’ (1999) method one step further by performing a transition analysis. Despite the development of these more recent aging methods (Kunos et al. 1999; DiGangi et al. 2009), the standard developed by İşcan and his colleagues remains the primary method of choice. Iliac auricular surface It is commonly reported that Sashin (1930) was the first to describe age-related changes of the auricular surface (Buckberry & Chamberlain 2002), followed by Kobayashi (1967); however, Sashin states that Meckel was the first to describe the sacroiliac joint in 1816, noting that the joint surfaces were smooth in youth and rougher in older individuals. Lovejoy and colleagues conducted the first systematic study of agerelated morphological changes of the auricular surface in 1985; Lovejoy and colleagues (1985b) devised a phase-based standard that described age related morphology for 5- or 10-year age intervals starting at age 20 years and ending at 60+. Their standard was based on a large reference sample of 500 individuals from the Hamann-Todd Osteological Collection, 250 from the Libben archaeological population, and fourteen forensic cases. Lovejoy and colleagues’ standard was originally designed for use with seriation to estimate the age distribution of a skeletal sample, but it has been routinely applied to individual cases for forensic anthropological analysis. The method was tested by Lovejoy and colleagues (1985a) on two samples selected from the Hamann-Todd collection (Mulhern & Jones 2005) to ascertain the accuracy and bias. Results indicate that the method is equally as accurate as pubic symphyseal aging, though admittedly

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more difficult to apply (Lovejoy et al. 1985a; Lovejoy et al. 1985b); as a result the reliability of the method remains a concern15. Several studies have attempted to improve upon the original method. Buckberry and Chamberlain (2002) revised Lovejoy and colleagues (1985b) method and developed a component system that estimated age based on the composite score. The authors claim that the revised technique was easier to apply and had lower levels of inter- and intraobserver error than Lovejoy and colleagues’ (1985b) method. Igarashi and colleagues (2005) also developed a newer method for age estimation, based on the iliac auricular surface based on 700 modern Japanese skeletons with recorded age. This technique took a different approach to description of the morphology, noting the presence or absence of certain relief and texture features16 and then selecting the parameter estimates of each feature. Subsequently the age estimation was obtained by summing these parameter estimates. Igarashi and colleagues (2005) claimed their method was more accurate than other methods, particularly at the older age ranges, and that reliability was better for both males and females. Osborne and colleagues (2004) used Lovejoy’s standards for aging the auricular surface to estimate age for a sample of 266 individuals from the Terry (194) and Bass Donated (72) collections. Using the original age-phase correlation scheme, only 33% of the sample was correctly aged. The authors suggested that the 5- and 10-year age ranges published by Lovejoy and colleagues (1985b) were too narrow for use in forensic contexts. As a result, Osborne and colleagues (2004) modified the auricular surface aging system to include only six phases, combining phases with means that were not

15 16

The reader is referred to the discussion of weaknesses of the auricular surface later in this chapter. Nine features for males and seven for females.

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statistically significantly different. For the new method, inaccuracy was lowest for middle decades (40-59) and highest for the oldest decade (80-89); the method overestimated age for lower decades (20-49) and underestimated age for higher decades (50-89). Mulhern and Jones (2005) tested both Lovejoy and colleague’s (1985b) method and the revised auricular aging standard by Buckberry and Chamberlain (2002) on 309 individuals of known age sex and race from the Terry and Huntington Collections. The authors were interested in determining whether the two methods were comparable in terms of accuracy. Their results indicated that the revised method was less accurate than the original for individuals under 50, but more accurate between 50-70 years. Although I have had no training or experience with Buckberry and Chamberlain’s (2002) aging method for the auricular surface, their age estimates for older age groups are not constrained by the 50+ value assigned to the terminal phase of Lovejoy and colleague’s (1985b) standard. However, the revised method uses the same terminology as the original, and this does not assist those already struggling with the interpretation of Lovejoy and colleague’s (1985b) feature descriptions. Multiple-trait approaches Both intrinsic and extrinsic factors modify aging patterns, implying that a single indicator will only reflect one part of a complex process (Kemkes-Grottenthaler 2002). Despite continuous modification and recalibration of existing methods, the paradigm has shifted from single-indicator to multiple-indicator approaches. The reasons were to minimize the error of single indicators by using several individual methods to estimate

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age (Cox 2000) and to combine multiple indicators to produce an internally consistent age distribution (Wright & Yoder 2003). In 1970, Acsádi and Nemeskéri created the Complex Method, which evaluated four features: structural changes of the spongiosa of the humeral and femoral epiphyses, symphyseal face of the pubis, and endocranial suture closure. In 1985, Lovejoy and colleagues developed the multifactorial aging method using the Hamann-Todd Osteological Collection as the reference sample (Lovejoy et al. 1985a), despite known problems with the sample. This standard incorporated age information from multiple indicators, including the auricular surface, pubic symphysis, and clavicular and proximal femoral radiographs, following Lovejoy and colleagues (1985b), Meindl and colleagues (1985), and Walker and Lovejoy (1985), respectively. Both the complex and multifactorial methods produce better accuracy for age estimates in older individuals (Jackes 1985). Transition analysis A novel approach to skeletal age at death estimation took hold in the 1990s. Boldsen and colleagues (2002), like others preceding them, had the goal of revising the means of age estimation of adult skeletons, this time in light of the Rostock Manifesto; the Rostock Manifesto called for a number of improvements to be made in the field of paleodemography, including the development of more reliable and more vigorously validated age estimation methods (Hoppa & Vaupel 2002). Due to known problems with phase based techniques, the authors sought to develop a new kind of age estimation method. The culmination of their osteological experience and statistical changes was a new multi-factorial age estimation method that returned to a component system for the 58

pubic symphysis, introduced a component scoring system for the auricular surface, and evaluated five different points along cranial sutures with a new descriptive scale. The authors’ approach follows the logic of McKern and Stewart (1957), who divided the pubic symphysis into individually scored components, each with their own set of morphological changes to score; like other age indicators, these morphological changes are a series of unidirectional stages. Boldsen and colleagues (2002) believe that this approach better reflects the complex changes observed for age indicators than do static phase descriptions, because senescent changes do not occur simultaneously. As the authors state, it is difficult to force a complex anatomical structure into one particular stage (Boldsen et al. 2002). In addition, a component scoring system allows osteologists to take full advantage of the meager information that might be available in poorly preserved age indicators, especially if they are fragmentary. Boldsen and colleagues’ (2002) newly developed scoring system, as well as the features scored, were derived from previous descriptions as well as extensive experience working with thousands of prehistoric and historic skeletons. Pubic symphyseal and iliac auricular surface characteristics were defined using American skeletal samples and Danish archaeological remains. Only one of the auricular surface features, specifically the posterior iliac exostoses, was not present for the archaeological remains. Boldsen and colleagues (2002) only observed this trait in elderly individuals in the Terry Collection. This feature, along with the breakdown of the dorsal margin of the pubic symphysis, is considered to be a trait characteristic of old age. Instead of assigning a static age range to the stages observed for each feature scored, the authors chose to use a different statistical technique: transition analysis.

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Boldsen and colleagues (2002) describe transition analysis as an estimation procedure that allows for inferences to be made about the timing of transition from one stage to the next. The ADBOU age estimator program calculates a maximum likelihood point estimate for age at death and associated confidence intervals for each individual. In the ADBOU age estimator program, the observer can choose the sex, race, and hazard model, which is either a uniform prior distribution or an informed prior. If the uniform prior distribution is chosen, the program assumes that all target individuals have an equal chance of being all ages, ignoring the target sample’s age distribution (Konigsberg and Frankenberg 1994). This approach is criticized by BocquetAppel and Masset (1982), and later by Di Bacco and colleagues (1999), because it weights extremely old and highly unlikely ages-at-death heavily. Boldsen and colleagues (2002), acknowledge the critique but counter with the claim that uniform prior use is precedented (Konigsberg et al. 1998). If the informed prior is chosen, the program uses documentary information from either United States national homicide data from 1996 (Peters et al. 1998) or 17th Century rural Danish parish records (Johansen 1998). Bayesian prediction allows for a direct visualization of the variability because age at death is assessed by the probability that it belongs to a set chronological interval (Schmitt et al. 2002). The prior probability is the probability of an individual belonging to a specific age category, given no information other than the assumption that the individual is similar to the reference sample to be used. The likelihood is the probability that an individual with a particular score belongs to a particular age, based on the age distribution of the reference set for that point’s score. The posterior probability is

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indicative of the chance that an individual belongs to a particular age group taking into account the prior probability and the likelihood (Aykroyd et al. 1999).

Application of American aging standards to target groups An enormous amount of research has been conducted on skeletal aging over the last century. Since an exhaustive survey of every osteological-based aging study would be voluminous, only highlights related to the dissertation question are presented. The application of aging standards developed from one sample to any given target sample assumes that both samples possess the same biological aging characteristics (KemkesGrottenthaler 2002); however, an abundance of research has identified factors influencing individual variability in the aging process. This literature review focuses on those American aging standards tested in this research, highlighting the performance of each by sex, race, nationality, and time, as well as emphasizing the standards’ strengths and weaknesses.

Cranial sutures Differences in the synostosis of cranial sutures between the sexes have been reported. In 1955, Brooks noted a difference between female pubic mean age and cranial suture closure age; this slower rate of suture obliteration in females ranged anywhere from five to twenty-five years. Singer (1953) found a similar lag in suture obliteration for females. In contrast, Brooks (1955) reported that males had a higher correlation between pubic and cranial mean ages. Galera and colleagues (1998) found sex-specific and population differences in cranial suture closure for a sample of skeletons drawn from the Terry Collection using four independent scoring standards, including that of Meindl 61

and Lovejoy; Nawrocki (1998) also found a correlation between suture closure and sex for the Terry Collection. Masset (1989) provided some presumptive evidence for a secular trend in cranial suture closure, finding a slight difference between the means of closure in two Portuguese samples with death years approximately fifty years apart: Lisbon and Coimbra. BocquetAppel and Masset (1995) published additional evidence supporting a secular trend in suture obliteration. Using the same Lisbon sample, they compared cranial suture closure to a sample from Prague with years of death nearly a century later. Bocquet-Appel and Masset (1995) plotted the percentage of lambdoidal suture closure against age and reported that at 60-70 years of age, the Lisbon group appears 20-30 years younger than their actual age when the modern Prague sample’s standards were applied.

Strengths and weaknesses Meindl and Russell (1998) report that age estimation based on cranial suture closure fell into disfavor in the 1950s, due to critiques by Singer (1953), Brooks (1955), and McKern and Stewart (1957); decades later, the value of suture closure as an estimator of age at death is still questioned (İşcan & Loth 1989; Buikstra & Ubelaker 1994; Hershkovitz et al. 1997; Galera et al. 1998; Boldsen et al. 2002). Though some have argued that the lateral-anterior sutures are more reliable than the vault sites, ectocranial suture closure is generally considered inaccurate, providing only a quick and rough impression of age (Singer 1953; Brooks 1955; McKern & Stewart 1957; Meindl et al. 1983; Ritz-Timme et al. 2000). Cranial sutures appear to obliterate with increasing age, but synostosis is affected by other factors like mechanical stress and genetic contributions (Cohen Jr. 1993). Regardless, the large variability in rates of closure makes age 62

estimation problematic (Singer 1953; Brooks 1955; McKern & Stewart 1957; Krogman & İşcan 1986; Masset 1989; Saunders et al. 1992; Buikstra & Ubelaker 1994; BocquetAppel & Masset 1995; Galera et al. 1998; Rösing et al. 2007). This variability means that the determination of an individual’s age at death is only possible between very wide limits, which do not allow for any meaningful estimation of age (Acsádi & Nemeskéri 1970; Hershkovitz et al. 1997). Even Boldsen and colleagues (2002), admit that they only include suture obliteration in their aging standard because the cranium is often the only element recovered in forensic cases. Thus, cranial suture closure is now returning as part of standards utilizing multiple indicators of age (Meindl & Russell 1998), with the caveat that in ambiguous cases, postcranial age indicators should be weighted more heavily than cranial suture scores (Buikstra & Ubelaker 1994). The anthropological literature reports that the correlation between cranial suture closure and chronological age is low to non-existent (Singer 1953; Brooks 1955; Masset 1971; Perizonius 1984; Hershkovitz et al. 1997) and includes documentation of very old individuals with open sutures (Perizonius 1984; Aykroyd et al. 1999). Variables possibly affecting suture obliteration include epidemic diseases, and vascular, hormonal, genetic, biomechanical, and local factors (Persson et al. 1978; Masset & de Castro e Almeida 1990, as cited in Jackes 2000; Cohen Jr. 1993; Kanisius & Luke 1994). Asymmetry is also a concern, as marked differences between right and left sides have been noted; the lateral-anterior sites of Meindl and Lovejoy system, as well as the coronal suture, are vulnerable to asymmetry (Zivanović 1983; Kemkes-Grottenthaler 1996, as cited in Jackes 2000). If only one side is present, there is a risk of erroneous age assessment. Other problems with cranial suture closure aging standards include unclear

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descriptions of obliteration stages (Hershkovitz et al. 1997) and limitations due to a clear end point (complete obliteration) to the standard that can be reached long before death (Jackes 2000). Despite the presence of significant individual variation, Todd and Lyon (1924) emphasize that cranial suture synostosis has a clear, ordered sequence of changes. Another benefit of cranial suture closure aging standards, specifically that of Meindl and Lovejoy, is low interobserver error (Galera et al. 1995). In contrast to other reports, Meindl and colleagues (1983) report a moderate (0.65) correlation between cranial suture closure and chronological age.

Pubic symphysis In terms of skeletal aging, differences between the sexes—both in morphology and rate of maturation—have been observed for the pubic symphysis (Todd 1921; Gilbert & McKern 1973; Gilbert 1973; Brooks & Suchey 1990; Sharma et al. 2008). Originally, Todd reported no significant differences in pubic aging between the sexes. But soon after, Todd (1921) discovered that females developed the ventral rampart formation two to three years later than males, and that females exhibited dorsal flattening two to three years earlier than males. Differences in the rate of skeletal morphological change between the sexes was also described by Gilbert and McKern (1973), who stated that female pubic symphyses appeared ten years older than their male counterparts. Because sex-specific differences have been consistently observed for the pubic symphysis, more recent revisions to the Todd system, including Brooks and Suchey (1990) and Hartnett (2007), have devised aging standards with separate age ranges and/or different phase descriptions for males and females. 64

The greater variability observed for female pubic morphology (Jackes 1985; Katz & Suchey 1986; Kemkes-Grottenthaler 2002; Djurić et al. 2007) translates to less reliability in age estimates and has been attributed to hormonal changes and trauma related to childbearing by numerous authors (Todd 1921; Putschar 1976; Suchey et al. 1979; Bergfelder & Hermann 1980). For this reason, Brooks and Suchey (1990) suggest that dorsal lipping should not be relied upon as an indication of age in females. However, a crude analysis comparing small samples of low-parity and high-parity females from the historic Spitalfields cemetery did not detect significant differences in the variation of mean stage by age (Hoppa 2000). Despite this, Hoppa (2000) did find that female pubic symphyses appeared younger than males after the age of 40 years in some samples. In his earliest evaluation of pubic symphyseal aging, Todd (1920) did not discuss differences in the rate of metamorphosis between American Blacks and Whites drawn from the Hamann-Todd Osteological Collection. Similarly, Brooks (1955) did not note any differences between races for the pubic symphysis. But when Katz and Suchey (1989) tested for racial differences in pubic symphyseal metamorphosis using a welldocumented multiracial American sample of 704 male pubic bones collected after autopsy at the Los Angeles County coroner’s office in 1977, significant differences in age were found across racial groups. The relationship between estimated age using a sixstage modified Todd system and chronological age was examined as a function of race, which was classified as White, Black, or Mexican. The authors analyzed the data twice, once using linear regression models and once incorporating an analysis of variance. The authors observed that Blacks and Mexicans with advanced pubic symphyseal patterns tended to have lower chronological ages than Whites exhibiting the same morphology

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(Katz & Suchey 1989). As a result, Klepinger and colleagues (1992) encourage the use of a racially specific variant of the Suchey-Brooks pubic symphyseal aging standard for males. Conflicting results were reported when American aging standards were applied to skeletal samples from Asia. Schmitt (2004) conducted a blind study of the SucheyBrooks pubic symphysis method on a Thai sample of known sex and age at death. The Thai sample was composed of unclaimed bodies/indigents, as well as willed remains from the Department of Anatomy at the University of Chiang Mai, Thailand. Results found that both bias and inaccuracy increased with age, and that the chronological age tended to be underestimated. A blanket conclusion that age assessment based on American standards should not be used for samples from Asia was made, because Thai individuals retain earlier phase morphology even in advanced age, resulting in lower age estimates than actual chronological age (Schmitt 2004). The degree of inaccuracy was striking, up to 32 years for individuals over the age of 60. In contrast, when Hanihara (1952) scored Japanese pubic symphyses using Caucasian standards, the Japanese individuals appeared two to three years older than their actual ages. Similarly, Sakaue (2006) tested the Suchey-Brooks system on a recent Japanese sample (n=416) and found that the differences between the mean ages of Japanese and American samples for all six stages were not statistically significant. This contrast hinted that these trends might be affected by potential underlying differences in socioeconomic status, health, and/or nutrition. Pal and Tamankar (1983) and Sinha and Gupta (1995) reported differences in pubic symphyseal aging between a sample of males from India and American White

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males using the Todd ten-phase standard and the McKern-Stewart component method. When compared to the reference sample (see Todd 1927), the Indian males had statistically significant lower mean ages of development for phases II, III, and VI-X, as well as differences in developmental timing of several pubic symphyseal components. Specifically, the Indian males had an advanced development of the dorsal margin and formation of the symphyseal rim. Indian males also exhibited a delay in the completion of the dorsal plateau, ventral beveling and rampart, and the symphyseal rim (Sinha & Gupta 1995). The Suchey-Brooks standard has been tested on European samples as result of recent international forensic anthropological investigations of mass graves. Djurić and colleagues (2007) evaluated the Suchey-Brooks method using a Serbian sample consisting of 52 males and 33 females from the Institute for Forensic Medicine, University of Belgrade (1999-2002). The authors found the Suchey-Brooks method to be more accurate in males (89.7%) than for females (72%); the greater inaccuracy for females was expected due to the increased variability in their age indicators (Djurić et al. 2007). The oldest individuals were underaged, regardless of sex. Kimmerle and colleagues (2008a) also tested whether American aging standards for the pubic symphysis would reliably estimate the age at death for Balkan skeletal remains. The pubic symphyses of 212 male and 84 female Balkans were scored using the SucheyBrooks method. The comparative sample of 2,078 American males and females (Blacks and Whites) was drawn from the Forensic Data Bank; their Todd scores were converted to the 6-phase Suchey-Brooks stages. To test for population differences in aging, Kimmerle and colleagues (2008a) used proportional odds probit regression, an analysis of

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deviance, and an improvement chi square statistic. Results showed statistically significant differences between American and Balkan females (Kimmerle et al. 2008a), but no difference between the aging processes of males and the total populations (sexes pooled). Berg (2008) also reported differences in the median ages for pubic symphyseal phases between American and Balkan females. Hoppa (2000) used the Suchey-Brooks standard to identify differences in agerelated changes of the pubic symphysis between the original Brooks and Suchey (1990) reference sample and two different target samples: a 20th century forensic sample of similar composition to that of the Suchey-Brooks reference sample (Klepinger et al. 1992) and an archaeological sample derived from the 18-19th Century cemetery at Spitalfields (Molleson & Cox 1993). Hoppa (2000) found that the mean phase of each 10-year group within the reference and both target samples was not the same, indicating that the rates of skeletal change occurring in the three samples are significantly different. The differences observed were particularly significant for females over the ages of 30 in the archeological target and 40 in the forensic target; both the forensic sample and archaeological sample females have younger looking morphology than the reference sample at comparable actual ages (Hoppa 2000).

Strengths and weaknesses Todd’s pubic symphysis aging standard has significant methodological problems, including reference sample age documentation, sample size, age range, and sex distribution (Brooks 1955; Djurić et al. 2007). It is well documented that the Terry and Hamann-Todd collections contain individuals whose ages are not truly known (Boldsen et al 2002; Hunt & Albanese 2005; Konigsberg et al. 2008). This problem may be the 68

result of several circumstances: 1) individuals did not provide accurate information regarding their age because either their true age is unknown or is misrepresented for cultural reasons; 2) an inconsistency between stated age and what the morgue physician thought the body condition was consistent with; or 3) morgue physicians assigned an age to individuals of undocumented age at death based on the physical appearance of the body at autopsy examination (Howell 1976; Meindl et al. 1983; Lovejoy et al. 1985a; Usher 2002; Hunt & Albanese 2005). Questions about the accuracy of ages for the sample implies that all aging methods based on the Hamann-Todd collection should not be trusted unconditionally, because less than one in six cadavers from Cleveland hospitals had sufficiently documented ages and the difference between stated and observed ages were often 15-20 years (Meindl et al. 1983; Lovejoy et al. 1985a). In a separate study, Lovejoy and colleagues (1985a) found only three “cadaver records” had legal documentation of birth date in their sample drawn from the Hamann-Todd collection. However, several of the same authors claim in a later publication (Meindl et al. 1990) that their sample of 512 individuals selected from the Hamann-Todd collection each had a legal age at death recorded on their United States Revised Death Certificate, which was filed at the Vital Records Division of Cleveland City Hall; they stressed that the next of kin providing the information are listed and that it is, as it was then, illegal to falsify such information. It is unclear exactly how the number of individuals with legal documentation of age at death increased so dramatically in five years, but perhaps it was related to the location of the information: cadaver records versus the Vital Records Division.

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Of course, it is impossible to know for certain which ages are accurate and which are not. In the Hamann-Todd collection, the uncertainty of some “documented” ages is marked by “?” or “ca.” However, these designations are not the only indicators of potential problems with the recorded age at death. The anatomists working on the Hamann-Todd collection described/rated each of the cadavers “stated age” according to their certainty of the documented age, using terms like “certainly correct” and “certainly incorrect” (Meindl et al. 1983; Lovejoy et al. 1985a). Stated ages that were deemed “certainly correct” were those falling within a +/-5 year range of the observed age. When the stated age of an individual was 30 years but unfused epiphyses were observed, the stated age was classified as “certainly incorrect” (Lovejoy et al. 1985a). Finally, It is well documented that ages cluster at five-year intervals in the Terry and Hamann-Todd collections, probably as the result of estimated ages or individuals who rounded off their reported age (Todd 1920; Cobb 1952; Katz & Suchey 1986; Boldsen et al. 2002; Hunt & Albanese 2005; Konigsberg et al. 2008). The Todd pubic symphysis aging standard also suffers from significant inaccuracy, due in part to the variability observed in the symphyseal face, which results in morphology that is difficult to classify into one stage or another (McKern & Stewart 1957). So much variability was present within the Hamann-Todd sample that Todd could not get over a quarter of his sample to fit into his aging scheme (Jackes 1985). As a result, Todd purposefully reduced the sample variation, deleting problematic skeletons that did not fit the standards for skeletal development existing at the time (Angel et al. 1986; Katz & Suchey 1986; Brooks & Suchey 1990; Gillett 1991). Todd’s method does not account for individual variation according to Katz and Suchey (1986). The Suchey-

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Brooks method addressed this issue by modifying Todd’s standard, using a much larger reference sample with legal documentation of age at death. Brooks and Suchey’s (1990) sample had better age at death, geographic, and racial representation, so it is preferable because it allowed for normal variation to be evaluated (Gillett 1991); this is reflected in the large age intervals associated with the Suchey-Brooks phases and the significant overlap in 95% confidence intervals for age estimates (Brooks & Suchey 1990; Schmitt 2004; Kimmerle et al. 2008b). However, substance abuse in 20th Century autopsy samples may be an issue (Klepinger et al. 1992). Taylor (2000) evaluated age-related changes in the sternal end of 173 ribs from individuals with known chronic substance abuse problems that were collected at autopsy at the King County Medical Examiner’s Office in Seattle, Washington. Taylor (2000) found that that chronic substance abuse affected the reliability and accuracy of the İşcan and colleagues’ rib aging method; in contrast, Hartnett (2007) did not find a statistically significant difference between groups17 when the observed and actual pubic symphyseal phases were compared. Other critiques of pubic symphyseal aging standards include low repeatability and low reliability (Saunders et al. 1992; Molleson & Cox 1993; Hoppa 2000; Schmitt et al. 2002; Rösing et al. 2007). Though Galera and colleagues (1995) did not find significant interobserver error for either the Todd or Suchey-Brooks standard when testing the methods on 963 skeletons from the Terry Collection, Kimmerle and colleagues (2008b) found that correlations between observer’s scores for Todd’s method varied from low to high for a sample of identified individuals from Kosovo. Significant interobserver error

17

The groups compared were chronic substance abusers and those with no history of substance abuse. Both groups were drawn from an autopsy sample at the Maricopa County Forensic Science Center.

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may be due in part to confusion between the development and degeneration of a single feature: the ventral rampart (Suchey & Katz 1998; Kimmerle et al. 2008b). It may also be the result of the difficulty of applying upper phase designations based on photos and insufficient phase descriptions (Gillett 1991); Brooks and Suchey (1990) attempted to solve this problem as well, commissioning the casting of reference plaques and formulating clear phase descriptors for both males and females (Gillett 1991). Interestingly, Kimmerle and colleagues (2008b) reported more observer variation for the Suchey-Brooks method than for Todd’s; despite their improvements, some suggest that characteristics scored for the Suchey-Brooks standard are still difficult to assess, resulting in large interobserver error (Saunders et al. 1992; Baccino et al. 1999), as well as accuracy and precision that were less than desired (Klepinger et al. 1992). Like other aging standards, the phase-based methods relying solely on the pubic symphysis tend to overestimate the age of the young and underestimate the age of the old (Martrille et al. 2007). Meindl and colleagues (1983) determined that pubic symphysis aging standards were biased, such that the error increases with age. Numerous authors have emphasized that age assessment from the pubic symphysis is not reliable past the fourth decade (Hanihara & Suzuki 1978; Suchey 1979; Meindl et al. 1983; Lovejoy et al. 1985a; Lovejoy et al. 1985b; Meindl et al. 1985; Katz & Suchey 1986; Klepinger et al. 1992; Lovejoy et al. 1997; Meindl & Russell 1998; Sakaue 2006; Djurić et al. 2007; Martrille et al. 2007; Sharma et al. 2008). Age estimation methods based on the pubic symphysis require sex-specific standards; sex-specific changes are particularly noticeable for those older than 40 years (Gilbert & McKern 1973) and are likely related to the degenerative process (Schmitt et

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al. 2002). While revisions to the Todd method do attempt to better describe age ranges and/or differing morphology associated with males and females, the original Todd method uses the same standard for both sexes. Population-specific standards may also be necessary, as the timing of age-related morphological changes in Asian and African samples appear to be different from those of European samples (Schmitt et al. 2002). At this point, only Boldsen and colleagues’ Transition Analysis method considers race when estimating the age and confidence intervals, though the choice is limited to Blacks and Whites. Another concern with the reliability of the pubic symphyseal aging standards is that Hoppa (2000) observed differences among samples18 in the rate of early development and later degeneration of this indicator, as scored by the Suchey-Brooks method; however, Konigsberg and Frankenberg (2002) argue this difference was the result of interobserver error. Regardless, the numerous modifications to the Todd system of aging the pubic symphysis have not solved the controversy of whether variations exist in age estimates due to sex, race, population, inter-observer variability, or method reliability (Kimmerle et al. 2008a). Age estimates produced by pubic symphyseal standards may be affected by numerous factors, including childbirth, physical inactivity due to trauma or debilitation, and asymmetry. Stewart (1957) reported differences in the dorsal margin of multiparous women that resulted in overaging. In contrast, Klepinger and colleagues (1992) noted that physical inactivity was associated with severe underestimation of chronological age; the authors cite an example of an individual in his/her 50s, who was estimated to be in

18

See previous section for a discussion of this paper. Samples compared were Brooks and Suchey’s original reference sample, a 20th century forensic sample of similar composition to that of the SucheyBrooks reference sample (Klepinger et al. 1992), and an archaeological sample derived from the 18-19th century cemetery at Spitalfields (Molleson et al. 1993).

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his/her 20s based on skeletal indicators. Accordingly, Klepinger and colleagues (1992) stressed the need to note variables like extremes in body weight, alcoholism, trauma, and physical disability, as they affect age estimates. Additionally, the developmental and degenerative processes forming the age indicator are not necessarily symmetrically stable within an individual; this is well documented in the literature and several models have been proposed to explain skeletal asymmetry, including genetic determinants, biomechanical factors, and environmental stress (Jones et al. 1977; Schell et al. 1985; Albert & Greene 1999; Halgrimsson 1999). Overbury and colleagues (2009) reported asymmetry in the pubic symphysis Suchey-Brooks age phases for over 60% of a sample of 20th Century White males (n=130) drawn from the Hamann-Todd anatomical collection; however, the authors note that the presence of asymmetry does not compromise the accuracy of the method if the morphologically advanced symphyseal face is used to age the asymmetrical individual. Despite these criticisms, pelvic indicators are considered superior to cranial ones (Meindl & Lovejoy 1989; Nagar & Hershkovitz 2004), and the age-related changes of the pubic symphysis are regarded by many anthropologists to be the best indicator of age at death in adults, providing the greatest accuracy and reliability (McKern & Stewart 1957; Stewart 1979; Meindl et al. 1985; Suchey et al. 1986; Steele & Bramblett 1988; Ubelaker 1989a; Buikstra & Ubelaker 1994; Bass 1995; Suchey & Katz 1998; Boldsen et al. 2002). The pubic symphysis is clearly the most studied indicator of age in adult humans, and it is both the most commonly used and most widely trusted indicator (Todd 1920, 1921; Brooks 1955; McKern & Stewart 1957; Stewart 1957; Gilbert & McKern 1973; Hanihara

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& Suzuki 1978; Suchey 1979; Meindl et al. 1985; Katz & Suchey 1986; Meindl & Lovejoy 1989; Gillett 1991; Aykroyd et al. 1999). Since developmental changes are fairly constrained, and senescent changes are more variable, Boldsen and colleagues (2002) suggested that the pubic symphysis is the most informative indicator of adult age at death because it includes both developmental and degenerative changes; the auricular surface, sternal rib end, and cranial sutures exhibit only degenerative changes. Because of the development of the ventral rampart of the pubic symphysis, the pubic symphysis is considered the most accurate indicator for young adults (Mant 1984; Meindl et al. 1985; Bedford et al. 1993; Sirohiwal et al. 1998; Martrille et al. 2007). Klepinger and colleagues (1992) assessed the performance of three pubic symphyseal aging techniques using the mean absolute deviation of true age from the scored interval mean falling within +/- 1 and +/- 2 standard deviations. Their study was based on a sample of 202 female and 116 male pubic symphyses collected at autopsy by Suchey from the Office of the Chief Medical Examiner-Coroner, County of Los Angeles, and Micozzi and Carroll from the Dade County Medical Examiner, Miami Florida, respectively. Based on this research, Klepinger and colleagues (1992) concluded that among the Suchey-Brooks, Gilbert-McKern, and McKern-Stewart methods, the SucheyBrooks standard is best for forensic casework. The Suchey-Brooks standard for estimating age from the pubic symphysis is considered one of most reliable macroscopic age estimation methods (Telmon et al. 2005). Even when considering other standards based on the pubic symphysis, it is least open to be criticized based on methodological issues for reasons mentioned previously.

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The Suchey-Brooks system is currently used as the worldwide standard for estimating age from the pubic symphysis (Kimmerle et al. 2008a). Although Hartnett (2007) and Berg (2008) have modified existing pubic symphyseal aging methods by adding an additional phase, Boldsen and colleagues’ (2002) Transition Analysis standard for scoring the pubic symphysis is the most recent original aging method developed. The most significant benefits of this new standard include finer detail in scoring morphological change by using a component approach and a more accurate estimated age distribution, particularly for those aged 50+. The latter is particularly important because the ability to estimate the age at death of older individuals has been elusive in the past.

Auricular surface For Lovejoy and colleagues’ (1985b) auricular surface aging standard, neither Murray and Murray (1991) nor Osborne and colleagues (2004) detected significant differences between males and females or Blacks and Whites. This contrasts with Mensforth and Lovejoy’s (1985) findings, which indicate that the female auricular surface changes less rapidly than that of males. As mentioned in the previous section on the pubic symphysis, Schmitt (2004) studied a Thai sample of known age and sex. In addition to using the Suchey-Brooks standard, she also tested the Lovejoy and colleagues (1985b) iliac auricular surface method. Results were similar, finding that both bias and inaccuracy increased with age, as well as that the standard produced a systematic underestimation of chronological age. Osborne and colleagues (2004) tested Lovejoy and colleagues’ (1985b) auricular surface aging standard and did not find a significant difference between American 76

samples drawn from the late 19th and early 20th Century Terry Collection and the later 20th Century Bass Documented Collection. Specifically, Osborne and colleagues (2004) tested the effects of age, sex, ancestry, and collection/time of auricular surface morphological change and found that only age influences the observed changes.

Strengths and weaknesses Based on Murray and Murray’s (1991) conclusion that there are neither sex nor geographic differences in auricular aging, Konigsberg and Frankenberg (1992) argue that the auricular surface is useful in paleodemography because it satisfies the Uniformitarian assumption; this assumption is met because Murray and Murray (1991) find that both living and past human populations age in a similar manner at identical rates (Meindl & Russell 1998). In addition, the auricular surface itself is often the best-preserved indicator of age in archaeological remains (Aykroyd et al. 1999; Cox 2000) and as a result, Meindl and colleagues (1983) recommend that the auricular surface should be a primary method of age determination for these samples. When compared to other traditional phase based standards, the auricular surface has been touted to be at least as accurate in predicting older ages as the pubic symphysis, if not better (Meindl & Lovejoy 1989; Saunders et al. 1992; Bedford et al. 1993). In fact, Meindl and colleagues (1983) claim that the overall correlation of age with the auricular surface is 0.72. One major drawback to Lovejoy and colleagues’ auricular surface aging standard is that many authors suggest that too much variability occurs within any given individual’s auricular surface morphology, making it difficult to classify those that are ambiguous (Buckberry & Chamberlain 2002). Even authors contributing to the development of the auricular surface aging standard state that it is difficult to interpret 77

due to the complexity of the solely degenerative changes (Meindl & Lovejoy 1989). Predictably, Rogers (1990) could not replicate Lovejoy and colleagues’ results in older samples, and Jackes (1992) found significant interobserver disagreement, though this may be partially explained by the fragmentary nature of the indicator (Meindl & Russell 1998). Many investigators suggest that the method suffers from low repeatability and reliability (Murray & Murray 1991; Jackes 1992; Saunders et al. 1992; Molleson & Cox 1993; Hoppa 2000; Schmitt et al. 2002). But Galera and colleagues (1995) did not find significant interobserver error when they tested the on 963 skeletons from the Terry Collection. Murray and Murray (1991) tested Lovejoy and colleagues’ (1985b) auricular surface aging method on 189 individuals from the Terry Collection to determine if the method was applicable to individuals in forensic contexts. The authors suggested that the age ranges provided by the standard, particularly for older individuals, were too large for forensic cases (Murray & Murray 1991). For example, those between the ages of 50-60 years were underestimated by approximately 10.5 years, and individuals older than 60 years were underaged by an average of 24 years. When tested on samples taken from the Bass Donated and Terry Anatomical collections, inaccuracy was lowest for middle decades 40-69 year olds and highest for 80-89 year olds (Osborne et al. 2004). Bias was positive—overestimating age—for the young, and negative for the old (Murray & Murray 1991; Saunders et al. 1992; Osborne et al. 2004; Mulhern & Jones 2005; Martrille et al. 2007). This is to be expected due to the statistical methodology used, because regression toward the mean19 returns results of systematic over- and under-aging (Konigsberg &

19

See discussion later in this chapter.

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Frankenberg 1994; Konigsberg et al. 1997; Aykroyd et al. 1997; Nawrocki 1998; Aykroyd et al. 1999). Other tests of the auricular surface aging standard conducted by Murray and Murray (1991) and Santos (1996) discovered that the method governs the age estimates of target populations; instead of being characteristic of the target Terry and Coimbra samples, it reflects the built-in biases of the method (Bocquet-Appel & Masset 1982; Jackes 2000). Other critiques of the Lovejoy and colleagues auricular surface aging standard emphasize its inability to allow for individual variation in skeletal aging (Bedford et al. 1993), narrow age ranges (Aykroyd et al. 1999; Buckberry & Chamberlain 2002; Schmitt 2004), and subjectivity to the effects of motion at the sacroiliac joint during pregnancy (Sashin 1930).

Fourth rib Sex-specific differences have been reported for the sternal rib ends, resulting in separate age ranges and/or different phase descriptions for male and female standards (İşcan et al. 1984; İşcan et al. 1985; Loth & İşcan 1989). In addition, İşcan and colleagues (1987) specifically designed their study to test for racial differences in the age estimation from the sternal end of the rib. They tested their fourth rib aging standard, which was developed from a White American population, on a sample of Black Americans from an autopsy sample from Broward County, Florida. While the authors acknowledge that the Black sample was limited in size and age range, they did find differences between races in both the rate and pattern of metamorphosis. After the age of 30 years, differences between Blacks and Whites were apparent; specifically, İşcan and colleagues (1987) reported that American Blacks were overaged in phases 5-7. In 79

subsequent publications, Loth (1988) and Loth and İşcan (1989) noted differences in the rate and pattern of sternal rib aging specifically between Black and White females, with Black females appearing younger than their White counterparts. In contrast, Russell and colleagues’ (1993) observed slight, non-significant delays in sternal rib end changes for American Blacks compared to American Whites in a sample of males drawn from the Hamann-Todd collection. Similarly, Oettlé and Steyn (2000) found a tendency toward slower age-related morphological change in the sternal end of the ribs for South African Blacks compared to Americans. Oettlé and Steyn (2000) applied İşcan’s American rib aging standards to a large sample of Black males and females collected between 19941996 from Gauteng Province, South Africa. The morphology of the South African Blacks was such that the observed phases of rib age were younger than the expected phase according to chronological age. This effect may be result of disease exposure, physical activity level, genetic and cultural differences, and socioeconomic and nutritional disparities (Sanders 1966; Oettlé & Steyn 2000). Similarly, Yavuz and colleagues (1988) noted that a modern White autopsy sample of Turks, which is geographically, genetically, and culturally different from the American White sample from which İşcan’s rib aging standard was devised, generally tended to attain phases later than Americans, though these differences were not statistically significant. As mentioned previously, Yoder and colleagues (2001) tested the applicability of İşcan and colleagues’ right fourth rib aging technique on left and right ribs 2-3 and 5-9. This study was undertaken as a response to critiques of İşcan’s rib aging method, which included difficulty applying the standard as a result of poor preservation of the sternal end and/or the inability to isolate the fourth rib. Yoder and

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colleagues (2001) found that most ribs provided age estimates similar to that of the fourth rib and that a composite score20 yields the same age as the fourth rib, suggesting that other ribs can used with İşcan’s and colleagues’ age estimation method.

Strengths and weaknesses As with all skeletal aging standards, reports documenting the interobserver rates are contradictory for İşcan’s fourth rib aging method; Dudar and colleagues (1993) found high interobserver error, but tests conducted by Galera and colleagues (1995) on 963 skeletons from the Terry Collection did not find significant interobserver error. Unsurprisingly, tests conducted by Loth and İşcan (1989) after developing the standard revealed minimal interobserver error and no significant difference in scoring by experience level. Similarly, Oettlé and Steyn (2000) determined that the standard’s repeatability was acceptable. In general, İşcan and colleagues’ fourth rib standard produces large standard error in age estimates (Rösing et al. 2007), and many osteologists have found that the method is not useful in isolation (Russell et al. 1993; Loth 1995; Aykroyd et al. 1999). In contrast, Ritz-Timme and colleagues (2000) recommend using İşcan and colleagues’ fourth rib standard for cadavers, skeletal remains, historic and archaeological cases, particularly for individuals under 40 years of age, as published standard errors are as low as +/- 2-4 years. While all traditional American aging standards have greater inaccuracy as chronological age increases, Martrille and colleagues (2007) found that İşcan’s fourth rib aging standard had the least inaccuracy of all the methods for older individuals

20

The average of an individual's phase scores for multiple ribs, omitting the fourth rib.

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examined from the Terry Anatomical Collection; similar conclusions were reported by Saunders and colleagues (1992), Russell and colleagues (1993), and Dudar and colleagues (1993). Methodological bias results in the tendency to overestimate the age of the young and to underestimate the age of the old (Russell et al. 1993; Martrille et al. 2007); however, systematic biases in age estimates have been reported for İşcan and colleagues’ fourth rib aging technique: both a Canadian autopsy sample and a Canadian archaeological sample were consistently underaged (Dudar et al. 1993), and autopsied French individuals were consistently overaged (Baccino et al. 1999). Another concern in forensic and archaeological contexts is that it is difficult to isolate the fourth rib if the remains are incomplete or fragmentary (Kunos et al. 1999); however, this obstacle may be overcome by using a suitable, alternative rib (Loth et al. 1994; Yoder et al. 2001). Finally, although Kunos and colleagues (1999) believe that the rib cage is not subjected to physiological stress in the same way that pelvic indicators of age are, the authors do suggest that the lower ribs may be affected by mechanical stresses. Rösing and colleagues (2007) echo this concern, proposing that age changes in the sternal end of the ribs may be dependent on activity patterns.

Transition Analysis Boldsen and colleagues (2002) noted differences in the point estimates and confidence intervals produced by Transition Analysis between the sexes, as well as between American Blacks and Whites, despite inputting the same observations. However, these differences have less of an impact if all components for all indicators are

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used to compute the age estimates. The authors attribute these sex- and race-based age estimate disparities to genetic differences and/or diverse lifetime experiences.

Strengths and weaknesses For traditional phase based aging methods, osteologists sometimes find it difficult to distinguish between two sequential stages in a particular bony feature, thus leaving the determination open to interpretation (Kimmerle et al. 2008b). This problem might arise because the particular skeletal feature is anomalous, modified through a pathological process, or damaged after burial (Dr. George Milner, personal communication, 2009). Phase based methods have discrete age intervals, often with a constant width, to describe imprecision in estimations (Boldsen et al. 2002); but this approach assumes that all individual age estimates have the same degree of error (Boldsen et al. 2002). The benefit of using Transition Analysis is that scores components of aging indicators separately, better reflecting the complex changes observed, and it allows every skeleton to have its own degree of error, which is dependent on its particular suite of traits (Boldsen et al. 2002). Another benefit is that the ADBOU Age Estimator program allows for the observer to record a score between phases (ex. 2-3) if he or she is unsure for a certain component. Kimmerle and colleagues (2008b) support the continuous nature of categories and the need for Transition Analysis in calculating age at death estimates. The point estimates and confidence intervals for age vary among individuals because the suite of traits is different for each; unlike traditional phase based methods, Boldsen and colleagues’ Transition Analysis is designed to analyze trait data and optimize the output. Aykroyd and colleagues (1999) argue that by using Bayesian analyses, Transition Analysis reduces the trend of underestimating the age of the old and performs better than 83

traditional methods by providing smaller average differences between predicted and actual age, as well as a smaller 95% confidence interval for the point estimate. Bayesian prediction and maximum likelihood estimates of age are better because they are calculated from a combination of stages for different variables and priors can be generated from the collection at hand. The maximum likelihood age estimates produced by Transition Analysis are independent of the distribution in the original reference sample (Boldsen 1997; Aykroyd et al. 1999; Schmitt et al. 2002). As a result, it appears that age estimates produced by Transition Analysis using an appropriate informed prior are not subject to mimicry as are traditional, regression analysis aging standards (Boldsen et al. 2002). However, Bayesian estimation also has limitations. In a study conducted by Aykroyd and colleagues (1999), it produced systematic bias; it was not determined if the bias was inherent in method or was the result of the dataset tested. According to its authors, Transition Analysis using pubic, auricular, and cranial indicators should perform the best because it allows for the different rate of change in components of the indicator and it combines all data to get the most accurate estimate (Boldsen et al. 2002); formal testing by Boldsen and colleagues (2002) confirmed this result, finding a 0.88 correlation of the estimate with chronological age. Boldsen and colleagues’ Transition Analysis using pubic symphysis scores alone performed nearly as well, with a correlation of 0.86, followed by the auricular surface (0.82) and cranial sutures (0.66) (Boldsen et al. 2002). The primary critique of this aging standard is that the reference collection used by Boldsen and colleagues (2002) includes three Black females over the age of 90; Hoppa and Vaupel (2002) argue that these individuals, who are part of the Terry Anatomical

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Collection, probably died at younger ages, and should be excluded from future analyses. Other critiques are not specific to this method; as with other skeletal aging standards, the results from Transition Analysis include broader age ranges for older individuals than for younger ones. The likelihood curves produced by Boldsen and colleagues’ Transition Analysis become broader as age increases, indicating the error associated with estimating age of older individuals. This may result in part because many components, like dorsal margin of pubic symphysis, progress quickly through stages in early adulthood then plateau or only change slightly later on (Boldsen et al. 2002).

Critiques of estimating age from the adult skeleton The estimation of age at death from the skeleton is subject to a number of concerns, including the inherent variation in the aging process, methodological issues, and statistical problems.

Inherent variation in the aging process Skeletal biologists need to understand the fundamental biological process of aging in the human skeleton because the inherent variation in that process is a primary source of error for all current age estimation standards (Bocquet-Appel & Masset 1982; Lovejoy et al. 1997; Hoppa 2000; Schmitt et al. 2002). This complex variability in the process of skeletal aging (Ferenbach et al. 1980; Maples 1989; Stini 1994; Schmitt et al. 2002) is the nature of human senescence, complicating the estimation of chronological age from biological/skeletal indicators (Spirduso 1995). Senescent changes in bone are degenerative, not developmental, and as a result they are far more variable, producing age estimates with what Boldsen and colleagues 85

(2002) argue is a considerable degree of error; the degeneration of the body and skeleton is complex and does not occur at same rate for all individuals (Meindl et al. 1983; Wittwer-Backofen et al. 2008; Samworth & Gowland 2007) or even for multiple indicators within an individual (Kemkes-Grottenthaler 2002). The skeleton is more plastic than is generally assumed (Stewart 1957; Kemkes-Grottenthaler 2002), and it participates in the overall metabolism of the organism (Acsádi & Nemeskéri 1970). As a result, bone responds to internal and external influences by changing its morphology. Individual aging is influenced by a number of factors (Krogman 1970; Ferenbach et al. 1980; Angel & Caldwell 1984; İşcan & Loth 1985; İşcan et al. 1987; Vaupel et al. 1998; Jackes 2000), and is determined by continual interactions among genes, culture, and environment (Arking 1998; Schmitt et al. 2002). The skeletal aging process is influenced by many variables, including genetic factors (Angel 1984; Kemkes-Grottenthaler 2002), growth (Tanner 1962; Bogin 1999), diet/nutrition (Acsádi & Nemeskéri 1970; Angel 1984; Plato et al. 1994; Samworth & Gowland 2007), living conditions (Acsádi & Nemeskéri 1970), health status/disease (Steinbock 1976; Acsádi & Nemeskéri 1970; Angel 1984; Ortner 2003), occupation (Trotter 1937; Ubelaker 1979; Kennedy 1989; Plato et al. 1994; Samworth & Gowland 2007), lifestyle and physical activity levels (Stewart 1980; Angel 1984; Plato et al. 1994; Samworth & Gowland 2007), environmental changes (Acsádi & Nemeskéri 1970; Kemkes-Grottenthaler 2002), biomechanics, endocrine function (Krogman 1970; Maples 1981), bone mineral density (Arking 1998) substance abuse (Saville 1965; Wolf 1981; Angel 1984), and other factors (Buckberry & Chamberlain 2002; Prince 2004).

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There is strong evidence for low individual variability of morphological aging criteria during young ages, but variability among individuals increases as they age due to their unique interactions with the environment, as well as the accumulation of genetic and behavioral influences (Harper & Crews 2000; Wittwer-Backofen et al. 2008). In fact, numerous authors have noted that traditional osteological age indicators typically do not accurately estimate age for older individuals, finding that the older the individual, the greater the error in the age estimate (Angel 1984; Rösing et al. 2007; Wittwer-Backofen et al. 2008). This inaccuracy is reflected in the broad age ranges (i.e. 50+ years) produced by several standards for older individuals. This problem is so severe that several prominent anthropologists have argued that it may be impossible to age older individuals with any precision using current methods (Hanihara & Suzuki 1978; Suchey et al. 1986; Meindl & Lovejoy 1989; Milner et al. 2000; Boldsen et al. 2002; Berg 2008).

Methodological problems Multiple skeletal age indicators can be correlated with each other, meaning that the information provided by each indicator is not independent of the others (Boldsen et al. 2002). If age indicators are related or closely linked, the standards will reinforce a central tendency rather than providing a better (or more precise) age estimate (Jackes 2000). In contrast, Boldsen and colleagues (2002) suggest that if the correlation of skeletal traits is purely attributed to age, then the indicators would be independent if age is controlled for (Boldsen 1997). This assumption of “conditional independence” (Boldsen 1997) may work well for senescent changes in skeletal morphology as the mutation accumulation mechanism—one theory of aging—suggests (Rose 1991).

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Boldsen and colleagues (2002) found this assumption to hold true for the Terry Collection. Osteological age indicators have been developed and tested in a variety of contexts, and the summation of these results indicates a high degree of variation in the accuracy of age prediction (Meindl & Russell 1998). Bocquet-Appel and Masset (1982) argue that inaccuracy and unreliability is inherent in all osteological aging standards, which is a result of the low correlation between skeletal features and chronological age. Bias and inaccuracy should also be a source of concern for investigators. Bias is defined as the sum of the estimated age minus chronological age, divided by the number of individuals. Bias is directional, so the sign is important. Inaccuracy is defined as the sum of the absolute value of estimated age minus real age, divided by the number of individuals; here, the sign is not important (Meindl & Russell 1998; KemkesGrottenthaler 2002). All of the standards tested in this research have well-documented inaccuracy that increases with age (Buikstra & Konigsberg 1985; Lovejoy et al. 1985a; Katz & Suchey 1986; Murray & Murray 1991; Dudar et al. 1993; Bedford et al. 1993; Santos 1996; Nagar & Hershkovitz 2004), though Meindl and Russell (1998) argue that using multiple age estimation standards minimizes this problem. Unfortunately, bias is harder to deal with because certain methods work well for particular ages but not others; this is due to reference population age structure influences, which will be discussed in more detail below. Finally, advanced training—and more importantly, experience—is required for the accurate application of osteological age estimation methods (Ritz-Timme et al. 2000;

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Rösing et al. 2007). Without the proper application of the aging standard to any given target sample, researchers should be skeptical of the results presented.

Statistical problems Statistical problems also thwart the process of age estimation from skeletal indicators (Samworth & Gowland 2007). Two prominent, related problems include regression toward the mean and age structure mimicry.

Regression toward the mean Regardless of the indicator used, most current American age estimation standards tend to overestimate the ages of the young and underestimate the ages of the old in target samples (Murray & Murray 1991; Russell et al. 1993; Osborne et al. 2004; Djuric et al. 2007; Hartnett 2007; Martrille et al. 2007; Berg 2008). Numerous authors suggest that these systematic errors are the result of regression toward the mean (Katz & Suchey 1986; Konigsberg & Frankenberg 1994; Konigsberg et al. 1997; Aykroyd et al. 1997; Aykroyd et al. 1999; Schmitt et al. 2002; Rösing et al. 2007). Most traditional phase-based standards use linear regression to correlate the morphology of the indicator with chronological age. The calculated regression line equation is then applied to convert scores to a predicted age estimate (Schmitt et al. 2002). With this technique, though, lower correlations between the skeletal indicator and chronological age results in greater bias (Aykroyd et al. 1997; Aykroyd et al. 1999). If age at death is regressed on a set of skeletal age indicators, the output is an estimate of age for each value of the indicator; while this is what researchers want, these estimates are influenced by the age composition of the reference sample. For example, if the young 89

in the reference sample outnumber the old, then archaeological samples aged using techniques based on this reference sample look younger than their actual age, feeding into the belief that past populations lived much shorter life spans. Age structure mimicry One of the most significant sources of error in skeletal age estimation arises from problems with the reference collection from which the methods are based (Masset 1971; Bocquet-Appel & Masset 1982; Masset 1989). Documented skeletal reference series have distorted age compositions and/or selection criteria (Wittwer-Backofen et al. 2008), often over-representing older, non-Hispanic White individuals, particularly for those collections established in the latter half of the 20th Century. The published claims of systematic errors in age estimation are likely due in part to differences between reference samples and target populations. Although Masset (1971) noted differences in the underlying age-structures of the method’s reference sample and target groups, it was Bocquet-Appel and Masset’s (1982) seminal work that raised concerns about methodological problems in paleodemography and sparked heated debate in the anthropological community. The authors’ critiques, which have been reiterated in later works (Bocquet-Appel & Masset 1985; Masset 1989, 1993; Bocquet-Appel 1994; Bocquet-Appel & Masset 1996), focused on two points: first, that methods of age estimation were too imprecise and biased to produce useable results for demographic analyses; and second, that aging methods reflect the age structure of the reference sample. The latter problem, age structure mimicry (Mensforth 1990), results in the replication of the reference sample mortality profile in target samples and unreliable age estimators, with error increasing when indicators correlate poorly with age (Bocquet90

Appel & Masset 1982; Konigsberg et al. 1994; Meindl & Russell 1998; Kimmerle et al. 2008a). Schmitt and colleagues (2002) summarized the problem of age-structure influences effectively. Each stage of a particular standard has a corresponding mean age that was calculated from the reference population. The mean age of each stage depends greatly on the overall age structure of the reference population, and as a result, when the standard is applied to a target archaeological sample, its distribution will be similar to that of the reference sample. A study by Gillett (1991), comparing age estimates for an archaeological site on the eastern shore of the San Francisco Bay produced by the Todd and Suchey-Brooks pubic symphysis standards, clearly illustrates the problem of age structure mimicry. When Gillet (1991) looked at predicted survivorship and probability of death, the Todd standards resulted in a lack of representation of older individuals (40+ years). The Todd standard predicted that the chance of survival for these individuals sharply declined at age 40, but the Suchey-Brooks standard predicted that the chance of survival would steadily decline in the older ages (Gillett 1991). The difference observed between the two predicted population distributions reflects the difference between the phase age limits between the two methods, especially for upper decades (Gillett 1991). Gillett (1991) concluded that the usual assumption that prehistoric Californians died by the age of 50-55 is likely a reflection of the standard from which their ages were estimated. This problem also exists for the application of osteological aging standards on an individual level in forensic contexts (Schmitt et al. 2002), but to a lesser extent (Komar & Buikstra 2008). Bocquet-Appel and Masset argued that imprecise age estimates and mimicry preclude the ability to answer fundamental questions of interest in target groups, and

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thus, declared the death of paleodemography. Another critique of paleodemography showed that the inverse of the mean age at death is approximately equal to the crude birth rate in a non-stationary population (Sattenspiel & Harpending 1983); this argument suggested that traditional paleodemographic data was more informative about fertility than mortality in populations with an unknown growth rate, a conclusion that was counterintuitive to most researchers at the time. Several years later, Buikstra and colleagues (1986) used regression analysis to illustrate that birth rate was indeed more highly correlated with the death proportion than the crude death rate in non-stationary populations. In contrast to the grim outlook posited by Bocquet-Appel and Masset (1982), other researchers countered that the “death” of paleodemography was exaggerated and extreme (Van Gerven & Armelagos 1983; Buikstra & Konigsberg 1985; Greene et al. 1986; Konigsberg & Frankenberg 1992, 1994). Van Gerven and Armelagos (1983) argued that skeletal samples do not invariably reflect the structure of their reference populations and that age estimates do not produce the random fluctuations predicted by Bocquet-Appel and Masset’s (1982) a priori criteria for age estimation. Buikstra and Konigsberg (1985) argued that Bocquet-Appel and Masset’s (1982) critiques were extreme, but they acknowledged that the imprecision in age indicators, particularly for older adults, and interobserver error remain significant problems. Beginning in the mid-1980s, researchers attempted to address some of the critiques of paleodemography by testing alternative methods. Jackes (1985) suggested that the mean and standard deviation for age at death within pubic symphyseal phases could be used to probabilistically assign ages at death in the target sample. She used the

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normal distributions of age within phases to get smooth distributions, but Frankenberg and Konigsberg (2002, 2006) have criticized her methodology. The authors argue that it is unlikely that age is normally distributed within stages and that the age distribution is still dependent, in part, on the reference sample. Another development was the application of a hazards model to paleodemography as an alternative to using the traditional life table approach (Gage & Dyke 1986; Gage 1988; Wood et al. 1992). The benefits to hazards analysis include the replacement of the life table values l(x) with a survivorship function21, d(x) with a smooth function22, and q(x) with a hazard rate. Hazards models allow for variation in the age ranges that are assigned to individual skeletons and can be adjusted for growth rate, assuming some estimate of the growth rate is available (Asch 1976; Milner et al. 2000). However, Frankenberg and Konigsberg (2006) stress that hazards models alone do not circumvent the non-stationarity problem. Konigsberg and Frankenberg (1992) stated that the perceived paucity of older individuals in archaeological samples was the result of using inappropriate methods of age estimation; the authors suggested the solution to this age structure mimicry problem was to use maximum likelihood estimates of life table or hazard functions to incorporate the uncertainty of age estimates. They argued that when age is estimated rather than known, the traditional method of assigning individuals to age classes will produce biased estimates of age structure; this bias was known in other areas of study and a potential solution was drawn from the fisheries literature. The "iterated age-length key" uses a contingency table of an age indicator against known age classes in a reference sample to

21 22

The probability of survival to age “a.” The probability density function of age at death.

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infer the age at death structure in a target sample. In addition, Konigsberg and Frankenberg (1992) suggested a future course for research that included the revision and development of new aging methods and the incorporation of uncertainty of age estimates in parameters for life tables using maximum likelihood or hazards methods. However, Konigsberg and Frankenberg did not anticipate the use of ordinal parametric models like logistic or probit regression to describe the development of age indicators that are phasebased (Skytthe & Boldsen 1993). Boldsen brought this approach, termed transition analysis because it models the age to transition between indicator phases, to paleodemography. Subsequently, the Transition Analysis aging method was introduced (Milner et al. 2000; Boldsen et al. 2002). In 1996, Bocquet-Appel and Masset presented their results using iterative proportional fitting (IPFP) in simulations; the authors applied sets of conditional probabilities in order to estimate the age at death structure for the target sample. Bocquet-Appel and Masset (1996) argued that their method differed from that outlined in Konigsberg and Frankenberg (1992); however, Frankenberg and Konigsberg (2002, 2006) disagreed. In 2002, when Konigsberg and Frankenberg compared the two, finding that the methods were essentially the same statistic; they argued that both methods used on the same data should produce the same results. Bocquet-Appel and Masset’s (1996) test produced differing results, a conclusion that Konigsberg and Frankenberg (2002) do not accept. Konigsberg and Frankenberg (2002) also criticized Jackes’ (2000) use of the IPFP; in this case, they argued that Jackes violated the statistical assumptions of the model by using more age groups than there were morphological stages in the aging method.

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Amidst the decades of conflict and controversy, a collaborative effort to advance the field emerged. A workshop of invited researchers came together at the Max Planck Institute for Demographic Research in Rostock, Germany, to focus on biostatistical methods and adult aging techniques within the scope of paleodemography (Hoppa & Vaupel 2002). The workshop provided the attendees with an identical dataset on which to test their techniques. One of the most significant outcomes was the realization that the theoretical framework in which varying statistical methods were placed was critical. The Rostock Manifesto is the theoretical approach adopted by the researchers. The Manifesto calls for the development of more reliable and more vigorously validated age indicator stages relating skeletal morphology to known chronological age, the use of a multidisciplinary approach to develop models and methods to estimate the probability of observing a suite of skeletal characteristics “c,” given known age “a,” the recognition by osteologists that what is of interest in paleodemographic research is the probability that the skeletal remains are from a person who died at age “a,” given the evidence concerning “c”23, and the calculation of the probability distribution of lifespans in the target population must be done first, before individual estimates of age (Hoppa & Vaupel 2002). The outcomes of this collaborative effort are published in a text edited by Hoppa and Vaupel (2002); within, numerous North American and European researchers present their approaches to solving problems in paleodemography. Despite significant contributions in the past quarter century, researchers are still struggling with accurate adult age estimation from skeletons (Storey 2007). This is particularly problematic for the estimating ages of older individuals, as evidenced in the

23

Note that this is different from the probability referred to in the previous clause.

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recent work by Wittwer-Backofen and colleagues (2008). In their study, thirteen independent observers24 using a variety of aging techniques analyzed the skeletal remains of 121 adults from Lauchheim, an early medieval cemetery. The age ranges and mean age estimations were compared, and the results indicated smaller age ranges for younger individuals and broader age ranges for older age groups, regardless of the method used. In summary, to some degree, all age estimates derived from conventional, phasebased methods are affected by mimicry and imprecision. However, the maximum likelihood age estimates produced by Transition Analysis are independent of the age distribution in the original reference sample (Boldsen 1997) and should not be subject to mimicry (Boldsen et al. 2002).

Summary Hoppa and Saunders (1998) suggest that anthropologists only have a basic knowledge of population differences for skeletal age change. Conflicting results regarding the influence of sex and race on age estimation have emerged from the published osteological research. The anthropological literature demonstrates that the relationship between the skeletal indicator and chronological age varies among samples drawn from different geographical regions (Schmitt 2004), implying that a single standard of senescence for populations of different origins is not appropriate (Hoppa 2000). These population differences may actually be the result of diverse genetic backgrounds, behaviors, environments, or other factors. Secular trends could have a significant impact on age estimation if, for instance, contemporary individuals skeletally

24

The observers were often the developers of the method they scored.

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mature earlier or senesce at different rates than their historical counterparts (Klepinger 2001). Klepinger (2001) found a secular trend for increasing childhood obesity, which was associated with a trend for accelerated skeletal maturation; though according to Klepinger (2001) this secular trend is real, the author determined that it is negligible for the estimation of age, simply contributing to the noise of population variation. Only a few recent studies have directly addressed the issue of a secular trend in the rate of senescence of joint surfaces, and the results are contradictory. American aging standards do not appear to be uniformly applicable to all target populations worldwide. But does this problem exist for American target samples differing in genetic background, environmental factors, and time, from those used to develop American aging standards? Currently, American aging standards are applied to prehistoric, historic, and forensic samples alike, despite the fact that the standards are developed from samples primarily composed of individuals living during the 19th and early 20th Centuries, which are not even representative of the populations from which they were derived. If secular change, genetic and environmental differences, or other factors strongly influence the aging process of the human skeleton, these standards may not be appropriate for age estimation of target samples differing from the reference sample, even if they belong to the same population. As a result, age estimates for target samples may not be reliable or accurate, impacting downstream analyses in bioarchaeological and paleodemographic endeavors as well as having legal implications for the admissibility of forensic anthropological evidence in court. Rules for the admissibility of scientific evidence require publication of the method in peer-reviewed journals as well as an assessment of the validity and reliability

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of the standard. Interestingly, forensic tests of aging standards have focused on the application of American standards to foreign target samples, likely due to the large scale investigations of mass graves and necessity to age Eastern European samples where no population-specific standards exist. It is unclear if American aging standards perform as well on recent American target samples as they do for the reference populations from which they were developed. To date, no large-scale investigation of this question has been undertaken using documented American skeletal samples. To address this problem, this dissertation research will explicitly compare large documented skeletal samples drawn from older American reference series and more recent documented American osteological collections to determine whether these samples age at a different rate. American Blacks and Whites of both sexes were drawn from the Terry Anatomical, Hamann-Todd Osteological, Bass Donated, Maxwell Museum Documented, and the Maricopa County Forensic Science Center autopsy collections so that differences between older and more recent groups can be explored. Morphological indicators of age were scored according to the following standards to determine if changes are absent, ubiquitous, patterned, or random in nature: Todd (1920) pubic symphysis; Suchey and Brooks (1990) pubic symphysis; Hartnett and Fulginiti (2007) pubic symphysis; Lovejoy and colleagues (1985) auricular surface; İşcan and Loth (1986) sternal end of the fourth rib; Meindl and Lovejoy (1985) cranial sutures; and Boldsen and colleagues (2002) pubic symphysis, auricular surface, and cranial sutures.

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Chapter 4 Research Design The goal of this research is to determine whether older American skeletal series progress through the senescent changes of skeletal indicators at a different rate than more recent ones. The answer to this question determines the validity of the current practice of universally applying American aging standards to all American skeletal series, regardless of differences in sex, race, genetic background, living conditions, health status, time, and geographic region is valid. If differences in aging exist between older and more recent American skeletal samples, then existing standards, many of which are based on these older samples, will not produce reliable age estimates for forensic or archaeological remains. The data used to address this question include demographic data and scores of the morphology for four skeletal indicators according to phases/stages defined by seven established American aging standards. Four osteological age indicators were examined: the pubic symphysis, auricular surface, sternal end of the fourth rib, and cranial sutures. American aging standards tested include the Todd, Suchey-Brooks, Hartnett-Fulginiti, and Boldsen and colleagues pubic symphysis methods, the Lovejoy and colleagues and Boldsen and colleagues auricular surface methods, the İşcan and colleagues sternal rib end method, the Meindl and Lovejoy and Boldsen and colleagues methods for scoring cranial suture closure, and the Boldsen and colleagues methods combining the pubic symphysis, auricular surface, and cranial suture indicators. These data were recorded for nearly one thousand remains drawn from five documented American skeletal collections.

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Materials Nine hundred and seventy-one sets of adult human skeletal remains, aged 20 years or older, were examined to test whether American osteological aging standards are universally applicable to diverse American target samples. The dataset included the remains of Blacks and Whites drawn from the Terry, Hamann-Todd, Bass Documented, and Maxwell Museum collections, as well as samples curated at the Maricopa County Forensic Science Center. These collections comprise skeletal remains collected for over a century from autopsy, cadaver, unclaimed, and donated bodies. These collections are an ideal data source for this dissertation research because, with the caveats previously described, the remains have known sex, age, race, and date of birth and/or death information. Morphology of the pubic symphysis, auricular surface, sternal rib ends, and cranial sutures were examined for a sample of individuals born between the early 19th and late 20th Centuries. Analyses were limited to Blacks and Whites because the selected collections have comparatively few individuals from other groups. Nonetheless, these two groups are important in historic research and contemporary forensic analyses in the United States, and the results will clarify the potential impact of often-overlooked variables on age estimation in other American groups. The skeletal series utilized in this project can be roughly divided into older “Reference” samples and more “Recent” collections. This is done for two reasons: first, to distinguish samples that have traditionally been used as the references for traditional American aging standards from those that are not; and second, to maximize sample sizes such that a wider range of variation can be examined, particularly for Blacks. For this research, the older Reference American skeletal samples have an average birth year of 100

1878, with a range of 1828-1943. Many of these individuals lived through tumultuous times, including the American Civil War and Reconstruction Period; living standards declined after the war, particularly for those living in the South (Carson 2006). The more Recent American skeletal samples have an average birth year of 1939, with a range of 1889-1985; many of these individuals benefited from the first commercially available antibacterial antibiotic, which was available in the early 1930s. While vaccines for smallpox and plague were available in the 19th Century, the 20th Century saw the development of and widespread immunization for many more infectious diseases, including cholera, typhoid, tuberculosis, influenza, polio, measles, mumps, and rubella (Centers for Disease Control and Prevention 2006). Some members of each group experienced the Great Depression; this event may have significantly affected the health of these individuals, as well as the quality and quantity of food consumed. These samples provide a relatively diverse group and long temporal continuity of American documented skeletal remains, which is important to effectively test the research hypotheses. The Reference series serve as the source population for the development of many of the osteological aging standards tested during the data analysis phase of this research. Specific information for each collection regarding collection strategy, source populations, and biases are presented below.

Reference Collections Anatomical Collections Anatomists, not physical anthropologists, pioneered the first collections of entire human skeletons. As a result, older collections were primarily drawn from anatomy 101

school dissecting rooms and unclaimed morgue remains. The majority of the individuals included in these samples were born in the early 19th through the early 20th Centuries. Hamann-Todd Collection (HTH) As described in Chapter 2, the Hamann-Todd Osteological Collection is the product of two of the great anatomists of the early 20th Century: Carl August Hamann and Thomas Wingate Todd (Quigley 2001). The collection contains approximately 3,700 skeletons (Cobb 1981; Moore-Jansen 1989) and is the single largest comparative anatomical collection in the United States (Moore-Jansen 1989). It is currently housed at the Laboratory of Physical Anthropology within the Cleveland Museum of Natural History. The series contains the remains of American Whites and Blacks primarily from the Cleveland area, with the remainder from elsewhere in Ohio. These individuals were born between 1823 and 1934. The collection contains the remains of individuals from the anatomy department’s dissection labs, unclaimed or indigent burials, as well as those willing their bodies to science and research (Usher 2002). Documentation accompanying the remains includes age at death, race, sex, date of birth and/or date of death, as well as place of birth, occupation, and cause of death when available (Cobb 1959; Thompson 1982; Moore-Jansen 1989). For individuals with a specified place of birth, Cobb (1952) found that 60% of the Whites in the sample were born abroad, including origins in Scandinavia, Britain, Germany, and eastern and southern Europe. Native-born Whites have Ohio, New York and Pennsylvania listed as birthplaces (Cobb 1952). The majority of Black individuals migrated to the Cleveland area, with southern U.S. birthplaces including Georgia, Alabama, the Carolinas, Tennessee, Virginia, Kentucky, Mississippi, and Arkansas (Cobb 1952). The series 102

contains a large number of males (82%) and adults (Moore-Jansen 1989); only 25-30 skeletons are 16 years or under (Gottlieb 1982). As a whole, the individuals within the collection are considered to be generally of lower socioeconomic strata based on the cause of death listed (Cobb 1952; White 1991). The Hamann-Todd collection has served as the documented reference material for a number of commonly used aging methods, most notably the Todd method for scoring the metamorphosis of the pubic symphysis, the standards Lovejoy and colleagues developed for aging the iliac auricular surface, and the Meindl and Lovejoy technique for scoring cranial suture closure. Although significant concerns have been leveled against the Hamann-Todd collection regarding the reliability of age at death information25, the collection has not been rendered useless. As Meindl and colleagues (1990) suggest, the careful selection of remains can result in a viable sample; I was cognizant of these issues and adjusted the sampling strategy26 for data collection accordingly, justifying the use of these remains for this research. Terry Collection (TC) Two scientists were also integral to the formation of the Robert J. Terry Anatomical Skeletal Collection: Robert Terry, a medical doctor, and noted anthropologist, Mildred Trotter; the formation of the collection is detailed in Chapter 2. The Terry Collection contains 1,728 skeletons (Hunt 2009) and is the second largest anatomical skeletal collection in the United States (Moore-Jansen 1989). It is currently

25

The reader is referred to Chapter 3 for a more detailed discussion of known issues with recorded age at death. 26 See the Sample Selection Protocol in the Data Collection Methods section, this chapter.

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housed at the National Museum of Natural History at the Smithsonian Institution in Washington, DC. The series contains the remains of American Whites and Blacks from St. Louis and, to a lesser extent, other locations in Missouri (Hunt & Albanese 2005). These individuals were born between 1828 and 1943. Like the Hamann-Todd collection, the Terry collection contains the remains of individuals obtained from local hospital and institutional morgues, assembled from 1914 to 1965 (Murray & Murray 1991). The demography of the source population, specifically indigents and city morgue cadavers, initially presented an overabundance of older Black and White males; young, White women were originally underrepresented compared to males in the collection (Hunt 2009). Trotter attempted to correct the sex and race biases inherent in the collection by both replacing and adding new skeletal remains (Trotter 1981), though this did not eliminate all sampling problems. The majority of these supplemental females were willed donations because of the Willed Body Law of Missouri (passed in the mid-1950s), which required a signed release by the next-of-kin; this law resulted in a shift away from the lower socioeconomic status typical of earlier cadavers to a middle and upper income bracket for newer remains (Quigley 2001). Like the Hamann-Todd collection, the socioeconomic status of the majority of the early Terry individuals is low, an assumption based on the documented cause of death, which were often diseases of poverty and exposure (Moore-Jansen 1989). In contrast to the Hamann-Todd collection, most individuals in the Terry collection were native born (Moore-Jansen 1989). As with the Hamann-Todd Collection, the remains have documentation as to sex, age, race, cause of death, and date of birth and/or death (Thompson 1982). Morgue records also contain the name of the individual, morgue or institution of origin, permit

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number, and the dates of embalming (Hunt 2009). Other documentation held by the museum includes skeletal inventories, dental charts, autopsy reports, photographs and anthropometric measurements for two-thirds of the cadavers, over 800 plaster death masks, and 1050 hair samples (Hunt 2009). Like the Hamann-Todd series, the Terry collection has played an integral role in physical anthropology by serving as a reference sample for bone changes associated with age, sex, and race; in addition, the collection was also the basis for Trotter’s stature estimation equations (Quigley 2001). Another similarity to the Hamann-Todd collection includes uncertainty for some of the reported ages at death; again, the sample selection protocol for this thesis attempts to alleviate this problem.

Recent Collections More recent documented skeletal remains will be drawn from both ongoing skeletal donation programs and an autopsy sample.

Documented Collections Bass Collection (UTK) At present, the William M. Bass Donated Skeletal Collection, named for its founder, contains over 750 skeletons (Smithsonian Institution 2009) and is continually growing as a result of the University of Tennessee Forensic Anthropology Center’s active skeletal donation program. The collection is currently housed at the Forensic Anthropology Center, which is located within the Department of Anthropology at the University of Tennessee in Knoxville. The series predominantly contains the remains of

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American Whites and Blacks, with a smaller portion of Hispanic ancestry. Individuals are generally from Tennessee, with some derived from other states in the nation (University of Tennessee Forensic Anthropology Center 2005). Birth years range from the 1890’s to the 2000’s, with ages at death ranging from fetal to 101 years old. The source population includes forensic cases and donated remains (Usher 2002), and the series is biased towards males and Whites. The remains in the Bass Documented Collection have documentation as to sex, age, race, cause of death, date of birth and/or death, adult stature, weight, handedness, and education level. The current donor information form also requests information regarding occupation, childhood socioeconomic status, and medical history (University of Tennessee Forensic Anthropology Center 2005). Maxwell Museum Collection (MMA) The Maxwell Museum collection currently consists of over 260 individuals, and like the Bass Donated collection, is continually growing as a result of the Maxwell Museum of Anthropology’s active skeletal donation program. The collection is currently housed at the Maxwell Museum of Anthropology’s Laboratory of Human Osteology, which is in the Anthropology building at the University of New Mexico in Albuquerque. The series contains the remains of predominantly White Americans from the state of New Mexico. Birth years range from 1887 to 1971. The source population includes forensic cases and donated remains (Quigley 2001; Usher 2002; Laboratory of Human Osteology Maxwell Museum of Anthropology 2009), and the series is biased towards older Whites. Most individuals in the collection have documented sex, age, population affinity, and cause of death. Since the mid-1990s, a new donor information sheet has been 106

distributed to prospective donors, which requests both demographic data as well as additional information about health history and occupation (Laboratory of Human Osteology Maxwell Museum of Anthropology 2009).

Autopsy Collections Maricopa County Forensic Science Center autopsy sample (MCFSC) The Maricopa County Forensic Science Center autopsy collection consists of only pubic symphyses and bilateral sternal fourth rib ends. These samples were collected at the time of autopsy with the consent of the next of kin, between 2005 and 2006 by Kristen Hartnett as part of her dissertation research. The Maricopa County Forensic Science Center autopsy collection contains bone samples from 602 individuals; 582 individuals had both pubic symphyses and ribs (Hartnett 2007). The collection is housed at the Maricopa County Forensic Science Center in Phoenix, Arizona. While the sample includes both Black and White individuals, the preserved collection has an abundance of White males and females, as well as older individuals (Hartnett 2007). Individuals identified as Hispanic were included in the White27 category. Hartnett (2007) provided little justification for this action, although she did mention that this was in accordance with the Maricopa County Forensic Science Center’s classification system and that law enforcement often combines the two groups. All individuals are from Maricopa County, Arizona, in accordance with the Forensic Science Center’s jurisdiction. Birth years range from 1906 to 1988, with ages at death ranging from 18 to 99 years old with a mean of 54.1 (Hartnett 2007). All

27

Hartnett (2007) uses the term Caucasian.

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remains fall under the county’s medicolegal jurisdiction, including unattended and nonnatural deaths. The pubes and ribs were drawn from decedents of known age, sex, and race, and were collected at the time of autopsy/examination by the pathologist. Drug and alcohol history was also obtained, but Hartnett (2007) admits that it was unclear if this information was provided by a qualified medical source. The Institutional Review Board for human subjects research at Arizona State University did not allow for the collection of other antemortem information. During the course of gaining consent from the next of kin, it was noted that consent was more likely given for elderly individuals than younger ones and more likely for Caucasians than Blacks, Native Americans, Hispanics, or Asians. This is in part due to lower population numbers for Blacks and Asians in the Phoenix area and religious beliefs for Hispanics and Native Americans (Hartnett 2007).

Data Collection Methods Sample Selection Protocol Sample selection sought to balance four factors: documented chronological age, sex, race, and year of birth. Individual ages were recorded and each individual was placed in one of seven arbitrary age sets (20-29 years, 30-39 years, 40-49 years, 50-59 years, 60-69 years, 70-79 years, and 80+ years). The sampling strategy devised for this research project called for groups of ten same sex and race individuals (Black female, Black male, White female, and White male) per ten-year age set, for a maximum of 280 individuals drawn from each skeletal series.

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As discussed in Chapter 3, the Terry and Hamann-Todd collections are known to contain individuals whose ages are not truly known; the sampling strategy employed for this research attempted to avoid known problems with these collections by excluding individuals with ages designated as “?” or “ca,” as well as those with ages ending in “0” or “5.” This targeted elimination sought to reduce the potential bias in the dataset introduced by including individuals of suspect age. Some remains of unknown age could be estimated to belong to age sets other than that for their true chronological age; there is no definitive way to calculate the imprecision of the estimates. Researchers must rely on Todd’s and others assessments of whether the stated age was congruent with the condition of the soft and hard tissues of the body. Chronological age is an essential component of the statistical analyses used; imprecise ages could affect the results obtained. Importantly, the elimination of individuals with ages ending in “0” and “5” may also introduce bias, removing the morphology and variation present for those whose chronological ages actually are multiples of five. However, I think any bias introduced by the elimination of remains with potentially estimated ages is negligible when compared to that introduced by knowingly including imprecise ages.

Dataset Samples selected from the Terry and Hamann-Todd collections closely approximated the maximum set of 280 individuals. Due to their relative lack of Black decedents and smaller collection sizes, the samples drawn from the Bass Donated, Maxwell Museum Documented, and Maricopa County autopsy series were reduced to approximately half the size of those for the Terry and Hamann-Todd collections (Table 1). The total number of skeletal remains included in the dataset was 971, with 56% 109

(n=544) from reference anatomical series and 44% (n=427) from more recent donated and autopsy collections (Figure 1). Table 1: Number of skeletal remains in the dataset, by series and sex-race category

Black females White females Black males White males Total

HamannTodd 66 70 69 70 275

Terry

Bass Donated 3 55 29 69 156

67 64 70 68 269

Maricopa County 3 70 16 60 149

Maxwell Museum 1 49 4 68 122

Total 140 308 188 335 971

The dataset was also nearly evenly split between the sexes, with a composition of 54% (n=523) males and 46% (n=448) females. The number of males and females within the Maricopa County, Terry, and Hamann-Todd series was equivalent; a disparity between the sexes was observed for the Maxwell Museum and Bass Donated collections, both with an abundance of males that account for roughly two-thirds of the remains composing each sample (Figure 2). Figure 1: Number of skeletal remains in the dataset by series Composition of Dataset 122 275

149

156 269 HTH

TC

UTK

MCFSC

MMA

HTH=Hamann-Todd TC=Terry UTK=Bass Donated MCFSC=Maricopa County MMA=Maxwell Museum

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Figure 2: Number of Males and Females by Series

Number of Males and Females by Series 600 500

72 50

400

76

73

98

300

58

200

131

138

136

139

females

males

100

MMA MCFSC UTK TC HTH

0

HTH=Hamann-Todd TC=Terry UTK=Bass Donated MCFSC=Maricopa County MMA=Maxwell Museum

The distribution of races within skeletal samples is not balanced, however. Blacks only compose one third (n=328) of the total dataset. The three Recent American skeletal series have significantly more individuals classified as White than Black (Figure 3). This disparity is the result of the demographic composition of these collections as a whole. As mentioned previously, few African Americans are residents of Maricopa County and few tend to donate their remains to skeletal collections, either in Tennessee or New Mexico. The mean chronological age at death was fairly uniform across skeletal samples; only the Maxwell Museum sample was considerably older on average than the other samples (Figure 4). The similarity among collections for mean age at death was to be expected, based on the sample selection scheme. The Maxwell Museum collection

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includes an abundance of older individuals, and as a result, the mean age of the selected sample reflects the paucity of younger individuals. Figure 3: Number of Blacks and Whites by Series

Number of Blacks and Whites by Series 700 600

117 5

500 400

MCFSC UTK

124

32

300

MMA

130

19

TC

200 100

137

132

135

140

black

white

HTH

0

HTH=Hamann-Todd TC=Terry UTK=Bass Donated MCFSC=Maricopa County MMA=Maxwell Museum

Figure 4: Average Age by Collection

Average Age by Collection 80

Age-at-Death (Years)

70 68.58

60 50

54.11

55.88

58.69

54.42

40 30 20 10 0 HTH

TC

UTK

MMA

MCFSC

Skeletal Sample

HTH=Hamann-Todd TC=Terry UTK=Bass Donated MCFSC=Maricopa County MMA=Maxwell Museum

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Table 2: Sample Sizes by Age Cohort

20-29 30-39 40-49 50-59 60-69 70-79 80+ total

HamannTodd 40 40 40 40 40 40 35 275

Terry 30 40 40 40 39 40 40 269

Bass Donated 11 13 31 27 27 23 24 156

Maricopa County 23 21 25 17 20 22 21 149

Maxwell Museum 3 14 8 23 23 28 23 122

total 107 128 144 147 149 153 143 971

Table 2 shows the division of each sample by ten-year age cohorts. The data clearly demonstrate the paucity of younger individuals in the Maxwell Museum Documented and Bass Donated samples. Descriptive statistics for age at death by skeletal series are summarized in Table 3. The average age at death for each sex-race category, by series, is presented in Figure 5.

Table 3: Age at Death Statistics for Series in the Dataset

min max mean median mode

Hamann-Todd 21 96 54 54 22

Terry 20 102 56 56 30

Bass Donated 20 101 59 58 49

Maricopa County 20 97 54 52 43

Maxwell Museum 22 101 63 66 68

The average birth year by series is presented in Figure 6, which clearly illustrates the difference in time between Reference and Recent samples. Average birth years for sex-race groups by collection are presented in Table 4. The mean year of birth was fairly uniform across sex-race groups when the skeletal samples are pooled: the average age of birth was 1914 for Black females, 1913 for Black males, 1913 for White females, and 1913 for White males. The similarity among sex-race groups for mean birth year was expected because of the sample selection scheme. 113

Adult stature information was available for 58% of the dataset; no stature data was collected for the Maricopa County autopsy sample. Of the remaining 822 individuals in the dataset, 565 had documentation of stature: 99% of the Hamann-Todd sample, 37% of the Terry sample, 74% of the Maxwell Museum Documented sample, and 67% of the Bass Donated sample. Average adult stature for sex-race groups is Figure 5: Average Age at Death by Sex-Race Category

Average Age

70

50

40

30 20

MMA UTK

10

MCFSC

0 BF

WF Sex-Race Cat

HTH BM

mp le

TC Sa

Age-at-Death (Years)

60

WM

egory

HTH=Hamann-Todd TC=Terry UTK=Bass Donated MCFSC=Maricopa County MMA=Maxwell Museum BF=Black females WF=White females BM=Black males WM=White males

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Figure 6: Average Year of Birth by Collection

Average Year of Birth by Collection 1960 1950

1940 Year of Birth

1939 1920

1925

1900 1880 1860

1874

1880

1840 1820 HTH

TC

MMA

UTK

MCFSC

Skeletal Sample

HTH=Hamann-Todd TC=Terry UTK=Bass Donated MCFSC=Maricopa County MMA=Maxwell Museum

Table 4: Average Birth Year for Sex-Race Groups by Series

Black females White females Black males White males

Hamann-Todd 1877 1871 1876 1872

Maxwell Museum 1932 1921 1910 1928

Terry 1885 1887 1879 1879

Bass Donated 1927 1937 1941 1940

Maricopa County 1947 1950 1957 1949

Table 5: Average Stature by Sex-Race Groups

Black females White females Black males White males TOTAL

Hamann-Todd 64.4 61.8 68.0 67.0 65.3

Maxwell Museum N/A 63.2 69.8 68.8 66.9

Terry 62.9 62.5 67.3 66.8 65.6

Bass Donated 68.3 66.7 67.2 67.3 67.1

average 63.6 62.5 68.3 67.5

presented in Table 5. As is expected, the average adult height is sexually dimorphic for this dataset, with American males approximately 5 inches taller on average than their female counterparts, regardless of race.

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Data collection The pubic symphysis, auricular surface, sternal end of the fourth rib, and cranial sutures were examined for each individual. To avoid investigator bias, phase values/component scores were assigned based on morphological characteristics without prior knowledge of the individual’s age at death. The Maricopa County autopsy sample lacks auricular surfaces and cranial material as a result of Hartnett’s (2007) acquisition protocol; accordingly, only the pubic symphyses and sternal ends of the fourth rib were evaluated. When present, both right and left sides were examined for the pubic symphysis, auricular surface, and rib ends for all remains in the dataset. To avoid observer bias, only after completion of work at each institution were the demographic information collated with the morphological observations. A single observer recorded all data to maintain uniformity in phase assessments and eliminate the potential error introduced by multiple scorers. As noted previously, current aging standards are divided into either stage/phase based or transition analysis methods. Transition analysis allows for flexibility in the varying rates of separate bony modifications, as the age-related changes of the indicators are not necessarily simultaneous. This method avoids the problematic nature of phases/stages defined by a suite of characteristics that may not be an accurate description of the actual set of morphological traits observed.

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Pubic Symphysis Phase/Stage Based Standards The pubic symphysis undergoes a general progression of morphological change defined by deep ridges and furrows that with age fill in to produce a smooth surface with a ridge of bone on the ventral surface; then a bony rim forms around the face before the face and rim deteriorate. Specific features scored for the pubic symphysis include the surface relief, degree of delimitation of upper and lower extremities, extent of ventral rampart and dorsal plateau formation, appearance of dorsal lipping and ventral bony ligamentous outgrowths, rim erosion, and face shape. Based on the combination of features observed, the age progressive changes of the pubic symphysis were categorized into one of six phases according to the SucheyBrooks phase descriptions (Suchey & Katz 1986; Brooks & Suchey 1990). This method was chosen because Klepinger and colleagues (1992) have shown that it is most appropriate for forensic applications because it has the best accuracy and precision of the methods tested. The features of the pubic symphyseal face were also classified according to phase descriptions defined by Todd (1920, 1921) and Hartnett and Fulginiti (2007). These methodological variants exemplify the oldest and the most recent phase-based standards developed for assessing age from the pubic symphysis. Unlike the SucheyBrooks method, which was based on an autopsy sample from the 1950s, the Todd phase descriptions were established using the Hamann-Todd collection as the reference source. The Hartnett-Fulginiti phase descriptions are based on a recent autopsy sample and include an additional phase to that of the Suchey-Brooks method. This phase was created

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to more accurately describe face changes after the age of 50 years, though the method still forces varying morphological features into stages/phases. Transition Analysis The morphological stages of five pubic symphyseal features, specifically symphyseal relief, symphyseal texture, superior apex, and dorsal and ventral symphyseal margins, were observed for use with transition analysis following standards published by Boldsen and colleagues (2002). The symphyseal relief changes from deep to shallow to residual billowing, and then flattens before changing to an irregular surface. Symphyseal texture, which is scored on the dorsal demiface, changes from fine-grained to coarse grained; subsequently microporosity and macroporosity is paramount. The superior apex metamorphoses from no protuberance, to the presence of a protuberance, to its integration into the symphyseal face. The ventral symphyseal margin changes from serrated with pronounced ridges and furrows, to beveled/flattened billows, to the formation and completion of the rampart, to rim formation, and finally to rim breakdown. The dorsal symphyseal margin follows a similar pattern of metamorphosis as the ventral margin, minus the rampart. The dorsal margin changes from serrated with pronounced ridges and furrows, to flat, to rim formation, and finally to rim breakdown.

Auricular Surface Phase/Stage Based Standard The auricular surface of the ilium is thought to undergo a general metamorphosis, beginning with a fine granular surface and transversely organized billows on the superior and inferior demifaces. Next the transverse organization and billows fade and the 118

granulation becomes coarser; then the surface becomes more irregular, with areas of macroporosity and inferior lipping developing. Concurrently, rim formation at the apex occurs and the retroauricular area transforms from smooth to rugged. The auricular surface, including the apex, superior demiface, inferior demiface, and retroauricular area, was examined for the following morphological features: presence and degree of billowing, granulation, porosity, and transverse organization on the face of the auricular surface. Each auricular surface was then assigned to one of eight stages, as defined by Lovejoy and colleagues (1985b). Transition Analysis The morphological stages of nine features of the iliac auricular surface were scored for use with transition analysis following Boldsen and colleagues (2002). Features observed included superior demiface topography, inferior demiface topography, superior surface morphology, apical surface morphology, inferior surface morphology, inferior surface texture, and superior posterior iliac exostoses, inferior posterior iliac exostoses, and posterior iliac exostoses. The surface topography is scored for both the superior and inferior demifaces and follows predictable changes from an undulating surface, to an elevated central region, and finally to a flat or irregular surface. The surface morphology is scored separately for the superior, apical, and inferior aspects and metamorphoses from billowed to flat to bumpy; the inferior surface texture changes from smooth to porous, with independent scores for microporosity and macroporosity. Marginal bony proliferation is scored for the superior posterior and inferior posterior iliac exostoses; the surface starts as smooth, next changes to rounded bony elevations, then pointed, jagged, and touching exostoses, and terminates 119

as fusion to the sacrum. Finally, marginal bony proliferation scored for the posterior exostoses changes from no exostoses, to round and pointed exostoses.

Sternal End of the Fourth Rib Phase/Stage Based Standard The metamorphosis of the sternal end of the fourth rib begins as a slight indentation with rounded borders that pit deepens with increasing age, creating a V shape with a regularly scalloped border. As the pit continues to deepen, the shape widens to a U, and the scalloped border become more irregular; finally, the bone quality deteriorates, becoming more brittle and porous, and bony projections form. Specific features scored for the sternal end of the fourth rib include pit depth, pit shape, and rim and wall configuration. Based on the descriptions and combination of these individual features, the sternal rib end morphology was classified into one of the eight phases defined by İşcan and Loth (1986) and İşcan et al. (1984, 1985).

Ectocranial Suture Closure Phase/Stage Based Standard Meindl and Lovejoy’s (1985) method for cranial suture closure scores ectocranial sutures at ten locales: midlambdoid, lambda, obelion, anterior sagittal, bregma, midcoronal, pterion, sphenofrontal, and superior and inferior sphenotemporal. Scores follow a four-point scale ranging from zero (open) to 3 (obliterated).

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Transition Analysis Boldsen et al. (2002) scored only five craniofacial sites for the transition analysis method: lambdoidal-asterion, sagittal-obelica, coronal-pterion, zygomaticomaxillary, and interpalatine. This method scored suture closure on a five-point scale: 1) open, defined as a noticeable gap between the cranial bones; 2) juxtaposed; 3) partially obscured, with bony bridges present; 4) punctuated, characterized by an appearance of scattered small points or grooves; and 5) obliterated, when no evidence of a suture remains.

Other data In addition to recording phase/stage categories for each of the skeletal remains including in the dataset in a laptop computer database, digital photographs of the pubic symphyseal face, auricular surface, sternal end of the fourth rib, and cranial suture sites were taken to serve as documentation of the morphology scored. Collections curators provided demographic data, including documented age, sex, race, birth year, death year, and stature data.

Data Analysis Methods Data preparation All demographic data, the observed phase/stages for each indicator, and the observed transition analysis component scores for the pubic symphysis, auricular surface, and ectocranial suture closure were entered into an Excel spreadsheet. The expected phases/stages were then calculated using the documented age and the age ranges associated with the standard’s phases. For example, using the Todd 121

standard for estimating age from the pubic symphysis, a 32-year-old individual would have an expected phase of 6, as phase 6 is associated with an age range of 30-35 years. For standards with very large age ranges that overlap multiple phases, such as the Suchey-Brooks and Hartnett-Fulginiti methods, the expected phase was chosen using the mean age for phases. For example, using the Suchey-Brooks standard for estimating age from the pubic symphysis, a 32-year-old female could have an expected phase of 2, 3, 4, and 5. However the documented age of 32 is closest to the mean of phase 3 (30.7 years), so the expected phase assigned is 3. Additional steps were required to transform the raw data for ectocranial suture closure to a useable age estimate. First the suture sites were divided into vault and lateral-anterior sites. Cranial vault sites include midlambdoid, lambda, obelion, anterior sagittal, bregma, midcoronal, and pterion. Lateral-anterior sites include locales at midcoronal, pterion, sphenofrontal, inferior sphenotemporal, and superior sphenotemporal. Next the sites were summed to reach a composite vault score and a composite lateral-anterior score. The value of these composite scores is then associated with one “S” designation for the vault and another “S” designation for the lateral-anterior sites. These “S” designations were essentially viewed as phases, each with mean ages and confidence intervals. Like the Suchey-Brooks and Hartnett-Fulginiti methods, Meindl & Lovejoy’s standard for scoring ectocranial sutures for age estimation also has very large age ranges that overlap multiple “S” designations. As above, the expected “S” designation was chosen using the mean age closest to the documented age. Thus, for a 32-year-old individual, the expected vault “S” designation is S1 and the expected lateralanterior sites “S” designation is S1.

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The transition analysis raw component scores also required additional transformation before comparison to actual age. Raw data consisted of stage assessments for each individual component scored; without combining these component scores and inputting demographic information, Boldsen and colleagues’ method cannot equate these data directly to an age estimate or range. Accordingly, the sex, race, hazard, and observed component scores for transition analysis indicators were entered into the ADBOU Transition Analysis Age Estimator program. This program, provided by Dr. George Milner, combined the raw scores and demographic information to calculate an age at death using maximum likelihood estimation. The age at death calculation included both point estimates and 95% confidence intervals for each of the following indicators: pubic symphysis, auricular surface, and cranial suture closure (Boldsen et al. 2002). Age calculations were also produced based on a combination of these three indicators following two schemes: one assuming a uniform prior age distribution (UNI) and the other assuming the age distribution of the chosen forensic hazard (COR) (Boldsen et al. 2002). The pre-industrial Danish prior was not used because the individuals included in the dataset were born after the start of the American Industrial Revolution Once all of the expected values were determined, the difference between the observed and the expected phases/designations was calculated for each stage traditional standard. Because no phases exist for transition analysis, the predictive ability of the standard must be computed using a comparison of chronological age in years to the calculated point estimate from the ADBOU age estimator program. Accordingly, the difference between the observed/calculated and the expected/documented age in years was calculated for all transition analysis indicators: pubic symphysis, auricular surface,

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ectocranial suture closure, combined indicators assuming a uniform distribution and an informed prior distribution.

Analytical methods Right versus left side morphology A concordance correlation coefficient was calculated to determine whether the observed phase scores for right side morphology differed significantly from those of the left side. The statistic is similar to intra-class correlation (Nickerson 1997) and is used to evaluate the agreement between two values from the same sample (Lin 1989). The concordance correlation coefficient evaluated the agreement between the right and left side phase scores by measuring the variation from the concordance line, a 45-degree line through the origin (Lin 1989). If right and left phase scores are not significantly different, then observations from one side will be used to streamline data analysis and interpretation.

Intraobserver agreement A random subset of ten individuals from each skeletal series was drawn from the dataset to test for intraobserver agreement between first and second observations for each aging standard. The weighted Kappa statistic was used to test for intraobserver reliability (Landis & Koch 1977) and is appropriate because it accounts for the magnitude of disagreement between the first and second observations. These calculations are important for detecting any observer inconsistencies with a specific standard; if the

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observer was not reliable with a certain method, the statistical analyses using that data should be interpreted with caution.

Descriptive statistics Simple descriptive statistics for all variables, including means, standard deviations, and distributions, were calculated for each of the methods; this step was necessary to establish whether the assumption of normality for statistical testing is valid. In addition, Spearman’s correlations between age and phase, as well as between variables, were calculated. These statistics measured the degree of association between the two variables, providing an indication of whether that particular variable was correlated with chronological age and/or each other. The documented chronological ages were plotted according to their observed phase/stage for each standard, for both the Reference and Recent groups. This provided a visual representation of the variation in age at death encompassed by each phase of each standard for each skeletal sample and will provide visual summaries of the data, generating insights into whether any obvious differences existed in rates of senescence. The weighted Kappa statistic was used to test for agreement between observed phase/calculated age and expected phase/documented age. This data provided a measure of the degree of difference between the actual morphology of the indicator and what was expected for that individual based on their age at death. The calculated differences between the observed and actual values were plotted against year of birth by the total dataset, each of the five skeletal series, and ten-year age cohorts to explore whether patterns emerge in the amount of predictive error for each aging standard. Linear trend lines were fitted to each plot by series and age cohort. These plots and regression lines 125

served as a heuristic tool to identify possible changes in the rate of aging through time, independent of Reference or Recent designation. Once the differences between the observed and expected values were calculated (PE) for each individual for all aging standards scored, standard multiple regression was used to identify which of the following descriptive variables best predicted the PE value: sex, race, sex-race groups, adult stature28, year of birth, age at death, reference/recent group, series, and region of the United States. All categorical values were transformed into nominal data according to the following scheme: 1=female and 2=male; 1=Black and 2=White; 1=black females, 2=black males, 3= white females, and 4=white males; 1=reference and 2= recent; 1=Hamann-Todd, 2=Terry, 3=Maxwell Museum, 4=Bass Donated, and 5=Maricopa County; 1=Midwest US, 2=Southeast US, and 3=Southwest US. This was done so that all variables could be evaluated in the same regression model. Results will identify whether any of the variables tested influence how well the aging standards perform. Multiple regression assumes that the variables are normally distributed, there is a linear relationship between the independent and dependent variables, the variables are measured reliably/without error, and that the variance of errors do not differ at different values of the independent variable (Osborne & Waters 2002). These assumptions were

28

Environmental and psychosocial insults during childhood are known to affect bone growth and may influence adult stature (Peck & Lundberg 1995; Bogin 1999); however, some researchers have found that growth in length was maintained at the expense of cortical thickness, despite nutritional and disease stress (Himes 1978; Huss-Ashmore 1981). In addition, when negative effects like nutritional or disease stress are ameliorated, long bone lengths increase during these periods of growth recovery, but cortical thickness/bone mass did not (Huss-Ashmore et al. 1982). The effect of this catch-up growth on stature can be problematic for assessing stresses experienced during the growth years of individuals using adult stature. Although cortical bone thickness, as well as Harris lines, may be more sensitive indicators of childhood stress, it was beyond the scope of this project to measure these variables. Thus, adult stature data, which was readily available for much of the dataset, was used as a rough proxy for childhood living conditions.

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tested and results indicated that regression analysis was appropriate: the residuals of nearly all of the descriptive variables approximated a normal distribution, plots of the standardized residuals and standardized predicted values were linear for nearly all variables, variables were measured reliably, and most plots comparing the standardized residuals and standardized predicted values indicated homoscedasticity.

Research Questions and Hypothesis Testing To determine whether older American skeletal series age at a different rate than more recent ones, three questions were addressed using a combination of deductive and inductive statistical approaches. The sample size, mean age in years, standard error of the mean, 95% confidence interval for the mean, standard deviations, and observed age range were computed for each phase of each standard. These data were computed for each sex, race, sex-race group, and total sample within the Reference and Recent samples. This tabulates the proportion of cases that exhibit a particular phase of an indicator by documented age, provides a means for comparing this data to existing publications, and serves as a launching point for further hypothesis testing. Finally, the ages at transition were calculated using a log-age cumulative probit model in R (R Development Core Team 2008) for each of the aging standards, for both Reference and Recent groups. Data was calculated for the total sample for each group, pooling sex and race. Subsequently, each group was divided by sex and race, and the ages at transition were then calculated for each aging standard by these divisions. These data will numerically present a comparison of ages at transition for the groups examined.

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Question 1: Is the observed morphology of the aging indicator associated with the same chronological ages for both older Reference and more Recent American skeletal populations? To test whether variation in the morphological aging processes of the pubic symphysis, auricular surface, sternal rib end, and cranial sutures exist between Reference and Recent American skeletal series, the null hypothesis of no difference in rate of senescent change was tested for the following established American aging standards: Todd, Suchey-Brooks, Hartnett-Fulginiti, Lovejoy and colleagues, İşcan and colleagues, Meindl and Lovejoy, and Boldsen and colleagues. This question was tested using proportional odds probit regression and generalized linear regression, coupled with an analysis of deviance; these analyses provide a measure of the significance of the association between the proportions of cases from each sample that exhibit a particular phase of an indicator, conditional on age. Using proportional odds probit regression in R (R Development Core Team 2008), the observed phase was regressed onto the log-age and population. The model then was run with an additional term for the interaction between log-age and population. An analysis of deviance was used to compare the two models, and an improvement chisquare statistic with associated p-value formally tested the impact of the added interaction term (Fox 2002), which allowed for the slopes of the regression lines to differ. The null hypothesis for this test is that the addition of the interaction term is not important. If the reduction in deviance is not significantly greater than chance, then the added interaction term does not belong in the model; this outcome is indicative of regression line slopes that do not significantly different between groups. If the chi-square likelihood ratio

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statistic has an associated p-value of 0.05 or less, then the addition of the interaction term improves the model. This improvement indicates a significant association between the aging process of the indicator and the population. This analysis was run multiple times: 1) reference versus recent samples with pooled sex and race; 2) reference versus recent samples by sex; and 3) reference versus recent samples by race. This statistical approach is appropriate because probit regression models the dependence of the indicator on age. This is accomplished by calculating the means, standard deviations, log-likelihood, and standard error of the ages of transition for each phase of the indicator for each group. Subsequently, a measurement is produced that is indicative of the association between the proportions of cases from each sample that exhibited a particular phase of an indicator, conditional on age (see Kimmerle et al. 2008). A similar approach was taken to analyze whether a significant association exists between population and the point estimates of age produced by Boldsen and colleagues’ Transition Analysis. Proportional odds probit regression requires the dependent variable to be ordinal; however, the point estimates for age are continuous data, so a generalized linear model was fitted in R (R Development Core Team 2008). The point estimate of age was regressed onto the documented age and population, and then the model was run again with the additional interaction term for age*population. As above, an analysis of deviance was calculated and the associated p-value was used to formally test the impact of the added interaction term. Three potential outcomes exist. The first possibility was that no difference in the rate of aging between Reference and Recent samples was detected for any of the

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standards tested. The second outcome was that all standards tested produced a statistically significant difference in the rate of aging between populations. The third outcome was that some standards would produce statistically significant differences between samples and others would not. The expectation was that the third outcome was most likely. Two lines of evidence justified this expectation. First, the literature on temporal change for age indicators is mixed. Osborne et al. (2004) observed no change for the auricular surface when comparing samples drawn from the Terry and Bass Donated collections, while others have reported secular trends for cranial suture closure (Masset 1989, BocquetAppel & Masset 1995) and the pubic symphysis (Hoppa 2000). Second, a pilot study was conducted (Potter 2009) that produced both significant and non-significant trend lines for the difference between observed score versus expected phase regressed on year of birth. Question 2: Assuming that the third outcome from Question 1 is supported, is there a pattern that explains why some aging standards produce significant differences in the aging process of skeletal indicators between groups while others do not? The null hypothesis of no pattern was tested for the following groupings: 1) method type, specifically phase/stage based or component based/transition analysis; 2) indicator used, particularly the pubic symphysis, auricular surface, fourth rib, cranial sutures, or combined; 3) anatomical region, either postcranial or cranial; 4) time; 5) adult stature, categorically defined as short, average, and tall, 6) sex, either female or male; and 7) race, either Black or White.

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To test whether a pattern was present within the outcome, the results from Question 1 hypothesis testing were sorted into two groups: 1) standards producing statistically significant differences among series and 2) standards that do not produce differences among series. Subsequently, an inductive approach was used to identify patterns according to method type, indicator, anatomical region, time, adult stature, sex, or race. Two possible outcomes existed. The first outcome was that the results appear to be random, with no apparent pattern explaining why some standards produce statistically significant differences between older and more recent American skeletal populations and others do not. The second possible outcome was that a pattern was present. The expectation was that there would be non-random groupings of standards that have different chronological ages associated with specific indicator morphology for Reference and Recent American skeletal populations. Justification for the patterns that may explain the results was based on critiques of particular aging standards and published anthropological research on aging. Method type may influence the results because phase/stage based methods force the morphology of the indicator into a preset description of traits that may or may not accurately describe the features present. In contrast, transition analysis is flexible, allowing for differing rates of development or degeneration of individual components within a single indicator. Certain indicators of age, regardless of the method used, may produce similar results. This was expected for the pubic symphysis, because the Suchey-Brooks and Hartnett-Fulginiti methods were both derived from the original Todd scoring system. Similar morphological attributes are also examined as components of the pubic symphysis indicator for the Boldsen and

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colleagues’ Transition Analysis scheme, and thus may also group with these phase-based methods. Similarly, results may group according to the anatomical region of the indicator; postcranial—particularly pelvic—indicators clustered in Jackes’ (2000) reanalysis of Kemkes-Grottenthaler’s dissertation data. This may be the result of the suture obliteration method itself, because it had a definite “cutoff point” marking the cessation of morphological change that postcranial measures lack (Jackes 2000). The temporal difference between the Reference and Recent samples may also influence the results, particularly if most indicators and standards return a statistically significant difference between these populations. The average year of birth for the Reference sample is 1878 (range 1828-1943), while it is 1939 (range 1889-1985) for the Recent sample. The difference in birth years may allow for the recognition of subtle changes in the rate of progression of skeletal age changes, possibly resulting from secular trends, environmental factors, cultural practices, socioeconomic status, improvements in living conditions, diet, disease prevalence, and advances in health care and disease prevention. As mentioned previously, the adult height of individuals was used as a proxy for childhood health and socioeconomic status due to availability of the data. Environmental and psychosocial insults during childhood are known to affect bone growth and can influence adult attained stature, though catch-up growth may reduce or eliminate these effects. These childhood stresses may also affect skeletal aging. Patterns may also arise with regard to sex and race. Differences in the estimation of age exist between males and females (Kemkes-Grottenthaler 2002), and these differences are reflected in the use the same scoring system for both sexes with different means and age ranges ascribed to the phase based on sex (Meindl & Lovejoy 1985;

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Boldsen et al. 2002), as well as the use of aging standards that score males and females separately (İşcan & Loth 1986; Brooks & Suchey 1990; Hartnett 2007). Additionally, research supports the theory that females may be buffered from environmental insults, which may render them less susceptible to changes in nutrition, socioeconomic status, and other extrinsic variables (Bogin 1999). Some research on dental and skeletal development indicates that race may also affect age estimates. For example, Blacks are advanced compared to Whites in terms of dental development and eruption, and similar results have been noted for the development of ossification centers and the epiphyseal fusion of elements in the hand and wrist (Masse & Hunt 1963; Garn & Bailey 1978). Question 3: In the case of contrasting results from multiple aging standards for a single skeletal indicator, which standard is the best gauge of whether a difference in the rate of senescent change has occurred between older Reference and more Recent American samples? A three-fold approach was taken to identify which osteological aging standards were used to determine whether a change in the skeletal aging indicator had occurred for American samples. First, results from published studies, as reviewed in Chapter 3, were used to assess the strengths and weaknesses of the skeletal age indicators and standards tested in this research project. This endeavor identified which aging standards may have methodological biases or other problems that affect the reliability of the results obtained from hypothesis testing. Second, stepwise regression analysis was used on a pooled sample to determine which standards were the best predictors of actual age as determined by inclusion in the prediction formula. A pooled sample combining temporally and geographically distinct collections was chosen to create a more diverse sample that

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encompasses a wider range of variation observed for American populations. Finally, other criteria contributing to the assessment of the method’s reliability were considered, including intraobserver agreement values, correlation coefficients with chronological age, my experience and comfort level with the methods, and the robusticity of the statistical results within this dissertation. Aging standards that were free from methodological bias, had low intra-observer error, predicted chronological age well as defined by its inclusion in the stepwise model, and had easily recognized features that were highly correlated with chronological age, were considered more reliable than others. These aging methods were used as the measure of whether older American skeletal series aged at a different rate than more recent ones.

Assumptions The assumptions made in this research are broadly divided into two categories: those regarding the skeletal collections and those regarding the aging standards tested.

Documented Skeletal Collections The first major assumption made in this research is that the documented information for the individuals within these five American skeletal collections is accurate. Although these collections are considered “documented,” a closer look at the source of the recorded information warrants concern regarding the reliability of some data.

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Age at Death Some of the skeletal remains within Terry and Hamann-Todd collections do not have known chronological age at death (Boldsen et al 2002; Hunt & Albanese 2005; Konigsberg et al. 2008); this issue was discussed thoroughly in Chapter 3. To summarize, often this uncertainty was denoted by the addition of a “?” or “circa” to the documented age. In other instances, the age was estimated, rounding to the nearest multiple of five; for those cases that have ages ending in 0 or 5, it was less clear which were true ages and which were estimated. As stated earlier in this chapter, the sampling strategy employed for this research attempted to avoid the inclusion of those remains with questionable ages; however, it must be assumed that the ages recorded were correct for the individuals who were selected for the dataset.

Racial Designation The term race, as it is used in this manuscript, was clarified in Chapter 1. Different individuals do not apply the social construct of race, and its classification terms, in the same manner; perceptions have also changed over time. Race is a complicated and problematic concept that is likely inextricably intertwined with other variables, such as socioeconomic status, education, nutrition, and access to health care (Williams 1996, Cooper et al. 2001). In addition, race is a fluid concept that is interpreted in many ways (Herman 1996); how individuals are classified has changed significantly over time in the United States, both by themselves or when ascribed by others. In earlier skeletal collections, race was recorded from death certificates and was likely ascribed by morgue physicians based on physical features, which may not be an accurate refection of how the

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decedents would have identified themselves during life. In contrast, potential donors self-report their race on questionnaires for the Bass and Maxwell documented collections, particularly for the more recently acquired remains. This self-identification may not match opinions of observers in the community. This discrepancy in how race information is acquired is another potential source of bias inherent in comparing these skeletal series.

Osteological Aging Standards The second major set of assumptions made in this research lies in the reliability and validity of the osteological age at death estimation methods currently employed. The anthropological literature has called into question these very attributes of current aging standards.

Reliability of Aging Standards Reliability issues include observer error, age and sex dimorphism, asymmetry, intertrait correlation, causative factors, and heritability (Saunders 1989). Of principal concern is inter- and intra-observer error, because morphology-based age assessment indicators are subjectively interpreted despite a multitude of photographic and/or cast reference materials (Kemkes-Grottenthaler 2002). Experience is important, as the researcher’s ability to properly identify age-related morphological traits is essential to the standard’s predictive potential (Baccino et al. 1999; Kemkes-Grottenthaler 2002). For example, the Gilbert-McKern pubic symphysis standard’s scoring of the development and breakdown of the ventral rampart is particularly prone to observer error (Suchey 1979). Even with the introduction of their method for scoring the iliac auricular surface, 136

Lovejoy and colleagues (1985b) admit to the increased difficultly in the application of their standard over those for the pubic symphysis. As outlined in the analytical methods section of this chapter, intraobserver agreement was calculated for all standards used as part of this research and will identify which standards are more reliable when employed by the author.

Validity of Aging Standards Validity issues focus on the predictive value of the indicators used, based on how strongly correlated the indicator is to chronological age (Kemkes-Grottenthaler 2002). A compilation of correlation coefficients for several age indicators and age at death is presented in Table 6. Poorer correlations translate to greater bias (Aykroyd et al. 1999). What is the minimum acceptable correlation coefficient to indicate a reliable relationship between morphology and age? The answer varies by author and ranges anywhere from 0.7 to 0.9 (Lovejoy et al. 1985a; Bocquet-Appel & Masset 1982, respectively). However, the utility of correlation coefficients as a proxy for the predictive value of a trait is also questionable, because age indicators are typically discrete and the relationship between a phase/stage and its corresponding age range is not linear (Kemkes-Grottenthaler 2002). Gerontologists know that the aging phenomenon is highly variable among individuals (Bryant and Pearson 1994), and this is particularly evident for older individuals, because skeletal age-at-death markers become progressively more inaccurate with increased age (Angel 1984). But even intra-individual variability is problematic, as evidenced by asymmetrical cranial suture closure and auricular surface scores (Moore-Jansen & Jantz 1986; Kemkes-Grottenthaler 1996).

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Table 6: Correlation coefficients between age indicator and age at death (after Kemkes-Grottenthaler 2002) Indicator Pubic symphysis (Todd)1 Pubic symphysis (Todd)2 Auricular surface (Meindl et al.) Ectocranial sutures (Meindl & Lovejoy)3 Ectocranial sutures (Meindl & Lovejoy)4 1 2 3 4

Females -0.64 ---

Males 0.85 0.57 ---

0.34

0.59

Sexes combined -0.57 0.72 0.57 lateral-anterior sutures 0.50 vault sutures 0.56

Katz & Suchey (1986) Meindl et al. (1985) Meindl & Lovejoy (1985) Kemkes-Grottenthaler (1996) [cited in Kemkes-Grottenthaler (2002)]

It was assumed for this thesis that the accuracy of the age estimation methods tested here was acceptable, such that the hypothesis testing, regardless of outcome, produced meaningful results. In an attempt to approximate each method’s accuracy, three values were calculated for each aging standard: the correlation coefficient between the phase scores and age, bias, and inaccuracy.

Limitations The limitations of this research are the direct result of both information availability for American skeletal collections and issues related to sample bias and sample representativeness. Because of these obstacles, it is impossible to make definitive statements about causative agents of change and the population at-large. Only inferences can be made regarding variables that may contribute to or influence change in the rate of aging in the studied American skeletal samples.

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Availability of Information The hypotheses that are tested during the course of this dissertation focus on temporal and spatial changes in the relationship between the morphology of the skeletal indicator and chronological age. It is not possible to quantify all of the variables that may affect this relationship. While newer documented skeletal collections like the Bass Donated and Maxwell Museum series are now attempting to gather additional information about their donors, information on childhood health, nutrition, and living conditions, as well as socioeconomic status, occupation, and health history, are not generally available for individuals in older anatomical collections. While, this lack of individual information makes it difficult to draw conclusions about specific variables influencing the rate of senescent change in the human skeleton, it does not necessarily strongly impact age estimation, as this data is also unknown for target samples.

Issues of Sample Bias and Sample Representativeness Usher (2002) states that the reliability and representativeness of reference collections are usually taken for granted. Existing collections contain a subset of individuals from a larger population, which may be influenced by acquisition methods and collection strategies. This selected subset of individuals is not No skeletal collections are truly representative of the general population from which they are drawn (Usher 2002). Acquisition artifact is a well-known problem for documented collections; bias occurs by the inherent nature of skeletal collections as a result of the demographic profile, methods of collection, recording of the history of the materials, and curation/storage (Huxley 2005). Acquisition bias is present for all of the samples used in

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this project because not every person in the population at large has an equal chance of becoming part of the collections. Certain people are more likely to donate or to become medicolegal cases. For example, femoral length is correlated with donation type in the Terry Collection (Ericksen 1982) and with differences in achieved educational levels and socioeconomic status in the Bass Documented Collection (Wilson et al. 2007). Although all five of the skeletal samples examined for this research are American, there are likely significant differences between these samples with regard to genetic background, overall socioeconomic status, income, living conditions, nutrition, and health care during life. These specific differences cannot be fully understood or quantified because these data are not necessarily available for any given individual or sample, but they must be acknowledged as sources of bias that are inherent in this study. The source of the skeletons within the collections cannot be ignored (Morris 2007). While the Hamann-Todd and Terry anatomical collections are composed of predominantly lower socioeconomic status individuals drawn from hospital and institutional morgues, the Bass Donated and Maxwell Museum collections tend to contain willed bodies from more middle income backgrounds (Corruccini 1974; Hunt & Albanese 2005; Wilson et al. 2007). Therefore, generalized statements about income and socioeconomic status do not apply to all individuals included in the sample. The Terry collection, for example, contains both indigent and willed remains. Morris (2007) reports that self-donated/willed bodies are often those of well-educated individuals who have attained a higher social class than those of indigent acquisitions. As a result, Morris (2007) hypothesizes that differences in observations may reflect different lifestyles regardless of the biological origin of the group.

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Clearly, sampling bias is a significant concern; skeletal series do not represent all of the variability within the source population that is anticipated for any given geographic area or period of time (Usher 2002). This problem is exemplified in a study conducted by Komar and Grivas (2008), which compared the Maxwell Museum of Anthropology documented skeletal collection to three New Mexico samples: autopsy, deceased, and living. The Maxwell sample differed significantly from each of these populations in age, sex, race, cause of death, and manner of death. A significant overrepresentation of males, Whites, and elderly individuals in the Maxwell Documented Collection is present. This problem undoubtedly extends to the other samples studied for this research and must be acknowledged as a caveat when drawing conclusions from this data.

Summary To determine whether older American skeletal series age at a different rate than more recent ones, three research questions will be addressed. First, a determination as to which American aging standards produce significant and non-significant differences in age estimates between older reference and more recent documented skeletal series will be made though formal hypothesis testing. If some or all of the null hypotheses of no difference are rejected, then the next action will be to determine if patterns exist in the results according to method type, indicator used, anatomical region, time, sex, race, or adult stature. Next, an inductive approach to data analysis and a summary of the strengths and weaknesses of current aging standards will be used to determine which standards should be weighted more heavily when considering the primary dissertation question. Finally, all results will be combined to determine whether differences exist in the rate of osteological aging between older and more recent American skeletal 141

collections, using heavily weighted traits as a proxy for whether a generalized pattern of change has occurred.

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Chapter 5 Results Twelve different aging standards were applied to older and more recent American skeletal samples to determine whether both samples age at the same rate. Three hypotheses were tested; their results follow.

Preliminary Data Analysis Prior to hypothesis testing, the difference between right and left side scores was assessed, intraobserver agreement and descriptive statistics were computed, and assumptions of normality were tested.

Right versus left side morphology A concordance correlation coefficient was calculated using SAS v9.1 (SAS Institute, Cary, NC) to determine whether the observed phase scores for right side morphology differed significantly from those of the left side. Results are presented in Table 7. The concordance correlation coefficient increases as the true correlation increases and decreases as the within-subject variability increases. Thus, 0.9338, the Table 7: The estimated concordance correlation coefficient, with 95% confidence limits Statistic Sample Size Mean 1 Mean 2 Variance 1 Variance 2 Covariance Concordance Correlation Lower Confidence Limit Concordance Correlation Concordance Correlation Upper Confidence Limit

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Value 14919 3.9598 3.9584 4.5849 4.5908 4.284 0.9317 0.9338 0.9358

value calculated for the concordance correlation here, indicates a high true correlation and low variability between right and left side phase scores by individual. Phase scores for right and left sides were not significantly different, so observations from the right side were chosen arbitrarily to streamline data analysis and interpretation.

Intraobserver agreement A random subset of 50 individuals was drawn from the dataset to test for observer inconsistencies using the weighted Kappa statistic; ten individuals were randomly selected from each skeletal series to form the subset. Table 8 summarizes the weighted Kappa results for each aging standard scored. The bold text highlights the highest and lowest values. The observer had the highest agreement values for the Todd and SucheyBrooks pubic symphyseal scoring systems. This result was expected because the Todd Table 8: Agreement between first and second observation scores using the weighted Kappa statistic Method (obs1 vs. obs2)

Value*

ASE

Todd Pubic symphysis

0.9146

0.0337

0.8487

0.9806

Suchey-Brooks pubic symphysis

0.8628

0.0512

0.7624

0.9631

Hartnett-Fulginiti pubic symphysis

0.8164

0.0558

0.7070

0.9258

Lovejoy et al. auricular surface

0.6748

0.0743

0.5292

0.8204

Iscan et al. 4 rib end

0.7152

0.0801

0.5583

0.8722

Meindl & Lovejoy cranial sutures

0.8342

0.0154

0.8039

0.8644

Boldsen et al. transition analysis (all)

0.8113

0.0137

0.7844

0.8382

Boldsen et al. transition analysis (PS)

0.8296

0.0226

0.7854

0.8738

Boldsen et al. transition analysis (AS)

0.7793

0.0234

0.7334

0.8252

Boldsen et al. transition analysis (CS)

0.7645

0.0290

0.7076

0.8214

th

95% Confidence Limits

Total 0.8765 0.0069 0.8629 0.8900 All methods combined * All weighted Kappa values are significant at the p=0.05 level (Gwet 2002). A one-sided test of the Pr > Z and a two-sided test of the Pr > |Z| for the H0: weighted kappa = 0 both returned p values of <0.0001 for all comparisons.

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and Suchey-Brooks methods are the aging standards with which the observer had the most instruction and experience. Unsurprisingly, the Lovejoy and colleagues standard for aging the auricular surface had the lowest agreement value; this method was difficult to apply, and the anthropological literature has documented its high intra- and interobserver error rates (Lovejoy et al. 1985a; Lovejoy et al. 1985b; Murray & Murray 1991; Jackes 1992; Saunders et al. 1992; Molleson et al. 1993; Hoppa 2000; Schmitt et al. 2002). The low agreement value for the sternal end of the fourth rib may have been the result of poorer preservation and/or damage affecting the delicate diagnostic features of the rim. The author learned to score the pubic symphysis, auricular surface, and cranial sutures using Boldsen and colleagues’ Transition Analysis system shortly before data collection commenced. The author was self-taught using material kindly provided by Dr. George Milner, including a PowerPoint presentation with photographic examples illustrating key features of stages and a written description of each stage for all components scored. Despite no formal training with the standard, the author’s agreement values were good, suggesting the stage descriptions were clearly written and relatively straightforward in their application. Table 9 presents intraobserver agreement values by skeletal collection. The series are arranged according to the order in which they were visited for data collection, starting with the Maxwell Museum and ending with the Maricopa County Forensic Science Center. Predictably, intraobserver agreement increased as data collection progressed, suggesting that the added experience resulted in more consistent scoring of the indicators.

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Table 9: Intraobserver agreement values across all aging methods, by skeletal series Skeletal Series Weighted Kappa* 0.7850 Maxwell Museum Documented 0.8605 Bass Donated 0.8964 Hamann-Todd 0.8720 Terry 0.9015 Maricopa County Forensic Science Center * All weighted Kappa values are significant at the p=0.05 level (Gwet 2002). A one-sided test of the Pr > Z and a two-sided test of the Pr > |Z| for the H0: weighted kappa = 0 both returned p values of <0.0001 for all comparisons.

Data Analysis The research design employed for this study included a large amount of data, which could become cumbersome: right and left side scores for 19 variables, and unilateral scores for another 15 variables. No statistically significant difference existed between right and left side scores, suggesting that the analysis of one side or the other would yield comparable results. Accordingly, to avoid redundancy in result reporting, only right side scores were chosen to be included in subsequent statistical analyses.

Descriptive statistics The means, standard deviations, and standard errors for all variables were calculated for each of the methods to determine whether they were normally distributed. SAS v9.1 (SAS Institute, Cary, NC) was used to calculate the means, standard deviations, variances, and tests for normality for all variables in the dataset, including the age at death, year of birth, stature, and all aging standards, including the individual components of Boldsen and colleagues’ Transition Analysis method (Table 10). The distributions of all variables were significantly different from a normal distribution at the 0.05 level. For

146

Table 4.10: Summary table of descriptive statistics for all variables in the dataset age year of birth stature Todd Suchey-Brooks Hartnett-Fulginiti Boldsen symphyseal relief Boldsen symphyseal texture Boldsen superior apex Boldsen ventral symphyseal margin Boldsen dorsal symphyseal margin Lovejoy et al. Boldsen superior demiface topography Boldsen inferior demiface topography Boldsen superior surface morphology Boldsen apical surface morphology Boldsen inferior surface morphology Boldsen inferior surface texture Boldsen superior posterior iliac exostoses Boldsen inferior posterior iliac exostoses Boldsen posterior iliac exostoses Iscan et al. Meindl & Lovejoy midlambdoid Meindl & Lovejoy lambda Meindl & Lovejoy obelion Meindl & Lovejoy anterior sagittal Meindl & Lovejoy bregma Meindl & Lovejoy midcoronal Meindl & Lovejoy pterion Meindl & Lovejoy sphenofrontal Meindl & Lovejoy inferior sphenotemporal Meindl & Lovejoy superior sphenotemporal Boldsen lambdoidal-asterica Boldsen sagittal-obelica Boldsen coronal-pterica Boldsen zygomaticomaxillary Boldsen interpalatine

Mean 56.57 1904.7 65.95 2.13 8.6 4.91 4.93 4.56 3.45 5.8 4.08 6.12 2.36

Std Dev 19.872 37.204 4.0039 0.8976 1.9824 1.3189 1.4219 0.9247 0.8879 1.5995 1.0478 1.8423 0.6122

Std Error 0.63772 1.19393 0.1683 0.02974 0.06539 0.04351 0.04691 0.03041 0.03015 0.05291 0.0346 0.06626 0.02196

2.42

0.6049

0.02181

0.73764

0

3.73

0.8243

0.02959

0.74996

0

3.62 3.66 1.69 2.66

0.952 0.8852 0.8272 1.2172

0.03415 0.032 0.03026 0.04298

0.78912 0.76367 0.72648 0.80673

0 0 0 0

2.41

1.2265

0.0453

0.87338

0

1.49 5.77 1.49 1.76 2.28 1.99 1.55 1.49 2.06 1.96 1.07

0.6017 1.5066 1.0231 0.9922 0.9849 1.0298 0.9727 0.9545 0.9433 0.9479 0.8607

0.02194 0.06125 0.03602 0.03499 0.03467 0.03621 0.0342 0.0335 0.03323 0.03327 0.03019

0.70679 0.93132 0.85846 0.8626 0.72515 0.8116 0.87196 0.84786 0.823 0.83721 0.85368

0 5.00E-16 0 0 0 0 0 0 0 0 0

0.86

0.8301

0.02916

0.8051

0

2.15 3.5 3.74 2.56 3.17

0.9842 1.1734 1.162 0.8237 0.9739

0.03454 0.04131 0.04085 0.029 0.0357

0.86744 0.89488 0.85854 0.82427 0.88765

0 0 0 0 0

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Shapiro-Wilk Pr
the year of birth, age at death, and aging standard scores, this was the result of the design of the sampling strategy, which aimed to have an equal number of individuals in each age cohort. Descriptive statistics, including the means, standard deviations, and range, were calculated for the continuous variables in the dataset by skeletal series and Reference/Recent group (Table 11). Box plots illustrated the differences between skeletal series with regard to the means and distributions of age at death, year of birth, and stature. Table 11: Descriptive statistics for continuous variables Variable Age at death (years) Hamann-Todd Maricopa County Maxwell Museum Terry Bass Donated REFERENCE RECENT Year of birth Hamann-Todd Maricopa County Maxwell Museum Terry Bass Donated REFERENCE RECENT Adult stature (inches) Hamann-Todd Maricopa County Maxwell Museum Terry Bass Donated REFERENCE RECENT

N

Mean

Standard Deviation

Minimum

Maximum

971

56.6

19.9

20

102

275 149 122 269 156 544 427 971 275 149 122 269 156 544 427

54.3 54.4 63.2 55.9 58.7 55.1 58.5 1904.7 1873.9 1950.3 1924.6 1882.3 1938.6 1878.0 1938.7

19.5 21.4 18.3 20.1 18.6 19.8 19.8 37.2 18.9 21.2 18.5 23.6 18.5 21.8 21.9

21 20 22 20 20 20 20 1828 1833 1907 1889 1828 1892 1828 1889

96 97 101 102 101 102 101 1985 1911 1985 1970 1943 1980 1943 1985

566

65.9

4.0

51

77

272 N/A 90 99 105 544 427

65.3 N/A 66.9 63.6 67.2 65.4 67.0

3.8 N/A 4.7 3.5 4.0 3.7 4.3

51 N/A 53 55 56 51 53

73 N/A 77 74 75 74 77

With the exception of the Maxwell Museum collection, all series had similar means and distributions for age at death (Figure 7). This was expected based on the 148

sampling strategy for data collection for this research, which aimed to create samples from each skeletal collection that were equal in the number of individuals included in each ten-year age cohort. The box plot of year of birth clearly illustrated the division between the older Reference and the newer Recent skeletal collections (Figure 8). Both the Terry and Hamann-Todd samples have mean birth years prior to 1900; the Maxwell Museum, Bass Donated, and Maricopa County collections have mean birth years that are approximately 50 years after those of the Reference samples. The box plot of stature by skeletal series depicted the wide range of variation in adult height within each of the collections (Figure 9). No stature data was archived for the Maricopa County Forensic Science Center autopsy sample. Means were slightly lower for the Reference samples.

Figure 7: Box plot comparing age at death by skeletal series

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Figure 8: Box plot comparing year of birth by skeletal series

Figure 9: Box plot comparing stature by skeletal series

For the plots above, the series names were abbreviated as follows: Hamann-Todd (HTH), Maricopa County (MSCFSC), Maxwell Museum (MMA), Terry (TC), and Bass Donated (UTK). 150

Spearman’s Correlations Spearman’s correlations between age and phase, as well as among all variables, were calculated (see Appendix A). These statistics measured the degree of association between the two variables, providing an indication of whether that particular variable is correlated with chronological age and/or each other. Pearson’s correlations assume an underlying normal distribution of the population. In contrast, Spearman’s correlations did not assume any underlying distribution. Accordingly, Spearman’s correlations were considered more appropriate. Spearman’s correlations between variables scored and age at death All aging standards and components were significantly, positively correlated with age at death at the 0.05 level. Spearman’s correlation coefficients ranged from 0.155 to 0.723. Only five scoring systems demonstrated a fairly strong positive correlation with age at death: İşcan’s fourth rib (0.723), Lovejoy and colleagues’ auricular surface (0.711), the Suchey-Brooks standard for scoring the pubic symphysis (0.703), the Hartnett-Fulginiti modification of the Suchey-Brooks method (0.700), and the Todd pubic symphysis standard (0.697). These variables have the highest Spearman’s correlation coefficients with age because they were the only five methods scored for this research that were strictly phase based. The other methods, specifically Meindl and Lovejoy’s cranial suture closure and Transition Analysis, required the combination of multiple components, each with a stage-based scoring system, to estimate age. It was noted that the scores of the individual components may not correlate well with age, but when combined, each feature contributed a little toward a better estimate of age.

151

Fourteen of the fifteen variables (93%) with the lowest correlation coefficients were scores of cranial suture closure at specific cranial landmarks, from both the Transition Analysis and Meindl and Lovejoy standards. This was not entirely unexpected, as the value of cranial suture obliteration as an age indicator has been questioned (Singer 1953; Brooks 1955; McKern & Stewart 1957; Masset 1971; Meindl et al. 1983; İşcan & Loth 1989; Masset 1989; Buikstra & Ubelaker 1994; Hershkovitz et al. 1997; Galera et al. 1998; Boldsen et al. 2002). As mentioned above, this result may be influenced by the nature of the data, as individual cranial suture scores are components of the aging standard. Spearman’s correlations between variables scored When aging standard variables were compared to each other, some of the Spearman’s correlation coefficients were not statistically significant. The Transition Analysis auricular surface inferior posterior iliac exostoses component was not significantly correlated with any of the following variables: pubic symphyseal relief and texture; auricular surface topography, morphology, and texture; and ten of the fifteen cranial suture closure variables scored for Boldsen and colleagues’ and Meindl and Lovejoy’s standard). Regardless of standard, the majority of the cranial suture scores were not significantly correlated with pubic symphyseal texture and relief, auricular surface topography and texture, or the three iliac exostoses components. Among those variable comparisons with statistically significant Spearman’s correlation coefficients, the three phase-based methods for scoring the pubic symphyseal morphology had the strongest positive values. Unsurprisingly, the highest correlation observed was between the scores for the Suchey-Brooks and Hartnett-Fulginiti methods 152

(0.952); the latter standard was based strongly on the former, with the main difference being an added seventh phase in the Hartnett-Fulginiti method. The next highest correlation coefficient was noted between the Todd and Suchey-Brooks systems (0.937); again, this was to be expected considering that the Suchey-Brooks was a modification of Todd’s standard. The third highest correlation coefficient (0.899) was observed between the Todd and Hartnett-Fulginiti scores.

Plots of stage versus age at death Documented chronological ages were plotted according to their observed phase for each standard, for both the Reference and Recent groups. These plots provided a quick view of the variation in age at death encompassed by each phase of each standard by population (see Appendix B). When comparing Reference and Recent populations, these plots illustrate differences between certain aging standards with regard to the age distribution by phase. The ideal plot would illustrate a linear diagonal pattern of ages at death, which increased as the observed phase increased. Instead, a wide range of ages was encompassed by the observed phases. For the pubic symphysis, later phases for the Todd, Suchey-Brooks, and Hartnett-Fulginiti standards showed a wide range of ages, from young to old (for an example, see Figure 10, Suchey-Brooks). In contrast, the individual variables scored for the Boldsen and colleagues method showed wide ranges of ages for each phase (for an example, see Figure 11, symphyseal texture). This result was also prevalent for nearly all of the aging standards based on cranial suture closure (for an example, see Figure 12, coronal-pterica). The plots in Appendix B suggested that no radical differences were observed in the distribution of ages by phase for the standards tested by Reference or Recent population. 153

When the dataset was divided by skeletal series, the plots of two of the Recent skeletal series, the Maxwell Museum and Bass Donated collections, showed greater variation in Figure 10: Plot of observed Suchey-Brooks phase by age for Recent and Reference populations

Age by Phase: Suchey-Brooks standard 8 7

Phase

6 5 4 3 2 1 0 0

20

y = 0.0516x + 2.0793 R2 = 0.5408

40

60

80

Age at Death

Reference Linear (Reference)

Recent Linear (Recent)

100

120

y = 0.042x + 2.4652 R2 = 0.4422

Figure 11: Plot of observed Boldsen and colleagues’ symphyseal texture component score by age for Recent and Reference populations

Age by Phase: Boldsen et al. symphyseal texture 4.5 4 3.5 Phase

3 2.5 2 1.5 1 0.5 0 0

20

40

60 Age at Death

y = 0.0056x + 1.6409 R2 = 0.0175

Reference Linear (Reference)

154

80

100

120

y = 0.0127x + 1.6138 R2 = 0.0728

Recent Linear (Recent)

age by phase for traditional phase-based pubic symphyseal and auricular surface scoring standards, as well as many of the Boldsen and colleagues pubic symphyseal variables scored. Cranial suture scoring methods and most of the Boldsen and colleagues auricular surface variables scored showed considerable variation in age by observed phase for all skeletal series. Figure 12: Plot of observed Boldsen and colleagues coronal-pterica suture closure component score by age for Recent and Reference populations

Age by Phase: Boldsen et al. coronal-pterica 6 5

Phase

4 3 2 1 0 0

20 y = 0.0277x + 2.1551 2 R = 0.2165

40

60

80

Age at Death

Reference Linear (Reference)

Recent Linear (Recent)

100

120

y = 0.0255x + 2.2836 2 R = 0.1729

Comparison of observed and expected values The expected phases were calculated using the documented age and the published means associated with each standard’s phases. Individual cranial suture closure observations were summed according to their vault or lateral-anterior classifications and converted to an “S” designation for each classification. These “S” designations were viewed as phases, and the expected “S” designation was chosen using the mean age

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closest to the documented age. The ADBOU Transition Analysis Age Estimator program combined the raw Boldsen and colleagues’ component scores and demographic information to calculate an age at death point estimate, which was then directly compared to documented age at death.

Agreement between observed and expected values The weighted Kappa statistic was used to test for agreement between the observed phase/calculated age and the expected phase/documented age, by Reference and Recent group and by skeletal series (Table 12). The weighted Kappa value measured the degree of difference between the value of the actual morphology of the indicator and what was expected for that individual based on their age at death. Based on the weighted kappa values for the Recent and Reference groups, the agreement between the observed values and the expected values was greater for the Reference group for all aging standards tested. The highest agreement for observed and expected values occurred for the Todd pubic symphysis aging standard for the Reference group. The lowest agreement values were noted for all three cranial suture closure methods in the Recent group; these three aging standards also had the lowest agreement values of all methods for the Reference group. When the dataset was partitioned by skeletal series, the results were somewhat unexpected. Nearly all of the age estimation methods had the highest agreement between observed and expected phase values for the Maricopa County autopsy sample; the only exception was the Boldsen and colleagues’ Transition Analysis method using multiple indicators and a uniform prior distribution. Both the Maxwell Museum and Bass Donated skeletal series had the lowest agreement values for all methods and samples 156

tested, which drove down the average agreement for the Recent group and explained why the Reference group had higher overall values for agreement between the observed and expected scores. Table 12: Agreement between observed and expected values using the weighted kappa statistic Weighted kappa value* MMA UTK MCFSC HTH TC 0.35666 0.48223 0.77949 0.71836 0.69364 0.29704 0.47508 0.76496 0.62980 0.65395 0.23748 0.40564 0.66046 0.56774 0.54609 0.13892 0.28662 0.48603 0.40799 0.42834

Aging Standard REC REF 0.57843 0.70716 Todd 0.55010 0.64154 Suchey-Brooks 0.46363 0.55723 Hartnett-Fulginiti Transition Analysis pubic 0.31616 0.41893 symphysis 0.41410 0.65118 0.28565 0.51084 . 0.64808 0.65367 Lovejoy et al. Transition Analysis 0.34733 0.34894 0.37403 0.32900 . 0.34169 0.35786 auricular surface 0.52229 0.57640 0.24816 0.45478 0.66234 0.62252 0.52231 Iscan et al. Meindl & Lovejoy vault 0.13018 0.23141 0.17543 0.09190 . 0.19831 0.26521 system Meindl & Lovejoy lateral- 0.16235 0.26425 0.15928 0.15871 . 0.27880 0.25059 anterior system Transition Analysis cranial 0.12742 0.20929 0.15152 0.11019 . 0.21106 0.20779 sutures Transition Analysis combo, 0.38734 0.48997 0.23976 0.40237 0.48603 0.48086 0.49953 uniform Transition Analysis combo, 0.35437 0.43707 0.18741 0.34198 0.52596 0.44130 0.43245 forensic * All weighted Kappa values are significant at the p=0.05 level (Gwet 2002). A one-sided test of the Pr > Z and a two-sided test of the Pr > |Z| for the H0: weighted kappa = 0 both returned p values of <0.0001 for all comparisons.

The author expected to observe results supporting the hypothesis that methods based on a particular skeletal collection would perform better when applied to those same series in this analysis. This expectation only held true for the Hartnett-Fulginiti method, which had the highest agreement value for the Maricopa County autopsy sample. However, methods using the Hamann-Todd collection as their reference sample, including Todd’s pubic symphysis, Lovejoy and colleagues’ auricular surface, and Meindl and Lovejoy’s vault and lateral-anterior cranial suture standards, had good

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agreement values when compared to their application to other samples. Interestingly, when compared to documented age, the point estimate of age produced by Boldsen and colleagues’ Transition Analysis using the nine auricular surface components resulted in agreement values that were low but nearly identical for each of the skeletal series, ranging from 0.329 to 0.374.

Plots of differences between observed and expected values Individual plots of the differences between observed and expected values (PEs) and year of birth were created for each method’s scored variables for the total sample, each individual skeletal series, and ten-year age cohorts. Each graph had the individual points plotted, a trend line fitted by linear regression, and an R-square value associated with the fitted line. This endeavor produced numerous plots (see Appendices C-E; note that the “y” scale varies by plot). These plots were used as heuristic tools to identify possible shifts or changes in the performance of aging standards through time. A large amount of variation was observed for all methods, and the regression lines did not capture the much of the magnitude of this variation. Plots Highlighting Differences for the Entire Dataset Plots illustrating the differences between observed and expected values for each aging standard were created using the entire dataset, without regard to group, series, age at death, or other variables, to reveal any changes in the performance of aging standards over time (see Appendix C). For the total sample, all methods showed a slight increase over time, though the trend lines did not explain much of the variation in the differences

158

Figure 13: Plot exemplifying a tendency of the method to underestimate chronological age Iscan 4th Rib

Difference between observed and expected phases

4 2 0 1820

R2 = 0.0069 1840

1860

1880

1900

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-2 -4

-6 -8 -10 Year of birth

Figure 14: Plot exemplifying a tendency of the method to overestimate chronological age Boldsen et al Pubic Sym physis

Difference between observed and expected phases

100 80 60 40 20 0 1820 -20

R2 = 0.0004 1840

1860

1880

1900

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-40 -60 -80 Year of birth

between observed and expected values present. It was clear from these plots that certain methods tended to underestimate age in general (Figure 13) and that others tended to 159

slightly overestimate age in general (Figure 14). Standards underestimating age included İşcan fourth rib, Meindl and Lovejoy cranial vault and lateral-anterior sutures, and Boldsen and colleagues cranial suture closure. One method that tended to overestimate age was Boldsen and colleagues’ pubic symphysis. Plots Highlighting Differences Among Skeletal Series To elucidate the potential changes in the performance of aging standards, plots illustrating the differences between observed and expected values for each aging standard scored were color-coded by skeletal series: Hamann-Todd Osteological, Terry Anatomical, Maxwell Museum Documented, Bass Donated, and the Maricopa County autopsy (see Appendix D). These plots illustrated that several methods generally tend to underestimate the age of individuals within particular skeletal collections and that certain standards generally tend to overestimate age in others. The Boldsen and colleagues pubic symphysis standard tended to overestimate age for the Reference group, specifically the Hamann-Todd and Terry series, as well as the Recent Maricopa County autopsy sample (Figure 15). The Transition Analysis method with a uniform prior (UNI) also tended to overestimate age in the Recent Maricopa County skeletal series (Figure 16). Five aging standards had the general tendency to underestimate age for remains from the Recent Maxwell Museum Documented collection (Figure 17): Todd pubic symphysis, Suchey-Brooks pubic symphysis, İşcan fourth rib, Boldsen and colleagues’ cranial sutures, and Transition Analysis (COR). The İşcan fourth rib standard also tended to underage the remaining two Recent skeletal series: Bass Donated and Maricopa County autopsy (Figure 18).

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Figure 15: Plot illustrating overestimation of age in the Terry and Hamann-Todd series (Reference) Boldsen et al Transition Analysis: Uniform Distribution 80

Difference between observed and expected phases

60

40

20

0 1820

1840

1860

1880

1900

1920

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2000

-20

-40

-60

-80 Year of birth HTH

TC

MMA

UTK

Linear (HTH)

Linear (TC)

Linear (MMA)

Linear (UTK)

2

R = 0.0698

2

R = 0.1269

2

2

R = 0.2111

R = 0.1119

MCFSC Linear (MCFSC) 2

R = 0.0137

Figure 16: Plot illustrating overestimation of age in the Maricopa County sample (Recent) Boldsen et al Pubic Symphsis 100

Difference between observed and expected phases

80

60

40

20

0 1820

1840

1860

1880

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-20

-40

-60

-80 Year of birth

2

HTH

TC

MMA

UTK

Linear (HTH)

Linear (TC)

Linear (MMA)

Linear (UTK)

R = 0.0024

2

R = 0.0033

2

R = 0.125

161

2

R = 0.0319

MCFSC Linear (MCFSC) 2

R = 0.0137

2000

Figure 17: Example of an aging method that tended to underestimate age in the Maxwell sample Suchey-Brooks Symphysis 5

Difference between observed and expected phases

4

3

2

1

0 1820

1840

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-1

-2

-3

-4

-5 Year of birth HTH

TC

MMA

UTK

Linear (HTH)

Linear (TC)

Linear (MMA)

Linear (UTK)

2

R = 0.0093

2

R = 0.0781

2

MCFSC

2

R = 0.0274

R = 0.0711

Linear (MCFSC) 2

R = 0.0567

Figure 18: Plot of İşcan’s method, which tended to underage the Bass and Maricopa County series Iscan 4th Rib

Difference between observed and expected phases

4

2

0 1820

1840

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-2

-4

-6

-8

-10 Year of birth

2

HTH

TC

MMA

UTK

Linear (HTH)

Linear (TC)

Linear (MMA)

Linear (UTK)

R = 0.0955

2

R = 0.0675

2

R = 0.055

162

2

R = 0.0835

MCFSC Linear (MCFSC) 2

R = 0.0696

2000

Figure 19: The Transition Analysis (COR) method tended to underestimate the age for all series Boldsen et al Transition Analysis: Forensic Distribution

Difference between observed and expected phases

40

20

0 1820

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-20

-40

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-80 Year of birth HTH

TC

Linear (HTH) 2

MMA

Linear (TC)

R = 0.2652

UTK

Linear (MMA)

2

R = 0.3163

MCFSC

Linear (UTK)

Linear (MCFSC)

2

2

R = 0.3423

2

R = 0.3296

R = 0.2563

Figure 20: Example of steeper trend line slopes were observed for all skeletal series Meindl & Lovejoy Cranial Lateral-Anterior Sutures 8

Difference between observed and expected phases

6

4

2

0 1820

1840

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-2

-4

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-8 Year of birth HTH Linear (HTH) 2

R = 0.3796

TC Linear (TC)

MMA Linear (MMA)

163

2

R = 0.2135

2

R = 0.1874

UTK Linear (UTK) 2

R = 0.1922

1980

2000

In general, the Transition Analysis (COR) method tended to underestimate the age for all of the skeletal series, regardless of Reference and Recent group (Figure 19). Finally, steeper trend line slopes were observed for all skeletal series for the HartnettFulginiti pubic symphysis, Meindl and Lovejoy cranial vault and lateral-anterior suture closure, and Transition Analysis (COR) standards (Figure 20). Plots Highlighting Differences Among Age Cohorts Plots illustrating the differences between observed and expected values for each aging standard scored were color-coded by ten-year age cohorts (20-29, 30-39, 40-49, 5059, 60-69, 70-79, and 80+) to elucidate the potential changes in the performance of aging standards by age group over time (see Appendix E). It is clear from these plots that certain methods tended to underestimate the age of older age groups and that some

Figure 21: Example of an aging standard that overestimates age in younger cohorts

Todd Pubic Symphysis

Difference between observed and expected phase

8

6

4

2

0 1820

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-4

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2

20-29

30-39

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50-59

60-69

70-79

Linear (20-29)

Linear (30-39)

Linear (40-49)

Linear (50-59)

Linear (60-69)

Linear (70-79)

R = 0.004

2

R = 0.0027

2

R = 3E-05

2

R = 0.0463

164

2

R = 0.0333

2

R = 0.0031

80+ Linear (80+) 2

R = 0.0064

2000

Figure 22: Example of an aging standard that underestimates age in older cohorts Iscan 4th Rib 4

Difference between observed and expected phase

2

0 1820

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-2

-4

-6

-8

-10 Year of Birth 20-29 Linear (20-29) 2

R = 0.0004

30-39 Linear (30-39) 2

R = 7E-05

40-49 Linear (40-49) 2

R = 0.0088

50-59 Linear (50-59) 2

60-69 Linear (60-69) 2

R = 0.0019

R = 3E-07

70-79 Linear (70-79) 2

R = 0.0311

80+ Linear (80+) 2

R = 3E-05

Figure 23: Example of an aging method that illustrates a shift from overaging to a value closer to zero (see specifically the 20-29 year age cohort) Meindl & Lovejoy Cranial Vault Sutures

Difference between observed and expected phase

6

4

2

0 1820

1840

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-2

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-6 Year of Birth 20-29 Linear (20-29) 2

R = 0.3056

30-39 Linear (30-39) 2

R = 0.0156

40-49 Linear (40-49) 2

R = 0.0113

50-59 Linear (50-59) 2

R = 0.0151

165

60-69 Linear (60-69) 2

R = 0.0165

70-79 Linear (70-79) 2

R = 0.0496

80+ Linear (80+) 2

R = 0.0406

2000

Figure 24: Example of an aging method that illustrates a shift from underaging to a value closer to zero (see specifically the 70-79 and 80+ year age cohorts) Boldsen et al Transition Analysis: Uniform Distribution 80

Difference between observed and expected phase

60

40

20

0 1820

1840

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-20

-40

-60

-80 Year of birth 20-29 Linear (20-29) 2

R = 0.003

30-39 Linear (30-39) 2

R = 0.0046

40-49 Linear (40-49) 2

R = 0.0515

50-59 Linear (50-59) 2

R = 0.0042

60-69 Linear (60-69) 2

R = 0.0001

70-79 Linear (70-79) 2

R = 0.0688

80+ Linear (80+) 2

R = 0.0261

overestimated the age of younger age groups. Methods overestimating the age of younger cohorts included the Todd pubic symphysis (<30), Suchey-Brooks pubic symphysis (<30), Lovejoy and colleagues’ auricular surface (<50), and Meindl and Lovejoy cranial vault and lateral-anterior suture standards (<40) (Figure 21). A shift from overaging, denoted by a positive difference value, to a value closer to zero was noted for the 60-69 cohort for the Boldsen and colleagues’ pubic symphysis standard and the 20-29 cohort for the Lovejoy and colleagues’ auricular surface and Meindl and Lovejoy cranial vault and lateral-anterior suture closure standards (Figure 23). A shift from underaging to an error closer to zero was observed for the 70-79 and

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80+ cohorts for the Boldsen and colleagues’ auricular surface and Transition Analysis (UNI) standards (Figure 24).

Identification of the best predictors of the difference between observed and expected phases Before hypothesis testing commenced, the differences between the observed and actual values for each aging standard were calculated (PE) and then regressed on year of birth, sex, race, sex-race category, group, series, stature, geographic region of the United States, and ten-year age cohorts to determine which variables significantly contributed to the variation observed in the PEs. Results are summarized in Table 13. The summary of the stepwise regression is presented in Appendix F. The three variables that appeared to have the largest impact on the majority of the standards tested were the year of birth, age at death, and geographic region of the United States. Admittedly, all three of these variables were related to the group variable. Though an attempt was made to sample an equal number of individuals from each tenyear age range from all skeletal series examined, the Reference samples were more likely to have older individuals than more Recent ones simply because of their birth years; also, the Reference samples had greater numbers of younger individuals as a result of their collection methods. While birth years overlap between these groups, the Reference group contains a greater number of earlier birth years than does the Recent group. Finally, region of the United States was somewhat related to Reference and Recent groups, as both Reference skeletal series were classified as Midwest, the Bass series was classified as Southeast, and the remaining two Recent skeletal series were classified as Southwest.

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When year of birth was determined to be a statistically significant predictor in the prediction models, it typically did not contribute much to explaining the variation observed for the PE; partial R-squared values ranged from 0.010 to 0.039. A similar effect was noted for region, with partial R-squared values ranging from 0.004 to 0.035. When age at death was determined to be a significant predictor, however, it explained a larger portion of the variation observed in the differences between the observed and

Table 13: Summary of significant (α=0.05) predictors of calculated differences between observed and expected values (PE) by aging standard Aging standard

Significant predictors of PE

Todd Pubic Symphysis

Year of birth Region Year of birth Region Height Year of birth Group Sex Height Age at death Region 10-year age cohort Age at death Region Height 10-year age cohort Age at death Series Age at death Sex Year of birth Region Sex-race category Year of birth Age at death Region Sex Age at death Sex Race Year of birth Age at death Region Race Year of birth Age at death Race

Suchey-Brooks Pubic Symphysis

Hartnett-Fulginiti Pubic Symphysis

Lovejoy et al. Auricular Surface Iscan 4th Rib

Meindl & Lovejoy Vault Sutures Meindl & Lovejoy Lateral-Anterior Sutures Boldsen et al. Pubic Symphysis

Boldsen et al. Auricular Surface

Boldsen et al. Cranial Sutures

Transition Analysis: Uniform Distribution

Transition Analysis: Forensic Distribution

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actual values for each aging standard, with partial R-squared values ranging from 0.120 for Boldsen and colleagues’ combined indicators with a uniform prior to 0.414 for Meindl and Lovejoy’s cranial vault sutures. When sex and race were included in the prediction models, neither typically contributed much to the model or was statistically significant at the 0.05 level; when included in the model, the contribution to the variation explained by sex ranged from 0.007 to 0.038 and the contribution of race ranged from 0.017 to 0.030. Overall, the tested variables did not account for a large portion of the variation observed in the calculated differences between observed and expected values for the aging standards tested. Model R-Squared values ranged from 0.071 for Boldsen and colleagues’ pubic symphysis to 0.425 for Meindl and Lovejoy’s cranial vault sutures. At their best, a combination of these variables accounted for only 42% of the variation observed in the difference between observed and expected values. This result implies that most of the variation observed cannot be explained by the nine variables tested; this variation may be stochastic in nature, accounted for by a variable not tested in this research, or a combination of the two. Results indicated that, in general, most of the descriptive variables tested do not heavily influence how well aging standards perform, based on the degree of error between the observed and expected values. Only the age at death appeared to significantly influence the calculated value of the difference between observed and expected values for each aging standard, and this was only the case for half of the standards tested. This was not surprising, as one might expect to have a larger difference between the observed and expected phases for older individuals as a result of the greater

169

variation present with increasing age. These data suggest that it would be appropriate to pool the sexes and races for the subsequent analyses comparing the Reference and Recent groups. Though it was not necessary to separately test groupings based on sex or race, more powerful statistics could identify differences between these groups. Thus, these groups will remain separate for subsequent statistical analyses.

Calculation of bias and inaccuracy Bias and inaccuracy were calculated for all aging methods used in this research (Tables 14 and 15). Bias was calculated as the sum of the estimated age minus the chronological age, divided by the total number of individuals. For phase-based methods, which do not produce point estimates of age, bias was calculated as the sum of the observed phase minus the expected phase, divided by the total number of individuals. The resulting value and sign describe the degree and direction of systematic over- or under-estimation of age at death for the sample. Inaccuracy was calculated in a similar manner, except the value was the sum of the absolute value of the estimated age minus the chronological age, divided by the total number of individuals. Similarly, for phasebased methods, inaccuracy was calculated using observed and expected phases. All methods tested were biased toward underestimating age except for Todd and Boldsen and colleagues’ Transition Analysis for the pubic symphysis. Bias was worst for Transition Analysis using multiple indicators and a forensic prior and Meindl and Lovejoy’s cranial vault. Bias was greater for the Recent group for all age estimation methods except the following: Todd, Transition Analysis pubic symphysis, Transition Analysis auricular surface, and Transition Analysis using all indicators and a uniform prior distribution. 170

Table 14: Bias and inaccuracy values for traditional phase-based aging methods Todd

SucheyBrooks

Hartnett- Lovejoy Iscan et Meindl & Meindl & Fulginiti et al. al. Lovejoy vault Lovejoy lat-ant

Total

0.141

-0.128

-0.099

-0.077

-0.261

-0.704

-0.339

Reference

0.307

-0.046

0.004

0.042

-0.228

-0.636

-0.053

Recent

-0.07

-0.232

-0.23

-0.23

-0.302

-0.792

-0.703

Total

0.936

0.505

0.77

0.72

0.852

1.85

2.409

Reference

0.857

0.467

0.721

0.761

0.737

2.188

2.759

Recent

1.021

0.541

0.824

0.656

0.963

1.4

1.944

Bias*

Inaccuracy*

* In this table, bias and inaccuracy are measured in terms of phases because traditional phase-based methods do not provide a point estimate of age.

Table 15: Bias and inaccuracy values for Boldsen and colleagues’ Transition Analysis methods Transition Analysis: Pubic Symphysis

Transition Analysis: Auricular Surface

Transition Analysis: Cranial Sutures

Transition Analysis: Uniform Distribution

Transition Analysis: Forensic Distribution

Total

4.122

-1.382

-3.595

-2.468

-9.064

Reference

5.601

-3.704

-1.563

-3.463

-8.443

Recent

2.237

1.576

-6.185

-1.199

-9.855

Total

18.606

12.966

18.74

13.87

13.35

Reference

17.252

15.085

21

11.82

12.21

Recent

20.148

10.087

15.707

16.309

14.651

Bias*

Inaccuracy*

* In this table, bias and inaccuracy are measured in terms of years because the Transition Analysis method provides a point estimate of age.

Inaccuracy was worst for Boldsen and colleagues’ Transition Analysis using the pubic symphysis and cranial sutures, both with values approaching twenty years. Inaccuracy was particularly large for Meindl and Lovejoy’s phase-based cranial suture aging methods. Inaccuracy was greater for the Recent group for all age estimation methods except the following: Lovejoy and colleagues, İşcan and colleagues, Meindl and Lovejoy, and Transition Analysis using the auricular surface and cranial sutures.

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It is important to note the difference in scale between Tables14 and 15. While it appears that the Transition Analysis methods have a large degree of inaccuracy, these values are calculated based on the point estimate of age at death produced by the ADBOU program, not the 95% prediction interval that accompanied the estimate. Comparing these values to a fewer number of phases—as few as six and as many as ten—is misleading, as these phases encompass wide ranges of ages. Regardless, bias and inaccuracy values are comparable to those reported in the literature.

Hypothesis Testing To address the primary dissertation question of whether there are differences in the aging processes of osteological indicators between Reference and Recent American Skeletal populations, three hypotheses were evaluated.

Question 1 Is the observed morphology of the aging indicator associated with the same chronological ages for both older Reference and more Recent American skeletal populations? The sample size, mean age in years, standard error of the mean, 95% confidence intervals for the mean, standard deviations, and observed age ranges were computed for each phase of each osteological aging standard; these data were computed for each sex, race, and total sample within the Reference and Recent samples (see Appendix G). This tabulated the number of cases that exhibit a particular phase of an indicator by documented age, providing a launching point for further hypothesis testing by probit regression. 172

Next, the ages at transition for each osteological aging standard were calculated using a log-age cumulative probit model in the statistical package R (R Core Development Team, 2008). Ages at transition were calculated for the total sample for both Reference and Recent groups. Tables with the ages at transition and plots of the data are presented in Appendix H. Four outcomes were observed: 1) a difference in the rate of change, such that the Recent group illustrated a slightly decelerated rate of progression through stages; 2) a difference in the rate of change, such that the Recent group illustrated an accelerated rate of progression through stages; 3) no difference in the rate of change, but a difference in the ages at transition; and finally 4) no difference in the rate of change or ages at transition. Examples of each, including a comparison of age at transition plots and values between the Reference and Recent groups are presented in Figures 25-28. Figure 25 presents a comparison in which there appeared to be a significant difference in the ages at transition between groups, with the Recent group showing a slightly decreased rate of progression through stages when compared to the Reference group. The data presented are for the Todd pubic symphysis standard; standards with similar results included Suchey-Brooks, Hartnett-Fulginiti, Boldsen and colleagues’ superior apex and dorsal and ventral symphyseal margin components of the pubic symphysis. Figure 26 illustrates a comparison in which there appeared to be a significant difference in the ages at transition between groups, with the Recent group showing a slightly increased rate of progression through stages when compared to the Reference group. The data presented are for Boldsen and colleagues’ auricular surface superior

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Figure 25: Example of a difference in the rate of change, illustrating the Recent group’s slower rate of progression through stages

Density 0

20

40

60

80

0 .01 .02 .03 .04 .05 .06

.06 .04 0

.02

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.08

.10

.07

Plots of ages at transition for the Todd method (Reference and Recent, respectively)

100

0

20

40

Age

60

80

100

Age

Ages at transition for the Todd method Transition (Todd Phase) I-II II-III III-IV IV-V V-VI VI-VII VII-VIII VIII-IX IX-X

Reference (age in years) 13.73281 16.94788 19.34549 22.34348 25.6066 31.47315 38.78029 41.9785 46.57818

Recent (age in years) -16.05893 17.05482 20.07991 24.1767 33.19188 38.88756 44.66757 55.54833

Difference in years -0.88895 2.29067 2.26357 1.4299 -1.71873 -0.10727 -2.68907 -8.97015

Plot of ages at transition for the Todd method

Plot of Ages at Transition (Todd) 60

Age in Years

50 40 Reference

30

Recent

20 10 0 I-II

II-III

III-IV

IV-V

V-VI

VI-VII

Transition

174

VII-VIII VIII-IX

IX-X

Figure 26: Example of a difference in the rate of change, illustrating the Recent group’s faster rate of progression through stages

.01

.02

Density

.03 .02

0

0

.01

Density

.03

.04

.04

.05

Plots of ages at transition for the Boldsen et al. auricular surface superior demiface topography component (Reference and Recent, respectively)

0

20

40

60

80

100

0

20

Age

40

60

80

100

Age

Ages at transition for the Boldsen et al. auricular surface superior demiface topography component Transition (SDT Phase) I-II II-III

Reference (age in years) 14.87479 65.79723

Recent (age in years) 15.92711 53.42181

Difference in years -1.05232 12.37542

Plot of ages at transition for the Boldsen et al. auricular surface superior demiface topography component

Plot of Ages at Transition (Boldsen superior demiface topography)

Age in Years

70 60 50 40

Reference

30 20

Recent

10 0 I-II

II-III Transition

175

Figure 27: Example of no difference in the rate of change, but different ages at transition between the Reference and Recent groups

.06 0

0

.02

.04

Density

.03 .02 .01

Density

.04

.08

.05

Plots of ages at transition for the Boldsen et al. auricular surface inferior demiface topography component (Reference and Recent, respectively)

0

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40

60

80

100

0

20

Age

40

60

80

100

Age

Ages at transition for the Boldsen et al. auricular surface inferior demiface topography component Transition (IDT Phase) I-II II-III

Reference (age in years) 14.62314 58.09248

Recent (age in years) 7.735717 48.69423

Difference in years 6.887423 9.398251

The highlighted cell indicated a value that was very low, which was likely an artifact of regression

Plot of ages at transition for the Boldsen et al. auricular surface inferior demiface topography component

Plot of Ages at Transition (Boldsen inferior demiface topography)

Age in Years

70 60 50 40

Reference

30 20

Recent

10 0 I-II

II-III Transition

176

Figure 28: Example of no difference in the rate of change or ages at transition between Reference and Recent groups

Density

0

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.05 .06 .07

Plots of ages at transition for the İşcan et al. method (Reference and Recent, respectively)

100

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Age

Ages at transition for the İşcan et al. method Transition (İşcan Phase) I-II II-III III-IV IV-V V-VI VI-VII VII-VIII

Reference (age in years) -17.26198 24.77808 31.53157 45.20274 60.83032 96.05648

Recent (age in years) -17.83161 22.9366 32.39673 45.51855 63.31529 89.76016

Difference in years --0.56963 1.84148 -0.86516 -0.31581 -2.48497 6.29632

Plot of ages at transition for the İşcan et al. method

Plot of Ages at Transition (İşcan et al.) 120

Age in Years

100 80 Reference

60

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40 20 0 I-II

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177

V-VI

VI-VII

VII-VIII

demiface topography component; Boldsen and colleagues’ pubic symphyseal relief component produced similar results. Figure 27 summarizes a comparison in which there did not appear to be a significant difference in the rate of progression through stages between groups, but the ages at transition between Reference and Recent groups differed. The data presented are for Boldsen and colleagues’ auricular surface inferior demiface topography component; Meindl and Lovejoy’s lateral-anterior suite of cranial sutures produced similar results. Finally, Figure 28 illustrates a comparison in which there did not appear to be a significant difference in the rate of progression through stages or the ages at transition between Reference and Recent groups. The data presented are for the İşcan fourth rib aging standard; Boldsen and colleagues’ coronal-pterica suture closure component produced similar results. The log-age cumulative probit model produced unrealistic age at transition estimates for many of the standards and components tested: 1) Boldsen and colleagues’ pubic symphyseal texture component; 2) seven of Boldsen and colleagues’ nine auricular surface components, including superior, apical and inferior surface morphology, inferior surface texture, and superior, inferior and posterior iliac exostoses; 3) four of Boldsen and colleagues’ five cranial suture components, including lambdoidal-asterion, sagittalobelica, zygomaticomaxillary, and interpalatine; and 4) Meindl and Lovejoy’s cranial vault system. Most of the unrealistic estimates consisted of very low ages for the initial transition and very high ages for the last transition(s). This outcome may be result of the statistical analysis used, as this problem has been documented for regression techniques. In addition, several of Boldsen and colleagues’ components, specifically the pubic

178

symphyseal texture, the three posterior iliac exostoses scores, and the four aforementioned cranial suture components, produced extreme values for the estimated ages at transition. These values indicated a problem with the statistical analysis of the data, whether due to some unidentified factor, severe regression effects, and/or very low correlations between the component scores and chronological age, which range from 0.09 to 0.36. Regardless, the results from these features were not utilized for hypothesis testing. To complicate the data analysis further, the Transition Analysis point estimates calculated for the pubic symphysis, auricular surface, cranial sutures, and combined indicators were based on the combination and statistical analysis of all of Boldsen and colleagues’ individual components scored; if many of those components produced unlikely or extreme values for age at transition estimates, then these may have impacted the overall age estimates produced by the Transition Analysis standard. Finally, a formal test of whether variation in the morphological aging processes of the pubic symphysis, auricular surface, sternal rib end, and cranial sutures exists between Reference and Recent American skeletal series was conducted. The null hypothesis of no difference was tested using regression and an analysis of deviance. Proportional odds probit regression was used for ordinal data resulting from traditional aging standards, and a generalized linear regression model was used for the point estimates produced by Transition Analysis. Reference versus Recent, sexes and races pooled When the total samples for the Reference and Recent American populations were compared, significant differences in the rate of progression through age-related phases were observed for the following osteological aging standards: Todd, Suchey-Brooks, 179

Hartnett-Fulginiti, four of the five traits scored for Boldsen and colleagues’ pubic symphysis, Lovejoy and colleagues auricular surface, two of the nine features scored for Boldsen and colleagues’ auricular surface, and all five of the variations of the Transition Analysis method (see Table 16-17). Components defined by Boldsen and colleagues that produced significant differences between populations included the pubic symphyseal texture, superior apex, and ventral and dorsal margins, the auricular surface superior demiface topography, and superior and inferior posterior iliac exostoses. However, several of these have been identified as unreliable due to the extreme ages estimated for the later transitions: Boldsen and colleagues’ pubic symphyseal texture component, Boldsen and colleagues’ superior, inferior and posterior iliac exostoses, and Boldsen and colleagues’ lambdoidal-asterion, sagittal-obelica, zygomaticomaxillary, and interpalatine cranial sutures. The effect on the point estimates of age that were produced by the combination of these components and others using the Transition Analysis standard was not known, and thus those results were also questioned. The unreliable results were not surprising considering the values presented in Tables 16 and 17, which provide evidence of poor fitting models for all aging methods, regardless of indicator. The poor fit of the models was indicated by residual deviances that greatly exceeding the residual degrees of freedom. Residual deviations less than the degrees of freedom are the mark of good fitting models; this was not observed for these analyses. I expected that the poor fit resulted from the violation of the assumption of an underlying normal distribution of data for probit models. Figures 29-31 are examples of graphs illustrating the number of individuals classified into each phase or component score, by Reference and Recent group (see

180

Table 16: Analysis of deviance and improvement chi-square output: Total CHI-SQUARE LIKELIHOOD RATIO TESTS: TOTAL Resid. Resid. Aging Standard Model Df Dev Test Df LR stat. Todd Suchey-Brooks Hartnett-Fulginiti Boldsen symphyseal relief Boldsen symphyseal texture Boldsen superior apex Boldsen ventral symphyseal margin Boldsen dorsal symphyseal margin Lovejoy et al. Boldsen superior demiface topog. Boldsen inferior demiface topography Boldsen superior surface morphology Boldsen apical surface morphology Boldsen inferior surface morphology Boldsen inferior surface texture Boldsen sup post iliac exostoses Boldsen inf post iliac exostoses Boldsen posterior iliac exostoses Iscan et al. Meindl & Lovejoy vault system Meindl & Lovejoy lateral-ant system Boldsen lambdoidalasterica Boldsen sagittalobelica Boldsen coronalpterica Boldsen zygomaticomaxillary Boldsen interpalatine

phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop phase=log(age) + pop phase=log(age) + pop + log(age):pop

908 907 912 911 911 910 918 917 906 905 862 861 906 905 911 910 764 763 773 772 765 764 770 769 771 770 759 758 743 742 795 794 726 725 748 747 597 596 794 793 797 796 806 805 801 800 803 802 801 800 738 737

181

2051.39 2028.777 1849.41 1839.078 2301.053 2291.121 2132.382 2131.169 2206.863 2201.804 1250.542 1233.833 2079.48 2053.544 1781.65 1754.093 2134.66 2125.002 1258.631 1258.584 1232.295 1229.338 1469.078 1469.067 1664.284 1664.239 1537.935 1537.865 1441.242 1439.37 2020.705 2009.59 2165.995 2145.147 1210.128 1208.648 1640.604 1640.6 2557.837 2557.664 2718.662 2718.662 2108.233 2107.2 2374.725 2374.442 2101.865 2101.852 1845.807 1845.704 1959.238 1959.233

Pr(Chi)

1 vs. 2

1

22.61351

1.98E-06

1 vs. 2

1

10.33357

0.00131

1 vs. 2

1

9.931722

0.00162

1 vs. 2

1

1.212204

0.2709

1 vs. 2

1

5.05854

0.0245

1 vs. 2

1

16.70875

4.36E-05

1 vs. 2

1

25.9361

3.53E-07

1 vs. 2

1

27.55682

1.53E-07

1 vs. 2

1

9.657344

0.00189

1 vs. 2

1

0.04707152

0.82824

1 vs. 2

1

2.956933

0.08551

1 vs. 2

1

0.01149554

0.91462

1 vs. 2

1

0.04474754

0.83247

1 vs. 2

1

0.07045612

0.79067

1 vs. 2

1

1.871958

0.17125

1 vs. 2

1

11.11511

0.00086

1 vs. 2

1

20.84712

4.97E-06

1 vs. 2

1

1.480229

0.22374

1 vs. 2

1

0.00445147

0.94681

1 vs. 2

1

0.1726718

0.67775

1 vs. 2

1

8.80E-05

0.99251

1 vs. 2

1

1.033002

0.30945

1 vs. 2

1

0.2831491

0.59464

1 vs. 2

1

0.01299699

0.90923

1 vs. 2

1

0.1026685

0.74865

1 vs. 2

1

0.0051994

0.94252

Table 17: Analysis of deviance output: Total ANALYSIS OF DEVIANCE TABLE (glm): TOTAL Aging Standard Transition Analysis pubic symphysis Transition Analysis auricular surface Transition Analysis cranial sutures Transition Analysis combo, uniform Transition Analysis combo, forensic

Model TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop

Resid. Df 957 956 818 817 811 810 968 967 968 967

Resid. Dev 854085 847924 372969 352528 566261 562475 496436 490474 255263 251194

Df

Deviance

Pr>(|Chi|)

1

6161.2

0.008398

1

20441

5.8720E-12

1

3785.7

0.0196

1

5962.3

0.0006

1

4068.7

7.5700E-05

Appendix I). Figure 29 shows one typical outcome: the majority of individuals classified into earlier phase scores. This result was observed for Boldsen and colleagues’ pubic symphyseal texture, auricular surface inferior surface texture, and three iliac exostoses components. Figure 30 illustrates the other typical outcome, which showed that the majority of individuals were classified into later phase scores. This was observed for most of the aging methods, including the following: Todd, Suchey-Brooks, HartnettFulginiti, İşcan and colleagues, Meindl and Lovejoy’s lateral-anterior sutures, and Boldsen and colleagues’ pubic symphyseal relief, superior apex, ventral and dorsal symphyseal margins, superior and inferior demiface topography, inferior demiface topography, and three surface morphology components. The aging method that most closely approached a normal distribution was Meindl and Lovejoy’s cranial vault sutures (see Figure 31). Based on the interpretation of the graphs in Appendix I, if the underlying normal distribution was the only factor contributing to the poor fit of the models, I would expect that Meindl and Lovejoy’s cranial vault suture method would have had the lowest ratio of residual deviance to residual degrees of freedom. However, this was not observed. Based on the data presented in Table 16, Boldsen and colleagues’ superior apex, dorsal 182

Figure 29: Example of methods with the majority of individuals classified into earlier phase scores

Number of Individuals by Observed Phase 350 300 250 200

Reference

150

Recent

100 50 0 1

2

3

.

Boldsen et al. Posterior Iliac Exostoses Phase

Figure 30: Example of methods with the majority of individuals classified into later phase scores

Number of Individuals by Observed Phase 300 250 200 Reference

150

Recent

100 50 0 1

2

3

.

Boldsen et al. Inferior Demiface Topography Phase

symphyseal margin, superior and inferior demiface topography, and superior surface morphology have the lowest ratios; contrary to expectations, Meindl and Lovejoy’s vault sutures had one of the largest ratios, along with Meindl and Lovejoy’s lateral-anterior sutures, Boldsen and colleagues’ cranial sutures, and Lovejoy and colleagues’ auricular 183

surface. While the non-normality of the data was likely a contributing factor to the poor fit of the models, it did not appear to be the only cause.

Figure 31: Closest approximation to a normal distribution of individuals classified into phases

Number of Individuals by Observed Phase 180 160 140 120 100 80 60 40 20 0

Reference Recent

0

1

2

3

4

5

6

.

Meindl & Lovejoy Cranial Vault Sutures Phase

To sum, formal hypothesis testing used to compare the aging processes of the pubic symphysis, auricular surface, cranial sutures, fourth rib, and combined osteological age indicators produced varied results. Some indicated a significant difference between Reference and Recent American skeletal populations, and others did not. When these data were combined with the estimated ages at transition, it was possible to compare the average rate of progression of Recent American skeletal remains through age-related phases to that of the Reference group. Faster and slower designations were reserved only for those aging standards that indicated a statistically significant difference in the rate of aging between the Reference and Recent groups, as determined by the analysis of deviance test and improvement chisquare statistic. The rates of aging were determined by examining the graphs and tables

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Table 18: Recent sample’s rate of progression through morphological stages, compared to the Reference sample Faster

Slower

No difference

Pubic symphysis Todd X Suchey-Brooks X Hartnett-Fulginiti X Boldsen symphyseal relief X Boldsen symphyseal texture X* Boldsen superior apex X Boldsen ventral symphyseal margin X Boldsen dorsal symphyseal margin X Auricular Surface Lovejoy et al. X Boldsen superior demiface topography X Boldsen inferior demiface topography X Boldsen superior surface morphology X Boldsen apical surface morphology X Boldsen inferior surface morphology X Boldsen inferior surface texture X Boldsen superior posterior iliac exostoses X* Boldsen inferior posterior iliac exostoses X* Boldsen posterior iliac exostoses X* Fourth Rib İşcan et al. X Cranial Suture Closure Meindl & Lovejoy vault system X Meindl & Lovejoy lateral-anterior system X Boldsen lambdoidal-asterion X* Boldsen sagittal-obelica X* Boldsen coronal-pterica X Boldsen zygomaticomaxillary X* Boldsen interpalatine X* * indicates age at transition estimates that were extremely unlikely, representing an artifact of the regression analysis itself, an extremely low correlation between the standard/component and chronological age, or some other unknown factor. These results were interpreted with caution.

of the ages at transition comparing Reference and Recent groups in Appendix H. Because of the way the graphs were plotted with the age on the Y-axis and the transition on the X-axis, the steeper the slope, the slower the rate of progression through phases. The steep slope depicts the increased number of years passed through to attain the next transition.

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Table 18 summarizes the Recent group’s rate of aging as compared to the Reference sample. Results showed that the Recent sample aged at a slower rate for nearly all of the standards and individual components that indicated a difference between the two groups. This outcome is particularly important for the pubic symphysis. The degree of this change was small for the Todd, Suchey-Brooks, and Hartnett-Fulginiti standards; for example, for every year that the Reference group took toward achieving the next transition, the Recent group took an average of 1.3 years, regardless of the standard used. For later transitions, this translates into differences of up to 15 years. In the table above, no difference indicated no significant difference between Reference and Recent groups in slopes for the rate of progression though stages; faster and slower designations were not assigned to these standards because any differences between the two groups was due to chance. As stated earlier, many of Boldsen and colleagues’ components produced extreme values for ages at transition. Although they have been included in the above tables, the results produced by these eight components were not reliable. The extreme estimated values for the ages at transition often were most pronounced for the last transition and for the Reference group. As a result, the significant difference in rate of aging between groups for those components were likely due to a skewed slope that was affected by the extremely large value of the Reference group’s final age at transition. For those components that showed no statistically significant difference in slope between the two groups, the result was due to extreme values at the later phase transitions for both the Reference and Recent groups. When both had nearly equally large values, the slopes both increased dramatically for the same transitions, paralleling each other.

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For subsequent analyses, the components with extreme values were eliminated, and only the standards producing significant differences in the rate of aging between Reference and Recent American skeletal collections were examined further. Aging methods and individual indicators that detected a significant difference between groups included the following: the Todd, Suchey-Brooks, and Hartnett-Fulginiti phase-based methods for scoring the pubic symphysis; Boldsen and colleagues’ superior apex, ventral symphyseal margin, and dorsal symphyseal margin of the pubis; Lovejoy and colleagues’ phase-based method for scoring the auricular surface; Boldsen and colleagues’ superior demiface topography of the auricular surface; and Boldsen and colleagues’ transition analysis method for the pubic symphysis, auricular surface, cranial sutures, and combined indicators using both the uniform and forensic priors.

Question 2 Is there a pattern that explains why some aging standards produce significant differences in the aging process of skeletal indicators between groups while others do not? The above results were sorted into three groups: 1) standards/components that produced statistically significant differences between American skeletal samples, 2) standards/components that did not produce differences between the groups, and 3) standards that were not reliable (see Table 19). An inductive approach was used to identify patterns according to method type, indicator, anatomical region, sex, race, time, and/or stature. Based on the summary table, a pattern was evident.

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Method type It was thought that method type might influence the results because phase-based methods force the morphology of the indicator into a preset description of traits that may or may not accurately describe the features present. In contrast, Transition Analysis was more flexible, allowing for differing rates of development or degeneration of individual components within a single indicator. All five of the Transition Analysis standards indicated statistically significant differences in the rate of aging between Reference and Recent groups, and while it appeared that method type explained the pattern observed, both traditional phase-based standards and Transition Analysis methods showed similar results for the pubic symphysis and auricular surface. In addition, several of Boldsen and colleagues’ individual components scored for the pubic symphysis and auricular surface also returned significant results. The pubic symphysis and auricular surface indicators account for two-thirds of the data input for each of the combined-indicator Transition Analysis standards (UNI and COR), and as a result, these two indicators heavily influence both the uniform and forensic prior forms. Only cranial suture obliteration standards differed by method type. Because both method types produced identical results for the pubic symphysis and auricular surface, it did not appear as though the pattern was determined by method type. Indicator used It was also hypothesized that certain indicators of age, regardless of the method used, might produce similar results. This was expected for the pubic symphysis indicator, because the Suchey-Brooks and Hartnett-Fulginiti methods were both derived

188

Table 19: Summary of the significant and non-significant differences between groups for the tested osteological aging standards Rate of senescence between American skeletal samples Significant No difference Unreliable difference between groups results between groups Pubic symphysis Todd Suchey-Brooks Hartnett-Fulginiti Boldsen symphyseal relief Boldsen symphyseal texture Boldsen superior apex Boldsen ventral symphyseal margin Boldsen dorsal symphyseal margin Auricular Surface Lovejoy et al. Boldsen superior demiface topography Boldsen inferior demiface topography Boldsen superior surface morphology Boldsen apical surface morphology Boldsen inferior surface morphology Boldsen inferior surface texture Boldsen superior posterior iliac exostoses Boldsen inferior posterior iliac exostoses Boldsen posterior iliac exostoses Fourth Rib İşcan et al. Cranial Suture Closure Meindl & Lovejoy vault system Meindl & Lovejoy lateral-anterior system Boldsen lambdoidal-asterion Boldsen sagittal-obelica Boldsen coronal-pterica Boldsen zygomaticomaxillary Boldsen interpalatine Transition Analysis Pubic symphysis Auricular surface Cranial sutures Combined indicators: UNI Combined indicators: COR

X X X X X X X X X X X X X X X X X X X X X X X X X X X* X* X* X* X*

* indicates standards weighted less heavily than others due to the potential influences of extremely unlikely data29

29

It is unclear how the extreme ages at transition produced by individual Transition Analysis components affect the point estimate of age calculated using combinations of these components. As Boldsen and colleagues claim, the combination of traits scored and indicators used may produce a better estimate of age because the individual biases of each are reduced. However, age estimates may still be affected, particularly for the cranial sutures, because four of the five components produced unrealistic output. As a result, the Transition Analysis methods for the pubic symphysis, auricular surface, cranial sutures, and all indicators combined are weighted less heavily than other aging standards that lack this potential problem.

189

from the original Todd scoring system. Similar morphological attributes were also examined as components of the pubic symphysis indicator for the Boldsen and colleagues’ Transition Analysis scheme, and thus would also group with these phasebased methods. This pattern was observed for the pubic symphysis, and to a lesser extent, the auricular surface. For the pubic symphysis, the three traditional phase-based aging standards, the Transition Analysis method, and three of the five individual components defined by Boldsen and colleagues, all produced the same results: a statistically significant difference in the rate of progression through age-related morphological changes between the Reference and Recent American skeletal samples. This result was also supported by the regression analysis conducted previously, which identified the year-of-birth cohort as a significant predictor of the calculated difference between the observed and actual values for each aging standard. Both the Transition Analysis and traditional phase-based standards for aging the auricular surface, as well as one of Boldsen and colleagues’ superior demiface topography component, produced identical results, though the strength of this pattern would be more convincing if a greater number of the individually scored components also indicated a change in the rate of aging. Anatomical region of the indicator Similarly, results were hypothesized to group according to the anatomical region of the indicator; postcranial indicators, specifically pelvic indicators, clustered in Jackes’ (2000) re-analysis of Kemkes-Grottenthaler’s dissertation data. While pelvic indicators may be under similar stresses as the result of pregnancy in females or its weight-bearing function, Jackes’ (2000) data may be skewed by the suture obliteration method itself; 190

unlike postcranial aging systems, the distinctiveness of cranial suture closure may be due to the fact that it had a definite cutoff point marking the cessation of morphological change (Jackes 2000). With the exception of the Transition Analysis cranial suture method, all standards producing significant differences between groups were pelvic or strongly influenced by pelvic indicators (TA UNI and TA COR), suggesting the anatomical region may play a part in explaining the pattern of mixed results observed. Sex It was hypothesized that patterns may also arise with regard to sex because the literature on human growth showed that females appear to be buffered from environmental insults, rendering them less susceptible to nutritional stress, changes in socioeconomic status, and other environmental variables (Greulich 1957; Bogin 1999). Differences in the estimation of age from the skeleton have also been documented between males and females (Kemkes-Grottenthaler 2002). These differences have been Table 20: Analysis of deviance and improvement chi-square output: Females CHI-SQUARE LIKELIHOOD RATIO TESTS: FEMALES Resid. Resid. Aging Standard Model Df Dev Test Df LR stat. Todd

phase=log(age) + pop phase=log(age) + pop + log(age):pop Suchey-Brooks phase=log(age) + pop phase=log(age) + pop + log(age):pop Hartnett-Fulginiti phase=log(age) + pop phase=log(age) + pop + log(age):pop Boldsen superior phase=log(age) + pop apex phase=log(age) + pop + log(age):pop Boldsen ventral phase=log(age) + pop symphyseal margin phase=log(age) + pop + log(age):pop Boldsen dorsal phase=log(age) + pop symphyseal margin phase=log(age) + pop + log(age):pop Lovejoy et al. phase=log(age) + pop phase=log(age) + pop + log(age):pop Boldsen superior phase=log(age) + pop demiface topography phase=log(age) + pop + log(age):pop

403 402 407 406 406 405 373 372 400 399 409 408 357 356 363 362

191

832.6623 831.7907 795.2503 795.2476 1083.695 1083.416 507.956 507.7817 861.7674 860.8668 789.8129 788.7389 993.9912 991.9908 540.1837 538.4183

Pr(Chi)

1 vs. 2

1

0.8716104

0.35051

1 vs. 2

1

0.00265724

0.95889

1 vs. 2

1

0.2790338

0.59734

1 vs. 2

1

0.1742582

0.67635

1 vs. 2

1

0.9006326

0.34261

1 vs. 2

1

1.073947

0.30006

1 vs. 2

1

2.000356

0.15726

1 vs. 2

1

1.765471

0.18394

Table 21: Analysis of deviance output: Females ANALYSIS OF DEVIANCE TABLE (glm): Females Aging Standard Transition Analysis pubic symphysis Transition Analysis auricular surface Transition Analysis cranial sutures Transition Analysis combo, uniform Transition Analysis combo, forensic

Model TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop

Resid. Df 439 438 371 370 367 366 445 444 445 444

Resid. Dev 422520 419831 163262 155136 202942 202625 240823 238011 122890 120598

Df

Deviance

Pr>(|Chi|)

1

2689.2

0.09394

1

8126.1

1.07E-05

1

316.74

0.4494

1

2812.5

0.02199

1

2292.4

0.003671

reflected in distinct means and age ranges ascribed to the phase of a scoring system based on sex (Meindl & Lovejoy 1985; Boldsen et al. 2002), as well as the development of aging standards that scores males and females separately (İşcan & Loth 1986; Brooks & Suchey 1990; Hartnett 2007). When Reference and Recent females were compared, only the Transition Analysis standards based on the auricular surface and the three indicators combined (UNI and COR) produced statistically significant differences (see Tables 20-21). In contrast, significant differences between Reference and Recent males were observed for the Todd, Suchey-Brooks, Hartnett-Fulginiti, Lovejoy and colleagues’ auricular surface, and Transition Analysis auricular surface, cranial suture, and both combined indicator (UNI and COR) standards (see Tables 22-23). However, as with the total sample, the residual deviance and residual degrees of freedom data for both sexes indicate poor fitting models for all aging methods. For complete results by sex, including tables with the ages at transition and plots of the data, see Appendix J. Significant differences between males by group were also noted for Boldsen and colleagues’ superior apex of the pubis, ventral pubic symphyseal margin, and dorsal pubic symphyseal margin components. The comparison of males between groups 192

produced nearly identical results to the comparison of Reference and Recent samples when both sexes and races were pooled; the only exception was that the Recent males lacked a significantly different slope from Reference males for Boldsen and colleagues’ auricular surface superior demiface topography component and the Transition Analysis pubic symphysis standard.

Table 22: Analysis of deviance and improvement chi-square output: Males

Aging Standard

CHI-SQUARE LIKELIHOOD RATIO TESTS: MALES Resid. Resid. Model Df Dev Test Df LR stat.

Todd

phase=log(age) + pop phase=log(age) + pop + log(age):pop Suchey-Brooks phase=log(age) + pop phase=log(age) + pop + log(age):pop Hartnett-Fulginiti phase=log(age) + pop phase=log(age) + pop + log(age):pop Boldsen superior phase=log(age) + pop apex phase=log(age) + pop + log(age):pop Boldsen ventral phase=log(age) + pop symphyseal margin phase=log(age) + pop + log(age):pop Boldsen dorsal phase=log(age) + pop symphyseal margin phase=log(age) + pop + log(age):pop Lovejoy et al. phase=log(age) + pop phase=log(age) + pop + log(age):pop Boldsen superior phase=log(age) + pop demiface topography phase=log(age) + pop + log(age):pop

495 494 498 497 497 496 484 483 498 497 496 495 398 397 406 405

1160.977 1132.787 1005.823 989.471 1130.818 1114.777 713.335 692.9743 1175.441 1145.341 950.6783 913.3633 1128.777 1121.791 693.4601 693.3587

Pr(Chi)

1 vs. 2

1

28.19044

1.10E-07

1 vs. 2

1

16.35171

5.26E-05

1 vs. 2

1

16.04166

6.20E-05

1 vs. 2

1

20.3607

6.41E-06

1 vs. 2

1

30.10007

4.10E-08

1 vs. 2

1

37.31505

1.01E-09

1 vs. 2

1

6.98569

0.00822

1 vs. 2

1

0.1013099

0.75026

Table 23: Analysis of deviance output: Males ANALYSIS OF DEVIANCE TABLE (glm): Males Aging Standard Transition Analysis pubic symphysis Transition Analysis auricular surface Transition Analysis cranial sutures Transition Analysis combo, uniform Transition Analysis combo, forensic

Model TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop

Resid. Df 515 514 444 443 441 440 520 519 520 519

Resid. Dev Df 428495 425600 207455 194600 337899 331672 255116 251860 132299 130413

Deviance

Pr>(|Chi|)

1

2895.2

0.0615

1

12854

6.32E-08

1

6226.7

0.004052

1

3255.3

0.009598

1

1886.5

0.006144

The rate of progression through age-related morphological changes differed between Reference and Recent American males; Table 24 summarizes the data, which

193

suggests that Recent males age at a slightly slower rate than their Reference counterparts for all standards producing a statistically significant result.

Table 24: Recent males rate of progression through morphological stages, compared to Reference males Faster

Slower

No difference

Pubic symphysis Todd Suchey-Brooks Hartnett-Fulginiti Boldsen superior apex Boldsen ventral symphyseal margin Boldsen dorsal symphyseal margin Auricular Surface Lovejoy et al. Boldsen superior demiface topography

X X X X X X X X

Race Race was also hypothesized to influence the pattern of results observed because research on dental and skeletal development indicates that race may affect age estimates. For example, Blacks were advanced compared to Whites in terms of dental development and eruption, and similar results were noted for the development of ossification centers and the epiphyseal fusion of elements in the hand and wrist (Masse & Hunt 1963; Garn & Bailey 1978). Some authors have suggested that African Blacks are genetically programmed to develop more rapidly and that their delay in development early in childhood was the result of a detrimental environment, including lack of nutrition, prevalence of disease, and lower socioeconomic status (Jones & Dean 1956; Garn & Bailey 1978). However, when American White and Black infants and children were matched for socioeconomic variables, Blacks were still consistently advanced in terms of the formation and eruption of permanent dentition and the radiological appearance of ossification centers and epiphyseal union (Garn & Bailey 1978). In addition, advanced 194

development was reported for soft tissue indicators of puberty in African American females when compared to American Whites (Bogin 1999).

Table 25: Analysis of deviance and improvement chi-square output: Blacks

Aging Standard

CHI-SQUARE LIKELIHOOD RATIO TESTS: BLACKS Resid. Resid. Model Df Dev Test Df LR stat.

Todd

phase=log(age) + pop phase=log(age) + pop + log(age):pop Suchey-Brooks phase=log(age) + pop phase=log(age) + pop + log(age):pop Hartnett-Fulginiti phase=log(age) + pop phase=log(age) + pop + log(age):pop Boldsen superior phase=log(age) + pop apex phase=log(age) + pop + log(age):pop Boldsen ventral phase=log(age) + pop symphyseal margin phase=log(age) + pop + log(age):pop Boldsen dorsal phase=log(age) + pop symphyseal margin phase=log(age) + pop + log(age):pop Lovejoy et al. phase=log(age) + pop phase=log(age) + pop + log(age):pop Boldsen superior phase=log(age) + pop demiface topography phase=log(age) + pop + log(age):pop

309 308 312 311 311 310 297 296 310 309 308 307 282 281 289 288

685.4239 684.8084 643.6478 643.6458 733.7793 733.7735 411.684 408.7432 676.0402 674.5049 594.8088 588.1771 767.8346 760.9095 458.9825 458.7299

Pr(Chi)

1 vs. 2

1

0.615499

0.43272

1 vs. 2

1

0.00204839

0.9639

1 vs. 2

1

0.00581296

0.93923

1 vs. 2

1

2.940764

0.08637

1 vs. 2

1

1.535329

0.21531

1 vs. 2

1

6.63167

0.01002

1 vs. 2

1

6.9251

0.0085

1 vs. 2

1

0.252629

0.61523

Table 26: Analysis of deviance output: Blacks ANALYSIS OF DEVIANCE TABLE (glm): Blacks Aging Standard Transition Analysis pubic symphysis Transition Analysis auricular surface Transition Analysis cranial sutures Transition Analysis combo, uniform Transition Analysis combo, forensic

Model TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop

Resid. Df 324 323 248 247 245 244 325 324 325 324

Resid. Dev Df 289684 288828 109243 107515 209540 207003 189232 188483 89395 89142

Deviance

Pr>(|Chi|)

1

855.91

0.3279

1

1727.7

0.04634

1

2537.1

0.08375

1

748.92

0.2565

1

253.04

0.3375

Significant differences between Reference and Recent Blacks were observed for Boldsen and colleagues’ dorsal pubic symphyseal margin component, Lovejoy and colleagues’ auricular surface standard, and the Transition Analysis auricular surface standard (see Tables 25-26). Appendix K contains additional results, including tables with the ages at transition and all plots of the data. Recent African Americans aged at a 195

slower rate than their Reference counterparts for both Boldsen and colleagues’ dorsal pubic symphyseal margin component and Lovejoy and colleagues’ auricular surface standard. In contrast, when Whites were compared between the Reference and Recent American populations, significant differences between the groups were observed for the Todd, Suchey-Brooks, Hartnett-Fulginiti, and Transition Analysis auricular surface, cranial suture, and both combined indicator standards (see Tables 27-28). Complete results are presented in Appendix K. Significant differences between Whites by population were also noted for Boldsen and colleagues’ superior apex of the pubis, ventral pubic symphyseal margin, dorsal pubic symphyseal margin, and auricular surface superior demiface topography components. The comparison of Whites between groups produced nearly identical results to the comparison of Reference and Recent samples when both sexes and races were pooled; the only exception was that the Recent Whites

Table 27: Analysis of deviance and improvement chi-square output: Whites

Aging Standard Todd

CHI-SQUARE LIKELIHOOD RATIO TESTS: WHITES Resid. Resid. Model Df Dev Test Df LR stat.

phase=log(age) + pop phase=log(age) + pop + log(age):pop Suchey-Brooks phase=log(age) + pop phase=log(age) + pop + log(age):pop Hartnett-Fulginiti phase=log(age) + pop phase=log(age) + pop + log(age):pop Boldsen superior phase=log(age) + pop apex phase=log(age) + pop + log(age):pop Boldsen ventral phase=log(age) + pop symphyseal margin phase=log(age) + pop + log(age):pop Boldsen dorsal phase=log(age) + pop symphyseal margin phase=log(age) + pop + log(age):pop Lovejoy et al. phase=log(age) + pop phase=log(age) + pop + log(age):pop Boldsen superior phase=log(age) + pop demiface topography phase=log(age) + pop + log(age):pop

589 588 593 592 592 591 560 559 588 587 597 596 473 472 480 479

196

1351.43 1327.307 1197.445 1185.036 1543.4 1533.169 829.3912 818.7804 1381.636 1362.193 1176.934 1153.285 1345.59 1343.511 755.3237 752.3295

Pr(Chi)

1 vs. 2

1

24.12359

9.03E-07

1 vs. 2

1

12.40883

0.00043

1 vs. 2

1

10.23097

0.00138

1 vs. 2

1

10.6108

0.00112

1 vs. 2

1

19.44301

1.04E-05

1 vs. 2

1

23.64971

1.16E-06

1 vs. 2

1

2.078069

0.14943

1 vs. 2

1

2.99421

0.08356

Table 28: Analysis of deviance output: Whites ANALYSIS OF DEVIANCE TABLE (glm): Whites Aging Standard Transition Analysis pubic symphysis Transition Analysis auricular surface Transition Analysis cranial sutures Transition Analysis combo, uniform Transition Analysis combo, forensic

Model TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop

Resid. Df 630 629 567 566 563 562 640 639 640 639

Resid. Dev Df 554647 552535 254734 241935 339486 335415 298880 295930 161337 159046

Deviance

Pr>(|Chi|)

1

2112.5

0.121

1

12799

4.45E-08

1

4071.7

0.009002

1

2950.2

0.0116

1

2290.6

0.002416

did not have a significantly different slope from Reference Whites for Lovejoy and colleagues’ auricular surface and the Transition Analysis pubic symphysis standard. In addition, Whites showed nearly identical results to males; the only differences observed were for Lovejoy and colleagues’ auricular surface standard and Boldsen and colleagues’ auricular surface superior demiface topography component. Differences between Reference and Recent American Whites in the rate of progression through age-related morphological changes were observed for all standards except Lovejoy and colleagues’ auricular surface method; Table 29 shows that the Recent Whites age at a slower rate than their Reference counterparts for all standards producing

Table 29: Recent Whites rate of progression through morphological stages, compared to Reference Whites Faster

Slower

No difference

Pubic symphysis Todd Suchey-Brooks Hartnett-Fulginiti Boldsen superior apex Boldsen ventral symphyseal margin Boldsen dorsal symphyseal margin Auricular Surface Lovejoy et al. Boldsen superior demiface topography

X X X X X X X X

197

a statistically significant result, except the superior demiface topography component of the auricular surface. The residual deviance and residual degrees of freedom data for both Blacks and Whites indicate poor fitting models for all aging methods, regardless of indicator. Sex-race category The ages at transition were calculated and an analysis of deviance was performed based on four sex-race categories to explore the possible interaction between sex and race effects (Tables 30-31). Tables with the ages at transition and plots of the data are presented in Appendix L. The sample size for Recent Black females was too small to calculate these values; results for this category were not obtained. Again, all models were poorly fitted to the data, regardless of aging method or sex-race category. Differences between Reference and Recent American sex-race categories in the rate of progression through age-related morphological changes were observed for all

Table 30: Analysis of deviance and improvement chi-square output: Sex-race categories CHI-SQUARE LIKELIHOOD RATIO TESTS: SEX-RACE CATEGORIES Resid. Resid. Aging Standard Model Df Dev Test Df LR stat. Pr(Chi) Todd

phase=log(age) + pop phase=log(age) + pop + log(age):pop Suchey-Brooks phase=log(age) + pop phase=log(age) + pop + log(age):pop Hartnett-Fulginiti phase=log(age) + pop phase=log(age) + pop + log(age):pop Boldsen superior phase=log(age) + pop apex phase=log(age) + pop + log(age):pop Boldsen ventral phase=log(age) + pop symphyseal margin phase=log(age) + pop + log(age):pop Boldsen dorsal phase=log(age) + pop symphyseal margin phase=log(age) + pop + log(age):pop Lovejoy et al. phase=log(age) + pop phase=log(age) + pop + log(age):pop Boldsen superior phase=log(age) + pop demiface topography phase=log(age) + pop + log(age):pop

908 907 912 911 911 910 862 861 906 905 911 910 764 763 773 772

198

2052.673 2029.499 1849.362 1838.391 2301.126 2289.208 1253.195 1232.994 2081.484 2051.077 1784.432 1762.248 2135.400 2122.252 1260.990 1259.896

1 vs. 2

1

23.17455 Pr(Chi)

1.48E-06

1 vs. 2

1

10.97097

0.000926

1 vs. 2

1

11.91767

0.000556

1 vs. 2

1

20.20041

6.97E-06

1 vs. 2

1

30.4067

3.50E-08

1 vs. 2

1

22.18320

2.48E-06

1 vs. 2

1

13.1479

0.000288

1 vs. 2

1

1.094386

0.2955014

Table 31: Analysis of deviance output: Sex-race categories ANALYSIS OF DEVIANCE TABLE (glm): Sex-race categories Aging Standard Transition Analysis pubic symphysis Transition Analysis auricular surface Transition Analysis cranial sutures Transition Analysis combo, uniform Transition Analysis combo, forensic

Model TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop

Resid. Df 957 956 818 817 811 810 968 967 968 967

Resid. Dev 558534 550698 263426 261571 499043 498804 308967 308960 149051 148510

Df

Deviance

Pr>(|Chi|)

1

7835.7

0.0002259

1

1854.8

0.01609

1

238.04

0.5341

1

7.0946

0.8783

1

541.35

0.06045

standards except Boldsen and colleagues’ auricular surface superior demiface topography component and the Transition Analysis cranial suture and combine indicator standards; Tables 32-33 showed that the Recent White males and Recent Black males age at a slower rate than their Reference counterparts.

Table 32: Recent White males rate of progression through morphological stages, compared to Reference White males Faster

Slower

No difference

Pubic symphysis Todd Suchey-Brooks Hartnett-Fulginiti Boldsen superior apex Boldsen ventral symphyseal margin Boldsen dorsal symphyseal margin Auricular Surface Lovejoy et al. Boldsen superior demiface topography

X X X X X X X X

Table 33: Recent Black males rate of progression through morphological stages, compared to Reference Black males Faster

Slower

No difference

Pubic symphysis Todd Suchey-Brooks Hartnett-Fulginiti Boldsen superior apex Boldsen ventral symphyseal margin Boldsen dorsal symphyseal margin Auricular Surface Lovejoy et al. Boldsen superior demiface topography

X X X X X X X X

199

Recent White males aged at a slightly decelerated rate for all of the pubic symphyseal standards and components that produced a statistically significant difference between total Reference and Recent skeletal samples. Pubic symphyseal aging standards did not appear to be equally applicable to Reference and Recent White males. Recent Black males also age at a decelerated rate for Lovejoy and colleagues’ auricular surface standard and all three of Boldsen and colleagues’ pubic symphyseal components that produced a statistically significant difference between total Reference and Recent skeletal samples; however, no significant difference between Reference and Recent Black males was present for the traditional phase-based pubic symphyseal standards. This result implied that only certain morphological features of the pubic symphyseal age indicator had shifted in terms of their rate of progression through agerelated stages. Accordingly, Meindl and Lovejoy’s auricular surface aging standard did not appear to be equally applicable to Reference and Recent Black males. Time, by ten-year birth cohorts It was hypothesized that the temporal difference between the Reference and Recent samples may also explain the pattern of mixed results. The average year of birth for the Reference sample was 1878 (range 1828-1943), while it was 1939 (range 18891985) for the Recent sample. The difference in birth years may allow for the recognition of subtle changes in the rate of progression of skeletal age changes, possibly resulting from secular trends, environmental factors, cultural practices, socioeconomic status, improvements in living conditions, diet, disease prevalence, and advances in health care and disease prevention (Eveleth 1975; Eveleth 1979; Frisancho 1978; Fogel 1986; Malina et al. 1987; Bogin 1999). 200

In the last two decades, numerous anthropological investigations have reported the presence of secular change in human skeletal dimensions and morphology; in addition, trends for earlier maturation were noted for the onset of menarche and the development of secondary sexual characteristics. A possible diachronic trend may also exist for developmental-based skeletal aging systems in American males and females (Webb & Suchey 1985). The shift in the timing of physiological and skeletal maturation hinted at potential changes in the rates of osteological degeneration and senescence as well. Tables 34-35 summarize the statistical output for differences among birth year cohorts. Bold type denotes statistically significant differences among cohorts, which included all pubic symphyseal standards and components that produced significant results for the comparison between Reference and Recent total samples except for the HartnettFulginiti method. However, the residual deviance and residual degrees of freedom data reported in the table indicate poor fitting models for all aging methods.

Table 34: Analysis of deviance and improvement chi-square output: 10-year birth cohorts CHI-SQUARE LIKELIHOOD RATIO TESTS: 10-YEAR BIRTH COHORTS Resid. Resid. Aging Standard Model Df Dev Test Df LR stat. Pr(Chi) Todd

phase=log(age) + pop phase=log(age) + pop + log(age):pop Suchey-Brooks phase=log(age) + pop phase=log(age) + pop + log(age):pop Hartnett-Fulginiti phase=log(age) + pop phase=log(age) + pop + log(age):pop Boldsen superior phase=log(age) + pop apex phase=log(age) + pop + log(age):pop Boldsen ventral phase=log(age) + pop symphyseal margin phase=log(age) + pop + log(age):pop Boldsen dorsal phase=log(age) + pop symphyseal margin phase=log(age) + pop + log(age):pop Lovejoy et al. phase=log(age) + pop phase=log(age) + pop + log(age):pop Boldsen superior phase=log(age) + pop demiface topography phase=log(age) + pop + log(age):pop

908 907 912 911 911 910 862 861 906 905 911 910 764 763 773 772

201

2077.080 2068.393 1857.601 1850.504 2309.186 2307.281 1261.522 1256.405 2096.774 2092.876 1804.104 1796.088 2136.237 2134.638 1259.167 1259.118

1 vs. 2

1

8.686526

0.00320571

1 vs. 2

1

7.097512

0.007719102

1 vs. 2

1

1.905282

0.1674883

1 vs. 2

1

5.116556

0.0236986

1 vs. 2

1

3.898507

0.04832903

1 vs. 2

1

8.016078

0.004636387

1 vs. 2

1

1.598872

0.2060632

1 vs. 2

1

0.04852136

0.8256565

Table 35: Analysis of deviance output: 10-year birth cohorts ANALYSIS OF DEVIANCE TABLE (glm): 10-year birth cohorts Aging Standard Transition Analysis pubic symphysis Transition Analysis auricular surface Transition Analysis cranial sutures Transition Analysis combo, uniform Transition Analysis combo, forensic

Model TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop

Resid. Df 957 956 818 817 811 810 968 967 968 967

Resid. Dev 564711 564636 274819 273801 507106 505795 307796 306803 149219 149194

Df

Deviance

Pr>(|Chi|)

1

74.21

0.723

1

1017.6

0.08141

1

1311.0

0.1474

1

992.93

0.07688

1

25.777

0.6827

The ages at transition were not calculated for the seventeen ten-year birth cohorts because there was an insufficient representation of the range of ages and observed stages necessary to produce robust data for most of the cohorts. The dataset was biased toward older individuals for the earliest birth years, which resulted in a lack of young ages and low phases for these cohorts. Likewise, the later cohorts were restricted to younger ages, simply because an individual born recently could not attain elderly ages and be included in skeletal series that were established in the 1980s. Even when cohort sizes were increased to twenty-five years, the earliest cohorts still lacked a sufficient number of younger individuals, preventing a statistically sound calculation of ages at transition. Little can be said about whether the differences in pubic symphyseal indicators were the result of birth year, genetic diversity, or environmental variables. Adult Stature Finally, adult stature was hypothesized to explain the pattern of results. The adult height of individuals was used as a proxy for childhood health and socioeconomic status due to availability of the data. The justification for this variable was literature on growth and development of children with varying socioeconomic backgrounds (Ericksen 1982; 202

Peck & Lundberg 1995; Bogin 1999), though catch-up growth may mitigate any negative effects on adult attained stature. Tables 36-37 summarize the statistical output for differences among short, average, and tall individuals. Bold type denotes statistically significant differences among the groups, which were only present for the Transition Analysis auricular surface and cranial suture closure standards. These findings suggest that adult stature had little impact on the explanation of the patterns of age-related rate changes between Reference and Recent groups; however, the models tested are a poor fit of the actual data. While markers of lower socioeconomic status in childhood was correlated with delayed skeletal development and maturation in the literature, it does not appear to contribute much to explaining rate changes in skeletal degenerative age markers. Perhaps adult socioeconomic, nutritional, and health data, which were conspicuously absent in the documentation of existing American osteological samples, would be better for explaining the patterns observed.

Table 36: Analysis of deviance and improvement chi-square output: Adult stature CHI-SQUARE LIKELIHOOD RATIO TESTS: STATURE Resid. Resid. Aging Standard Model Df Dev Test Df LR stat. Todd

phase=log(age) + pop phase=log(age) + pop + log(age):pop Suchey-Brooks phase=log(age) + pop phase=log(age) + pop + log(age):pop Hartnett-Fulginiti phase=log(age) + pop phase=log(age) + pop + log(age):pop Boldsen superior phase=log(age) + pop apex phase=log(age) + pop + log(age):pop Boldsen ventral phase=log(age) + pop symphyseal margin phase=log(age) + pop + log(age):pop Boldsen dorsal phase=log(age) + pop symphyseal margin phase=log(age) + pop + log(age):pop Lovejoy et al. phase=log(age) + pop phase=log(age) + pop + log(age):pop Boldsen superior phase=log(age) + pop demiface topography phase=log(age) + pop + log(age):pop

520 519 524 523 523 522 488 487 520 519 524 523 515 514 522 521

203

1261.451 1261.204 1139.065 1139.047 1358.167 1358.162 751.0584 749.3637 1237.644 1235.686 1074.060 1073.991 1461.386 1461.154 867.5873 867.4645

Pr(Chi)

1 vs. 2

1

0.2472045

0.6190504

1 vs. 2

1

0.01743218

0.8949598

1 vs. 2

1 0.004172073

0.9484992

1 vs. 2

1

1.694737

0.1929776

1 vs. 2

1

1.958401

0.1616845

1 vs. 2

1

0.06918113

0.7925328

1 vs. 2

1

0.2312174

0.6306226

1 vs. 2

1

0.1227854

0.7260329

Table 37: Analysis of deviance output: Adult stature ANALYSIS OF DEVIANCE TABLE (glm): Stature Aging Standard Transition Analysis pubic symphysis Transition Analysis auricular surface Transition Analysis cranial sutures Transition Analysis combo, uniform Transition Analysis combo, forensic

Model TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop TA point estimate=age + pop TA pt est=age + pop + age:pop

Resid. Df 555 554 562 561 558 557 563 562 563 562

Resid. Dev 347549 346766 203109 201427 363372 359928 153497 152608 89097 88662

Df

Deviance

Pr>(|Chi|)

1

782.63

0.2635

1

1682.1

0.03043

1

3443.7

0.02097

1

888.16

0.07052

1

434.36

0.09706

Synopsis The general pattern observed was suggestive of strong influences by age indicator, anatomical region of the indicator, sex, and race. Adult stature did not appear to be a major influence with respect to the pattern produced, and sampling issues plagued the analysis of birth year cohort data, rendering it statistically unsound. Both method types produced similar results, indicating that the pattern of significant results observed was not explained by this variable. In contrast, pelvic indicators, specifically the pubic symphysis and auricular surface, both resulted in significant outcomes that accounted for most of the pattern. When the Reference and Recent groups were divided by sex, race, and sex-race categories, White males emerged as the subset contributing most to the pattern of mixed results obtained, followed by Black males. In general, if a significant difference was observed between groups, the results indicate that, on average, the Recent group aged at a slightly slower rate of maturation than the Reference sample. The significant differences observed between the Reference and Recent populations for pelvic indicators of age appeared to be primarily driven by males and Whites. This may be the result of small sample sizes for females and Blacks. Though 204

the entire dataset was nearly evenly split between the sexes, with a composition of 54% (n=523) males and 46% (n=448) females, two of the skeletal series classified as Recent had an abundance of males that accounted for approximately two-thirds of the remains within each sample. In addition, African Americans only account for about one third (n=328) of the total dataset, because the three Recent skeletal series were significantly biased toward Whites. This sample bias may have influenced the trends observed.

Question 3 In the case of contrasting results from multiple aging standards for a single skeletal indicator, which standard is the true indicator of whether a change in the rate of aging has occurred? The comparison between Reference and Recent American skeletal samples produced results in which some standards for an indicator signified a statistically significant difference in the aging rate and others did not; this was the case for the pubic symphysis, auricular surface, and cranial sutures. Which components or standards should be used to determine whether a change in the rate of progression through the age-related morphological metamorphoses of these indicators has actually occurred between Reference and Recent groups? To select which standards will be used to address the primary dissertation question of whether changes in the aging processes of the pubic symphysis, auricular surface, and cranial sutures have occurred for American skeletal samples, a combination of information was used including published data on the strengths and weaknesses of aging standards, stepwise regression to determine which standards/components were the

205

best predictors of chronological age, and other factors contributing to the reliability of the applied standards. Standards supported by the anthropological literature Vigorous debate has ensued over the most accurate and reliable standard for age at death estimation from the skeleton. A summary of the strengths and weaknesses of each age indicator is presented in Table 38. All of the osteological age estimation standards tested for this research were known to have inaccuracy that increased with chronological age (Buikstra & Konigsberg 1985; Lovejoy et al. 1985a; Katz & Suchey 1986; Murray & Murray 1991; Dudar et al. 1993; Bedford et al. 1993; Santos 1996; Nagar & Hershkovitz 2004). Most anthropologists agreed that using a combination of indicators was the best approach to estimating age from the skeleton (Brooks 1955; Lovejoy et al. 1985a; Krogman & İşcan 1986; Acsádi & Nemeskéri 1970; Brooks & Suchey 1990; Aykroyd et al. 1999; Baccino et al. 1999; Ritz-Timme et al. 2000; Rösing et al. 2007); each indicator provided some information, and the error of each was assumed to be largely independent, thus improving the accuracy of the age estimate. If only a single indicator of age at death could be used, the general consensus was that the pubic symphysis was the best indicator and that cranial suture closure was the worst. However, comparatively little discussion has taken place about the strengths and weaknesses of Boldsen and colleagues’ Transition Analysis scoring standards. Pubic symphysis Standards and components indicating a statistically significant difference in the rate of aging of the pubic symphysis between Reference and Recent American samples 206

Table 38: Summary of strengths and weaknesses for each aging indicator from the literature Cranial suture closure (Meindl & Lovejoy) · clear, sequential age changes · low interobserver error

PROs

CONs

· unreliable · asymmetric · defined endpoint can be reached long before death · large variability in rates of closure · weak correlation between closure and age · some claim obliteration is independent of age · large error, esp. 50+

Pubic symphysis (T=Todd) (SB=Suchey-Brooks) (Hartnett-Fulginiti)

Auricular surface

Sternal end of the fourth rib

Transition Analysis

(Lovejoy et al.)

(İşcan et al.)

(Boldsen et al.)

· irreversible, sequential age changes · most studied · generally considered the best indicator · relatively high correlation with age · (SB) highly regarded, attempted to fix known problems with Todd’s standard · (SB) larger, more representative sample · (SB) clearer phase descriptions and reference casts

· good preservation of indicator · satisfies assumption of uniformitarianism · some claim performs better than the pubic symphysis for older individuals · allows for agerelated changes after age 50

· low bias for young adults · little difference between 4th rib and alternative numbers · not affected by mechanical stress like the pelvis · low interobserver error

· (T) known problems with the reference sample, including questionable documentation of age and purposeful elimination of variation · overestimates the age of young · inaccurate for older individuals · inconclusive results regarding interobserver error rates · variability in morphology, hard to classify into a single phase · potential for confusion with the developmental vs. the degenerative changes to the ventral rampart · possibly affected by childbirth

· low repeatability · low reliability · high interobserver error · needs larger age ranges · strong methodological bias · ambiguous morphology, hard to classify into a single phase · too variable · subject to the influences of pregnancy

· difficult to identify 4th rib in fragmentary remains · possibly affected my mechanical stress (lower ribs only) · poor preservation

· not subject to age mimicry · can handle variation in suite of morphological traits observed by scoring components of the age indicator · age assessment can be specialized to an individual · small average difference between predicted and actual ages · smaller 95% confidence interval · multiple indicators generally considered best · combination of pubic, auricular, and cranial indicators best, eliminating outliers and methodological biases · unproven in the literature · increase in variation seen as age increases (like other methods) · possible problem with older black females in the reference sample

207

included the following: Todd, Suchey-Brooks, Hartnett-Fulginiti, Boldsen and colleagues’ superior apex, ventral and dorsal margins, and Transition Analysis using a combination of all five Boldsen and colleagues’ pubic components. The anthropological literature clearly favors the pubic symphysis as the indicator of choice for estimating age from the human skeleton. While methodological concerns were reported for the Todd method and comparative data was lacking for Hartnett’s modifications and Transition Analysis, these standards were concordant with the Suchey-Brooks method, which was the most highly regarded osteological aging standard in the literature because it exhibited relatively high correlations with chronological age and diverse reference sample. Boldsen and colleagues’ pubic symphyseal components were subsumed within the phase descriptions defined for the traditional methods; this suggested that these components may be the portions of the pubic symphysis that were driving the rate difference between Reference and Recent groups. Based on the anthropological literature, the SucheyBrooks standard was weighted heaviest of all four pubic symphyseal aging methods. Auricular surface Standards and components indicating a statistically significant difference in the rate of aging of the auricular surface between Reference and Recent American samples included Lovejoy and colleagues’ phase-based auricular surface standard, Boldsen and colleagues’ superior demiface topography, and Transition Analysis using a combination of all nine Boldsen and colleagues’ auricular components. The anthropological literature presented mixed reviews of age estimation using the auricular surface indicator. Despite the claim that it provided better estimates for older individuals, Lovejoy and colleagues’ phase-based auricular surface standard has been criticized for low repeatability, low 208

reliability, high interobserver error, strong methodological bias, and ambiguous morphology that was hard to classify into a single phase. As with the pubic symphysis, there was a lack of critique in the literature specific to the Transition Analysis auricular surface method, though the method was generally highly regarded. Based on published anthropological reports, Lovejoy and colleagues’ standard was not weighted as heavily as Boldsen and colleagues’ Transition Analysis auricular surface method, though it was unclear exactly how heavily to weight the Transition Analysis standard itself. Fourth Rib İşcan and colleagues’ aging method did not indicate a difference in the rate of senescent change of the sternal end of the fourth rib. The anthropological literature reported that this aging standard had low bias for young adults and low interobserver error; in addition, the sternal end of the fourth rib was not affected by mechanical stress like pelvic indicators. The most significant drawbacks of this indicator included poor preservation in archaeological samples and difficulty isolating the fourth rib, though scoring adjacent ribs can rectify the latter problem. Cranial sutures For this research, only Transition Analysis using a combination of all five of Boldsen and colleagues’ sutural components produced a statistically significant difference in the rate of aging between Reference and Recent American samples. The anthropological literature presented predominantly negative reviews of age estimation based on cranial suture obliteration. Major critiques of age estimation using cranial suture obliteration included low reliability, large variability in rates of closure, weak 209

correlation between closure and age, error introduced by asymmetric obliteration, and large error for older individuals, specifically because the defined end-point of complete obliteration can be reached long before death. However, these critiques were specifically targeted at traditional cranial suture closure standards, like that of Meindl and Lovejoy; little critique has been directly aimed at age estimation from cranial sutures using Boldsen and colleagues’ components and Transition Analysis. Based on published anthropological reports, it was questionable how heavily Boldsen and colleagues’ Transition Analysis cranial suture closure method should be weighted. Multiple indicators: pubic symphysis, auricular surface, and cranial sutures Both the uniform- and forensic-prior Transition Analysis standard for multiple indicators produced a statistically significant difference in the rate of aging of Reference and Recent American skeletal populations. As with the Transition Analysis methods based on each of the indicators individually, no critiques specific to the Transition Analysis UNI and COR methods have been addressed in the anthropological literature. Again, Transition Analysis was generally regarded as better than traditional phase-based aging standards because it was not subject to age mimicry and it allowed for more variation in the scoring technique used to describe the observed morphology. The multiple indicator Transition Analysis standards were considered to be the best by their developers, because outliers and methodological biases present for each individual indicator were eliminated.

210

Standards selected for inclusion in the stepwise regression model A summary of the aging standards and indicator components that were selected for inclusion in the regression model to predict documented chronological age is presented in Table 39. The stepwise regression was performed using all aging standards and indicator components scored for the entire dataset. While the contributions of the first eight variables listed were statistically significant at the 0.05 level, only the addition of the first three added more than a negligible amount to the variation explained by the model.

Table 39: Summary of aging standards and indicator components selected for inclusion in the regression model Summary of Stepwise Selection: all standards and components Variable Entered

Partial R-Square

Model R-Square

C(p)

F Value

Pr > F

Transition Analysis combo, forensic

0.5349

0.5349

148.896

390.96

<.0001

Iscan et al.

0.0817

0.6165

65.3968

72.21

<.0001

Lovejoy et al.

0.0303

0.6468

35.6834

29.00

<.0001

Hartnett-Fulginiti

0.0123

0.6592

24.7670

12.20

0.0005

Boldsen inferior surface texture

0.0068

0.6659

19.6775

6.81

0.0095

Meindl & Lovejoy lateral-anterior

0.0066

0.6725

14.7975

6.72

0.0099

Transition Analysis combo, uniform

0.0057

0.6782

10.8285

5.92

0.0155

Boldsen sup post iliac exostoses

0.0066

0.6848

5.9689

6.92

0.0089

Transition Analysis cranial sutures

0.0021

0.6868

5.8181

2.18

0.1409

Boldsen zygomaticomaxillary

0.0022

0.6890

5.5571

2.30

0.1304

Boldsen coronal-pterica

0.0030

0.6919

4.4645

3.16

0.0762

Boldsen sagittal-obelica

0.0022

0.6942

4.1473

2.38

0.1238

Todd

0.0023

0.6964

3.7879

2.44

0.1196

The first three variables included in the model were Transition Analysis using a forensic prior with multiple indicators, İşcan and colleagues’ fourth rib standard and

211

Lovejoy and colleagues’ auricular surface standard. Together, these three variables accounted for approximately two-thirds of the variation in chronological age; if all thirteen variables selected for inclusion in the model were considered, only about 70% of the variation was explained. Both Transition Analysis standards using a combination of pubic, auricular, and sutural components were highly correlated with each other. The stepwise regression was performed again, this time only including the following aging standards: Todd pubic symphysis, Suchey-Brooks pubic symphysis, Hartnett-Fulginiti pubic symphysis, Lovejoy and colleagues auricular surface, İşcan and colleagues fourth rib, Meindl and Lovejoy’s vault sutures, Meindl and Lovejoy’s lateralanterior sutures, Transition Analysis pubic symphysis, Transition Analysis auricular surface, Transition Analysis cranial sutures, and a uniform and forensic prior Transition Analysis using multiple indicators. Individual components were excluded from this regression analysis. Table 40 provides a summary of the aging standards that were selected for inclusion in the second stepwise regression model to predict documented chronological age. While the contributions of the first seven variables listed were statistically significant at the 0.05 level, only the addition of the first three added more than a negligible amount to the variation explained by the model. These three variables were identical to those produced by the first regression model. Again, these three variables accounted for approximately two-thirds of the variation in chronological age; if all eight variables selected for inclusion in the model were considered, only about 68% of the variation was explained.

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Table 40: Summary of aging standards selected for inclusion in the regression model Summary of Stepwise Selection: aging standards Variable Entered

Partial R-Square

Model R-Square

C(p)

F Value

Pr > F

Transition Analysis combo, forensic

0.5281

0.5281

199.083

479.01

<.0001

Iscan et al.

0.0908

0.6190

80.7482

101.80

<.0001

Lovejoy et al.

0.0322

0.6511

40.1592

39.26

<.0001

Hartnett-Fulginiti

0.0142

0.6653

23.3990

17.98

<.0001

Transition Analysis combined indicators, uniform

0.0082

0.6734

14.6015

10.58

0.0012

Meindl & Lovejoy lateral-anterior system

0.0042

0.6776

11.0418

5.51

0.0194

Transition Analysis auricular surface

0.0038

0.6814

8.0046

5.04

0.0253

Todd

0.0022

0.6836

7.0992

2.92

0.0883

One variable selected for the second stepwise regression model that was not in the first model included Transition Analysis auricular surface method; this standard replaced the Transition Analysis cranial suture standard. Regardless of the model used, Transition Analysis using a forensic prior with multiple indicators, İşcan and colleagues’ fourth rib, and Lovejoy and colleagues’ auricular surface standards contributed most to predicting chronological age for this dataset. Pubic symphysis Both the Hartnett-Fulginiti and Todd standards were included in the regression model; however the Suchey-Brooks and Transition Analysis standards were noticeably absent. These standards may have been excluded because the contribution that they would make to the prediction of age was already accounted for by other included standards, like the Todd, Hartnett-Fulginiti, and Transition Analysis methods. Another possibility was that the Hartnett-Fulginiti method was preferred because it had an

213

additional phase to account for older ages, which would be beneficial in this case since the dataset contained many older individuals. Auricular surface Both the traditional phase-based and Transition Analysis standards were included in the model. Lovejoy and colleagues’ aging standard was the third variable included in and was statistically significant at the 0.05 level for both regression models; this method explained approximately 3% of the total variation in chronological age for each model. Fourth Rib İşcan and colleagues’ aging standard was the second variable included in each of the regression models predicting chronological age. Its contribution was statistically significant at the 0.05 level, explaining 8-9% of the total variation observed in chronological age. Cranial sutures Meindl and Lovejoy’s lateral-anterior suture system was statistically significant at the 0.05 level for both regression models. The Transition Analysis cranial suture closure method was only included in the first regression model, along with three of the individual suture component scores; however, none were significant at the 0.05 level. Multiple indicators: pubic symphysis, auricular surface, and cranial sutures Both Transition Analysis methods using multiple indicators were included in the regression models. These standards may be the best predictors of chronological age because they produce a point estimate of age in years, which is the same scale as the 214

dependent variable. It could also be due to the fact that these standards encompass the most variation because they use a combination of pubic, auricular, and sutural indicators. Standards supported by other data Other data, including intraobserver scores, the correlation of the standard with chronological age, bias and inaccuracy, the observer’s comfort and experience level with the standards, and results from statistical analyses from this research, were also factored into the decision of which aging standards should be weighted more heavily in the case of mixed results for an indicator. The results are summarized in Table 41. Pubic symphysis All three traditional phase-based standards for aging the pubic symphysis, several of Boldsen and colleagues’ symphyseal components, and the Transition Analysis method using a combination of all five pubic components indicated a statistically significant difference in the rate of morphological change. Only two of Boldsen and colleagues’ pubic symphyseal components, the symphyseal relief and texture, did not provide reliable data supporting a change in the rate of aging. In contrast, symphyseal texture, which explains the degree of compactness or porosity observed, produced extreme values for later ages at transition, indicating a problem with the data; this problem may be due to the component’s poor correlation with chronological age, which was 0.13 for the Reference sample and 0.26 for the Recent sample. It did not appear to be a problem with the observer’s reliability, which was 0.600 for the right side and 0.675 for the left side; these values were comparable to other standards and components scored.

215

Table 41: Summary of other data used to assess the reliability of age estimation methods Intraobserver agreement

Correlation coefficient

Bias and Inaccuracy

Experience level

Statistical results

Pubic symphysis

0.915 [T], 0.863 [SB], 0.816 [HF], 0.830 [TA]

0.70 average [T SB HF] 0.63 [TA]

Extensive [T SB]

Good, except for symphyseal texture

Auricular surface

0.675 lowest [Lovejoy] 0.779 [TA]

0.71 [Lovejoy] 0.56 [TA]

Good [Lovejoy]

Good [Lovejoy] Unreliable [TA]

Fourth rib

0.715 relatively low

0.72

Good

Good

Cranial sutures

0.834 [ML] 0.765 [TA]

0.32 [ML-V] 0.37 [ML-LA] 0.37 [TA]

Moderate [M&L]

Unreliable [TA]

Transition Analysis PS AS CS

0.811

0.68 [UNI] 0.67 [COR]

Relatively low bias Systematically underestimates age [except T, TA] Large degree of inaccuracy [TA] Relatively low bias [Lovejoy, TA] Relatively low inaccuracy [TA] Moderate bias Moderate inaccuracy Relatively large bias [M&L] Large inaccuracy [M&L. TA] Bias comparable to other methods [UNI] Bias greatest for TA COR, underestimating age by nearly a decade

Little

Paradoxical

For this dataset, the correlations with chronological age for the Todd, SucheyBrooks, and Hartnett-Fulginiti standards were 0.697, 0.703, and 0.700, respectively. Of all the indicators scored for this research, the observer was most comfortable with the pubic symphysis; the observer has received the most training for and has the most experience applying the Todd and Suchey-Brooks methods. This is evident in the intraobserver agreement values, which are 0.9146 and 0.8628, respectively. Agreement values were also relatively high for the Hartnett-Fulginiti and Transition Analysis pubic symphysis standards, 0.8164 and 0.8296, despite the lack of formal training with these methods. Bias was relatively low, but directionality depended on the aging standard used: the Suchey-Brooks and Hartnett-Fulginiti methods systematically underestimated 216

age, and the Todd and Boldsen and colleagues’ methods. Boldsen and colleagues’ Transition Analysis method showed a large degree of inaccuracy. Auricular surface The traditional phase-based standard for aging the auricular surface, three of Boldsen and colleagues’ auricular components, and the Transition Analysis method using a combination of all nine auricular components indicated a statistically significant difference in the rate of morphological metamorphosis. Most of the nine components defined by Boldsen and colleagues’ did not indicate a significant change in the rate of aging for the auricular surface: inferior demiface topography, inferior surface texture, posterior iliac exostoses, and superior, apical, and inferior surface morphology. However, age at transition estimates calculated for these components appeared to be influenced by the regression analysis, producing very young ages at transition for early transitions and very old estimates for later transitions. This is typically found using a normal transition distribution, which is symmetric; in this research, the log-normal distribution was used to reduce the likelihood of obtaining very low estimates (Konigsberg et al. 2008). Despite this compensation, the analysis still produced very low values. Of the three components that did produce significant differences between groups, two did not provide reliable data supporting a change in the rate of aging. The superior and inferior posterior iliac exostoses produced extreme values for the ages at transition calculated for this research. These components score marginal bony proliferation, which metamorphoses from a smooth surface, to rounded bony elevations, then to pointed, jagged, and touching exostoses, terminating as fusion to the sacrum. As with the pubic 217

symphyseal texture, the extreme values for the calculated ages at transition indicated a problem with the data; this problem may be the result of this component’s poor correlation with chronological age, which was 0.09 for the Reference sample and 0.36 for the Recent sample. For the total dataset, the Spearman’s correlation with age was 0.245 and 0.173 for the superior and inferior posterior iliac exostoses, respectively. Again, it did not appear to be a problem with the observer’s reliability, which ranged from 0.615 and 0.764. The influence of the extremely large age at transition estimates produced by most of the individual auricular surface components was of concern when considering that the Transition Analysis standard for the auricular surface was based on a combination of these components. It was unclear if these components came together to produce a better estimate of age or if they compounded the error known to exist at the individual component level. In the present analysis, the Transition Analysis auricular surface standard was not as robust as the anthropological literature portrayed it. For this analysis, the correlation of the Lovejoy and colleague’s auricular surface score with chronological age was 0.711. The intraobserver agreement for the Lovejoy and colleagues’ auricular surface standard was 0.675, the lowest of all standards used for this research; the observer was less comfortable with the method and encountered problems with assigning ambiguous morphology to one phase. Although no formal training was undertaken for the Transition Analysis auricular surface standard, intraobserver agreement was relatively high at 0.779. Considering all of the aging methods tested, bias was lowest for the Lovejoy and colleagues and Boldsen and colleagues aging standards for scoring age-related changes of

218

the auricular surface. Both systematically underestimated age. These two auricular surface scoring methods had relatively low degrees of inaccuracy when compared to other age indicators, but inaccuracy was approximately 13 years for the Transition Analysis standard. Fourth Rib İşcan and colleagues’ method did not indicate a statistically significant difference in the rate of sternal rib end senescence between Reference and Recent American skeletal samples. The observer was comfortable scoring the fourth rib, but interobserver agreement was relatively low. Despite this, the score of the sternal end was highly correlated with chronological age. İşcan and colleagues’ method indicated moderate levels of bias and inaccuracy, with a tendency to slightly underestimate actual age. Cranial sutures Only the Transition Analysis method, which used a combination of five cranial suture components, indicated a statistically significant difference in the rate of morphological metamorphosis between groups. None of the individual sutural components defined by Boldsen and colleagues’ produced significant differences; however, four of these did not generate reliable age at transition estimates. Scores for the lambdoidal-asterion and zygomaticomaxillary landmarks produced extreme values that may be the result of their low correlation with chronological age, which ranged from 0.14 to 0.24. Scores for the sagittal-obelica and interpalatine sutures appeared to be affected by the regression analysis, as the calculations for the ages at transition were very young for early transitions and very old estimates for later transitions. These oddities did not 219

appear to be a problem with the observer’s reliability, which was 0.765 for the Transition Analysis cranial suture closure method. As with the auricular surface, the influence of the unrealistic data produced by most of the individual cranial suture components was of concern when considering that the Transition Analysis standard for suture closure was based on a combination of these components. It was unclear if these components came together to produce a better estimate of age or if they compounded the error known to exist at the individual level, calling into question the reliability of the Transition Analysis cranial suture closure standard. Neither the vault nor the lateral-anterior systems scored for Meindl and Lovejoy’s cranial suture closure standard indicated a significant difference in the rate of suture obliteration between Reference and Recent American skeletal samples. The observer was only somewhat comfortable scoring cranial suture closure, regardless of the standard used. Difficulty determining between intermediate degrees of closure was the source of most uncertainty, though this was not reflected in the values of intraobserver reliability for either traditional or Transition Analysis standards. Bias was intermediate to large, with all cranial suture aging methods systematically underestimating age. Meindl and Lovejoy’s vault and lateral-anterior sites, as well as Boldsen and colleagues’ Transition Analysis suture obliteration standard, had the largest degrees of inaccuracy of all the aging methods tested for this research. Inaccuracy for Boldsen and colleagues’ Transition Analysis standard using cranial sutures was nearly two decades.

220

Multiple indicators: pubic symphysis, auricular surface, and cranial sutures Concern regarding the reliability of the UNI and COR Transition Analysis standards to accurately assess whether change has occurred in the aging process between older and more recent American skeletal samples stemmed from the problems with Boldsen and colleagues’ pubic, auricular, and sutural components that produced impossible values for ages at transition. It was unclear how each individual component affected the maximum likelihood estimation of age produced by the Transition Analysis method. Bias was intermediate for the uniform distribution and large for the forensic distribution, approximately 2.5 and 9 years, respectively; both systematically underestimated age. Interestingly, the inaccuracy of both was approximately 13 years, which is nearly equal to that of Transition Analysis using the auricular surface but significantly better than Transition Analysis using either the pubic symphysis or cranial suture closure. Evaluation of Hypothesis 3: combining multiple lines of evidence Based on a synthesis of data from the anthropological literature, stepwise regression analysis, and other considerations, the following standards were determined to be weighted more heavily than others to determine whether a difference in the rate of aging has occurred for skeletal age indicators between Reference and Recent American skeletal populations: Suchey-Brooks pubic symphysis and Meindl and Lovejoy cranial suture obliteration (see Table 42). As a result, it was determined that a change in the rate of aging of the pubic symphysis has occurred between older Reference and more Recent American samples because the Suchey-Brooks standard, as well as others, produced 221

statistically significant differences between groups. In contrast, it was decided that no change in the rate of cranial suture obliteration existed between groups because the statistically significant change produced by the Transition Analysis cranial suture method may be plagued by statistical problems and low correlations between scored features and chronological age.

Table 42: Summary of aging methods supported by the lines of evidence presented in this research

Pubic symphysis

Auricular surface

Anthropological literature Suchey-Brooks Boldsen et al. (Transition Analysis) Boldsen et al. (Transition Analysis)

Stepwise regression Hartnett-Fulginiti Todd

Other criteria All (particularly Todd and Suchey-Brooks)

Lovejoy et al. Boldsen et al. (Transition Analysis)

Fourth rib Cranial sutures

İşcan et al. Boldsen et al. (Transition Analysis)

İşcan et al. Meindl & Lovejoy lateral-anterior system

Transition Analysis PS AS CS

Boldsen et al. (Transition Analysis)

Boldsen et al. (Transition Analysis)

İşcan et al. Meindl & Lovejoy lateral-anterior system

While neither the Lovejoy and colleagues nor the Transition Analysis standards for estimating age from the auricular surface appear to be particularly robust, both suggested that change has occurred in the rate of aging for that indicator. Again, the Transition Analysis method for aging the auricular surface was deemed unreliable due to extremely unlikely values produced during the course of statistical analysis for this research, and Lovejoy and colleagues’ standard has long been criticized for high observer error, low repeatability, and low reliability. Based on the findings presented here, a conclusive assessment of whether changes in the aging of the auricular surface have

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occurred between older and more recent American skeletal samples cannot be made with any certainty without additional study.

Summary The results of this research showed that some osteological standards indicated a change in skeletal aging between older and more recent American populations while others did not. Most of the individual pubic symphyseal components and all of the pubic symphyseal aging standards produced a statistically significant difference in the rate of age-progressive change between groups. Similarly, several of the auricular surface components and both of the auricular surface aging standards produced a statistically significant difference between groups. In contrast, only the Transition Analysis method demonstrated a significant difference in the aging process of cranial sutures between the two samples. Finally, the one standard for aging the sternal end of the fourth rib did not indicate any significant change between the older and more recent American skeletal populations. Significant results clustered for the pubic symphysis and the auricular surface pelvic indicators, suggesting that the indicator used and the anatomical region of the indicator explains the pattern observed. When the samples were divided by sex and race, males and Whites mirrored the pattern observed for the comparison of the total Reference and Recent samples. Based on a thorough consideration of published anthropological reports critiquing current aging standards, stepwise regression analysis to determine which standards best predict chronological age, and other pertinent data, it was determined that a statistically significant difference was only observed between older Reference and more Recent American skeletal samples for the rate of aging of the pubic symphysis. The 223

manifestation of this difference was a slight deceleration of the aging process in the Recent group.

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Chapter 6 Discussion Hypotheses revisited To address the question of whether differences exist in the aging processes of osteological indicators between Reference and Recent American Skeletal populations, three questions were evaluated. The first question addressed whether the observed morphology of the aging indicator was associated with the same chronological ages for both older Reference and more Recent American skeletal populations. Results indicated a difference in the rate of senescent change between groups, but only for some aging methods. The second question addressed whether a pattern was present that explained why some aging standards produced significant differences in the aging process of skeletal indicators between groups while others did not. Results demonstrated that most methods indicating a difference between groups were either pubic or auricular, which were both pelvic indicators. Males and Whites also appeared to significantly influence the results observed. The final question addressed the problem of which aging method was the true indicator of whether a change in the rate of aging had occurred when contrasting results were produced by multiple aging standards for a single skeletal indicator.

Aging

standards supported by previous research, statistical analysis, and other criteria attesting to method reliability included the Suchey-Brooks pubic symphysis method and Meindl and Lovejoy’s lateral-anterior cranial suture obliteration standard. These methods were relied upon to indicate whether a change in the rate of senescence of the pubic symphysis 225

and suture closure had occurred. Neither auricular surface aging method was endorsed. As only one rib aging method was tested, no conflicting results were produced.

Differences in aging between American skeletal samples For the pubic symphysis, nearly all of the components and aging standards were in agreement, producing a statistically significant difference in the rate of age-progressive changes for Reference and Recent American skeletal samples. Aging standards based on pubic symphyseal changes were deemed reliable, and the differences observed between groups for the pubic symphysis were accepted. In contrast, only the Transition Analysis method demonstrated a statistically significant difference between groups for cranial suture closure. Neither Meindl and Lovejoy’s vault system nor their lateral-anterior system indicated a difference between Reference and Recent samples. Though no specific critiques of the Transition Analysis standard were present in the anthropological literature, four of the five individual sutural components scored produced extremely unlikely ages at transition during the course of this research. Thus, the standard was deemed unreliable and was weighted less heavily than the other traditional cranial suture scoring standards, resulting in the conclusion that no change in the rate of suture obliteration had occurred between Reference and Recent American skeletal samples. Only one rib aging method was tested for this research: İşcan and colleagues’ standard for scoring the fourth rib. This method did not indicate a change in the rate of aging of the sternal end of the fourth rib between Reference and Recent American skeletal samples. This aging standard was well regarded in the literature and produced statistically sound results for this research; accordingly, İşcan and colleagues’ standard 226

was deemed reliable, and the similarities between groups in the aging process of the sternal end of the fourth rib were accepted. Gauging whether change occurred in the aging process of the auricular surface was the most challenging. While both standards produced results indicating a significant difference in the auricular aging process between groups, each had significant flaws. Although it was a major contributor in the stepwise regression model developed to predict chronological age, Lovejoy and colleagues’ standard has been criticized in the anthropological literature for having low reliability, high error, and low repeatability; low intraobserver agreement for this research echoed these concerns. Problems with the Transition Analysis method were of an entirely different nature; of the nine individual components scored for the method, only three indicated a significant difference between the Reference and Recent groups. Of those three, two produced extremely unlikely ages at transition during the course of this research. Like the Transition Analysis cranial suture method, this standard was deemed unreliable. As a result of these concerns, a conclusive determination of whether change in the rate of auricular surface aging had occurred between older and more recent American skeletal samples was not attained.

Interpretation It was expected that a difference might be detected between American groups for cranial suture closure, following results from Masset (1989) and Bocquet-Appel and Masset (1995), which found differences between temporally distinct European samples; these studies found that the older Lisbon sample had younger looking skeletal morphology than their more recent Prague counterparts, particularly for the older age categories. However, this result was not obtained for the American samples tested here. 227

Only a difference in pubic symphyseal senescence was detected between Reference and Recent American skeletal samples. Detecting a change in pubic symphyseal senescence between American skeletal samples was not unexpected because Hoppa (2000) also found differences among samples from the United States for the pubic symphysis; his results suggested that females older than 30-40 years, drawn from a 20th Century American forensic sample, looked younger skeletally than their counterparts in the Suchey-Brooks reference sample. However, it was surprising that males primarily affected this difference in my research, in contrast to Hoppa (2000), who reported the most significant differences were for females. Other studies have also detected a difference in the rate of senescent change in females, but not males; both Berg (2008) and Kimmerle and colleagues (2008a) found statistically significant differences in the ages associated with senescent change of the pubic symphysis between American and Balkan females. It is unclear exactly why females did not show significant differences in the rate of senescent change in the present study. One possibility may be that females are known to exhibit more variation within aging features; this greater variability may have outweighed or masked any change that proved to be significant for their male counterparts. Another explanation is that female skeletal aging, like developmental milestones, is buffered from shifts in environmental variables. Yet another possibility is bias in the sample. Males and Whites primarily affected the difference in the rate of senescent change of the pubic symphysis between Reference and Recent American skeletal samples. I remind the reader that in this manuscript, I am referring to Whites as a social category; individuals ascribed to this category tend to have different social and environmental

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experiences than those categorized as Black, particularly for variables like class, diet, living conditions, healthcare, and activity levels that may affect aging. While genes may have some effect, any differences observed are probably due to the differences in social experiences and treatment, as well as the physiological consequences of being classified into one category or another. In sum, the social and environmental experiences of individuals typically vary by which race is ascribed to them. Thus any differences observed between groups described by the terms Black and White are likely the result of social treatment rather than genes. When compared to their Reference sample counterparts, the American Recent Whites and males indicated a slightly decelerated rate of senescent change for the pubic symphysis. The degree of this change was small for the Todd, Suchey-Brooks, and Hartnett-Fulginiti standards; for every year that the Reference group took toward achieving the next transition, the Recent group took an average of 1.3 years, regardless of the standard used. The average ratio across pubic symphyseal standards was 1:1.5 for males, 1:1.7 for Whites, and 1:1.5 for White males, with Recent groupings aging at a slower rate. The impact of this difference is particularly significant for later ages, translating into average differences of up to 15 years.

Changes in the American political, social, cultural, and technological landscape Many factors may have influenced the observed shift in aging of the pubic symphysis. The specimens used for this research have birth years that ranged from 1828 to 1985, representing individuals who lived sometime between 1828 and 2006, a span of

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178 years. In the last 200 years, the United States has undergone significant political, social, cultural, and technological changes. The lives of Americans have changed dramatically over time as a result of major events like the Industrial Revolution, slavery, the Civil War and Reconstruction, World War I, the Great Depression, World War II, the Korean, Vietnam and Cold Wars, and the Digital age. In 1812, when America went to war with Great Britain, it became apparent that the States needed to improve transportation and become more economically independent; these forces prompted the Industrial Revolution in the United States, which resulted in a focus on manufacturing, mechanization, and urbanization. There was a shift from handmade goods to machine-made items that were mass-produced in factories. Transportation also improved, particularly with interstate roads, steam engines, canals, the railroad, and eventually the automobile. These improvements further opened the west, connected raw materials to factories and markets, and facilitated trade and travel. The invention of the cotton gin at the close of the 18th Century provided a more efficient way to de-seed cotton; slaves were needed to grow the cotton, which was in increasingly more in demand. Slaves in the United States were clothed, fed, and housed minimally to ensure their capacity for labor; living and working conditions were typically poor. Black slaves did not have the ability to seek medical care for disease or injury. Similarly, free Blacks also had limited access to healthcare. Free Blacks were typically very poor, and as a result, generally received inadequate medical attention and suffered from high mortality rates (Savitt 2002). In 1808, however, Congress passed legislation to end the transatlantic slave trade; this restricted of the number of slaves available and may have resulted in the slave owner’s manipulation of nutrition and medical care afforded to

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slaves (Steckel 1992; Rees et al. 2003; Carson 2009). Carson (2009), found that the adult stature of Blacks declined between 1850 and 1870, but rebounded by the end of the 19th Century. He alluded that this change in stature was reflective of the general health of Blacks living during the Antebellum period, the Civil War, and the Reconstruction period. Concurrently, as industries and factories arose, people moved from rural areas to cities for work; at the same time, inventions like the reaper and steel plow advanced agricultural efficiency. With urbanization came crowded living and/or working conditions, which led to the spread of communicable diseases. While vaccines for smallpox and plague were available in the 19th Century, the 20th Century saw the first commercially available antibacterial antibiotic in the early 1930s, as well as the development of vaccines and widespread immunization for many more infectious diseases, including cholera, typhoid, tuberculosis, influenza, polio, measles, mumps, and rubella (Centers for Disease Control and Prevention 2006). Significant developments in medicine continued to emerge, for both infectious and natural diseases, with a particular focus on heart disease, diabetes, and cancer. A significant change in the American diet has also occurred over the last two centuries, and this was likely responsible in part for the health problems prevalent today. In the early 19th Century, meals were typically homemade. With the Industrial Revolution, processed foods were produced in large factories and canned foods became more prevalent. Eventually, distribution of processed foods improved with the invention and mass production of motorized vehicles. For many Americans, the Great Depression significantly impacted the quality and quantity of food consumed.

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With the deployment of enlisted men overseas for World War II, traditional gender roles and the division of labor shifted. Women moved, albeit often temporarily, from working in the domestic sphere to jobs in manufacturing, communications, and nursing to assist with the war effort (Higonnet et al. 1987; Goldstein 2001). During the Second World War, fast, prepared, and frozen foods gained popularity because many homemakers were working outside of the home, a trend that has largely continued in the ensuing decades. Although many foods today have been fortified with vitamins and minerals, processed foods with chemical preservatives and fillers abound and fast foods have become a staple of many Americans’ diets. By the close of the 20th Century, one out of every 12 meals in the United States was fast food and soft drink consumption had risen to an average of 47.5 gallons per American per year, a significant increase from the average of one pint consumed per capita in 1850 (Ensminger 1995). Finally, with continued industrialization and mechanization, tasks that once took days or longer to complete by hand were now mass-produced and available for purchase. This advancement increased the amount of leisure time for many Americans. Technological advances in broadcasting and the digital age have made televisions and personal computers more affordable to the general public. Television programs, movies, video/computer games, the Internet, and other pastimes that result in a largely sedentary lifestyle now occupy much of Americans’ free time. This lack of physical activity is mirrored at the workplace, particularly for white-collar occupations. Perhaps these differences in average level of physical activity between the Reference and Recent groups may be related to the slight delay of the Recent sample in achieving middle and later phase transitions when compared to the Reference. This speculation is supported by a

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study by Klepinger and colleagues (1992), which noted that physical inactivity due to disability was associated with severe underestimation of chronological age. Many factors could have influenced the observed shift in aging of the pubic symphysis. Certainly environmental variables and the population’s living conditions have changed considerably in the last century. One could speculate that the American population’s general health and nutrition have improved; in particular, the implementation of immunizations and the advent of antibiotics have likely had a major impact on more recent generations. However, it was not possible to conclusively determine the causes of the observed change in the rate of progression of pubic symphyseal metamorphosis. Other factors that are difficult to quantify, like sample bias and differences in genetic background, could have also played a role in the results obtained. A difference in the rate of pubic symphyseal senescence was detected between two American skeletal samples, but these samples span a time period in the United States marked by significant political, social, cultural, and technological changes. Change of this magnitude is unparalleled in human history, and it is currently unclear if the observed trend extends to pre-Industrial and/or prehistoric skeletal samples.

Implications In order to understand the evolutionary processes that have molded variation in our species, it is important that anthropologists be able to assess the age at death for individuals in past populations. This ability is also important for cases of medico-legal significance. It is important to note, however, that the implications of this research differ for bioarchaeological and forensic anthropological applications. Although these two 233

fields investigate many of the same questions (like sex and age), bioarchaeological studies operate at the population level, so if an age estimation method is unbiased for that population, meaning that it does not systematically over- or under-estimate age at death, then the impact of inaccuracy is less of a concern. In contrast, forensic examinations are concerned with the estimation of age on an individual level, thus age structure mimicry is less problematic in forensic contexts than for paleodemographers (Komar & Buikstra 2008). However, the age estimates for forensic casework need to be precise and accurate for individuals, that is, close to the actual chronological age as well as repeatable and reliable. These conditions must be satisfied to meet the Daubert Criteria for admission as scientific evidence in court. Results from this research contribute data that adds to a greater understanding of variation in aging among American skeletal samples, taking a step toward improving the accuracy, precision, and applicability of current American osteological aging standards to American target samples. While the difference in the rate of aging of the pubic symphysis between older Reference and more Recent American skeletal populations was statistically significant, the impact of these differences did not appear to be anthropologically significant until later ages. However, the broad age ranges associated with phases defined by the SucheyBrooks and Hartnett-Fulginiti aging methods, as well as the 95% prediction intervals produced by the ABDOU Age Estimator program for Transition Analysis, largely mitigate this problem for forensic assessments of age at death. No forensic anthropologist should report a point estimate of age for an individual without also providing an appropriate prediction interval. These broad age ranges may be of less assistance for paleodemographic analyses, however, if the researcher is categorizing

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adults by five or 10 year age cohorts. Clearly, considerable individual variation is present, particularly in older age groups, and this fact remains a problem for age estimation from the adult human skeleton. Results of this research impact forensic anthropologists, the next of kin of unidentified skeletal remains, bioarchaeologists, paleodemographers, social and cultural anthropologists, and researchers interested in human aging and longevity. Benefits include a greater understanding of variation in aging among American skeletal populations, clarifying the reliability and applicability of current osteological aging standards to samples of differing genetic background and environmental influences. Results contribute to the knowledge base of skeletal estimation of age, and in conjunction with additional research, will lead to more reliable methods, affecting age estimates in forensic casework and the analysis of past populations alike.

Summary The difference detected in the rate of senescent change in the pubic symphyses of American skeletal samples is minimal at early ages but may be anthropologically significant at older ages. Although the broad age categories produced by most pubic symphyseal aging standards currently in use may swamp the differences of 15 years in the average age at transition for later transitions, it is important to consider that older American skeletal series may not be the best reference source for age estimation methods that will be applied to more Recent samples and individuals. Most aging methods were not significantly biased, suggesting that they are applicable to bioarchaeological cemetery samples. The specific cause of the change in pubic symphyseal senescence is not known, but the last 200 years of history in the United States are marked by significant political, 235

social, cultural, and technological changes. Overall, the American population’s general health and nutrition have improved; in particular, the implementation of immunizations and the advent of antibiotics have likely had a major impact on more recent generations. Another factor may be differences in physical activity levels between the two groups. However, it was not possible to conclusively determine the causes of the observed change in the rate of progression of pubic symphyseal metamorphosis. Other factors that are difficult to quantify, like sample bias and differences in genetic background, could have also played a role in the results obtained.

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Chapter 7 Conclusions Estimating age is a critical part of the study of skeletons from archaeological or forensic contexts. Although numerous research results have been published on the estimation of age from the adult human skeleton, this endeavor still needs improvement in terms of accuracy and precision. Many current aging standards are based on older reference skeletal samples, and some authors have argued that these standards and reference collections are outdated due to secular changes in overall body size, health, activity, and nutritional status. This question of uniformity in skeletal age changes across populations is fundamental to all comparative work in skeletal biology. Whether an aging standard will work on target groups that differ in time, space, and background from the reference sample is essential for reliable, accurate age estimation. Skeletal remains provide one source of information about past populations; accurate individual age estimates are critical, as they have implications for the overall age distribution derived for archaeological populations. The potential impact of variation in the rate of development of age-related features at the population level is great, particularly for estimating the demographic parameters of older individuals in skeletal samples (Hoppa 2000). If age cannot be reliably estimated, then anthropologists and paleodemographers cannot comment on any other characteristics that are dependent on age, including the distribution of ages, population fluctuation, mean age at death, life expectancy at birth, survivorship, mortality patterns, and mortuary practices (Meindl et al. 1983; Molleson 1995; Schmitt et al. 2002; Hawkes & O’Connell 2005). For example, the accurate

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assessment of age at death from skeletal remains is crucial to the exploration of how aboriginal/native populations were affected by contact, particularly with the introduction of European diseases. Addressing whether a difference exists in the rate of aging between groups is also crucial for the discipline of forensic anthropology. If osteological aging standards derived from older American reference samples are not appropriate for more recent documented collections, then the reliability of age estimates reported for present-day forensic cases is questionable. For example, forensic anthropologists require robust estimates of age that are admissible in court under the Daubert Criteria, which requires expert witnesses to quantify the certainty of their assessments. A survey of the anthropological literature reveals that a single standard of aging for populations of different origins is not appropriate; American aging standards do not appear to be uniformly applicable to all target populations worldwide. But few researchers have approached this problem by testing these methods on documented American target samples differing from those used to develop the aging standards themselves. American aging standards are continually applied to prehistoric, historic, and forensic samples alike, despite the fact that the standards are primarily developed from individuals living during the 19th and early 20th Centuries. The results of this research support the determination that a statistically significant difference is only present between older Reference and more Recent American skeletal samples for the rate of aging of the pubic symphysis. Statistical problems undermine the reliability of the Transition Analysis methods for estimating age from the auricular surface and cranial sutures. An assessment as to whether change

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occurred in the aging of the auricular surface cannot be made with confidence based on the data presented here. The significant difference recorded for the aging of the pubic symphysis manifests itself as a slight deceleration of the rate of metamorphosis for the Recent group, though the Recent group attains the initial transitions slightly earlier than the Reference sample. On average, for every year that the Reference sample took toward attaining the next transition, the Recent group took 1.3 years; this difference magnifies with each transition, such that the error in age estimates for older individuals can be very large. Based on the results obtained, there is evidence of a shift in the aging process of the pubic symphysis between older Reference and more Recent American skeletal populations. This implies that, when osteological aging standards based on 19th and early 20th Century reference samples are applied to modern-day forensic cases, the age estimates produced may not be reliable, particularly for older individuals. However, the broad age ranges associated with phases defined by the Suchey-Brooks and Hartnett-Fulginiti aging methods, as well as the 95% prediction intervals produced by the ADBOU Age Estimator program, largely mitigate this problem for forensic assessments of age at death. However, these broad age ranges may be of less assistance for paleodemographic analyses, particularly if the researcher anticipates categorizing adults by five or ten year age cohorts. Certainly considerable individual variation in the rate of skeletal senescent change is present, particularly in older age groups. It is not possible with the available data to determine what variables might be contributing to this shift. However, Tanner (1962) stated that during last hundred years there has been a significant tendency toward the earlier timing of adolescence,

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particularly after 1930; maturational changes now occur approximately two years earlier than at turn of the 20th Century (Bogin 1999). Adult aging techniques are based on postmaturational changes, so this trend may impact the timing of earlier phase transitions. In addition, the lives of Americans have changed considerably as a result of the significant political, social, cultural, and technological changes that have taken place in the United States during the last 200 years.

Recommendations So what do the results of this research mean for age estimation in bioarchaeological and forensic anthropological settings? As discussed previously, bioarchaeological and paleodemographic studies operate at the population level; unbiased age estimation methods that do not systematically over- or under-estimate age at death are important. The inaccuracy of any one estimate is less of a concern than for forensic investigations. Forensic anthropological examinations are concerned with the estimation of age on an individual level; age estimation methods that are precise, accurate, and reliable are needed. The issue of age structure mimicry, which is of great importance to bioarchaeological and paleodemographic investigations, is much less problematic in forensic contexts (Komar & Buikstra 2008). While the difference in the rate of aging of the pubic symphysis between older Reference and more Recent American skeletal populations was statistically significant, the impact of these differences did not appear to be anthropologically significant until later ages. However, the broad age ranges associated with phases defined by the SucheyBrooks and Hartnett-Fulginiti aging methods, as well as the 95% prediction intervals produced by the ABDOU Age Estimator program for Transition Analysis, largely 240

mitigate this problem for forensic assessments of age at death. The narrow age ranges for Todd’s pubic phases are not appropriate for age estimation in forensic anthropological casework; additionally, Todd’s method does not provide any indicator of certainty of the age estimate, which is becoming necessary in forensic contexts as a result of recent critiques of the forensic sciences outlined by the National Academy of Sciences and the Daubert criteria.

Forensic investigations Based on the results of this analysis, researchers should use Transition Analysis with a combination of multiple indicators (forensic prior) and İşcan and colleagues’ fourth rib for forensic anthropological investigations when possible. The benefits of Transition Analysis include lower inaccuracy than other methods tested for this research, the ability to factor in sex and race information, and the reduction or elimination of the biases of any one skeletal age indicator. The benefits of İşcan and colleagues’ aging method also include relatively low inaccuracy and a high correlation with age. Of course, skeletal remains recovered from forensic settings are often incomplete. Many times only a cranium is recovered because it is easily recognized by the general public as human in origin. If the entire cranium is present, it is recommended that the researcher use both Transition Analysis and Meindl and Lovejoy aging methods for cranial suture closure. Although both aging standards are inaccurate, each measures different locales. An age estimate and range should consider the output from both methods. If only a partial skull is recovered, researchers should use the Transition Analysis aging standard, because it allows for missing data, the observer can score closure as a single stage or between stages (ex. 2-3), and it provides a 95% prediction 241

interval that will reflect the specific sex and race assessed for the unidentified skeletal remains. In this situation, Meindl and Lovejoy’s technique is not appropriate because it does not allow for missing data. Based on case experience, the auricular surface is often preserved, even when the pubic symphysis is weathered or damaged due to animal scavenging or recovery efforts. If only the auricular surface is available for aging, it is recommended that the researcher use both Transition Analysis and Lovejoy and colleagues’ auricular surface aging methods. Both have the lowest inaccuracy of all aging methods tested, but each also has weaknesses. Age ranges provided by both methods should be considered when estimating age from the auricular surface. However, if the auricular surface is fragmentary, Lovejoy and colleagues’ method is not feasible; in such circumstances, Transition Analysis should be the aging method chosen, as it has the capability of providing an age estimate and range using the available features. Caution should be used if the only features available are the iliac exostoses, as these produced unreliable statistical results for this research. Although it is uncommon to recover a pubic symphysis without any other indicator of age, the Suchey-Brooks method is recommended for aging the pubic symphysis. The Suchey-Brooks method had the low inaccuracy and provided a mean and range for the age estimate. If the pubic symphysis is fragmentary, the Transition Analysis method should be used with caution; in this analysis, this method proved to be inaccurate. In addition, only 70% of the 95% prediction intervals contained the documented chronological age.

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Bioarchaeological and paleodemographic analyses Based on the results of this analysis, researchers should use Transition Analysis with a combination of multiple indicators (uniform prior) for bioarchaeological and paleodemographic analyses. The benefits of Transition Analysis with multiple indicators and a uniform prior include low bias, the ability to factor in sex and race information, and the reduction or elimination of the biases of any one skeletal age indicator. Often excavated archaeological remains are incomplete due to preservation bias and/or prior disturbances. The auricular surface is the most likely age indicator to survive in archaeological settings. Both the Transition Analysis and Lovejoy and colleagues’ auricular surface aging methods should be used to estimate age from this indicator. Both have the lowest bias of all aging methods tested. Of course, if the auricular surface is fragmentary, Transition Analysis should be used to estimate age because this method can provide an estimate even when only a few of the individual components are able to be scored. As noted in the previous section, caution should be used if only the iliac exostoses can be scored, because these components produced unreliable statistical results for this research. Often the cranium, pubic symphysis, and ribs are not intact or preserved in archaeological material, whether due to mechanical damage or taphonomic processes. This is unfortunate, because the İşcan and colleagues rib aging technique, and the Todd, Suchey-Brooks, and Hartnett-Fulginiti pubic symphyseal aging methods all have low bias. As with forensic anthropological casework, cranial material should only be used to estimate age in combination with other indicators unless it is the only indicator of age available. 243

Future research Several questions still remain. Other researchers should evaluate the skeletal remains within this sample to determine if systematic bias in the author’s scoring is present, artificially creating a shift in the rate of aging between groups where there is none, or vice versa. Additionally, future research with larger samples and additional American documented skeletal remains of varying backgrounds should be conducted to confirm or refute the results obtained here, as sample bias and representativeness may contribute to the differences observed. If the change in pubic symphyseal aging between American skeletal samples is confirmed, further investigation is needed in order to quantify the degree of difference; this step is particularly important for future forensic anthropological analyses. While it was not the focus of this research to determine how the individual components defined by Boldsen and colleagues contributed to the Transition Analysis method’s production of a point estimate of age, this question is a potential avenue for future research. This endeavor is particularly important for shedding light on potential changes in the aging process of the auricular surface for American skeletal samples, as this question cannot be answered with any certainty here. Finally, while the goal of this research is not to identify variables affecting the rate of aging of certain indicators of the human skeleton, anthropologists will want to explore and understand what underlying causative factors may be influencing this shift in aging between human populations. Based on the results obtained from this research, investigators may want to focus on variables differing between the sexes and races, such as socioeconomic status, living and working conditions, and health care. Other 244

potentially interesting courses to follow include investigating the influence of physical activity levels on the rate of skeletal aging, particularly for the pubic symphysis.

Summary To address the question of uniformity in osteological age changes across populations, this research concentrated on identifying differences between American skeletal samples for four age indicators. The goal of this research was to determine if a change had occurred in the rate of aging between older Reference and more Recent skeletal populations for the pubic symphysis, auricular surface, sternal end of the fourth rib, and cranial sutures. A large sample of Black and White individuals of both sexes was selected from five documented American skeletal series: the Hamann-Todd Osteological, Robert J. Terry Anatomical, Maxwell Museum Documented, William Bass Donated, and Maricopa County autopsy collections. The Hamann-Todd and Terry collections are the two oldest and largest American skeletal samples, and they have served as the reference series for several current osteological aging standards, including Todd’s pubic symphysis, Lovejoy and colleagues’ auricular surface, and Meindl and Lovejoy’s cranial suture obliteration; combined, these two samples compose the Reference American group for this research. The remaining three skeletal samples are much more recent in origin, and together, these series compose the Recent American group. For each of the 971 skeletal remains included in the dataset, scores were recorded to describe the metamorphoses of four skeletal indicators of age: the pubic symphysis, auricular surface, sternal end of the fourth rib, and cranial sutures. Scores were attributed to these indicators according to the following established American osteological aging standards: Todd pubic symphysis, Suchey-Brooks pubic symphysis, Hartnett-Fulginiti 245

pubic symphysis, and Boldsen and colleagues’ Transition Analysis pubic symphysis, Lovejoy and colleagues’ auricular surface, Boldsen and colleagues’ auricular surface, İşcan and colleagues’ fourth rib end, Meindl and Lovejoy’s vault and lateral-anterior cranial sutures, Boldsen and colleagues’ Transition Analysis cranial sutures, and Boldsen and colleagues’ Transition Analysis using multiple indicators. Unlike traditional phasebased aging standards, which have an ordinal scale of phases describing a suite of morphological traits for an indicator, Boldsen and colleagues’ Transition Analysis methods are component-based; for each indicator, the Transition Analysis componentbased methods score many individual features on an ordinal scale. These individual components were then combined using maximum likelihood estimation to produce an estimate of age at death. To determine if there was a change in the rate of aging of skeletal age indicators between the older Reference and more Recent American skeletal populations, three research questions were addressed though the course of this research project. The first research question addressed whether the tested American aging standards produce significant differences between the aging processes of Reference and Recent documented skeletal series. The initial step was to calculate the ages at transition for each aging standard and component by Reference and Recent group. An unexpected result of this process was that it identified underlying problems with certain individual components and aging standards, specifically several of Boldsen and colleagues’ auricular surface and cranial suture components. Some of the problems were so severe that it called into question the reliability of the Transition Analysis output for these two indicators.

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In the next step, proportional odds probit regression and an analysis of deviance indicated a significant difference between groups for some osteological standards, but not others. All of the pubic symphyseal and auricular surface aging standards produced a statistically significant difference in the rate of age-progressive change between Reference and Recent American samples. Only one cranial suture closure aging standard, Boldsen and colleague’s Transition Analysis method, produced a significant difference between groups, but the İşcan and colleagues standard for aging the sternal end of the fourth rib did not. The second research question examined whether a pattern existed that explained why some standards produced significant results and others do not; possible patterns include the grouping of results by method type, indicator used, anatomical region of the indicator, birth year cohort, sex, race, sex-race category, and adult stature. Significant results clustered for the pubic symphysis and the auricular surface pelvic indicators, suggesting that the indicator used and the anatomical region of the indicator explained the pattern observed. When the samples were divided by sex and race, males and Whites paralleled the pattern observed for the comparison of the total Reference and Recent samples. The third and final question attempted to establish which standards should be trusted over others to determine whether a change in aging has occurred between groups, specifically for those indicators in which the aging standards produced opposing results. This determination was made considering several lines of evidence, including a review of the anthropological literature on the strengths and weaknesses of each aging standard, stepwise regression analysis to determine which standards best predict chronological age

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for the sample selected for this project, and other relevant data like the observer’s experience level, intraobserver agreement, and known problems with certain statistical output. Based on these data, the Suchey-Brooks pubic symphysis and Meindl and Lovejoy lateral-anterior cranial suture closure methods were selected as the most reliable aging standards for their respective indicators; when conflicting results were obtained, as was the case with cranial suture closure, the outcome produced by the most reliable aging method for that indicator was selected to determine whether a difference in the rate of skeletal senescence between groups was present. Accordingly, this research found a statistically significant difference in the rate of senescent change of the pubic symphysis between older Reference and more Recent American skeletal samples. Neither cranial sutures nor the sternal end of the fourth rib indicated a difference between groups. For the auricular surface, statistical problems undermined the reliability of the Transition Analysis method and significant critiques of the traditional phase-based technique presented in the literature, making an assessment as to whether change occurred in the senescence of this indicator inconclusive. For the pubic symphysis, the difference between groups was a slight deceleration of the rate of metamorphosis for the Recent group when compared to their Reference sample counterparts, particularly for males and Whites; however, the Recent group attained the initial transitions at slightly earlier ages than the Reference sample. Based on the results obtained, there is evidence of a shift in the aging process of the pubic symphysis between older Reference and more Recent American skeletal populations. This implies that, when osteological aging standards based on 19th and early 20th Century reference samples are applied to modern-day forensic cases, the age estimates produced

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may not be reliable, particularly for older individuals where this difference is more pronounced. Nonetheless, the broad age ranges associated with phases defined by all of the pubic symphyseal aging methods—with the exception of the Todd standard—appear to mitigate this problem for forensic assessments of age at death. In contrast, paleodemographic and bioarchaeological analyses may be affected to a greater extent, as these broad age ranges are not easily condensed into five or ten year age categories that are often desirable for such investigations. Regardless, these results support the conclusions of previous research, which report considerable individual variation in the rate of skeletal senescent change, particularly in older age groups.

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References Cited Acsádi GY, Nemeskéri J. 1970. History of human lifespan and mortality. Budapest: Akadémiai Kiadó. Aeby C. 1858. Ueber die Symphyse ossium pubis des Menschen nebst Beiträgen zur Lehre von hyalinen Knorpel und seiner Vernöcherungen. Zschr f rationelle Med Reihe 3(4):1-77. Albert AM, Greene DL. 1999. Bilateral asymmetry in skeletal growth and maturation as an indicator of environmental stress. Am J Phys Anthropol 110:341-349. Alkass K, Buchholz BA, Ohtani S, Yamamoto T, Druid H, Spalding KL. 2010. Age estimation in forensic sciences: application of combined aspartic acid racemization and radiocarbon analysis. Mol Cell Proteomics [2009 Dec 4. Epub ahead of print]. Angel JL. 1984. Variation in estimating age at death of skeletons. Collegium Anthropologicum 8(2):163-8. Angel JL, Caldwell PC. 1984. A forensic anthropological case from Wilmington, Delaware. In Rathbun TA, Buikstra JE (Eds): Human identification: case studies in forensic anthropology. Springfield, Illinois: Charles C. Thomas. Angel JL, Suchey JM, İşcan MY, Zimmerman MR. 1986. Age at death from the skeleton and viscera. In Zimmerman MR, Angel JL (Eds): Dating and age determination in biological materials. London: Croom Helm. Arking R. 1998. Biology of aging. Observations and principles. Sunderland, MA: Sinauer Associates, Inc.

250

Armelagos GJ, van Gerven DP. 2003. A century of skeletal biology and paleopathology: contrasts, contradictions, and conflicts. Am Anthropol 105(1):53-64. Armstrong GL, Conn LA, Pinner RW. 1999. Trends in infectious disease mortality in the United States during the 20th Century. JAMA 281:61-66. Asch D. 1976. The Middle Woodland population of the Lower Illinois Valley: a study in paleodemographic methods. Evanston: Northwestern Archaeological Program. Aykroyd RG, Lucy D, Pollard AM, Roberts CA. 1999. Nasty, brutish, but not necessarily short: a reconsideration of the statistical methods used to calculate age at death from adult human skeletal and dental age indicators. Am Antiq 64(1):55-70. Aykroyd RG, Lucy D, Pollard AM, Solheim T. 1997. Technical note: regression analysis in adult age estimation. Am J Phys Anthropol 104:259-265. Baccino E, Ubelaker DH, Hayek LA, Zerilli A. 1999. Evaluation of seven methods of estimating age at death from mature human skeletal remains. J Forensic Sci 44(5):931-936. Barnes JK, Woodward JJ, Smart C, Otis GA, Huntington DL. 1870. The Medical and Surgical History of the War of the Rebellion (1861-65). Washington, DC: U.S. Government Printing Office. Bass WM. 1968. Obituary of Charles Earnest Snow, 1910-1967. Am J Phys Anthropol 28:369-372. Bass WM. 1995. Human osteology: a laboratory and field manual. 15th ed. Columbia, Missouri: Missouri Archaeological Society. Bassett HE, Spradley MK, Jantz LM. 2003. The William M. Bass Donated Collection at

251

the University of Tennessee in Knoxville. Proceedings of the American Academy of Forensic Sciences 9:243-244. Abstract. Bedford ME, Russell KF, Lovejoy CO, Meindl RS, Simpson SW, Stuart-Macadam PL. 1993. Test if the multifactorial aging method using skeletons with known ages-atdeath from the Grant Collection. Am J Phys Anthropol 91:287-297. Berg GE. 2008. Pubic bone age estimation in adult women. J Forensic Sci 53(3):569-577. Bergfelder T, Hermann B. 1980. Estimating fertility on the basis of birth-traumatic changes in the pubic bone. J Hum Evol 9:611-613. Bill JH. 1862. Notes on arrow wounds. Med Rec 1862:365-367. Blumenbach JF. 1775. De Generis Humani Varietate Nativa. M.D., Friedrich Schiller University, Jena. Blumenbach JF. Decas Collectionis Sua Craniorun Diversarum Gentium Illustrata. Göttingen: Dieterich. Blumenbach JF. 1828. Nova Pentas Collectionis Suae Craniorum Diversarum Gentium Tanquam. Complementum Priorum Decadum. Göttingen: Dieterich. Boas F. 1912. Changes in bodily form of descendants of immigrants. Am Anthropol 14:530-562. Bocquet-Appel JP. 1994. Estimating the average for an unknown age distribution in anthropology. In Bogognini-Tarli S, Di Bacco M, Pacciani E: Statistical Tools in Human Biology. London: World Scientific. Bocquet-Appel JP, Masset C. 1982. Farewell to paleodemography. J of Hum Evol 11(4):321-33. Bocquet-Appel JP, Masset C. 1985. Paleodemography: resurrection or ghost? J Hum

252

Evol 14:107-111. Bocquet-Appel JP, Masset C. 1995. L'âge au décès dans les populations inhumées: comparaison de méthodes et de résultats. Antropologia Port 13:39-48. Bocquet-Appel JP, Masset C. 1996. Paleodemography: expectancy and false hope. Am J Phys Anthropol 99:571-583. Bogin B. 1999. Patterns of human growth. 2nd Ed. Cambridge: Cambridge University Press. Boldsen JL. 1997. Transitional analysis: a method for unbiased age estimation from skeletal traits. Am J Phys Anthropol Suppl 24:78. Abstract. Boldsen JL, Milner GR, Konigsberg LW, Wood JW. 2002. Transition analysis: a new method for estimating age from skeletons. In Hoppa RD, Vaupel JW: Paleodemography: age distributions from skeletal samples. Cambridge: Cambridge University Press. Bonn. 1777. Overhet Maaksel ende beweeglijke Loswording der Beenvereenigingen von het Bekken etc. Verh Bat Gen Rotterdam 3, Deel Pl 4. Borkan GA. 1986. Biological age assessment in adulthood. In Bittles AH, Collins KJ (Eds.): The biology of human aging. Cambridge: Cambridge University Press. Bowles FP. 1976. Measurement and Instrumentation in Physical Anthropology. Yearb Phys Anthropol 18:174-180. Brant LJ, Pearson JD. 1994. Modeling the variability in longitudinal patterns of aging. In Crews DE, Garruto RM: Biological anthropology and aging: perspectives on human variation over the life span. New York: Oxford University Press. Broca P. 1879. Instructions Relative À L’étude Anthropologique du Système Dentaire.

253

Bull Soc Anthropol Paris 2:128-152. Brooks ST. 1951. A Comparison of the Criteria of Age Determination of Human Skeletons by Cranial and Pelvis Morphology. Ph.D., University of California, Berkeley. Brooks ST. 1955. Skeletal age at death: the reliability of cranial and pubic age indicators. Am J Phys Anthropol 13(4):567-597. Brooks ST, Suchey JM. 1990. Skeletal age determination based on the os pubis: A comparison of the Acsádi-Nemeskéri and Suchey-Brooks methods. J Hum Evol 5:227-238. Broca P. 1861. Sur le volume et la forme du cerveau suivant les individus et suivant les races. Bull Soc Anthrop Paris 2:139-207. Buckberry JL, Chamberlain AT. 2002. Age estimation from the auricular surface of the ilium: a revised method. Am J Phys Anthropol 119:231-239. Buikstra, JE. 1979. Contributions of Physical Anthropologists to the Concept of Hopewell: A Historical Perspective. In Brose DS and Greber N: Hopewell Archaeology. Kent, OH: Kent State University Press. Buikstra JE. 2006. A Historical Introduction. In Buikstra JE, Beck LA. Bioarchaeology: the Contextual Analysis of Human Remains. Amsterdam: Academic Press. Buikstra JE. 2009. Introduction to the 2009 reprint edition. In Morton SG: Crania Americana (reprint edition). Davenport, Iowa: Gustav’s Library. Buikstra JE, Gordon CC. 1981. The study and restudy of human skeletal series: the importance of long-term curation. Ann NY Acad Sci 376:449-465. Buikstra JE, Konigsberg LW. 1985. Paleodemography: critiques and controversies. Am

254

Anthropol 87:316-334. Buikstra JE, Konigsberg LW, Bullington J. 1986. Fertility and the development of agriculture in the prehistoric Midwest. Am Antiq 51:528-546. Buikstra JE, Ubelaker DH. 1994. Standards for data collection from human skeletal remains: proceedings of a seminar at the Field Museum of Natural History. Arkansas Archeological Report Research Series No. 44. Fayetteville, Arkansas: Arkansas Archaeological Survey. Burns KR, Maples WR. 1976. Estimation of age from individual adult teeth. J For Sci 21:343-356. Carson SA. 2006. African-American and White living standards in the 19th Century American South: a biological comparison. CESifo Working Paper No. 1696. Munich: CESifo Group. Carson SA. 2009. African-American and white inequality in the nineteenth century American South: a biological comparison. J Popul Econ 22(3):739-755. Centers for Disease Control and Prevention. 2006. Vaccines Timeline: 50 Years of Vaccine Progress. http://www.cdc.gov/vaccines/pubs/vacc-timeline.htm Cobb WM. 1936. The Laboratory of Anatomy and Physical Anthropology of Howard University. Washington, DC: Howard University. Cobb WM. 1952. Skeleton. In Lansing AI: Cowdry’s problems of ageing, biological and medical aspects. 3rd Ed. Baltimore: the Williams and Wilkins Company. Cobb WM. 1959. Thomas Wingate Todd. J Natl Med Assoc 51:233-246. Cobb WM. 1981. Thomas Wingate Todd, 1885-1938. Am J Phys Anthropol 56:517-520. Cohen Jr MM. 1993. Sutural biology and the correlates of craniosynostosis. Am J Med

255

Genet 47:581-616. Cole F. 1931. George A. Dorsey. Am Anthropol 33:413-414. Committee on Identifying the Needs of the Forensic Sciences Community, National Research Council. 2009. Strengthening Forensic Science in the United States: A Path Forward. Washington, DC: The National Academies Press. http://www.nap.edu. Condon K, Charles DK, Cheverud JM, Buikstra JE. 1986. Cementum annulation and age determination in Homo sapiens. II. Estimates and accuracy. Am J Phys Anthropol 71:321-330. Cook DC. 2006. The Old Physical Anthropology and the New World: A Look at the Accomplishments of an Antiquated Paradigm. In Buikstra JE, Beck LA. Bioarchaeology: the Contextual Analysis of Human Remains. Amsterdam: Academic Press. Cooper RS, Kennelly JF, Durazo-Arvizu R, Oh H, Kaplan G, Lynch J. 2001. Relationship between premature mortality and socioeconomic factors in Black and White populations of US metropolitan areas. Pub Health Rep 116:464-473. Corruccini RS. 1974. An examination of the meaning of cranial discrete traits for human skeletal biological studies. Am J Phys Anthropol 40:425-446. Cox M. 2000. Ageing adults from the skeleton. In Cox M, Mays S (Eds.): Human Osteology in Archaeology and Forensic Science. London: Greenwich Medical Media Ltd. Cushing FH. 1890. Preliminary Notes on the Origins, Working Hypothesis, and

256

Primary Researches of the Hemenway Southwestern Archaeological Expedition. In Congres International des Americanistes. Compte-Rendu de la Septieme Session, Berlin,1888. de Arenosa D, Suchey JM. 1987. Determination of age in the male os pubis – composition of the sample. Poster presented at the 39th Annual Meeting of the American Academy of Forensic Sciences, San Diego, California. Di Bacco M, Ardito V, Pacciani E. 1999. Age-at-death diagnosis and age-at-death distribution estimate: two different problems with many aspects in common. Int J Anthropol 14(2-3): 161-169 DiGangi EA, Bethard JD, Kimmerle EH, Konigsberg LW. 2009. A new method for estimating age-at-death from the first rib. Am J Phys Anthropol 138:164-176. Dirkmaat, DC, Cabo LL, Ousley SD, Symes SA. 2008. New Perspectives in Forensic Anthropology. Yrbk Phys Anthropol 51:33–52. Djurić M, Djonić D, Nikolić S, Popović D, Marinković J. 2007. Evaluation of the Suchey-Brooks method for aging skeletons in the Balkans. J Forensic Sci 52(1):21-23. Dorandeu A, Coulibaly B, Piercecchi-Marti MD, Bartoli C, Gaudart J, Baccino E, Leonetti G. 2008. Age-at-death estimation based on the study of frontosphenoidal sutures. Forensic Sci Int 177(1):47-51. Dorsey GA. 1899. The skeleton in medicolegal anatomy. Chicago Med Recorder 16:172179. Driver HE. 1969. Indians of North America. Chicago: University of Chicago Press.

du Noüy PL. 1937. Biological Time. New York: The Macmillan Company. 257

Dudar JC, Pfeiffer S, Saunders SR. 1993. Evaluation of morphological and histological adult skeletal age-at-death estimation techniques using ribs. J Forensic Sci 38(3):677-685. Dwight T. 1878. The Identification of the Human Skeleton. A Medico-Legal Study. [Prize essay] Boston: Massachusetts Medical Society. Dwight T. 1890. The closure of the sutures as a sign of age. Boston Med Surg J 122:389535. El-Najjar MY, McWilliams KR. 1978. Forensic Anthropology: the Structure, Morphology, and Variation of Human Bone and Dentition. Springfield, Illinois: Charles C. Thomas. Ensminger AH. 1995. Concise encyclopedia of foods and nutrition. 2nd Ed. Boca Raton: CRC Press LLC. Ericksen MF. 1982. How “representative” is the Terry collection? Evidence from the proximal femur. Am J Phys Anthropol 59:345-350. Eveleth PB. 1975. Differences between ethnic groups in sex dimorphism of adult height. Ann Hum Biol 2:35-39. Eveleth PB, Tanner JM. 1976. Rate of maturation: population differences in skeletal, dental and pubertal development. In Eveleth PB, Tanner JM (Eds): Worldwide variation in human growth. Cambridge UK: Cambridge University Press. Eveleth PB. 1979. Population differences in growth: environmental and genetic factors. In Falkner F, Tanner JM (Eds.): Human growth: a comprehensive treatise. Volume 3: Methodology and ecological, genetic, and nutritional effects on growth. New York: Plenum Press.

258

Ferenbach D, Schwidetzky I, Stloukal M. 1980. Recommendations for age and sex diagnosis of skeletons. J Hum Evol 9(7):517-549. Flegal MD, Carroll RJ, Kuczmarski RJ, Johnson CL. 1998. Overweight and obesity in the United States: prevalence and trends, 1960-1994. Int J Obes Relat Metab Disord 22:39-47. Fogel RW. 1986. Nutrition and the decline in mortality since 1700: some preliminary findings. In Engerman SL, Gallman RE: Long-term factors in American economic growth. Chicago: the University of Chicago Press. Fox J. 2002. An R and S-plus companion to applied regression. Thousand Oaks: Sage Publications. Frédéric J. 1906. Untersuchungen über die normale Obliteration der Schädelnähte. Z Morph and Anthrop 9:373-456. Frankenberg SR, Konigsberg LW. 2006. A brief history of paleodemography from Hooton to hazards analysis. In Buikstra JE, Beck LA. Bioarchaeology: the Contextual Analysis of Human Remains. Amsterdam: Academic Press. Frisancho AR. 1978. Nutritional influences on human growth and maturation. Yearbook Phys Anthropol 21:174-191. Gage TB. 1988. Mathematical hazard models of mortality: an alternative to model life tables. Am J Phys Anthropol76(4):429-441. Gage TB, Dyke B. 1986. Parameterizing abridged mortality tables: the Siler threecomponent hazard model. Hum Biol 58:275-291. Galera V, Ubelaker DH, Hayek LA. 1995. Interobserver error in macroscopic methods of estimating age at death from the human skeleton. Int J Anthropol 10(4):229-239.

259

Galera V, Ubelaker DH, Hayek LA. 1998. Comparison of macroscopic cranial methods of age estimation applied to skeletons from the Terry Collection. J Forensic Sci 43(5):933-939. Garn SM, Bailey SM. 1978. Genetics of the maturational processes. In Falkner F, Tanner JM (Eds): Human growth, vol 1. New York: Plenum Press. Garn SM, Giles E. 1995. Earnest Albert Hooton, 1887-1954. Biographical Memoirs of the National Academy of Sciences 68:1-15. Gilbert BM. 1973. Misapplication to females of the standard for aging the male os pubis. Am J Phys Anthropol 38:39-40. Gilbert BM, McKern TW. 1973. A method for aging the female os pubis. Am J Phys Anthropol 38:31-38. Giles E. 1997. Hooton, E(arnest) A(lbert) (1887-1954). In Spencer F. (Ed.): History of Physical Anthropology: An Encyclopedia. Volume 1. New York: Garland Publishing. Giles E, Klepinger LL. 1999. The butcher who rendered his wife? Chicago’s Luetgert case and the beginning of American forensic anthropology. Proc Am Acad Forensic Sci 5:295-296. Abstract. Gillett RM. 1991. Determination of age at death in human skeletal remains: a comparison of two techniques. Int J Anthropol 6(2):179-189. Goldstein JS. 2001. War and Gender: How Gender Shapes the War System and Vice Versa. Cambridge: Cambridge University Press. Goldstein MS. 1953. Some vital statistics based on skeletal material. Hum Biol 25:3-12. Gottlieb M. 1982. Skeletons in the closet: who was Dr. T. Wingate Todd and what was he

260

doing with all those bones? Northern Ohio Live Magazine. Gould SJ. 1978a. Morton’s ranking of races by cranial capacity. Science 200(4341):503509. Gould SJ. 1978b. Flaws in a Victorian veil. Nat Hist 87(6):16-26. Gould SJ. 1996. The Mismeasure of Man: Revised and Expanded. New York: W.W. Norton. Gratiolet L.1 856. Mémoire sur le développement de la forme du crâne de l'homme, et sur quelques variations qu'on observe dans la marche de l'ossification de ses sutures. C R Acad Sci 43:428-431. Greene OL, VanGerven DP, Armelagos GJ. 1986. Life and death in ancient populations: bones of contention in paleodemography. Hum Evol 1:193-207. Greulich WW. 1957. A comparison of the physical growth and development of American-born and native Japanese children. Am J Phys Anthropol 15(4):489515. Griffin RC, Chamberlain AT, Hotz G, Penkman KEH, Collins MJ. 2009. Age estimation of archaeological remains using amino acid racemization in dental enamel: a comparison of morphological, biochemical, and known ages-at-death. Am J Phys Anthropol 140(2):244-252. Grisbaum GA, Ubelaker DH. 2001. An Analysis of Forensic Anthropology Cases Submitted to the Smithsonian Institution by the Federal Bureau of Investigation from 1962 to 1994. Smithsonian Contributions to Anthropology 45:1-15. Gustafson G. 1950. Age determinations on teeth. J Amer Dent Assoc 41:45-54. Gustafson G. 1966. Forensic Odontology. London: Staples Press.

261

Gustafson G, Simpson K. 1953. Dental data in crime investigation. In Simpson K (Ed.): Modern trends in forensic medicine. London: Butterworth. Gwet K. 2002. Computing inter-rater reliability with the SAS System. Statistical Methods for Inter-Rater Reliability Assessment, No. 3. Halgrimsson B. 1999. Ontogenetic patterning of skeletal fluctuating asymmetry in rhesus macaques and humans: evolutionary and developmental implications. Int J Primatol 20:121-151. Hanihara K. 1952. Age changes in the male Japanese pubic bone. J Anthrop Soc Nippon 62:245-260 (English summary). Hanihara K, Suzuki T. 1978. Estimation of age from the pubic symphysis by means of regression analysis. Am J Phys Anthropol 48:233-240. Harper AB, Laughlin WS. 1982. Inquiries into the Peopling of the New World: Development of Ideas and Recent Advance. In Spencer F: A History of American Physical Anthropology 1930-1980. New York: Academic Press. Harper GJ, Crews DE. 2000. Aging, senescence, and human variation. In Stinson S, Bogin B, Huss-Ashmore R, O’Rourke D (Eds): Human biology: an evolutionary and biocultural perspective. New York: Wiley-Liss. Hartnett KM. 2007. A Re-evaluation and Revision of Pubic Symphysis and Fourth Rib Aging Techniques. Ph.D. Dissertation, Arizona State University. Haury E. 1945. The Excavation of Los Muertos and Neighboring Ruins in the Salt River Valley, Southern Arizona. Papers of the Peabody Museum of American Archaeology and Ethnology. Vol 24: Cambridge, Massachusetts: Peabody Museum of American Archaeology and Ethnology.

262

Haviland WA. 1994. Wilton Marion Krogman 1903-1987. Biographical Memoirs of the National Academy of Sciences p294-320.. Hawkes K, O’Connell JF. 2005. How old is human longevity? J Hum Evol 49:650-653. Henle J. 1872. Bänder zwischen beiden Huftknochen. Handbuch der Bänderlehre des Menschen (Handbuch der systematischen Anatomie) 2e Aufl 121. Henry RS. 1964. The Armed Forces Institute of Pathology: Its First Century, 1862-1962. Washington, DC: Office of the Surgeon General, Department of the Army. Herman AA. 1996. Toward a conceptualization of race in epidemiologic research. Ethnicity and Disease 6:7-20. Hershkovitz I, Latimer B, Dutour O, Jellema LM, Wish-Baratz S, Rothschild C, Rothschild BM. 1997. Why do we fail in aging the skull from the sagittal suture? Am J Phys Anthropol 103(3):393-9. Higonnet MR, Jenson J, Michel S, Weitz MC. 1987. Behind the Lines: Gender and the Two World Wars. New Haven, CT: Yale University Press. Himes JH. 1978. Bone growth and development in protein-calorie malnutrition. World Rev Nutr Diet 28:143-187. Hinsley CM, Wilcox DR. 1996. The Southwest in the American Imagination: The Writings of Sylvester Baxter, 1881-1889. Vol. 1, The Southwest Center Series. Frank Hamilton Cushing and the Hemenway Southwestern Archaeological Expedition: 1886-1889. Tucson, AZ: University of Arizona Press. Hinsley CM, Wilcox DR. 2002. The Lost Itinerary of Frank Hamilton Cushing. Vol. 2,

263

The Southwest Center Series. Frank Hamilton Cushing and the Hemenway Southwestern Archaeological Expedition: 1886-1889. Tucson, AZ: University of Arizona Press. Hooton EA. 1925. The Ancient Inhabitants of the Canary Islands. Vol. VII, Harvard African Studies. Cambridge, MA: Peabody Museum of Harvard University. Hooton EA. 1930. The Indians of Pecos Pueblo: A Study of Their Skeletal Remains. Vol. 4, Papers of the Southwestern Expedition. New Haven: Yale University Press. Hooton EA. 1935. Development and Correlation of Research in Physical Anthropology at Harvard University. Proc Am Philos Soc 75:499-516. Hooton EA. 1943. Medico-legal aspects of physical anthropology. Clinics 1:1612-1624. Hoppa RD. 2000. Population variation in osteological aging criteria: an example from the pubic symphysis. Am J Phys Anthropol 111:185-191. Hoppa RD, Saunders S. 1998. The MAD legacy: how meaningful is mean age-at-death in skeletal samples. Hum Evol 13:1-13. Hoppa RD, Vaupel JW. 2002. The Rostock Manifesto for paleodemography. In Hoppa RD, Vaupel JW: Paleodemography: age distributions from skeletal samples. Cambridge: Cambridge University Press. Howell N. 1976. Notes on collection and analysis of demographic field data. In Marshall JF, S Polgar S (Eds.): Culture, natality, and family planning. Chapel Hill, NC: University of North Carolina. Howell N. 1982. Village composition implied by a paleodemographic life table: the Libben Site. Am J Phys Anthropol 59:263-269. Hrdlička A. 1895. Contribution to the general pathology of the insane. (Physical

264

examinations and measurements.) 24th Ann Rep Middletown State Homoeop Hosp 162-207. Hrdlička A. 1900. Arrangement and preservation of large collections of human bones for purposes of investigation. Am Nat 34:9-15. Hrdlička A. 1904. Directions for collecting information and specimens for physical anthropology. Bull US Nat Mus 39, part R. Hrdlička A. 1907a. Measurements of the cranial fossae. Proc US Nat Mus 32:177-232. Hrdlička A. 1907b. Skeletal remains suggesting or attributed to early man in North America. Washington, DC: Bull Bur Am Ethnol 33. Hrdlička A. 1908a. Physical anthropology and its aims. Sci 28(706):33-43. Hrdlička A. 1908b. Physiological and medical observations among the Indians of the Southwestern United States and Northern Mexico. Bureau of American Ethnology, Bulletin No. 34. Washington, DC: Government Printing Office. Hrdlička A. 1908c. Report on a collection of crania from Arkansas (made and donated to The National Museum, by Mr. Clarence B. Moore). J Acad Nat Sci Phila (2nd series) 13:558-563. Hrdlička A. 1908d. Sexual differences in the skull and other parts of the skeleton. Wash Med Ann 6:433-437. Hrdlička A. 1909a. Note sur la variation morphologique des Egyptiens depuis les temps prehistoriques ou predynastiques. Bull et Mem Soc d'Anthrop Paris (5th series) 10:143-144. Hrdlička A. 1909b. Report on the skeletal remains [recovered from the Earth-Lodge Ruins in eastern Nebraska]. Am Anthropol 11:79-84.

265

Hrdlička A. 1909c. Report on an additional collection of skeletal remains from Arkansas and Louisiana (made, and presented to the National Museum in 1509, by Mr. Clarence B. Moore). J Acad Nat Sci Phila (2nd series) 14:173-240. Hrdlička A. 1909d. Tuberculosis among certain Indian tribes of the United States, Bureau of American Ethnology, Bulletin No. 42. Washington, DC: Government Printing Office. Hrdlička A. 1910. Report on skeletal material from Missouri mounds, collected in 19061907 by Mr. Gerard Fowke. Bull Bur Am Ethnol 37:103-112. Hrdlička A. 1912a. The problems of the unity or plurality and the probable place of origin of the American aborigines. Am Anthropol 14:5-12. Hrdlička A. 1912b. Remains in eastern Asia of the race that peopled America. Smith Misc Coll 60(16):1-5. Hrdlička A. 1912c. Report on skeletal remains from a mound on Haley Place, near Red River, Miller Co., Arkansas. J Acad Nat Sci Phila (2nd series) 14:639-640. Hrdlička A. 1913. A report on a collection of crania and bones from Sorrel Bayou, Iberville Parish, Louisiana. J Acad Nat Sci Phila (2nd series) 14:95-99. Hrdlička A. 1914. Physical Anthropology in America. An historical sketch. Am Anthropol 16:508-554. Hrdlička A. 1917. The genesis of the American Indians. Proc. 19th Intern. Cong. Amer. pp 559-568. Hrdlička A. 1918. Physical anthropology: Its scope and aims. Philadelphia: The Wistar Institute of Anatomy and Biology. Hrdlička A. 1920. Anthropometry. Philadelphia: The Wistar Institute of Anatomy and

266

Biology. Hrdlička A. 1924. Catalogue of human crania in the United States National Museum collections. The Eskimo, Alaska and related Indians, northeastern Asiatics. Proc US Nat Mus 63, article 12. Hrdlička A. 1926. The race and antiquity of the American Indian. Sci Am 135:7-9. Hrdlička A. 1927. Catalogue of human crania in the United States National Museum collections. The Algonkin and related Iroquois, Siouan, Caddoan, Salish and Sahaptin, Shoshonean, and Californian Indians. Proc US Nat Mus 64, article 5. Hrdlička A. 1928. Catalogue of human crania in the United States National Museum collections. Australians, Tasmanians, South African Bushmen, Hottentots and Negroes. Proc US Nat Mus 69, article 24. Hrdlička A. 1931a. Catalogue of human crania in the United States National Museum collections. Pueblos, southeastern Utah Basket-Makers, Navaho. Proc US Nat Mus 78, article 2. Hrdlička A. 1931b. The problems of the origin and antiquity of the American aborigines in the light of recent explorations; a synopsis of four lectures. Bull Wagner Free Inst Sci Phila 6:10-14. Hrdlička A. 1934. Normal variation. Proc Am Phil Soc 74:253-261. Hrdlička A. 1935a. Normal variation of teeth and jaws, and orthodonty. Intern J Orthod Dent Child 21:1099-1114. Hrdlička A. 1935b. The Pueblos: with comparable data on the builds of the tribes of the Southwest and Northern Mexico. Am J Phys Anthropol 20(3):235-460. Hrdlička A. 1937. Biographical memoir of George Sumner Huntington 1861-1827.

267

National Academy of Sciences of the United States Biographical Memoirs 18. 11th Memoir. Washington, DC: National Academy Press. Hrdlička A. 1939. A practical anthropometry. Philadelphia: Wistar Institute. Hrdlička A. 1940. Catalog of human crania in the United States National Museum Collections. Indians of the Gulf States. Proc US Nat Mus 87:315-364. Hrdlička A. 1941. Anthropological connections between America and Siberia. Sci 103:441. Hrdlička A. 1942. Catalog of human crania in the United States National Museum Collections. Eskimo in general. Proc US Nat Mus 91:189-429. Hrdlička A. 1944. Catalog of crania in the United States National Museum collections: Non-Eskimo peoples of the northwest coast and Siberia. Proc US Nat Mus 93:1177. Hunt DR. 2009. The Robert J. Terry Anatomical Skeletal Collection. http://anthropology.si.edu/cm/terry.htm. Hunt DR, Albanese J. 2005. History and demographic composition of the Robert J. Terry Anatomical Collection. Am J Phys Anthropol 127:406-417. Hunter, J. 1771. Treatise on the Natural History of the Human Teeth. Part 1. Coll. Works, Palmer Ed. 1837. 4:315-318. Huss-Ashmore R. 1981. Bone growth and remodeling as a measure of nutritional stress. In Martin DL, Bumsted MP (Eds.): Biocultural Adaptation: Comprehensive Approaches to Skeletal Analysis. Amherst, Massachusetts: Department of Anthropology, University of Massachusetts at Amherst. Research Reports 20:8495.

268

Huss-Ashmore R, Goodman AH, Armelagos GJ. 1982. Nutritional inference from paleopathology. In Schiffer MB (Ed.): Advances in Archaeological Method and Theory. New York: Academic Press. Huxley AK. 2005. Gestational age discrepancies die to acquisition artifact in the forensic fetal osteology collection at the Nation Museum of Natural History, Smithsonian Institution, USA. Am J For Med Path 26(3):216-220. Igarashi Y, Uesu K, Wakebe T, Kanazawa E. 2005. New method for estimation of adult skeletal age at death from the morphology of the auricular surface of the ilium. Am J Phys Anthropol. 128(2):324-39. İşcan MY. 1988. Rise of forensic anthropology. Yearbook Phys Anthropol 31:203-230. İşcan MY. 1989a. Assessment of age at death in the human skeleton. In İşcan MY (Ed.): Age Markers in the Human Skeleton. Springfield, IL: Charles C. Thomas. İşcan MY. 1989b. Research strategies in age estimation: the multiregional approach. In İşcan MY (Ed.): Age Markers in the Human Skeleton. Springfield, IL: Charles C. Thomas. İşcan MY, Loth SR. 1984. Determination of age from the sternal rib in white males. A test of the phase method. J For Sci 31:122-132. İşcan MY, Loth SR. 1985. The effects of antemortem pathology and substance abuse on age determination from the skeleton. Florida Sci 48(Suppl 1):7. Abstract. İşcan MY, Loth SR. 1986. Estimation of age and determination of sex from the sternal rib. In Reichs KJ: Forensic Osteology: Advances in the Identification of Human Remains. Springfield: Charles C. Thomas. p 68-89. İşcan MY, Loth SR. 1989. Osteological manifestations of age in the adult. In İşcan MY,

269

Kennedy KAR (Eds.): Reconstruction of life from the skeleton. New York: Alan Liss. İşcan MY, Loth SR, Scheuerman EH. 1985. Determination of age from the sternal rib in white females. A test of the phase method. J For Sci 31:990-999. İşcan MY, Loth SR, Wright RK. 1984. Age assessment from the rib by phase analysis: White males. J Forensic Sci 29:1094-1104. İşcan MY, Loth SR, Wright RK. 1985. Age assessment from the rib by phase analysis: White females. J Forensic Sci 30:853-863. İşcan MY, Loth SR, Wright RK. 1987. Racial variation in the sternal extremity of the rib and its effect on age determination. J Forensic Sci 32(2):452-466. Ito PK. 1942. Comparative biometrical study of physique of Japanese women born and reared under different environments. Hum Biol 14:279-351. Jackes M. 1985. Pubic symphysis age distributions. Am J Phys Anthropol 68:281-299. Jackes M. 1992. Paleodemography: problems and techniques. In Saunders SR, Katzenberg MA: Skeletal biology of past peoples: research methods. New York: Wiley-Liss. Jackes M. 2000. Building the bases for paleodemographic analysis: adult age determination. In Katzenberg MA, Saunders R (Eds.): Biological anthropology of the human skeleton. New York, NY: Wiley-Liss. Jacobi KP. 2002. A Time Capsule of Physical Anthropology: Charles E. Snow’s WPA Letters, 1940-1941. Southeastern Archaeology 21:55-62. Jantz LM, Jantz RL. 1999. Secular change in long bone length and proportion in the United States, 1800-1970. Am J Phys Anthropol 110:57-67.

270

Jantz RL. 2001. Cranial change in Americans: 1850-1975. J. Forensic Sci 46(4):784-787. Jantz RL, Hunt DR, Falsetti AB, Key PJ. 1992. Variation among North Amerindians: analysis of Boas's anthropometric data. Hum Biol 64:435-461. Jarcho S. 1966. Human Paleopathology. New Haven: Yale University Press. Jefferson T. 1853. Notes on the State of Virginia. Richmond, Virginia: J.W. Randolph. Johansen HC. 1998. Four early Danish parish registers. Research Report 6. Odense: Danish Center for Demographic Research. Johnston FE, Zimmer LO. 1989. Assessment of growth and age in the immature skeleton. In İşcan MY, Kennedy KAR (Eds.): Reconstruction of life from the skeleton. New York: Alan R. Liss. Jones HH, Priest JD, Hayes WC, Tichenor CC, Nagel DA. 1977. Humeral hypertrophy in response to exercise. J Bone Joint Surg 59(A):204-208. Jones J. 1869. The aboriginal mound builders of Tennessee. Am Naturalist 3:57-73. Jones J. 1876. Explorations of the Aboriginal Remains of Tennessee. Smithsonian Contributions to Knowledge Vol 22(259), Article II. Washington, DC: Smithsonian Institution. Jones J. 1878. Exploration and Researches Concerning the Destruction of the Aboriginal Inhabitants of America by Various Diseases, as Syphilis, Matlazahuatl, Pestilence, Malarial Fever, and Small-Pox. New Orleans Med Surg J 5:926-941. Jones PRM, Dean RFA. 1956. The effects of Kwashiorkor on the development of the bones of the hand. J Trop Pediatr 2:51. Jones-Kern KF.1997. T. Wingate Todd and the Development of Modern American

271

Physical Anthropology 1900-1940. Doctoral Dissertation: Bowling Green State University. Jones-Kern KF, Latimer B. 1996. Skeletons out of the Closet. Explorer 38(1-2):26-28. Joyce C, Stover E. 1991. Witnesses from the grave. Boston: Little, Brown. Kanisius PH, Luke DA. 1994. Is the complexity of the human sagittal suture related to the size of the temporal muscle? Int J Anthropol 9:265-272. ten Kate H. 1892. Somatological observations on Indians of the Southwest. J Am Ethnol Arch 3(1):19-44. ten Kate H, Hovens P. 1995. Ten Kate's Hemenway Expedition Diary, 1887-1888. Journal of the Southwest 37(4):635-700. part of A Hemenway Portfolio: Voices and Views from the Hemenway Archaeological Expedition, 1886-1889. Katz D, Suchey JM. 1986. Age determination of the male os pubis. Am J Phys Anthropol 69(4):427-435. Katz D, Suchey JM. 1989. Race differences in pubic symphyseal aging patterns in the male. Am J Phys Anthropol 80(2):167-172. Kemkes-Grottenthaler A. 1996. Sterbealterbestimmung anhand des ektocranialen Nahtverschlusses: Eine Evaluierung der Meindl-Lovejoy-Methode. Rechtsmedizin 6:177-184. Kemkes-Grottenthaler A. 2002. Aging through the ages: historical perspectives on age indicator methods. In Hoppa RD, Vaupel JW: Paleodemography: age distributions from skeletal samples. Cambridge: Cambridge University Press. Kennedy KAR. 1989. Skeletal markers of occupational stress. In İşcan MY and Kennedy KAR (Eds.): Reconstruction of life from the skeleton. New York: Wiley-Liss.

272

Kerley ER. 1962. The microscopic determination of age in human bone. Doctoral Dissertation. Ann Arbor, (MI): University of Michigan, 1962 Kerley ER. 1970. Estimation of skeletal age after about age 30. In Stewart TD: Personal Identification in Mass Disasters. Washington, DC: Smithsonian Institute. Kern KF. 2006. T. Wingate Todd: pioneer of modern American physical anthropology. Kirtlandia 55:1-42. Key CA, Aiello LC, Molleson T. 1994. Cranial suture closure and its implications for age estimation. Int J Osteoarch 4:193-207. Kidder AV. 1924. An Introduction to the Study of Southwestern Archaeology with a Preliminary Account of the Excavations at Pecos. New Haven, Yale University Press. Kim YK, Kho HS, Lee KH. 2000. Age estimation by occlusal tooth wear. J Forensic Sci 45(2):303-9. Kimmerle EH, Jantz RL. 2005. Secular trends in craniofacial asymmetry studied by geometric morphometry and generalized Procrustes methods. In Slice DE: Modern Morphometrics in Physical Anthropology. New York: Kluwer Academic/Plenum Publishers. Kimmerle EH, Konigsberg LW, Jantz RL, Baraybar JP. 2008a. Analysis of age-at-death estimation through the use of pubic symphyseal data. J Forensic Sci 53(3):558568. Kimmerle EH, Prince DA, Berg GE. 2008b. Inter-observer variation in methodologies involving the pubic symphysis, sternal ribs, and teeth. J Forensic Sci 53(3):594600.

273

Klepinger LL. 2001. Stature, maturation variation and secular trends in forensic anthropology. J Forensic Sci 46(4):788-790. Klepinger LL. 2006. Fundamentals of Forensic Anthropology. Hoboken, New Jersey: John Wiley & Sons, Inc. Klepinger LL, Katz D, Micozzi MS, Carroll L. 1992. Evaluation of cast methods for estimating age from the Os pubis. J Forensic Sci 37:763-770. Kobayashi K. 1967. Trends in human life based upon human skeletons from prehistoric to modern times in Japan. J Fac Sci Univ Tokyo. Sect 3:107-162. Komar DA, Grivas C. 2008. Manufactured populations: what do contemporary reference skeletal collections represent? A comparative study using the Maxwell Museum documented collection. Am J Phys Anthropol 137(2):224-33. Komar DA, Buikstra JE. 2008. Forensic Anthropology: Contemporary Theory and Practice. New York: Oxford University Press. Konigsberg LW, Frankenberg SR. 1992. Estimation of age structure in anthropological demography. Am J Phys Anthropol 89:235-256 Konigsberg LW, Frankenberg SR. 1994. Paleodemography: not quite dead. Evol Anth 3:92-105. Konigsberg LW, Frankenberg SR. 2002. Deconstructing death in paleodemography. Am J Phys Anthropol 117(4):297-309. Konigsberg LW, Frankenberg SR, Walker RB. 1997. Regress what on what: paleodemographic age estimation as a calibration problem. In Paine RR (Ed): Integrating archaeological demography: multidisciplinary approaches to prehistoric populations. Carbondale, Illinois: Southern Illinois University.

274

Konigsberg LW, Hens SM, Jantz LM, Jungers WL. 1998. Stature estimation and calibration: Bayesian and maximum likelihood perspectives in physical anthropology. Am J Phys Anthropol 107(S27):65-92. Konigsberg LW, Herrmann NP, Wescott DJ, Kimmerle EH. 2008. Estimation and evidence in forensic anthropology: age-at-death. J Forensic Sci 53(3):541-557. Krogman WM. 1939. A guide to the identification of human skeletal material. FBI Law Enf Bull 12(4):17-40. Krogman WM. 1962. The Human Skeleton in Forensic Medicine. Springfield, Illinois: Charles C. Thomas. Krogman WM. 1970. Physical anthropology and forensic medicine. In von Mering O, Kasdan L (Eds): Anthropology and the behavioral and health sciences. Pittsburgh, Pennsylvania: University of Pittsburgh Press. Krogman WM. 1976. Fifty Years of Physical Anthropology: The Men, the Material, the Concepts, the Methods. Ann Rev Anthropol 5:1-15. Krogman WM, İşcan MY. 1986. The human skeleton in forensic medicine. 2nd ed. Springfield, Illinois: Charles C. Thomas. Kunos C, Simpson S, Russell K, Hershkovitz I. 1999. First rib metamorphosis: its possible utility for human age-at-death estimation. Am J Phys Anthropol 110:303323. Laboratory of Human Osteology: Maxwell Museum of Anthropology. 2009. Documented skeletal collection. http://osteolab.unm.edu/coll_doc.html Lamb DS. 1917. The Army Medical Museum in American Anthropology. Proc XIX Internat Congress of Americanists.

275

Landis JR, Koch GG. 1977. The measurement of observer agreement for categorical data. Biometrics 33:159-174. Laugier H. 1955. Age biologique et âge chronologique. Proceedings of the World Population Conference, 1954. Vol. 3. United Nations. 773-740. Lear, WJ. 2002. William Montague Cobb Medical Professor, Civil Rights Activist. Am J Public Health. 92(2):193. Legoux P. 1966. Détermination de l’âge dentaire de quelques fossils de la ligne humaine. Révue Française d’Odonto-stomatologie 9:1165-1214,1317-1330 and 10:10311048. Lehmann-Hartleben K. 1943. Archaeological notes: Thomas Jefferson, archaeologist. Am J Archaeol 47(2): 161-163. Lengeyel I. 1968. Biochemical aspects of early skeletons. In Brothwell DR: The Skeletal Biology of Earlier Human Populations. Symposia Soc. Study Hum. Biol. VIII. Oxford & London. Leopold D, von Jagow G. 1960. Das Röntgenbild des Kehlkopes—eine Möglichkeit zu Altersbest immungen. Beiträge zur Gerichtlichen Medizin 21:181-190. Lin LI. 1989. A concordance correlation coefficient to evaluate reproducibility. Biometrics 45(1):255-268. Little MA, Buikstra JE, Spencer F. 1995. The records of biological anthropology. In Silverman S & Perezo NJ (Eds.): Preserving the Anthropological Record, Second Edition. New York: Wenner-Gren Foundation for Anthropological Research. Loerzel R. 2003. Alchemy of Bones: Chicago’s Luetgert Murder Case of 1897. Urbana, Illinois: University of Illinois Press.

276

Loth SR. 1988. Analysis of the Terry Collection Black ribs. Presented at the International Congress of Anthropological and Ethnological Sciences. Zagreb, Yugoslavia. Loth SR. 1995. Age assessment of the Spitalfields cemetery population by rib phaseanalysis. Am J Hum Biol 7:465-471. Loth SR, İşcan MY. 1989. Morphological assessment of age in the adult: the thoracic region. In İşcan MY (Ed.): Age markers in the human skeleton. Springfield, Illinois: Charles C. Thomas. Loth SR, İşcan MY. 1994. Morphological indicators of skeletal aging: Implications for paleodemography and paleogerontology. In Crews DE, Garruto RM: Biological anthropology and aging: Perspectives on human variation over the life span. Oxford: Oxford University Press. Loth SR, İşcan MY, Scheuerman EH. 1994. Intercostal variation in the sternal rib end. For Sci Int 65:135-143. Lovejoy CO. 1985. Dental wear in the Libben population: its functional pattern and role in the determination of adult skeletal age at death. Am J Phys Anthropol 68:4756. Lovejoy CO, Barton TJ. 1980. A simple, rapid method of obtaining geometrical properties from sections or laminograms of long bones. J Biomech 13:65-67. Lovejoy CO, Burstein AH. 1977. Geometrical properties of bone sections determined by laminography and physical section. J Biomech 10:527-528. Lovejoy CO, Meindl RS, Mensforth RP, Barton TJ. 1985a. Multifactorial age determination of skeletal age at death: a method and blind test of its accuracy. Am J Phys Anthropol 68:1-14.

277

Lovejoy CO, Meindl RS, Pryzbeck TR, Barton TS, Kotting D, Heiple KG. 1977. The palaeodemography of the Libben site, Ottawa County, Ohio. Science 198:291293. Lovejoy CO, Meindl RS, Pryzbeck TR, Mensforth RP. 1985b. Chronological metamorphosis of the auricular surface of the ilium: A new method for the determination of adult skeletal age at death. Am J Phys Anthropol 68:15-28. Lovejoy CO, Meindl RS, Tague RG, Latimer B. 1997. The comparative senescent biology of the hominoid pelvis and its implications for the use of age-at-death indicators in the human skeleton. In Paine R (Ed): Integrating anthropological demography: multidisciplinary approaches to prehistoric population. Carbondale, Illinois: Center for Archaeological Investigations. Malina RM, Zalaveta AN, Little BB. 1987. Secular changes in the stature and weight of Mexican American school children in Brownsville, Texas, between 1928-1983. Hum Biol 59:509-522. Mant AK. 1984. Taylor’s principles and practice of medical jurisprudence. 13th ed. Edinburgh: Churchill Livingstone. Maples WR. 1981. Fooling mother nature and the forensic anthropologist. Am Acad Forensic Sci Program. Maples WR. 1989. The practical application of age-estimation techniques. In İşcan MY (Ed.): Age markers in the human skeleton. Springfield, Illinois: Charles C. Thomas. Martrille L, Ubelaker DH, Cattaneo C, Seguret F, Tremblay M, Baccino E. 2007.

278

Comparison of four skeletal methods for the estimation of age at death on White and Black adults. J Forensic Sci 52(2):302-307. Masse G, Hunt EE Jr. 1963. Skeletal maturation of the hand and wrist in West African children. Hum Biol 35:3-25. Masset C. 1971. Erreurs systématiques dans la détermination de l'âge par les sutures Crâniennes. English summary. Bulletins et Mémoires de la Société d'anthropologie de Paris, No. 1:85-105. Masset C. 1989. Age estimation on the basis of cranial sutures. In İşcan MY (Ed.): Age Markers in the Human Skeleton. Springfield, IL: Charles C. Thomas. Masset C. 1993. Encore l’age des Adultes. Bull et Mem de la Societe d’Anthropologie de Paris 5:217-224. Masset C, de Castro e Almeida ME. 1990. Âge et sutures crâniennes. Catania, Italy: Proceedings of the Mediterranean Academy of Sciences. pg 277. Matthews W. 1900. The cities of the dead. Land of the Sunshine 12: 213-21. Matthews W, Wortman JL, Billings JS. 1893. Human Bones of the Hemenway Collection in the United States Army Medical Museum. Memoirs of the National Academy of Sciences 6:139-286. McCarthy P. 1994. American headhunters: ghoulish war souvenirs turn up in living rooms and landfills. Omni 16:14. McKern TW, Stewart TD. 1957. Skeletal age changes in young American males. Analysed from the standpoint of age identification. Environmental Protection Research Division Technical Report EP-45. Natick, Massachusetts: Quartermaster Research and Development Center.

279

Meindl RS, Lovejoy CO. 1985. Ectocranial suture closure: a revised method for the determination of skeletal age at death based on the lateral-anterior sutures. Am J Phys Anthropol 68(1):57-66. Meindl RS, Lovejoy CO. 1989. Age changes in the pelvis: implications for paleodemography. In İşcan MY (Ed.): Age markers in the human skeleton. Springfield, Illinois: Charles C. Thomas. Meindl RS, Lovejoy CO, Mensforth RP. 1983. Skeletal age at death: accuracy of determination and implications for human demography. Hum Biol 55(1):73-87. Meindl RS, Lovejoy CO, Mensforth RP, Walker RA. 1985. A revised method of age determination using the os pubis, with a review and tests of accuracy and other current methods of pubic symphyseal aging. Am J Phys Anthropol 68:29-45. Meindl RS, Russell KF. 1998. Recent advances in method and theory in paleodemography. Ann Rev Anthropol 27:375-399. Meindl RS, Russell KF, Lovejoy CO. 1990. Reliability of age at death in the HamannTodd Collection: validity of subselection procedures used in blind test of the summary age technique. Am J Phys Anthropol 83:349-357. Mensforth RP, Lovejoy CO. 1985. Anatomical, physiological, and epidemiological correlates of the aging process: a confirmation of multifactorial age determination in the Libben skeletal population. Am J Phys Anthropol 68:87-106. Mensforth RP. 1990. Paleodemography of the Carlston Annis (15Bt5): a late Archaic skeletal population. Am J Phys Anthropol 82:81-99. Merbs CF. 1980. Catalogue of the Hrdlička Paleopathology Collection. Tyson RA, Dyer Alcauskas ES (Eds). San Diego: San Diego Museum of Man.

280

Merbs CF. 2002. Washington Matthews and the Hemenway Expedition of 1887-88. Journal of the Southwest 44(3):303-335. Miles AEW. 1963. Dentition in the estimation of age. J Dent Res 42:255-263. Milner GR, Jacobi KP. 2006. A New Deal for Human Osteology. In Buikstra JE, Beck LA (Eds.): Bioarchaeology: The Contextual Analysis of Human Remains. Amsterdam: Academic Press. Milner GR, Wood J, Boldsen J. 2000. Paleodemography. In Katzenberg MA, Saunders R (Eds.): Biological anthropology of the human skeleton. New York, NY: WileyLiss. Molleson TI, Cox M (Eds). 1993. The Spitalfields project. The middling sort. Volume 2– The Anthropology. York: Council for British Archaeology. Research Report 86. Molleson TI. 1995. Rates of ageing in the eighteenth century. In Saunders SR, Herring A (Eds.): Grave reflections: portraying the past through cemetery studies. Toronto: Canadian Scholars’ Press Inc. Moore-Jansen PH. 1989. Multivariate craniometric analysis of secular change and variation among recent North American populations. Ph.D. Dissertation, the University of Tennessee. Moore-Jansen PH, Jantz RL. 1986. A computerized skeletal data bank for forensic anthropology. Knoxville, TN: Department of Anthropology, University of Tennessee. Moore-Jansen PH, Ousley SD, Jantz RL. 1994. Data collection procedures for forensic skeletal material: report of investigations. No. 48. Knoxville, TN: University of Tennessee Department of Anthropology.

281

Moorees CFA, Fanning EA, Hunt EE. 1963a. Formation and resorption of three deciduous teeth in children. Am J Phys Anthropol 21:205-213. Moorees CFA, Fanning EA, Hunt EE. 1963b. Age formation by stages for ten permanent teeth. J Dent Res 42:1490-1502. Morris AG. 2007. Documentation: history and the sources of skeletons in collections. In Cassman V, Odegaard, Powell J (Eds.): Human remains: Guide for museums and academic institutions. Oxford: AltaMira Press. Morton SG. 1839. Crania Americana; or, a Comparative View of the Skulls of Various Aboriginal Nations of North and South America. Philadelphia: J. Dobson. Morton SG. 1849. Catalogue of Skulls of Man and the Inferior Animals, in the Collection of Samuel George Morton. Philadelphia: Merrihew & Thompson, Partners. Mulhern DM, Jones EB. 2005. Test of revised method of age estimation from the auricular surface of the ilium. Am J Phys Anthropol 126(1):61-5. Murray KA, Murray T. 1991. A test of the auricular surface aging technique. J Forensic Sci 36(4):1162-1169. Nagar Y, Hershkovitz I. 2004. Interrelationship between various aging methods, and their relevance to palaeodemography. Hum Evol 19:145-156. Nawrocki SP. 1998. Regression formulae for estimating age at death from cranial suture closure. In Reichs KJ: Forensic Osteology: Advances in the Identification of Human Remains, 2nd Ed. Springfield, IL: Charles C. Thomas. Nemeskéri J, Harsányi L, Acsádi G. 1960. Methoden zur diagnose des lebensalters von skelettfunden. Anth Anzeiger 24:70-95. Nickerson CAE. 1997. A note on "A concordance correlation coefficient to evaluate

282

Reproducibility." Biometrics 53(4):1503-1507. Oettlé AC, Steyn M. 2000. Age estimation from sternal ends of ribs by phase analysis in South African Blacks. J Forensic Sci 45(5):1071-1079. Olsen SL, Shipman P 1994. Cutmarks and perimortem treatment of skeletal remains on the Northern Plains. In Owsley DW, Jantz RL (Eds.): Skeletal Biology in the Great Plains: Migration, Warfare, Health, and Subsistence. Washington, DC: Smithsonian Institution Press. Orosz JJ. 1990. Curators and Culture: The Museum Movement in America, 1740-1870. Tuscaloosa: University of Alabama Press. Ortner DJ. 2003. Identification of pathological conditions in human skeletal remains. 2nd Ed. San Diego: Academic Press. Osborne DL, Simmons TL, Nawrocki SP. 2004. Reconsidering the auricular surface as an indicator of age at death. J Forensic Sci 49(5):905-911. Osborne J, Waters E. 2002. Four assumptions of multiple regression that researchers should always test. Practical Assessment, Research & Evaluation, 8(2). http://PAREonline.net/getvn.asp?v=8&n=2 Otis GA, Woodward JJ. 1865. Reports on the Extent and Nature of the Materials Available for the Preparation of a Medical and Surgical History of the Rebellion. Philadelphia: Printed for the Surgeon General's Office by J.B. Lippincott & Co. Overbury RS, Cabo LL, Dirkmaat DC, Symes SA. 2009. Asymmetry of the os pubis: implications for the Suchey-Brooks method. Am J Phys Anthropol 139(2):261268. Owsley DW, Mann RW, Baugh TG. 1994. Culturally modified human bones from the

283

Edwards Site. In Owsley DW, Jantz RL (Eds.): Skeletal Biology in the Great Plains: Migration, Warfare, Health, and Subsistence. Washington, DC: Smithsonian Institution Press. Paine RR, Harpending HC. 1998. Effect of sample bias on paleodemographic fertility estimates. Am J Phys Anthropol 105:231-240. Pal GP, Tamankar BP. 1983. Determination of age from the pubic symphysis. Indian J Med Res 99:694-701. Parker WT. 1883. Concerning arrow wounds. Phila Med Times 14:127-129. Peck MN, Lundberg O. 1995. Short stature as an effect of economic and social conditions in childhood. Soc Sci Med 41(5):733-738. Perizonius WRK. 1984. Closing and non-closing in 256 crania of known age and sex from Amsterdam (A.D. 1883-1909). J Hum Evol 13:201-213. Persson M, Magnusson BC, Thilander B. 1978. Sutural closure in rabbit and man: a morphological and histochemical study. J Anat 125:313-321. Peters KD, Kochanek KD, Murphy SL. 1998. Deaths: final data for 1996. National Vital Statistics Reports 47:9. Plato CC, Fox KM, Tobin JD. 1994. Skeletal changes in human aging. In Crews DE, Garruto RM: Biological anthropology and aging: perspectives on human variation over the life span. New York: Oxford University Press. Pommerol F. 1869. Sur la synostose des os du crane, consideree an point de vue normal et pathologique chez les differents races humaines. Thèse. Paris. Potter WE. 2009. Assessment of Secular Change in Osteological Aging Methods. Am J Phys Anthropol Suppl 48:214.

284

Powell ML, Cook DC, Bogdan G, Buikstra JE, Castro MM, Horne PD, Hunt DR, Koritzer RT, Souza SFM, Sandford MK, Saunders L, Sene GAM, Sullivan L, Swetnam JJ. 2006. Invisible hands: women in bioarchaeology. In Buikstra JE, Beck LA (Eds.): Bioarchaeology: The Contextual Analysis of Human Remains. Amsterdam: Academic Press. Pratt HS. 1928. Harris Hawthorne Wilder. Science 67:479-481. Prince DA. 2004. Estimation of adult skeletal age-at-death from dental root translucency. Ph.D. Dissertation, The University of Tennessee. Putschar WG. 1976. The structure of the human symphysis pubis with special consideration of parturition and its sequelae. Am J Phys Anthropol 45:589-594. Quigley C. 2001. Skulls and skeletons: human bone collections and accumulations. Jefferson, NC: McFarland & Company, Inc., Publishers. R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org. Rakita GFM. 2006 Hemenway, Hrdlička, and Hawikku: A Historical Perspective on Bioarchaeological Research in the American Southwest. In Buikstra JE, Beck LA (Eds.): Bioarchaeology: The Contextual Analysis of Human Remains. Amsterdam: Academic Press. Rankin-Hill LM, Blakey ML. 1994. W. Montague Cobb (1904-1990): Physical Anthropologist, Anatomist, and Activist. Am Anthropol 96(1):74-96. Redfield A. 1970. A new aid to aging immature skeletons: development of the occipital bone. Am J Phys Anthropol 33:217-220.

285

Rees R, Komlos J, Long N, Woitek U. 2003. Optimal food allocation in a slave economy. J Popul Econ 16:21–36. Ribbe FC. 1885. L’ordre d’oblitération des sutures du crane dans les races humaines. Thèse, Paris. Ritz S, Turzynski A, Schütz HW. 1994. Estimation of age at death based on aspartic acid racemization in noncollagenous bone proteins. Forensic Sci Int 69(2):149-59. Ritz-Timme S, Cattaneo C, Collins MJ, Waite ER, Schütz HW, Kaatsch HJ, Borrman HI. 2000. Age estimation: the state of the art in relation to the specific demands of forensic practise. Int J Legal Med 113(3):129-36. Rogers T. 1990. A test of the auricular surface method of estimating age-at-death and a discussion of its usefulness in the construction of paleodemographic lifetables. Presented at the 18th Annual Meeting of the Canadian Association of Physical Anthropologists. Banff, Alberta. Rose MR. 1991. Evolutionary biology of aging. New York: Oxford University Press. Rösing FW, Graw M, Marré B, Ritz-Timme S, Rothschild MA, Rötzscher K, Schmeling A, Schröder I, Geserick G. 2007. Recommendations for the forensic diagnosis of sex and age from skeletons. Homo 58(1):75-89. Ruff CB. 1981. A reassessment of demographic estimates for Pecos Pueblo. Am J Phys Anthropol 54:147-151. Russell KF, Simpson SW, Genovese J, Kinkel MD, Meindl RS, Lovejoy CO. 1993. Independent test of the fourth rib aging technique. Am J Phys Anthropol 92(1):53-62. Sakaue K. 2006. Application of the Suchey-Brooks system of pubic age estimation to

286

recent Japanese skeletal material. Anth Sci 114:59-64. Samworth R, Gowland R. 2007. Estimation of adult skeletal age-at-death: statistical assumptions and applications. Int J Osteoarch 17:174-188. Sanders CF. Sexing by costal cartilage calcification. Br J Radiol 39:233-234. Santos AL. 1996. How old is this pelvis? A comparison of age at death estimation using the auricular surface of the ilium and os pubis. In Pwiti G, Soper R (Eds.): Aspects of African Archaeology. Papers from the 10th Congress of the PanAfrican Association for Prehistory and Related Studies. Harare, Zimbabwe: University of Zimbabwe. SAS Institute Inc., SAS 9.1 Cary, NC: SAS Institute Inc., 2002-2003. Sashin D. 1930. A critical analysis of the anatomy and pathologic changes of the sacroiliac joints. J Bone Joint Surg 12:891-910. Sattenspiel L, Harpending H. 1983. Stable populations and skeletal age. Am Antiq 48:489-498) Saunders SR. 1989. Nonmetric skeletal variation. In İşcan MY and Kennedy KAR (Eds.): Reconstruction of life from the skeleton. New York: Wiley-Liss. Saunders SR, Fitzgerald C, Rogers T, Dudar C, McKillop H. 1992. A test of several methods of skeletal age estimation using a documented archaeological sample. Can Soc Forensic Sci J 25(2):97-118. Saville PD. 1965. Changes in bone mass with age and alcoholism. J Bone Joint Surg 47A(3):492-499. Savitt TL. 2002. Medicine and slavery: the diseases and health care of Blacks in antebellum Virginia. Urbana, IL: University of Illinois Press.

287

Schell LM, Johnston FE, Smith DR, Paolone AM. 1985. Directional asymmetry of body dimensions among White adolescents. Am J Phys Anthropol 67:317-322. Scheuer L, Black S. 2000. Developmental juvenile osteology. London: Academic Press. Schick EA (Ed.). 1997. Clyde Collins Snow. Current Biography Yearbook. New York: H.W. Wilson. Schmitt A. 2004. Age-at-death assessment using the os pubis and the auricular surface of the ilium: a test on an identified Asian sample. Int J Osteoarchaeol 14:1-6. Schmitt A, Murail P, Cunha E, Rougé. 2002. Variability of the pattern of aging on the human skeleton: evidence from bone indicators and implications on age at death estimation. J Forensic Sci 47(6):1203-1209. Schranz D. 1959. Age determination from the internal structure of the humerus. Am J Phys Anthropol 17:273-278. Schultz AH. 1945. Biographical Memoir of Aleš Hrdlička 1869-1943. Biogr Mem Natl Acad Sci 23:305-338. Schwartz JH. 1998. What the Bones Tell Us. Tucson: University of Arizona Press. Sharma G, Gargi J, Kalsey G, Singh D, Rai H, Sandhu R. 2008. Determination of age from pubic symphysis: an autopsy study. Med Sci Law 48(2):163-169. Shrestha LB. 2006. Life Expectancy in the United States. CRS Report for Congress. http://aging.senate.gov/crs/aging1.pdf Silverberg R. 1968. Mound Builders of Ancient America: The Archaeology of a Myth. Greenwich, CT: New York Graphic Society. Simonin C. 1948. Identification des corps des soldats Américans inconnus. Acta Med Leg Soc (Liège) 1:382-386.

288

Singer R. 1953. Estimation of age from cranial suture closure. J For Med 1:52-59. Sinha A, Gupta V. 1995. A study on estimation of age from pubic symphysis. Forensic Sci Int 75:73-78. Sirohiwal BL, Singh RK, Paliwal PK, Yadav DR. 1998. Evaluation of McKern and Stewart’s and Gilbert and McKern’s technique for aging the Indian female os pubis. J Ind Acad Forensic Med 20(1):20-23. Skytthe A, Boldsen JL. 1993. A method for construction of standards for determination of skeletal age at death. Am J Phys Anthropol 16 (suppl.):182. Sledzick PS, Barbian L. 2001. From Privates to Presidents: Past and Present Memoirs from the Anatomical Collections of the National Museum of Health and Medicine. In Williams E: Human Remains: Conservation, Retrieval, and Analysis. Proceedings of a Conference Held in Williamsburg, VA, Nov. 7-11th 1999. Oxford: Archaeopress. Sledzik PS, Ousley S. 1991. Analysis of six Vietnamese trophy skulls. J Forensic Sci 6:520-530. Smithsonian Institution. 2009. Written in bone: research collections. http://anthropology.si.edu/writteninbone/skeletal_research_collections.html Snow CC. 1982. Forensic anthropology. Ann Rev Anthropol 34:97-131. Snow CE. 1948. The identification of the unknown war dead. Am J Phys Anthropol 6:323-328. Spencer F. 1983. Samuel George Morton's doctoral thesis on bodily pain: the probable source of Morton's polygenism. Trans Stud Coll Physicians Phila 5:321-328. Spirduso WW. 1995. Physical dimensions of aging. Champaign, IL: Human Kinetics.

289

Stanton WR. 1960. The Leopard’s Spots: Scientific Attitudes toward Race in America 1815-59. Chicago: University of Chicago Press. Steckel R. 1992. Work, disease and diet in the health and mortality of American slaves. In Fogel RW, Engerman SL (Eds.): Without consent or contract: the rise and fall of American slavery, conditions of slave life and the transition to freedom: technical papers. New York: W.W. Norton. Steele GD, Bramblett CA. 1988. The anatomy and biology of the human skeleton. College Station, Texas: Texas A&M University Press. Steinbock RT. 1976. Paleopathological diagnosis and interpretation. Springfield, Illinois: Charles C. Thomas. Stevenson PH. 1924. Age order of epiphyseal union in man. Am J Phys Anthropol 7:5393. Stevenson PH. 1929. On racial differences in stature long bone regression formulae for the Chinese. Biometrika 21:303-318. Stewart TD. 1930.Anthropology and Dental Caries [Abstract]. Am J Phys Anthropol 14(1):89. Stewart TD. 1931. Incidence of Separate Neural Arch in the Lumbar Vertebrae of Eskimos. Am J Phys Anthropol 16(1):51-62. Stewart TD. 1940. The Life and Writings of Dr. Aleš Hrdlička (1869-1939). Am J Phys Anthropol 26(1):3-40. Stewart TD. 1953. Research in human identification. Science 118:3. Stewart TD. 1957. Distortion of the pubic symphyseal face in females and its effect on age determination. Am J Phys Anthropol 15:9-18.

290

Stewart TD. 1979. Essentials of forensic anthropology. Springfield, Illinois: Charles C. Thomas. Stewart TD. 1980. Responses of the skeleton to changes in the quality of life. J Forensic Sci 25:912-921. Stini WA. 1994. Nutrition and aging: intraindividual variation. In Crews DE, Garruto RM: Biological anthropology and aging: perspectives on human variation over the life span. New York: Oxford University Press. Storey R. 2007. An elusive paleodemography? A comparison of two methods for estimating the adult age distribution of deaths at late classic Copan, Honduras. Am J Phys Anthropol 132(1):40-47. Stout SD, Paine RR. 1992. Brief communication: histological age estimation using rib and clavicle. Am J Phys Anthropol 87:111-116. Stover E. 1997. The Grave at Vukovar. Smithsonian 27(12):40-51 Suchey JM. 1979. Problems in the aging of females using the os pubis. Am J Phys Anthropol 51(3):467-470. Suchey JM, Brooks ST, Katz D. 1988. Instructions for the Use of the Suchey-Brooks System for Age Determination of the Female Os Pubis. Instructions Material Accompanying the Female the Female Pubic Symphyseal Models of the SucheyBrooks System. Distributed by Diane France Casting, Bellevue, Colorado. Suchey JM, Katz D. 1986. Skeletal age standards derived from an extensive multiracial sample of modern Americans. Paper presented at the 55th Annual Meeting of the American Association of Physical Anthropologists, Albuquerque, New Mexico. Suchey JM, Katz D. 1998. Application of pubic age determination in a forensic setting. In

291

Reichs K (Ed.): Forensic osteology: advances in the identification of human remains. Springfield, Illinois: Charles C. Thomas. Suchey JM, Owings PA, Wiseley DV, Noguchi TT. 1984. Skeletal aging of unidentified persons. In Rathbun TA, Buikstra JE (Eds): Human identification: case studies in forensic anthropology. Springfield, Illinois: Charles C. Thomas. Suchey JM, Wiseley DV, Katz D. 1986. Evaluation of the Todd and McKern-Stewart methods for age the male Os-Pubis. In Reichs KJ (Ed.): Forensic osteology: advances in the identification of human remains. pp. 33-67. Springfield, IL: C.C. Thomas. Suchey JM, Wiseley DV, Green RF, Noguchi TT. 1979. Analysis of dorsal pitting in the os pubis in an extensive sample of modern American females. Am J Phys Anthropol 51(4):517-40. Tanner JM. 1962. Growth at adolescence. Oxford: Blackwell. Taylor KM. 2000. The effects of alcohol and drug abuse on the sternal end of the fourth rib. PhD Dissertation. Department of Anthropology, University of Arizona. Telmon N, Gaston A, Chemla P, Blanc A, Joffre F, Rougé D. 2005. Application of the Suchey-Brooks method to three-dimensional imaging of the pubic symphysis. J Forensic Sci 50(3):507-512. Thompson DD. 1979. The core technique in the determination of age at death in skeletons. J Forensic Sci 24:902-915. Thompson DD. 1982. Forensic anthropology. In Spencer F: A history of American Physical Anthropology, 1930-1980. New York: Academic Press. Thomas DH. 2000. Skull Wars: Kennewick Man, Archaeology, and the Battle for Native

292

American Identity. New York: Basic Books. Tobias PV. 1991. On the scientific, medical, dental, and educational value of collections of human skeletons. Int J Anthropol 6(3); 277-280. Todd TW. 1920. Age changes in the pubic bone: I. The White male pubis. Am J Phys Anthropol 3:467-470. Todd TW. 1921. Age changes in the pubic bone: II-IV. Am J Phys Anthropol 4:1-70. Todd TW. 1927. Skeletal records of mortality. Sci Monthly 24(6):481-496. Todd TW, Lyon DW. 1924. Endocranial suture closure: its progress and age relationship. Part I. Adult males of white stock. Am J Phys Anthropol 7:325-384. Todd TW, Lyon DW. 1925a. Cranial suture closure: its progress and age relationship. Part II. Ectocranial suture closure in adult males of white stock. Am J Phys Anthropol 8:23-45. Todd TW, Lyon DW. 1925b. Cranial suture closure: its progress and age relationship. Part III. Endocranial suture closure in adult males of Negro stock. Am J Phys Anthropol 8:46-71. Todd TW, Lyon DW. 1925c. Cranial suture closure: its progress and age relationship. Part IV. Ectocranial suture closure in adult males of Negro stock. Am J Phys Anthropol 8:149-168. Topinard P. 1885. Élements d’Anthropologie générale. Paris: A. Delahaye et É. Lecrosnier. Trotter M. 1937. Accessory sacro-iliac articulations. Am J Phys Anthropol 22:247-255. Trotter M. 1981. Robert J. Terry, 1871-1966. Am J Phys Anthropol 56:503-508. Trotter M, Gleser GC. 1952. Estimation of stature from long bones of American whites

293

and Negroes. Am J Phys Anthropol 10:463-514. Ubelaker DH. 1979. Skeletal evidence for kneeling in prehistoric Ecuador. Am J Phys Anthropol 51:679-686. Ubelaker DH. 1987. Estimating age at death from immature human skeletons: an overview. J Forensic Sci 32:1254-1263. Ubelaker DH. 1989a. Human skeletal remains. 2nd ed. Washington, DC: Taraxacum Press. Ubelaker DH. 1989b. The estimation of age at death from immature human bone. In İşcan MY (Ed.): Age Markers in the Human Skeleton. Springfield, IL: Charles C. Thomas. Ubelaker DH. 2000. T. Dale Stewart's perspective on his career as a forensic anthropologist at the Smithsonian. J Forensic Sci 45(2):269-78. Ubelaker DH. 2001. Contributions of Ellis R. Kerley to forensic anthropology. J Forensic Sci 46(4):773-776. Ubelaker DH. 2006a. The Changing Role of Skeletal Biology at the Smithsonian. In Buikstra JE, Beck LA. Bioarchaeology: the Contextual Analysis of Human Remains. Amsterdam: Academic Press. Ubelaker DH. 2006b. Thomas Dale Stewart 1901-1997. Biographical Memoirs of the National Academy of Sciences 88:1-16. Ubelaker DH, Grant LG. 1989. Human skeletal remains: preservation or reburial? Yearbook of Physical Anthropology 32:249-287. Ubelaker DH, Hunt DR. 1995. The influence of William M. Bass III on the development of American forensic anthropology. J Forensic Sci 40(5):729-734.

294

University of Tennessee: Forensic Anthropology Center. 2005. Collections and research. http://web.utk.edu/~fac/facilities.shtml. UNM Maxwell Museum of Anthropology 2001-2010. History and Mission of the Museum. http://www.unm.edu/~maxwell/. Usher BM. 2002. Reference samples: the first step in linking biology and age in the human skeleton. In Hoppa RD and Vaupel JW: Paleodemography: age distributions from skeletal samples. Cambridge: Cambridge University Press. Vandervael F. 1952. Critères d’estimation de l’âge des squelettes entre 18 et 38 ans. Bull du Comité Intern pour la Standardisation Anthropol Synthetique (Bologna) 2526:67-82. Vandervael F. 1953. L’identification anthropologique des mort inconnus de al guerre dans l’armée américaine. Rev Méd Liége 8:617-621. Van Gerven DP, Armelagos GJ. 1983. “Farewell to Paleodemography?” Rumors of its death have been greatly exaggerated. J Hum Evol 12:353-360. Vaupel JW, Carey JR, Christensen K, Johnson TE, Yashin Ai, Holm NV, Iachine IA, Kannisto V, Khazaeli AA, Liedo P, Longo VD, Zeng Y, Manton KG, Curtsinger JW. 1998. Biodemographic trajectories of longevity. Science 280:855-860. Waite ER, Collins MJ, Ritz-Timme S, Schutz HW, Cattaneo C, Borrman HIM. 1999. A review of the methodological aspects of aspartic acid racemization analysis for use in forensic science. For Sci Int 103:113–124. Waldron T. 1987. The relative survival of the human skeleton: implications for paleodemography. In Boddington A, Garland AN, Janaway RC (Eds.): Death, Decay, and Reconstruction. Manchester: Manchester University Press.

295

Walker PL. 2000. Bioarchaeological ethics: a historical perspective on the value of human remains. In Katzenberg MA, Saunders SR (Eds): Biological Anthropology of the Human Skeleton. New York: Wiley-Liss, Inc. Walker RA, Lovejoy CO. 1985. Radiographic changes in the clavicle and proximal femur and their use in the determination of skeletal age at death. Am J Phys Anthropol 68:67-78. Warren JC. 1822. A Comparative View of the Sensorial and Nervous Systems in Man and Animals. Boston: Ingraham. Warren JC. 1837. North American antiquities. On some crania found in the Ancient Mounds in North America. Am J Sci Arts 34:47-49. Warren J. 1911. Thomas Dwight, M.D., LL.D. Anat Rec 5:431-439. Washington University School of Medicine, St. Louis, Missouri. 2004-2009. Missouri women in the health sciences. Bernard Becker Medical Library. http://beckerexhibits.wustl.edu/mowihsp/bios/trotter.htm Webb PA, Suchey JM. 1985. Epiphyseal union of the anterior iliac crest and medial clavicle in a modern multiracial sample of American males and females. Am J Phys Anthropol 68(4):457-466. Weiss KM. 1973. Demographic models for anthropology. Mem Soc Am Archaeol. no. 27. White TD. 1991. Human Osteology. San Diego: Academic Press. White TD, Toth N. 1991. The question of ritual cannibalism at Grotta Guattari. Curr Anthropol 32(21):118. Wigmore JH. 1898. The Luetgert case. Am Law Rev 32:187-207.

296

Wilder HH, Wentworth B. 1918. Personal Identification: Methods for the Identification of Individuals, Living or Dead. Boston: Gorham. Willey GR, Sabloff JA. 1980. A History of American Archaeology. 2nd Ed. San Francisco: W.H. Freeman. Willey P, Emerson TE. 1993. The osteology and archaeology of the Crow Creek massacre. Plains Anthropol 38:227-269. Williams DR. 1996. Race/ethnicity and socioeconomic status: measurement and methodological issues. Int J Health Serv 26(3):483-505. Williams HU. 1932. The Origin and Antiquity of Syphilis. The Evidence from Diseased Bones. Arch Pathol 13:779-814, 931-983. Wilson D. 1876. Prehistoric Man: Researches into the Origin of Civilization in the Old and the New World. 3rd ed. London: Macmillan. Wilson RJ, Algee-Hewitt B, Jantz LM. 2007. Demographic trends within the Forensic Anthropology Center’s body donation program. Am J Phys Anthropol suppl 44:252. Abstract. Wilson T. 1901. Arrow wounds. Am Anthropol New Series. 3:513-531. Wingerd J, Peritz E, Sproul A. 1974. Race and stature in the skeletal maturation of the hand and wrist. Ann Hum Biol 1:201-210. Wissler C. 1942-1943. The American Indian and the American Philosophical Society. Proc Am Phil Soc 86:189-204. Wittwer-Backofen U, Buckberry J, Czarnetzki A, Doppler S, Grupe G, Hotz G, Kemkes

297

A, Larsen CS, Prince D, Wahl J, Fabig A, Weise S. 2008. Basics in paleodemography: a comparison of age indicators applied to the early medieval skeletal sample of Lauchheim. Am J Phys Anthropol 137:384-396. Wolf DJD. 1981. The effects of long-term drug abuse upon skeletal aging criteria among adult, Caucasian males. Am Acad Forensic Sci Program. Wood JW, Holman DJ, Weiss KM, Buchanan AV, Lefor B. 1992. Hazards models for human population biology. Yearb Phys Anthropol. 35:43-87. Wright LE, Yoder CJ. 2003. Recent progress in bioarchaeology: approaches to the osteological paradox. J Archaeol Res 11(1):43-70. Wyman J. 1868. Report of the Curator. Reports of the Peabody Museum of American Archaeology and Ethnology 1 (Second Annual Report of the Trustees):5-18. Wyman J. 1869. Report of the Curator. Reports of the Peabody Museum of American Archaeology and Ethnology 1 (Second Annual Report of the Trustees):5-20. Wyman J. 1871. Report of the Curator. Fourth Annual Report of the Trustees of the Peabody Museum of American Archaeology and Ethnology. Boston: Kingman. Yavuz MF, İşcan MY, Cöloğlu AS. 1988. Age assessment by rib phase analysis in Turks. Forensic Sci Int 98(1-2):47-54. Yoder C, Ubelaker DH, Powell JF. 2001. Examination of variation in sternal rib end morphology relevant to age assessment. J Forensic Sci 46(2):223-227. Zivanović S. 1983. A note on the effect of asymmetry in suture closure in mature human skulls. Am J Phys Anthropol 60(4):431-435.

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Appendices Appendix A: Spearman’s correlations Appendix B: Plots of documented age by phase Appendix C: Plots of the difference between observed and expected phases by year of birth Appendix D: Plots of the difference between observed and expected phases by skeletal series Appendix E: Plots of the difference between the observed and expected phase by 10-year birth cohort Appendix F: Regression output for identification of the best descriptive-variable predictors of the difference between observed and expected phases Appendix G: Descriptive data for phases by group, sex, and race Appendix H: Age at transition data by group Appendix I: Graphs of the number of individuals per observed phase, by Reference and Recent groups Appendix J: Age at transition data by sex Appendix K: Age at transition data by race Appendix L: Age at transition data by sex-race category

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Appendix A: Spearman’s correlations Spearman Correlation Coefficients / Prob > |r| under H0: Rho=0 / Number of Observations age

T_PS

SB_PS

HF_PS

TA_SR

TA_ST

TA_SA

TA_VSM

TA_DSM

age

1.00000 971

0.69694 <.0001 919

0.70321 <.0001 919

0.69973 <.0001 919

0.37807 <.0001 925

0.20500 <.0001 911

0.60490 <.0001 867

0.66311 <.0001 914

0.62782 <.0001 917

T_PS

0.69694 <.0001 919

1.00000

0.93687 <.0001 918

0.89855 <.0001 918

0.47135 <.0001 912

0.23558 <.0001 902

0.77170 <.0001 862

0.91075 <.0001 912

0.86080 <.0001 910

SB_PS

0.70321 <.0001 919

0.93687 <.0001 918

1.00000

0.95188 <.0001 919

0.47711 <.0001 912

0.26177 <.0001 902

0.75482 <.0001 862

0.90822 <.0001 912

0.87111 <.0001 910

HF_PS

0.69973 <.0001 919

0.89855 <.0001 918

0.95188 <.0001 919

1.00000

0.51019 <.0001 912

0.30229 <.0001 902

0.74438 <.0001 862

0.87700 <.0001 912

0.84125 <.0001 910

TA_SR

0.37807 <.0001 925

0.47135 <.0001 912

0.47711 <.0001 912

0.51019 <.0001 912

1.00000

0.27226 <.0001 911

0.46964 <.0001 860

0.48947 <.0001 906

0.48566 <.0001 913

0.20500 <.0001 911

0.23558 <.0001 902

0.26177 <.0001 902

0.30229 <.0001 902

0.27226 <.0001 911

1.00000

0.20723 <.0001 849

0.21731 <.0001 898

0.24681 <.0001 904

0.60490 <.0001 867

0.77170 <.0001 862

0.75482 <.0001 862

0.74438 <.0001 862

0.46964 <.0001 860

0.20723 <.0001 849

1.00000

0.77515 <.0001 858

0.72987 <.0001 857

0.66311 <.0001 914

0.91075 <.0001 912

0.90822 <.0001 912

0.87700 <.0001 912

0.48947 <.0001 906

0.21731 <.0001 898

0.77515 <.0001 858

1.00000

0.83575 <.0001 905

0.62782 <.0001 917

0.86080 <.0001 910

0.87111 <.0001 910

0.84125 <.0001 910

0.48566 <.0001 913

0.24681 <.0001 904

0.72987 <.0001 857

0.83575 <.0001 905

1.00000

L_AS

0.71145 <.0001 773

0.69250 <.0001 740

0.69262 <.0001 740

0.69405 <.0001 740

0.33412 <.0001 741

0.24006 <.0001 736

0.57877 <.0001 689

0.67374 <.0001 737

0.63536 <.0001 737

TA_SDT

0.34246 <.0001 777

0.27982 <.0001 742

0.25626 <.0001 742

0.27145 <.0001 742

0.22794 <.0001 744

0.09649 0.0087 739

0.26832 <.0001 691

0.25390 <.0001 739

0.22533 <.0001 740

TA_IDT

0.33414 <.0001 769

0.28990 <.0001 736

0.27287 <.0001 736

0.28871 <.0001 736

0.21438 <.0001 737

0.09400 0.0109 732

0.28891 <.0001 688

0.27304 <.0001 733

0.21664 <.0001 733

TA_SSM

0.43515 <.0001 776

0.37460 <.0001 741

0.37614 <.0001 741

0.36861 <.0001 741

0.23127 <.0001 743

0.14055 0.0001 738

0.35948 <.0001 690

0.34919 <.0001 738

0.34725 <.0001 739

TA_APM

0.44878 <.0001 777

0.36794 <.0001 742

0.36159 <.0001 742

0.35955 <.0001 742

0.27384 <.0001 744

0.14910 <.0001 739

0.36870 <.0001 691

0.33036 <.0001 739

0.34354 <.0001 740

TA_ISM

0.44575 <.0001 765

0.37167 <.0001 732

0.36105 <.0001 732

0.35324 <.0001 732

0.19316 <.0001 734

0.11470 0.0019 729

0.32912 <.0001 685

0.34863 <.0001 729

0.30944 <.0001 731

TA_IST

0.29262 <.0001 747

0.20403 <.0001 717

0.25193 <.0001 717

0.26264 <.0001 717

0.11170 0.0027 719

0.22698 <.0001 714

0.17107 <.0001 670

0.20496 <.0001 714

0.19821 <.0001 716

TA_SPE

0.24500 <.0001 802

0.19467 <.0001 764

0.20329 <.0001 764

0.19335 <.0001 764

0.17276 <.0001 766

0.09030 0.0131 754

0.16605 <.0001 714

0.19071 <.0001 760

0.15022 <.0001 762

TA_ST

TA_SA

TA_VSM

TA_DSM

919

919

919

925

300

911

867

914

917

Spearman Correlation Coefficients / Prob > |r| under H0: Rho=0 / Number of Observations age

T_PS

SB_PS

HF_PS

TA_SR

TA_ST

TA_SA

TA_VSM

TA_DSM

TA_IPE

0.17300 <.0001 733

0.14037 0.0002 704

0.14479 0.0001 704

0.16294 <.0001 704

0.06279 0.0960 704

0.01314 0.7297 694

0.13277 0.0006 661

0.11872 0.0017 699

0.13999 0.0002 701

TA_PIE

0.29649 <.0001 752

0.25978 <.0001 724

0.25105 <.0001 724

0.25912 <.0001 724

0.17015 <.0001 724

0.06596 0.0773 718

0.22612 <.0001 676

0.24331 <.0001 720

0.24741 <.0001 720

I_R4

0.72334 <.0001 605

0.64210 <.0001 590

0.60818 <.0001 589

0.60413 <.0001 589

0.40349 <.0001 592

0.16891 <.0001 584

0.55873 <.0001 568

0.58337 <.0001 588

0.57632 <.0001 587

SOD_ML

0.27959 <.0001 807

0.21858 <.0001 762

0.21590 <.0001 762

0.22334 <.0001 762

0.15514 <.0001 764

0.08054 0.0272 752

0.20274 <.0001 710

0.21869 <.0001 758

0.20343 <.0001 759

SOD_L

0.25181 <.0001 804

0.21970 <.0001 760

0.20691 <.0001 760

0.20705 <.0001 760

0.11663 0.0013 762

0.07991 0.0287 750

0.22564 <.0001 708

0.23110 <.0001 756

0.20958 <.0001 757

SOD_O

0.20685 <.0001 807

0.17055 <.0001 763

0.15774 <.0001 763

0.15586 <.0001 763

0.04837 0.1814 765

0.03858 0.2904 753

0.17970 <.0001 711

0.17814 <.0001 759

0.17145 <.0001 760

SOD_AS

0.20698 <.0001 809

0.20021 <.0001 765

0.17635 <.0001 765

0.18000 <.0001 765

0.09544 0.0082 767

0.04844 0.1837 755

0.20083 <.0001 713

0.20942 <.0001 761

0.18534 <.0001 762

SOD_B

0.24832 <.0001 809

0.23140 <.0001 764

0.20843 <.0001 764

0.19833 <.0001 764

0.13249 0.0002 766

0.04091 0.2619 754

0.25160 <.0001 712

0.23014 <.0001 760

0.20014 <.0001 761

SOD_MC

0.22739 <.0001 812

0.24812 <.0001 767

0.22361 <.0001 767

0.22209 <.0001 767

0.13648 0.0001 769

0.02464 0.4984 757

0.20908 <.0001 715

0.26053 <.0001 763

0.20681 <.0001 764

SOD_P

0.39582 <.0001 806

0.33739 <.0001 763

0.32719 <.0001 763

0.31670 <.0001 763

0.21338 <.0001 764

0.09298 0.0107 752

0.32437 <.0001 712

0.31914 <.0001 759

0.29960 <.0001 759

SOD_SF

0.34115 <.0001 812

0.30243 <.0001 767

0.30138 <.0001 767

0.29287 <.0001 767

0.20924 <.0001 769

0.07992 0.0279 757

0.30283 <.0001 715

0.28583 <.0001 763

0.27086 <.0001 764

SOD_IST

0.26054 <.0001 813

0.16478 <.0001 768

0.16066 <.0001 768

0.13596 0.0002 768

0.06408 0.0755 770

-0.00710 0.8453 758

0.16304 <.0001 716

0.15600 <.0001 764

0.12115 0.0008 765

SOD_SST

0.15510 <.0001 810

0.13718 0.0001 765

0.13123 0.0003 765

0.12644 0.0005 765

0.06948 0.0544 767

-0.00988 0.7863 755

0.14186 0.0001 713

0.13579 0.0002 761

0.10488 0.0037 762

TA_LA

0.25464 <.0001 812

0.18481 <.0001 767

0.17322 <.0001 767

0.17614 <.0001 767

0.09737 0.0069 769

0.07128 0.0499 757

0.16263 <.0001 715

0.17546 <.0001 763

0.15239 <.0001 764

TA_SO

0.20712 <.0001 807

0.13004 0.0003 763

0.12275 0.0007 763

0.11755 0.0011 763

0.03295 0.3627 765

0.05096 0.1624 753

0.13804 0.0002 711

0.14080 <.0001 759

0.11763 0.0012 760

TA_CP

0.44184 <.0001 809

0.36803 <.0001 765

0.35524 <.0001 765

0.34836 <.0001 765

0.26878 <.0001 767

0.11161 0.0021 755

0.36326 <.0001 713

0.35703 <.0001 761

0.32233 <.0001 762

TA_ZM

0.18636 <.0001 807

0.14292 <.0001 763

0.14349 <.0001 763

0.11153 0.0020 763

0.14442 <.0001 765

0.00836 0.8187 753

0.15047 <.0001 711

0.15522 <.0001 759

0.13754 0.0001 760

TA_IP

0.24035 <.0001 744

0.18872 <.0001 703

0.17548 <.0001 703

0.15974 <.0001 703

0.14682 <.0001 704

0.02760 0.4685 692

0.19805 <.0001 657

0.17344 <.0001 699

0.18046 <.0001 699

301

Spearman Correlation Coefficients / Prob > |r| under H0: Rho=0 / Number of Observations L_AS

TA_SDT

TA_IDT

TA_SSM

TA_APM

TA_ISM

TA_IST

TA_SPE

age

0.71145 <.0001 773

0.34246 <.0001 777

0.33414 <.0001 769

0.43515 <.0001 776

0.44878 <.0001 777

0.44575 <.0001 765

0.29262 <.0001 747

0.24500 <.0001 802

T_PS

0.69250 <.0001 740

0.27982 <.0001 742

0.28990 <.0001 736

0.37460 <.0001 741

0.36794 <.0001 742

0.37167 <.0001 732

0.20403 <.0001 717

0.19467 <.0001 764

SB_PS

0.69262 <.0001 740

0.25626 <.0001 742

0.27287 <.0001 736

0.37614 <.0001 741

0.36159 <.0001 742

0.36105 <.0001 732

0.25193 <.0001 717

0.20329 <.0001 764

HF_PS

0.69405 <.0001 740

0.27145 <.0001 742

0.28871 <.0001 736

0.36861 <.0001 741

0.35955 <.0001 742

0.35324 <.0001 732

0.26264 <.0001 717

0.19335 <.0001 764

TA_SR

0.33412 <.0001 741

0.22794 <.0001 744

0.21438 <.0001 737

0.23127 <.0001 743

0.27384 <.0001 744

0.19316 <.0001 734

0.11170 0.0027 719

0.17276 <.0001 766

TA_ST

0.24006 <.0001 736

0.09649 0.0087 739

0.09400 0.0109 732

0.14055 0.0001 738

0.14910 <.0001 739

0.11470 0.0019 729

0.22698 <.0001 714

0.09030 0.0131 754

TA_SA

0.57877 <.0001 689

0.26832 <.0001 691

0.28891 <.0001 688

0.35948 <.0001 690

0.36870 <.0001 691

0.32912 <.0001 685

0.17107 <.0001 670

0.16605 <.0001 714

TA_VSM

0.67374 <.0001 737

0.25390 <.0001 739

0.27304 <.0001 733

0.34919 <.0001 738

0.33036 <.0001 739

0.34863 <.0001 729

0.20496 <.0001 714

0.19071 <.0001 760

TA_DSM

0.63536 <.0001 737

0.22533 <.0001 740

0.21664 <.0001 733

0.34725 <.0001 739

0.34354 <.0001 740

0.30944 <.0001 731

0.19821 <.0001 716

0.15022 <.0001 762

L_AS

1.00000

0.36538 <.0001 772

0.36489 <.0001 768

0.53970 <.0001 771

0.53645 <.0001 771

0.51923 <.0001 765

0.45049 <.0001 746

0.17552 <.0001 762

TA_SDT

0.36538 <.0001 772

1.00000

0.58065 <.0001 768

0.39599 <.0001 775

0.35474 <.0001 776

0.33082 <.0001 764

0.14408 <.0001 746

0.10238 0.0046 765

0.36489 <.0001 768

0.58065 <.0001 768

1.00000

0.38024 <.0001 766

0.36317 <.0001 767

0.35102 <.0001 764

0.20433 <.0001 747

0.08631 0.0174 759

0.53970 <.0001 771

0.39599 <.0001 775

0.38024 <.0001 766

1.00000

0.71670 <.0001 776

0.61656 <.0001 763

0.23699 <.0001 744

0.11556 0.0014 764

0.53645 <.0001 771

0.35474 <.0001 776

0.36317 <.0001 767

0.71670 <.0001 776

1.00000

0.63958 <.0001 763

0.24354 <.0001 745

0.13567 0.0002 765

0.51923 <.0001 765

0.33082 <.0001 764

0.35102 <.0001 764

0.61656 <.0001 763

0.63958 <.0001 763

1.00000

0.26779 <.0001 745

0.11417 0.0017 756

0.45049 <.0001 746

0.14408 <.0001 746

0.20433 <.0001 747

0.23699 <.0001 744

0.24354 <.0001 745

0.26779 <.0001 745

1.00000

0.08411 0.0222 739

0.17552 <.0001 762

0.10238 0.0046 765

0.08631 0.0174 759

0.11556 0.0014 764

0.13567 0.0002 765

0.11417 0.0017 756

0.08411 0.0222 739

773

TA_IDT

TA_SSM

TA_APM

TA_ISM

TA_IST

TA_SPE

777

769

776

302

777

765

747

1.00000 802

Spearman Correlation Coefficients / Prob > |r| under H0: Rho=0 / Number of Observations L_AS

TA_SDT

TA_IDT

TA_SSM

TA_APM

TA_ISM

TA_IST

TA_SPE

TA_IPE

0.14347 0.0001 708

-0.02643 0.4826 708

-0.00543 0.8855 707

0.04918 0.1915 707

0.02594 0.4907 708

0.02992 0.4276 705

0.04774 0.2091 694

0.25313 <.0001 732

TA_PIE

0.25340 <.0001 741

0.14487 <.0001 742

0.08592 0.0194 740

0.15085 <.0001 741

0.12080 0.0010 742

0.08809 0.0168 737

0.07489 0.0440 724

0.13548 0.0002 751

I_R4

0.62415 <.0001 450

0.31627 <.0001 452

0.32478 <.0001 448

0.42330 <.0001 451

0.46821 <.0001 452

0.38298 <.0001 447

0.17576 0.0002 439

0.21168 <.0001 476

SOD_ML

0.26312 <.0001 760

0.05329 0.1411 764

0.05490 0.1315 756

0.20300 <.0001 763

0.22776 <.0001 764

0.19967 <.0001 752

0.05691 0.1234 734

0.12166 0.0006 789

SOD_L

0.22325 <.0001 757

0.05961 0.1004 761

0.04836 0.1850 753

0.15582 <.0001 760

0.20138 <.0001 761

0.15665 <.0001 749

0.03173 0.3916 731

0.08171 0.0220 786

SOD_O

0.20648 <.0001 760

0.10351 0.0042 764

0.07481 0.0397 756

0.15847 <.0001 763

0.18029 <.0001 764

0.14477 <.0001 752

0.03761 0.3088 734

0.04956 0.1643 789

SOD_AS

0.19883 <.0001 763

0.05223 0.1484 767

0.05190 0.1531 759

0.14141 <.0001 765

0.17647 <.0001 766

0.13832 0.0001 755

0.05735 0.1198 737

0.05456 0.1252 791

SOD_B

0.20326 <.0001 762

0.04579 0.2055 766

0.06311 0.0825 758

0.10651 0.0032 765

0.13582 0.0002 766

0.15671 <.0001 754

0.04492 0.2235 736

0.05037 0.1570 791

SOD_MC

0.21485 <.0001 765

0.02677 0.4585 769

0.05747 0.1132 761

0.13407 0.0002 768

0.15138 <.0001 769

0.16496 <.0001 757

0.05415 0.1414 739

0.04850 0.1722 794

SOD_P

0.32702 <.0001 760

0.15391 <.0001 763

0.14523 <.0001 756

0.25951 <.0001 762

0.28068 <.0001 763

0.28919 <.0001 752

0.08122 0.0278 734

0.11155 0.0017 789

SOD_SF

0.28568 <.0001 764

0.14582 <.0001 768

0.12692 0.0005 760

0.25820 <.0001 767

0.23013 <.0001 768

0.23695 <.0001 756

0.03703 0.3151 738

0.08144 0.0218 793

SOD_IST

0.15009 <.0001 765

0.12580 0.0005 769

0.08063 0.0261 761

0.15914 <.0001 768

0.14424 <.0001 769

0.13892 0.0001 757

0.01368 0.7104 739

0.05008 0.1586 794

SOD_SST

0.08461 0.0195 762

0.03606 0.3189 766

0.01694 0.6415 758

0.08609 0.0172 765

0.06287 0.0820 766

0.08831 0.0153 754

-0.04349 0.2386 736

0.04936 0.1655 791

TA_LA

0.22437 <.0001 764

0.03424 0.3433 768

0.04399 0.2257 760

0.13033 0.0003 767

0.16228 <.0001 768

0.15671 <.0001 756

0.04147 0.2605 738

0.05528 0.1199 793

TA_SO

0.17566 <.0001 760

0.09454 0.0089 764

0.07134 0.0499 756

0.12995 0.0003 763

0.16625 <.0001 764

0.13882 0.0001 752

0.02298 0.5341 734

0.06899 0.0527 789

TA_CP

0.36721 <.0001 763

0.15864 <.0001 766

0.12671 0.0005 759

0.26416 <.0001 765

0.28455 <.0001 766

0.28883 <.0001 755

0.12215 0.0009 737

0.12817 0.0003 792

TA_ZM

0.12819 0.0004 760

0.12871 0.0004 763

0.05626 0.1222 756

0.13651 0.0002 762

0.10195 0.0048 763

0.12612 0.0005 752

-0.01991 0.5902 734

0.04153 0.2436 790

TA_IP

0.14212 0.0002 699

0.14843 <.0001 703

0.13908 0.0002 695

0.15596 <.0001 702

0.17722 <.0001 703

0.20624 <.0001 691

0.00558 0.8849 675

0.03988 0.2829 727

303

Spearman Correlation Coefficients / Prob > |r| under H0: Rho=0 / Number of Observations TA_IPE

TA_PIE

age

0.17300 <.0001 733

0.29649 0.72334 <.0001 <.0001 752 605

I_R4 SOD_ML SOD_L SOD_O 0.27959 0.25181 0.20685 <.0001 <.0001 <.0001 807 804 807

0.20698 0.24832 <.0001 <.0001 809 809

0.22739 0.39582 <.0001 <.0001 812 806

T_PS

0.14037 0.0002 704

0.25978 0.64210 <.0001 <.0001 724 590

0.21858 0.21970 0.17055 <.0001 <.0001 <.0001 762 760 763

0.20021 0.23140 <.0001 <.0001 765 764

0.24812 0.33739 <.0001 <.0001 767 763

SB_PS

0.14479 0.0001 704

0.25105 0.60818 <.0001 <.0001 724 589

0.21590 0.20691 0.15774 <.0001 <.0001 <.0001 762 760 763

0.17635 0.20843 <.0001 <.0001 765 764

0.22361 0.32719 <.0001 <.0001 767 763

HF_PS

0.16294 <.0001 704

0.25912 0.60413 <.0001 <.0001 724 589

0.22334 0.20705 0.15586 <.0001 <.0001 <.0001 762 760 763

0.18000 0.19833 <.0001 <.0001 765 764

0.22209 0.31670 <.0001 <.0001 767 763

TA_SR

0.06279 0.0960 704

0.17015 0.40349 <.0001 <.0001 724 592

0.15514 0.11663 0.04837 <.0001 0.0013 0.1814 764 762 765

0.09544 0.13249 0.0082 0.0002 767 766

0.13648 0.21338 0.0001 <.0001 769 764

TA_ST

0.01314 0.7297 694

0.06596 0.16891 0.0773 <.0001 718 584

0.08054 0.07991 0.03858 0.0272 0.0287 0.2904 752 750 753

0.04844 0.04091 0.1837 0.2619 755 754

0.02464 0.09298 0.4984 0.0107 757 752

TA_SA

0.13277 0.0006 661

0.22612 0.55873 <.0001 <.0001 676 568

0.20274 0.22564 0.17970 <.0001 <.0001 <.0001 710 708 711

0.20083 0.25160 <.0001 <.0001 713 712

0.20908 0.32437 <.0001 <.0001 715 712

TA_VSM

0.11872 0.0017 699

0.24331 0.58337 <.0001 <.0001 720 588

0.21869 0.23110 0.17814 <.0001 <.0001 <.0001 758 756 759

0.20942 0.23014 <.0001 <.0001 761 760

0.26053 0.31914 <.0001 <.0001 763 759

TA_DSM

0.13999 0.0002 701

0.24741 0.57632 <.0001 <.0001 720 587

0.20343 0.20958 0.17145 <.0001 <.0001 <.0001 759 757 760

0.18534 0.20014 <.0001 <.0001 762 761

0.20681 0.29960 <.0001 <.0001 764 759

L_AS

0.14347 0.0001 708

0.25340 0.62415 <.0001 <.0001 741 450

0.26312 0.22325 0.20648 <.0001 <.0001 <.0001 760 757 760

0.19883 0.20326 <.0001 <.0001 763 762

0.21485 0.32702 <.0001 <.0001 765 760

TA_SDT

-0.02643 0.4826 708

0.14487 0.31627 <.0001 <.0001 742 452

0.05329 0.05961 0.10351 0.1411 0.1004 0.0042 764 761 764

0.05223 0.04579 0.1484 0.2055 767 766

0.02677 0.15391 0.4585 <.0001 769 763

TA_IDT

-0.00543 0.8855 707

0.08592 0.32478 0.0194 <.0001 740 448

0.05490 0.04836 0.07481 0.1315 0.1850 0.0397 756 753 756

0.05190 0.06311 0.1531 0.0825 759 758

0.05747 0.14523 0.1132 <.0001 761 756

TA_SSM

0.04918 0.1915 707

0.15085 0.42330 <.0001 <.0001 741 451

0.20300 0.15582 0.15847 <.0001 <.0001 <.0001 763 760 763

0.14141 0.10651 <.0001 0.0032 765 765

0.13407 0.25951 0.0002 <.0001 768 762

TA_APM

0.02594 0.4907 708

0.12080 0.46821 0.0010 <.0001 742 452

0.22776 0.20138 0.18029 <.0001 <.0001 <.0001 764 761 764

0.17647 0.13582 <.0001 0.0002 766 766

0.15138 0.28068 <.0001 <.0001 769 763

TA_ISM

0.02992 0.4276 705

0.08809 0.38298 0.0168 <.0001 737 447

0.19967 0.15665 0.14477 <.0001 <.0001 <.0001 752 749 752

0.13832 0.15671 0.0001 <.0001 755 754

0.16496 0.28919 <.0001 <.0001 757 752

TA_IST

0.04774 0.2091 694

0.07489 0.17576 0.0440 0.0002 724 439

0.05691 0.03173 0.03761 0.1234 0.3916 0.3088 734 731 734

0.05735 0.04492 0.1198 0.2235 737 736

0.05415 0.08122 0.1414 0.0278 739 734

TA_SPE

0.25313 <.0001 732

0.13548 0.21168 0.0002 <.0001 751 476

0.12166 0.08171 0.04956 0.0006 0.0220 0.1643 789 786 789

0.05456 0.05037 0.1252 0.1570 791 791

0.04850 0.11155 0.1722 0.0017 794 789

304

SOD_AS SOD_B SOD_MC

SOD_P

Spearman Correlation Coefficients / Prob > |r| under H0: Rho=0 / Number of Observations TA_IPE

TA_PIE

I_R4 SOD_ML SOD_L SOD_O

TA_IPE

1.00000 733

0.26446 0.14758 <.0001 0.0018 714 445

0.12871 0.08825 0.0005 0.0181 720 717

-0.0484 0.1938 721

0.07192 0.10878 0.0534 0.0034 722 722

0.12903 0.06855 0.0005 0.0658 725 721

TA_PIE

0.26446 <.0001 714

1.00000 0.30529 <.0001 752 451

0.11314 0.09786 0.06091 0.0021 0.0078 0.0978 740 737 740

0.09843 0.10196 0.0073 0.0054 742 742

0.10224 0.11279 0.0052 0.0021 745 741

I_R4

0.14758 0.0018 445

0.30529 1.00000 <.0001 451 605

0.22897 0.21563 0.22239 <.0001 <.0001 <.0001 473 470 474

0.17822 0.21012 <.0001 <.0001 475 474

0.17592 0.37304 0.0001 <.0001 476 475

SOD_ML

0.12871 0.0005 720

0.11314 0.22897 0.0021 <.0001 740 473

1.00000 0.66436 0.37873 <.0001 <.0001 807 801 804

0.53208 0.53032 <.0001 <.0001 805 805

0.48535 0.41982 <.0001 <.0001 807 800

SOD_L

0.08825 0.0181 717

0.09786 0.21563 0.0078 <.0001 737 470

0.66436 1.00000 0.60607 <.0001 <.0001 801 804 803

0.68906 0.59933 <.0001 <.0001 803 802

0.44932 0.40443 <.0001 <.0001 804 796

SOD_O

-0.04845 0.1938 721

0.06091 0.22239 0.0978 <.0001 740 474

0.37873 0.60607 1.00000 <.0001 <.0001 804 803 807

0.61252 0.44706 <.0001 <.0001 806 805

0.29059 0.30777 <.0001 <.0001 807 799

SOD_AS

0.07192 0.0534 722

0.09843 0.17822 0.0073 <.0001 742 475

0.53208 0.68906 0.61252 <.0001 <.0001 <.0001 805 803 806

1.00000 0.64229 <.0001 809 807

0.49239 0.35337 <.0001 <.0001 809 801

SOD_B

0.10878 0.0034 722

0.10196 0.21012 0.0054 <.0001 742 474

0.53032 0.59933 0.44706 <.0001 <.0001 <.0001 805 802 805

0.64229 1.00000 <.0001 807 809

0.62266 0.42289 <.0001 <.0001 809 801

SOD_MC

0.12903 0.0005 725

0.10224 0.17592 0.0052 0.0001 745 476

0.48535 0.44932 0.29059 <.0001 <.0001 <.0001 807 804 807

0.49239 0.62266 <.0001 <.0001 809 809

1.00000 0.46087 <.0001 812 804

SOD_P

0.06855 0.0658 721

0.11279 0.37304 0.0021 <.0001 741 475

0.41982 0.40443 0.30777 <.0001 <.0001 <.0001 800 796 799

0.35337 0.42289 <.0001 <.0001 801 801

0.46087 1.00000 <.0001 804 806

SOD_SF

0.05995 0.1070 724

0.08316 0.31063 0.0233 <.0001 744 477

0.37380 0.34988 0.27017 <.0001 <.0001 <.0001 806 802 805

0.31421 0.40408 <.0001 <.0001 807 807

0.39827 0.80378 <.0001 <.0001 810 806

SOD_IST

-0.01535 0.6798 725

0.01415 0.19787 0.6998 <.0001 745 477

0.23449 0.23126 0.22003 <.0001 <.0001 <.0001 807 803 806

0.23394 0.25264 <.0001 <.0001 808 808

0.22035 0.43154 <.0001 <.0001 811 806

SOD_SST

-0.03572 0.3378 722

-0.00947 0.14535 0.7967 0.0015 742 474

0.24243 0.24540 0.20028 <.0001 <.0001 <.0001 804 801 803

0.22589 0.20776 <.0001 <.0001 805 805

0.18137 0.36793 <.0001 <.0001 808 803

TA_LA

0.06278 0.0914 724

0.06343 0.21707 0.0838 <.0001 744 476

0.66776 0.55868 0.38168 <.0001 <.0001 <.0001 806 802 805

0.46586 0.50252 <.0001 <.0001 807 807

0.43365 0.39830 <.0001 <.0001 810 805

TA_SO

-0.05979 0.1087 721

0.03248 0.17003 0.3777 0.0002 740 474

0.40305 0.60310 0.84605 <.0001 <.0001 <.0001 804 803 807

0.62257 0.48185 <.0001 <.0001 806 805

0.30690 0.30935 <.0001 <.0001 807 799

TA_CP

0.11136 0.0027 723

0.15183 0.37977 <.0001 <.0001 743 476

0.46671 0.46437 0.34836 <.0001 <.0001 <.0001 805 801 804

0.41039 0.48935 <.0001 <.0001 806 806

0.47686 0.69008 <.0001 <.0001 809 803

TA_ZM

-0.07239 0.0520 721

0.05260 0.18582 0.1526 <.0001 741 475

0.18928 0.22383 0.24345 <.0001 <.0001 <.0001 801 797 800

0.22511 0.22149 <.0001 <.0001 802 802

0.19062 0.41160 <.0001 <.0001 805 802

TA_IP

-0.04662 0.2306 663

0.00970 0.27123 0.8003 <.0001 682 440

0.20834 0.19395 0.21707 <.0001 <.0001 <.0001 738 734 738

0.17875 0.17817 <.0001 <.0001 739 739

0.10907 0.34830 0.0029 <.0001 742 740

305

SOD_AS SOD_B SOD_MC

SOD_P

Spearman Correlation Coefficients / Prob > |r| under H0: Rho=0 / Number of Observations SOD_SF

SOD_IST

SOD_SST

TA_LA

TA_SO

TA_CP

TA_ZM

TA_IP

age

0.34115 <.0001 812

0.26054 <.0001 813

0.15510 <.0001 810

0.25464 <.0001 812

0.20712 <.0001 807

0.44184 <.0001 809

0.18636 <.0001 807

0.24035 <.0001 744

T_PS

0.30243 <.0001 767

0.16478 <.0001 768

0.13718 0.0001 765

0.18481 <.0001 767

0.13004 0.0003 763

0.36803 <.0001 765

0.14292 <.0001 763

0.18872 <.0001 703

SB_PS

0.30138 <.0001 767

0.16066 <.0001 768

0.13123 0.0003 765

0.17322 <.0001 767

0.12275 0.0007 763

0.35524 <.0001 765

0.14349 <.0001 763

0.17548 <.0001 703

HF_PS

0.29287 <.0001 767

0.13596 0.0002 768

0.12644 0.0005 765

0.17614 <.0001 767

0.11755 0.0011 763

0.34836 <.0001 765

0.11153 0.0020 763

0.15974 <.0001 703

TA_SR

0.20924 <.0001 769

0.06408 0.0755 770

0.06948 0.0544 767

0.09737 0.0069 769

0.03295 0.3627 765

0.26878 <.0001 767

0.14442 <.0001 765

0.14682 <.0001 704

TA_ST

0.07992 0.0279 757

-0.00710 0.8453 758

-0.00988 0.7863 755

0.07128 0.0499 757

0.05096 0.1624 753

0.11161 0.0021 755

0.00836 0.8187 753

0.02760 0.4685 692

TA_SA

0.30283 <.0001 715

0.16304 <.0001 716

0.14186 0.0001 713

0.16263 <.0001 715

0.13804 0.0002 711

0.36326 <.0001 713

0.15047 <.0001 711

0.19805 <.0001 657

TA_VSM

0.28583 <.0001 763

0.15600 <.0001 764

0.13579 0.0002 761

0.17546 <.0001 763

0.14080 <.0001 759

0.35703 <.0001 761

0.15522 <.0001 759

0.17344 <.0001 699

TA_DSM

0.27086 <.0001 764

0.12115 0.0008 765

0.10488 0.0037 762

0.15239 <.0001 764

0.11763 0.0012 760

0.32233 <.0001 762

0.13754 0.0001 760

0.18046 <.0001 699

L_AS

0.28568 <.0001 764

0.15009 <.0001 765

0.08461 0.0195 762

0.22437 <.0001 764

0.17566 <.0001 760

0.36721 <.0001 763

0.12819 0.0004 760

0.14212 0.0002 699

TA_SDT

0.14582 <.0001 768

0.12580 0.0005 769

0.03606 0.3189 766

0.03424 0.3433 768

0.09454 0.0089 764

0.15864 <.0001 766

0.12871 0.0004 763

0.14843 <.0001 703

TA_IDT

0.12692 0.0005 760

0.08063 0.0261 761

0.01694 0.6415 758

0.04399 0.2257 760

0.07134 0.0499 756

0.12671 0.0005 759

0.05626 0.1222 756

0.13908 0.0002 695

TA_SSM

0.25820 <.0001 767

0.15914 <.0001 768

0.08609 0.0172 765

0.13033 0.0003 767

0.12995 0.0003 763

0.26416 <.0001 765

0.13651 0.0002 762

0.15596 <.0001 702

TA_APM

0.23013 <.0001 768

0.14424 <.0001 769

0.06287 0.0820 766

0.16228 <.0001 768

0.16625 <.0001 764

0.28455 <.0001 766

0.10195 0.0048 763

0.17722 <.0001 703

TA_ISM

0.23695 <.0001 756

0.13892 0.0001 757

0.08831 0.0153 754

0.15671 <.0001 756

0.13882 0.0001 752

0.28883 <.0001 755

0.12612 0.0005 752

0.20624 <.0001 691

TA_IST

0.03703 0.3151 738

0.01368 0.7104 739

-0.04349 0.2386 736

0.04147 0.2605 738

0.02298 0.5341 734

0.12215 0.0009 737

-0.01991 0.5902 734

0.00558 0.8849 675

TA_SPE

0.08144 0.0218 793

0.05008 0.1586 794

0.04936 0.1655 791

0.05528 0.1199 793

0.06899 0.0527 789

0.12817 0.0003 792

0.04153 0.2436 790

0.03988 0.2829 727

306

Spearman Correlation Coefficients / Prob > |r| under H0: Rho=0 / Number of Observations SOD_SF

SOD_IST

SOD_SST

TA_LA

TA_SO

TA_CP

TA_ZM

TA_IP

TA_IPE

0.05995 0.1070 724

-0.01535 0.6798 725

-0.03572 0.3378 722

0.06278 0.0914 724

-0.05979 0.1087 721

0.11136 0.0027 723

-0.07239 0.0520 721

-0.04662 0.2306 663

TA_PIE

0.08316 0.0233 744

0.01415 0.6998 745

-0.00947 0.7967 742

0.06343 0.0838 744

0.03248 0.3777 740

0.15183 <.0001 743

0.05260 0.1526 741

0.00970 0.8003 682

I_R4

0.31063 <.0001 477

0.19787 <.0001 477

0.14535 0.0015 474

0.21707 <.0001 476

0.17003 0.0002 474

0.37977 <.0001 476

0.18582 <.0001 475

0.27123 <.0001 440

SOD_ML

0.37380 <.0001 806

0.23449 <.0001 807

0.24243 <.0001 804

0.66776 <.0001 806

0.40305 <.0001 804

0.46671 <.0001 805

0.18928 <.0001 801

0.20834 <.0001 738

SOD_L

0.34988 <.0001 802

0.23126 <.0001 803

0.24540 <.0001 801

0.55868 <.0001 802

0.60310 <.0001 803

0.46437 <.0001 801

0.22383 <.0001 797

0.19395 <.0001 734

SOD_O

0.27017 <.0001 805

0.22003 <.0001 806

0.20028 <.0001 803

0.38168 <.0001 805

0.84605 <.0001 807

0.34836 <.0001 804

0.24345 <.0001 800

0.21707 <.0001 738

SOD_AS

0.31421 <.0001 807

0.23394 <.0001 808

0.22589 <.0001 805

0.46586 <.0001 807

0.62257 <.0001 806

0.41039 <.0001 806

0.22511 <.0001 802

0.17875 <.0001 739

SOD_B

0.40408 <.0001 807

0.25264 <.0001 808

0.20776 <.0001 805

0.50252 <.0001 807

0.48185 <.0001 805

0.48935 <.0001 806

0.22149 <.0001 802

0.17817 <.0001 739

SOD_MC

0.39827 <.0001 810

0.22035 <.0001 811

0.18137 <.0001 808

0.43365 <.0001 810

0.30690 <.0001 807

0.47686 <.0001 809

0.19062 <.0001 805

0.10907 0.0029 742

SOD_P

0.80378 <.0001 806

0.43154 <.0001 806

0.36793 <.0001 803

0.39830 <.0001 805

0.30935 <.0001 799

0.69008 <.0001 803

0.41160 <.0001 802

0.34830 <.0001 740

SOD_SF

1.00000

0.47393 <.0001 812

0.38284 <.0001 809

0.39152 <.0001 811

0.28390 <.0001 805

0.57755 <.0001 808

0.43392 <.0001 806

0.34063 <.0001 743

0.56596 <.0001 810

0.27773 <.0001 812

0.24923 <.0001 806

0.33190 <.0001 809

0.46398 <.0001 807

0.32308 <.0001 744

0.23529 <.0001 809

0.20451 <.0001 803

0.26231 <.0001 806

0.41558 <.0001 805

0.29587 <.0001 742

0.40000 <.0001 805

0.38847 <.0001 808

0.20596 <.0001 806

0.19695 <.0001 743

0.36654 <.0001 804

0.26534 <.0001 800

0.22322 <.0001 738

0.28324 <.0001 804

0.27563 <.0001 741 0.34110 <.0001 740

812 SOD_IST

0.47393 <.0001 812

1.00000

SOD_SST

0.38284 <.0001 809

0.56596 <.0001 810

1.00000

0.39152 <.0001 811

0.27773 <.0001 812

0.23529 <.0001 809

1.00000

0.28390 <.0001 805

0.24923 <.0001 806

0.20451 <.0001 803

0.40000 <.0001 805

1.00000

0.57755 <.0001 808

0.33190 <.0001 809

0.26231 <.0001 806

0.38847 <.0001 808

0.36654 <.0001 804

1.00000

0.43392 <.0001 806

0.46398 <.0001 807

0.41558 <.0001 805

0.20596 <.0001 806

0.26534 <.0001 800

0.28324 <.0001 804

1.00000

0.34063 <.0001 743

0.32308 <.0001 744

0.29587 <.0001 742

0.19695 <.0001 743

0.22322 <.0001 738

0.27563 <.0001 741

0.34110 <.0001 740

TA_LA

TA_SO

TA_CP

TA_ZM

TA_IP

813

810

307

812

807

809

807

1.00000 744

Appendix B: Plots of documented age by phase Age by Phase: Todd standard 14 12

Phase

10 8 6 4 2 0 0

20

40

60

80

Age at Death y = 0.0751x + 4.5313 Reference R2 = 0.5292

Recent Linear (Recent)

Linear (Reference)

100

120

y = 0.0625x + 4.9288 R2 = 0.4057

Age by Phase: Suchey-Brooks standard 8 7

Phase

6 5 4 3 2 1 0 0

20

y = 0.0516x + 2.0793 R2 = 0.5408

40

60

80

Age at Death

Reference Linear (Reference)

308

Recent Linear (Recent)

100 y = 0.042x + 2.4652 R2 = 0.4422

120

Age by Phase: Hartnett-Fulginiti standard 8 7

Phase

6 5 4 3 2 1 0 0

20 y = 0.0565x + 1.8501 R2 = 0.5526

40

60

80

Age at Death

Reference Linear (Reference)

Recent Linear (Recent)

100

120

y = 0.0448x + 2.3205 R2 = 0.4387

Age by Phase: Boldsen et al. symphyseal relief 7 6

Phase

5 4 3 2 1 0 0

20 y = 0.018x + 3.5045 2 R = 0.1518

40

60

80

Age at Death

Reference Linear (Reference)

309

Recent Linear (Recent)

100

120

y = 0.0189x + 3.5387 R2 = 0.1521

Age by Phase: Boldsen et al. symphyseal texture 4.5 4 3.5 Phase

3 2.5 2 1.5 1 0.5 0 0

20

40

60

80

100

Age at Death y = 0.0056x + 1.6409 2 R = 0.0175

Reference Linear (Reference)

Recent Linear (Recent)

120

y = 0.0127x + 1.6138 2 R = 0.0728

Age by Phase: Boldsen et al. superior apex 6

Phase

5 4 3 2 1 0 0

20 y = 0.03x + 1.8118 2 R = 0.4139

40

60

80

Age at Death

Reference Linear (Reference)

310

Recent Linear (Recent)

100

120

y = 0.0224x + 2.1461 2 R = 0.2744

Age by Phase: Boldsen et al. ventral symphyseal margin 10 8 Phase

6 4 2 0 0

20 y = 0.0605x + 2.5 2 R = 0.5128

40

60

80

100

Age at Death

Reference Linear (Reference)

Recent Linear (Recent)

120

y = 0.0446x + 3.1937 2 R = 0.331

Age by Phase: Boldsen et al. dorsal symphyseal margin 7 6

Phase

5 4 3 2 1 0 0

20 y = 0.0395x + 1.9595 2 R = 0.4962

40

60

80

Age at Death

Reference Linear (Reference)

311

100

120

y = 0.0283x + 2.3704 2 R = 0.3121

Recent Linear (Recent)

Age by Phase: Lovejoy et al. auricular surface 12 10 Phase

8 6 4 2 0 0

20 y = 0.0753x + 1.9053 2 R = 0.5934

40

60

80

Age at Death

Reference Linear (Reference)

100

120

y = 0.0544x + 3.0248 2 R = 0.3738

Recent Linear (Recent)

Age by Phase: Boldsen et al. superior demiface topography 3.5 3 Phase

2.5 2 1.5 1 0.5 0 0

20

40

y = 0.0117x + 1.6704 2 R = 0.1532 Reference

60

80

Age at Death

Linear (Reference)

312

Recent Linear (Recent)

100

120

y = 0.0114x + 1.77 2 R = 0.1057

Age by Phase: Boldsen et al. inferior demiface topography 3.5 3

Phase

2.5 2 1.5 1 0.5 0 0

20 y = 0.0125x + 1.691 2 R = 0.169

40

60

80

Age at Death

Reference Linear (Reference)

Recent Linear (Recent)

100

120

y = 0.0075x + 2.0457 2 R = 0.0527

Age by Phase: Boldsen et al. superior surface morphology 6

Phase

5 4 3 2 1 0 0

20 y = 0.0191x + 2.6057 2 R = 0.1984

40

60

80

Age at Death

Reference Linear (Reference)

313

Recent Linear (Recent)

100

120

y = 0.016x + 2.9166 2 R = 0.1539

Age by Phase: Boldsen et al. apical surface morphology 6

Phase

5 4 3 2 1 0 0

20 y = 0.0226x + 2.2765 2 R = 0.2062

40

60

80

100

Age at Death

Reference Linear (Reference)

Recent Linear (Recent)

120

y = 0.0193x + 2.68 2 R = 0.176

Age by Phase: Boldsen et al. inferior surface morphology 6 5 Phase

4 3 2 1 0 0

20

40

y = 0.0204x + 2.4802 2 R = 0.1935 Reference

60

80

Age at Death

Linear (Reference)

314

Recent Linear (Recent)

100

120

y = 0.0188x + 2.6748 2 R = 0.1811

Age by Phase: Boldsen et al. inferior surface texture 3.5 3 Phase

2.5 2 1.5 1 0.5 0 0

20

y = 0.0108x + 1.0569 2 R = 0.0643

40

60

80

Age at Death

Reference Linear (Reference)

Recent Linear (Recent)

100

120

y = 0.0149x + 0.9011 2 R = 0.1139

Age by Phase: Boldsen et al. superior posterior iliac exostoses 7 6

Phase

5 4 3 2 1 0 0

20

40

y = 0.0103x + 1.9204 2 R = 0.0314 Reference

60

80

Age at Death

Linear (Reference)

315

Recent Linear (Recent)

100

120

y = 0.0247x + 1.5081 2 R = 0.1248

Age by Phase: Boldsen et al. inferior posterior iliac exostoses 7 6

Phase

5 4 3 2 1 0 0

20

40

60

80

Age at Death y = 0.0056x + 2.0794 2 R = 0.0099 Reference Recent

Linear (Reference)

100

120

y = 0.0282x + 0.7681 2 R = 0.1324

Linear (Recent)

Age by Phase: Boldsen et al. posterior iliac exostoses 3.5 3

Phase

2.5 2 1.5 1 0.5 0 0

20

40

60

80

Age at Death y = 0.0079x + 1.0511 2 R = 0.0695 Reference Recent

Linear (Reference)

316

100

120

y = 0.0109x + 0.8507 2 R = 0.0987

Linear (Recent)

Phase

Age by Phase: Iscan et al. Fourth Rib 9 8 7 6 5 4 3 2 1 0 0

20

40

y = 0.0566x + 2.7126 2 R = 0.529 Reference

60

80

Age at Death

Linear (Reference)

100

120

y = 0.0528x + 2.9091 2 R = 0.4923

Recent Linear (Recent)

Age by Phase: Meindl & Lovejoy Vault Sutures 7 6 Phase

5 4 3 2 1 0 0

20 y = 0.0243x + 2.5174 2 R = 0.118

40

60

80

Age at Death

Reference Linear (Reference)

317

Recent Linear (Recent)

100

120

y = 0.0185x + 2.7889 2 R = 0.0749

Age by Phase: Meindl & Lovejoy Lateral-Anterior Sutures 8 7

Phase

6 5 4 3 2 1 0 0

20 y = 0.0343x + 2.9402 2 R = 0.1623

40

60

80

Age at Death

Reference Linear (Reference)

Recent Linear (Recent)

100

120

y = 0.0296x + 2.9401 2 R = 0.1095

Age by Phase: Meindl & Lovejoy midlambdoid 3.5 3 Phase

2.5 2 1.5 1 0.5 0 0

20

y = 0.0102x + 1.1666 2 R = 0.0538

40

60

80

Age at Death

Reference Linear (Reference)

318

100

120

y = 0.0073x + 1.4233 2 R = 0.0251

Recent Linear (Recent)

Phase

Age by Phase: Meindl & Lovejoy lambda 3.5 3 2.5 2 1.5 1 0.5 0 0

20 y = 0.0084x + 1.4825 2 R = 0.0377

40

60

80

Age at Death

Reference Linear (Reference)

Recent Linear (Recent)

100

120

y = 0.0019x + 1.89 2 R = 0.0016

Phase

Age by Phase: Meindl & Lovejoy obelion 3.5 3 2.5 2 1.5 1 0.5 0 0

20

y = 0.0066x + 2.0881 2 R = 0.0263

40

60

80

Age at Death

Reference Linear (Reference)

319

Recent Linear (Recent)

100

120

y = -0.0002x + 2.4943 2 R = 2E-05

Age by Phase: Meindl & Lovejoy anterior sagittal 3.5 3

Phase

2.5 2 1.5 1 0.5 0 0

20 y = 0.007x + 1.8381 2 R = 0.0252

40

60

80

100

Age at Death

Reference Linear (Reference)

Recent Linear (Recent)

120

y = 0.008x + 1.5859 2 R = 0.0267

Age by Phase: Meindl & Lovejoy bregma 3.5 3

Phase

2.5 2 1.5 1 0.5 0 0

20

40

60

80

Age at Death y = 0.0075x + 1.4151 2 R = 0.0306 Reference Recent

Linear (Reference)

320

100

120

y = 0.0028x + 1.5342 2 R = 0.0042

Linear (Recent)

Phase

Age by Phase: Meindl & Lovejoy midcoronal 3.5 3 2.5 2 1.5 1 0.5 0 0

20 y = 0.0102x + 1.1722 2 R = 0.0559

40

60

80

Age at Death

Reference Linear (Reference)

Recent Linear (Recent)

100

120

y = 0.0003x + 1.5749 2 R = 6E-05

Age by Phase: Meindl & Lovejoy pterion 3.5 3 Phase

2.5 2 1.5 1 0.5 0 0

20 y = 0.0163x + 1.2493 2 R = 0.1465

40

60

80

Age at Death

Reference Linear (Reference)

321

Recent Linear (Recent)

100

120

y = 0.0126x + 1.4526 2 R = 0.0907

Age by Phase: Meindl & Lovejoy sphenofrontal 3.5 3

Phase

2.5 2 1.5 1 0.5 0 0

20 y = 0.015x + 1.2505 2 R = 0.1217

40

60

80

Age at Death

Reference Linear (Reference)

Recent Linear (Recent)

100

120

y = 0.0116x + 1.3564 2 R = 0.0676

Phase

Age by Phase: Meindl & Lovejoy inferior sphenotemporal 3.5 3 2.5 2 1.5 1 0.5 0 0

20 y = 0.0066x + 1.053 2 R = 0.0434

40

60

80

Age at Death

Reference Linear (Reference)

322

Recent Linear (Recent)

100 y = 0.0105x + 0.9142 2 R = 0.0642

120

Phase

Age by Phase: Meindl & Lovejoy superior sphenotemporal 3.5 3 2.5 2 1.5 1 0.5 0 0

20 y = 0.0054x + 1.0023 2 R = 0.0337

40

60

80

100

Age at Death

Reference Linear (Reference)

Recent Linear (Recent)

120

y = 0.0051x + 1.156 2 R = 0.0155

Age by Phase: Boldsen et al. lambdoidal-asterion 6

Phase

5 4 3 2 1 0 0

20 y = 0.0111x + 1.4888 2 R = 0.0504

40

60

80

Age at Death

Reference Linear (Reference)

323

Recent Linear (Recent)

100

120

y = 0.0131x + 1.4383 2 R = 0.06

Age by Phase: Boldsen et al. sagittal-obelica 6

Phase

5 4 3 2 1 0 0

20 y = 0.0155x + 2.6051 2 R = 0.0667

40

60

80

Age at Death

Reference Linear (Reference)

Recent Linear (Recent)

100

120

y = 0.0102x + 2.9515 2 R = 0.0271

Age by Phase: Boldsen et al. coronal-pterica 6 5

Phase

4 3 2 1 0 0

20 y = 0.0277x + 2.1551 2 R = 0.2165

40

60

80

Age at Death

Reference Linear (Reference)

324

Recent Linear (Recent)

100

120

y = 0.0255x + 2.2836 2 R = 0.1729

Age by Phase: Boldsen et al. zygomaticomaxillary 6 5 Phase

4 3 2 1 0 0

20 y = 0.0073x + 2.1518 2 R = 0.0347

40

60

80

Age at Death

Reference Linear (Reference)

Recent Linear (Recent)

100

120

y = 0.0071x + 2.131 2 R = 0.0204

Age by Phase: Boldsen et al. interpalatine 6

Phase

5 4 3 2 1 0 0

20 y = 0.0122x + 2.503 2 R = 0.0616

40

60

80

Age at Death

Reference Linear (Reference)

325

Recent Linear (Recent)

100 y = 0.0119x + 2.442 2 R = 0.0493

120

Appendix C: Plots of the difference between observed and expected phases by year of birth Todd Pubic Symphysis

Difference between observed and expected phases

8

6

4

2 R2 = 0.0069 0 1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

-2

-4

-6 Year of birth

Suchey-Brooks Pubic Symphysis

Difference between observed and expected phases

5 4 3 2 1 2

R = 0.0039 0 1820 -1

1840

1860

1880

1900

1920

-2 -3 -4 -5 Year of birth

326

1940

1960

1980

2000

Hartnett-Fulginiti Pubic Symphysis

Difference between observed and expected phases

5 4 3 2 1 0 1820 -1

2

R = 0.0354 1840

1860

1880

1900

1920

1940

1960

1980

2000

-2 -3 -4 -5 Year of birth

Boldsen et al Pubic Sym physis

Difference between observed and expected phases

100 80 60 40 20 0 1820 -20

R2 = 0.0004 1840

1860

1880

1900

1920

-40 -60 -80 Year of birth

327

1940

1960

1980

2000

Lovejoy et al Auricular Surface

Difference between observed and expected phases

8

6

4

2 2

R = 0.0208 0 1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

-2

-4

-6 Year of birth

Boldsen et al Auricular Surface

Difference between observed and expected phases

100 80 60 40 20 0 1820 -20

R2 = 0.0855 1840

1860

1880

1900

1920

-40 -60 -80 -100 Year of birth

328

1940

1960

1980

2000

Meindl & Lovejoy Cranial Vault Sutures

Difference between observed and expected phases

6

4

2 R2 = 0.069 0 1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

-2

-4

-6 Year of birth

Meindl & Lovejoy Cranial Lateral-Anterior Sutures

Difference between observed and expected phases

8 6 4 2 2

R = 0.0305 0 1820

1840

1860

1880

1900

1920

-2 -4 -6 -8 Year of birth

329

1940

1960

1980

2000

Boldsen et al Cranial Sutures

Difference between observed and expected phases

100 80 60 40 20 0 1820 -20

R2 = 0.0088 1840

1860

1880

1900

1920

1940

1960

1980

2000

-40 -60 -80 -100 Year of birth

Iscan 4th Rib

Difference between observed and expected phases

4 2 0 1820

R2 = 0.0069 1840

1860

1880

1900

1920

-2 -4

-6 -8 -10 Year of birth

330

1940

1960

1980

2000

Boldsen et al Transition Analysis: Uniform Distribution

Difference between observed and expected phases

80 60 40 20 R2 = 0.0554 0 1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

-20 -40 -60 -80 Year of birth

Boldsen et al Transition Analysis: Forensic Distribution

Difference between observed and expected phases

40

20 R2 = 0.0846 0 1820

1840

1860

1880

1900

1920

-20

-40

-60

-80 Year of birth

331

1940

1960

1980

2000

Appendix D: Plots of the difference between observed and expected phases by skeletal series Todd Pubic Symphysis

Difference between observed and expected phases

8

6

4

2

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

-2

-4

-6 Year of birth HTH

TC

Linear (HTH) 2

R = 0.051

MMA

Linear (TC) 2

R = 0.1252

UTK

Linear (MMA) 2

R = 0.0439

MCFSC

Linear (UTK) 2

R = 0.0795

Linear (MCFSC) 2

R = 0.1287

Suchey-Brooks Symphysis 5

Difference between observed and expected phases

4

3

2

1

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

-1

-2

-3

-4

-5 Year of birth

2

HTH

TC

MMA

UTK

Linear (HTH)

Linear (TC)

Linear (MMA)

Linear (UTK)

R = 0.0093

2

R = 0.0781

2

R = 0.0274

332

2

R = 0.0711

MCFSC Linear (MCFSC) 2

R = 0.0567

2000

Hartnett-Fulginiti Pubic Symphysis 5

Difference between observed and expected phases

4

3

2

1

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

-1

-2

-3

-4

-5 Year of birth HTH

TC

MMA

UTK

Linear (HTH)

Linear (TC)

Linear (MMA)

Linear (UTK)

2

R = 0.1121

2

R = 0.2463

2

2

R = 0.1774

R = 0.2966

MCFSC Linear (MCFSC) 2

R = 0.3233

Boldsen et al Pubic Symphsis 100

Difference between observed and expected phases

80

60

40

20

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

-20

-40

-60

-80 Year of birth

2

HTH

TC

MMA

UTK

Linear (HTH)

Linear (TC)

Linear (MMA)

Linear (UTK)

R = 0.0024

2

R = 0.0033

2

R = 0.125

333

2

R = 0.0319

MCFSC Linear (MCFSC) 2

R = 0.0137

2000

Lovejoy et al Auricular Surface

6

4

2

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

-2

-4

-6 Year of birth HTH Linear (HTH) 2

R = 0.2124

TC Linear (TC)

MMA Linear (MMA)

UTK Linear (UTK)

2

2

R = 0.2233

2

R = 0.1476

R = 0.1365

Boldsen et al Auricular Surface 100

80 Difference between observed and expected phases

Difference between observed and expected phases

8

60

40

20

0 1820

1840

1860

1880

1900

1920

1940

1960

-20

-40

-60

-80

-100 Year of birth HTH Linear (HTH) 2

R = 0.1556

TC Linear (TC) 2

R = 0.1766

334

MMA Linear (MMA) 2

R = 0.0677

UTK Linear (UTK) 2

R = 0.0851

1980

2000

Meindl & Lovejoy Cranial Vault Sutures

Difference between observed and expected phases

6

4

2

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

1960

1980

2000

-2

-4

-6 Year of birth HTH

TC

MMA

Linear (HTH)

Linear (TC)

Linear (MMA)

2

2

R = 0.3587

R = 0.2186

2

R = 0.1774

UTK Linear (UTK) 2

R = 0.2815

Meindl & Lovejoy Cranial Lateral-Anterior Sutures 8

Difference between observed and expected phases

6

4

2

0 1820

1840

1860

1880

1900

1920

1940

-2

-4

-6

-8 Year of birth HTH Linear (HTH) 2

R = 0.3796

TC Linear (TC)

MMA Linear (MMA) 2

2

R = 0.1874

R = 0.2135

335

UTK Linear (UTK) 2

R = 0.1922

Boldsen et al Cranial Sutures 100

Difference between observed and expected phases

80

60

40

20

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

1980

2000

-20

-40

-60

-80

-100 Year of birth HTH Linear (HTH)

TC Linear (TC) 2

2

UTK Linear (UTK) 2

2

R = 0.0594

R = 0.144

MMA Linear (MMA)

R = 0.0954

R = 0.0646

Iscan 4th Rib

Difference between observed and expected phases

4

2

0 1820

1840

1860

1880

1900

1920

1940

1960

-2

-4

-6

-8

-10 Year of birth

2

HTH

TC

MMA

UTK

Linear (HTH)

Linear (TC)

Linear (MMA)

Linear (UTK)

R = 0.0955

2

R = 0.0675

2

R = 0.055

336

2

R = 0.0835

MCFSC Linear (MCFSC) 2

R = 0.0696

Boldsen et al Transition Analysis: Uniform Distribution 80

Difference between observed and expected phases

60

40

20

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

-20

-40

-60

-80 Year of birth HTH

TC

MMA

UTK

Linear (HTH)

Linear (TC)

Linear (MMA)

Linear (UTK)

2

2

R = 0.0698

R = 0.1269

2

2

R = 0.2111

R = 0.1119

MCFSC Linear (MCFSC) 2

R = 0.0137

Boldsen et al Transition Analysis: Forensic Distribution

Difference between observed and expected phases

40

20

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

-20

-40

-60

-80 Year of birth

2

HTH

TC

MMA

UTK

Linear (HTH)

Linear (TC)

Linear (MMA)

Linear (UTK)

R = 0.2652

2

R = 0.3163

2

R = 0.3423

337

2

R = 0.3296

MCFSC Linear (MCFSC) 2

R = 0.2563

2000

Appendix E: Plots of the difference between the observed and expected phase by 10-year birth cohort Todd Pubic Symphysis 8

Difference between observed and expected phase

6

4

2

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

-2

-4

-6 Year of Birth 20-29

30-39

40-49

50-59

60-69

70-79

80+

Linear (20-29)

Linear (30-39)

Linear (40-49)

Linear (50-59)

Linear (60-69)

Linear (70-79)

Linear (80+)

2

2

R = 0.004

2

2

R = 0.0027

R = 0.0463

R = 3E-05

2

R = 0.0333

2

R = 0.0031

2

R = 0.0064

Suchey-Brooks Pubic Symphysis 5

Difference between observed and expected phase

4

3

2

1

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

-1

-2

-3

-4

-5 Year of Birth 20-29 Linear (20-29) 2

R = 0.0056

30-39 Linear (30-39) 2

R = 0.0076

40-49 Linear (40-49) 2

R = 0.0008

50-59 Linear (50-59) 2

R = 0.0089

338

60-69 Linear (60-69) 2

R = 0.0437

70-79 Linear (70-79) 2

R = 0.0016

80+ Linear (80+) 2

R = 0.0008

2000

Hartnett-Fulginiti Pubic Symphysis 5

Difference between observed and expected phase

4

3

2

1

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

-1

-2

-3

-4

-5 Year of Birth 20-29 Linear (20-29) 2

R = 0.016

30-39 Linear (30-39) 2

R = 0.0194

40-49 Linear (40-49) 2

R = 0.002

50-59 Linear (50-59)

60-69 Linear (60-69) 2

2

R = 0.0458

R = 0.0373

70-79 Linear (70-79) 2

R = 0.0017

80+ Linear (80+) 2

R = 0.0023

Boldsen et al Pubic Symphysis 100

Difference between observed and expected phase

80

60

40

20

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

-20

-40

-60

-80 Year of Birth 20-29 Linear (20-29) 2

R = 0.0005

30-39 Linear (30-39) 2

R = 0.0106

40-49 Linear (40-49) 2

R = 0.0133

50-59 Linear (50-59) 2

R = 0.0029

339

60-69 Linear (60-69) 2

R = 0.0255

70-79 Linear (70-79) 2

R = 8E-07

80+ Linear (80+) 2

R = 0.0027

2000

Lovejoy et al Auricular Surface

Difference between observed and expected phase

8

6

4

2

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

-2

-4

-6 Year of Birth 20-29 Linear (20-29) 2

R = 0.0804

30-39 Linear (30-39) 2

R = 0.0064

40-49 Linear (40-49) 2

R = 0.012

50-59 Linear (50-59) 2

60-69 Linear (60-69) 2

R = 0.0109

R = 0.0087

70-79 Linear (70-79) 2

R = 0.0016

80+ Linear (80+) 2

R = 0.0488

Boldsen et al Auricular Surface 100

Difference between observed and expected phase

80

60

40

20

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

-20

-40

-60

-80

-100 Year of Birth 20-29 Linear (20-29) 2

R = 0.0024

30-39 Linear (30-39) 2

R = 0.0148

40-49 Linear (40-49) 2

R = 3E-05

50-59 Linear (50-59) 2

R = 0.0275

340

60-69 Linear (60-69) 2

R = 0.0463

70-79 Linear (70-79) 2

R = 0.0986

80+ Linear (80+) 2

R = 0.1605

2000

Meindl & Lovejoy Cranial Vault Sutures

Difference between observed and expected phase

6

4

2

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

-2

-4

-6 Year of Birth 20-29 Linear (20-29) 2

R = 0.3056

30-39 Linear (30-39) 2

R = 0.0156

40-49 Linear (40-49) 2

R = 0.0113

50-59 Linear (50-59) 2

R = 0.0151

60-69 Linear (60-69) 2

R = 0.0165

70-79 Linear (70-79) 2

R = 0.0496

80+ Linear (80+) 2

R = 0.0406

Meindl & Lovejoy Cranial Lateral-Anterior Sutures 8

Difference between observed and expected phase

6

4

2

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

-2

-4

-6

-8 Year of Birth 20-29 Linear (20-29) 2

R = 0.3186

30-39 Linear (30-39) 2

R = 0.1071

40-49 Linear (40-49) 2

R = 0.0159

50-59 Linear (50-59) 2

R = 6E-05

341

60-69 Linear (60-69) 2

R = 0.0039

70-79 Linear (70-79) 2

R = 0.0113

80+ Linear (80+) 2

R = 0.0156

2000

TBoldsen et al Cranial Sutures 100

Difference between observed and expected phase

80

60

40

20

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

-20

-40

-60

-80

-100 Year of birth 20-29

30-39

40-49

50-59

60-69

70-79

80+

Linear (20-29)

Linear (30-39)

Linear (40-49)

Linear (50-59)

Linear (60-69)

Linear (70-79)

Linear (80+)

2

R = 0.0557

2

R = 0.0227

2

2

R = 0.0059

R = 0.0498

2

R = 0.0206

2

R = 0.0003

2

R = 0.0132

Iscan 4th Rib

Difference between observed and expected phase

4

2

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

-2

-4

-6

-8

-10 Year of Birth 20-29 Linear (20-29) 2

R = 0.0004

30-39 Linear (30-39) 2

R = 7E-05

40-49 Linear (40-49) 2

R = 0.0088

50-59 Linear (50-59) 2

R = 0.0019

342

60-69 Linear (60-69) 2

R = 3E-07

70-79 Linear (70-79) 2

R = 0.0311

80+ Linear (80+) 2

R = 3E-05

2000

Boldsen et al Transition Analysis: Uniform Distribution 80

Difference between observed and expected phase

60

40

20

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

-20

-40

-60

-80 Year of birth 20-29 Linear (20-29) 2

R = 0.003

30-39 Linear (30-39) 2

R = 0.0046

40-49 Linear (40-49) 2

R = 0.0515

50-59 Linear (50-59) 2

R = 0.0042

60-69 Linear (60-69) 2

R = 0.0001

70-79 Linear (70-79) 2

R = 0.0688

80+ Linear (80+) 2

R = 0.0261

Boldsen et al Transition Analysis: Forensic Distribution

Difference between observed and expected phase

40

20

0 1820

1840

1860

1880

1900

1920

1940

1960

1980

-20

-40

-60

-80 Year of Birth 20-29 Linear (20-29) 2

R = 0.0037

30-39 Linear (30-39) 2

R = 0.0021

40-49 Linear (40-49) 2

R = 0.0337

50-59 Linear (50-59) 2

R = 0.0018

343

60-69 Linear (60-69) 2

R = 0.0101

70-79 Linear (70-79) 2

R = 0.0159

80+ Linear (80+) 2

R = 0.0008

2000

Appendix F: Regression output for identification of the best descriptive-variable predictors of the difference between observed and expected phases Summary of Stepwise Selection (Todd) Variable Step Entered

Number Vars In

Partial R-Square

1 age

1

0.0865

0.0865 47.6152

50.12 <.0001

2 reg

2

0.0456

0.1322 20.9188

27.75 <.0001

3 YOB

3

0.0224

0.1545

8.8477

13.94

0.0002

4 height

4

0.0055

0.1600

7.4051

3.43

0.0647

5 group

5

0.0043

0.1643

6.7118

2.69

0.1016

4

0.0002

0.1641

4.8215

0.11

0.7408

5

0.0040

0.1681

4.2960

2.53

0.1120

6 7 sex

Variable Removed

age

344

Model R-Square

C(p)

F Value

Pr > F

Summary of Stepwise Selection (Suchey-Brooks) Variable Step Entered

Number Vars In

Partial R-Square

1 age

1

0.0489

0.0489 46.0543

27.17 <.0001

2 reg

2

0.0351

0.0840 26.8909

20.25 <.0001

3 height

3

0.0218

0.1058 15.7680

12.84

0.0004

4 YOB

4

0.0200

0.1258

5.6975

12.05

0.0006

3

0.0018

0.1240

4.7689

1.07

0.3014

4

0.0054

0.1294

3.5449

3.23

0.0727

5 6 group

Variable Removed

age

345

Model R-Square

C(p)

F Value

Pr > F

Summary of Stepwise Selection (Hartnett-Fulginiti) Variable Step Entered

Number Vars In

Partial R-Square

1 age

1

0.2023

0.2023 45.7454

2 height

2

0.0195

0.2218 33.7394

13.24

0.0003

3 sex

3

0.0065

0.2283 31.0406

4.47

0.0350

4 reg

4

0.0036

0.2319 30.4822

2.44

0.1189

5 YOB

5

0.0322

0.2641

9.3605

6 group

6

0.0102

0.2742

4.0680

7.33

0.0070

5

0.0002

0.2740

2.2104

0.14

0.7053

7

Variable Removed

age

346

Model R-Square

C(p)

F Value

Pr > F

134.13 <.0001

22.97 <.0001

Summary of Stepwise Selection (Lovejoy et al.) Variable Step Entered

Variable Removed

Number Vars In

Partial R-Square

1 age

1

0.2563

0.2563 7.1763

2 reg

2

0.0123

0.2686 0.4329

347

Model R-Square

C(p)

F Value

Pr > F

179.86 <.0001 8.79

0.0032

Summary of Stepwise Selection (Iscan et al.) Variable Step Entered

Variable Removed

Number Vars In

Partial R-Square

1 age

1

0.2338

0.2338 25.3020

2 reg

2

0.0339

0.2677 11.6483

3 cohort

3

0.0185

0.2862

4 height

4

0.0104

0.2966

348

Model R-Square

C(p)

F Value

Pr > F

101.01 <.0001 15.25

0.0001

5.0830

8.54

0.0037

2.2717

4.85

0.0283

Summary of Stepwise Selection (Meindl & Lovejoy vault sutures) Variable Step Entered

Variable Removed

Number Vars In

Partial R-Square

1 age

1

0.4135

0.4135 5.8190

2 cohort

2

0.0059

0.4193 2.2645

5.56

0.0187

3 categ

3

0.0032

0.4226 1.2045

3.08

0.0800

4 series

4

0.0026

0.4251 0.7794

2.44

0.1186

349

Model R-Square

C(p)

F Value

Pr > F

389.86 <.0001

Summary of Stepwise Selection (Meindl & Lovejoy lateral-anterior sutures) Variable Step Entered

Variable Removed

Number Vars In

Partial R-Square

1 age

1

0.3805

0.3805 10.0886

2 sex

2

0.0078

0.3883

5.0447

7.02

0.0083

3 series

3

0.0086

0.3969 -0.7142

7.83

0.0053

350

Model R-Square

C(p)

F Value

Pr > F

339.08 <.0001

Summary of Stepwise Selection (Boldsen et al. pubic symphysis) Variable Step Entered

Variable Removed

Number Vars In

Partial R-Square

1 categ

1

0.0278

0.0278 22.3335

2 reg

2

0.0123

0.0401 17.0625

3 YOB

3

0.0263

0.0664

3.4425

4 series

4

0.0045

0.0709

2.7560

351

Model R-Square

C(p)

F Value

Pr > F

15.90 <.0001 7.09

0.0080

15.64 <.0001 2.70

0.1011

Summary of Stepwise Selection (Boldsen et al. auricular surface) Variable Step Entered

Variable Removed

Number Vars In

Partial R-Square

1 age

1

0.1315

0.1315 47.5420

85.26 <.0001

2 YOB

2

0.0389

0.1705 22.2556

26.38 <.0001

3 sex

3

0.0245

0.1950

7.0712

17.09 <.0001

4 reg

4

0.0063

0.2013

4.6716

352

Model R-Square

C(p)

F Value

4.40

Pr > F

0.0363

Summary of Stepwise Selection (Boldsen et al. cranial suture closure) Variable Step Entered

Variable Removed

Number Vars In

Partial R-Square

1 age

1

0.1307

0.1307 94.7793

84.05 <.0001

2 race

2

0.0934

0.2241 26.7598

67.16 <.0001

3 sex

3

0.0376

0.2617

28.39 <.0001

353

Model R-Square

C(p)

0.5426

F Value

Pr > F

Summary of Stepwise Selection (Boldsen et al. PS AS & CS: UNI) Variable Step Entered

Number Vars In

Partial R-Square

1 age

1

0.1197

0.1197 44.3325

76.71 <.0001

2 race

2

0.0297

0.1494 25.8858

19.65 <.0001

3 series

3

0.0047

0.1541 24.6559

3.12

0.0781

4 reg

4

0.0220

0.1761 11.5309

14.95

0.0001

5 YOB

5

0.0095

0.1856

6.9710

6.55

0.0108

4

0.0000

0.1856

4.9713

0.00

0.9860

5

0.0041

0.1897

4.1476

2.83

0.0929

6 7 categ

Variable Removed

series

354

Model R-Square

C(p)

F Value

Pr > F

Summary of Stepwise Selection (Boldsen et al. PS AS & CS: COR) Variable Step Entered

Variable Removed

Number Vars In

Partial R-Square

1 Age

1

0.3401

0.3401 39.0730

2 Race

2

0.0165

0.3567 26.0025

14.48

0.0002

3 Reg

3

0.0032

0.3599 25.0642

2.83

0.0929

4 YOB

4

0.0272

0.3871

355

Model R-Square

C(p)

2.3028

F Value

Pr > F

290.72 <.0001

24.88 <.0001

Appendix G: Descriptive data for phases by group, sex, and race Pubic symphysis Todd REFERENCE AMERICAN Todd phase FEMALE I II III IV V VI VII VIII IX X MALE I II III IV V VI VII VIII IX X BLACK I II III IV V VI VII VIII IX X WHITE I II III IV V VI VII VIII IX X TOTAL I II III IV V VI VII VIII IX X

95% CI

RECENT AMERICAN

Std Dev

age range

n

mean

SE

1 1 3 9 10 27 19 15 10 156

29.0 24.0 26.3 27.8 35.7 34.5 36.9 45.5 47.0 64.8

2.9 2.8 6.1 2.1 1.6 3.1 2.1 1.2

14.1 21.4 21.9 30.2 33.6 39.0 42.2 62.5

38.6 34.2 49.5 38.8 40.2 52.1 51.9 67.2

4.9 8.4 19.3 10.9 6.9 11.9 6.8 14.7

4 4 4 9 17 39 10 26 157

21.8 25.0 24.3 28.4 30.9 38.8 43.4 49.0 67.6

0.9 4.0 1.7 1.6 1.3 1.8 1.5 2.6 1.1

19.0 12.2 19.0 24.7 28.1 35.1 40.1 43.7 65.5

24.5 37.8 29.5 32.2 33.7 42.6 46.7 54.3 69.7

3 5 7 12 28 26 16 18 151

23.3 23.8 23.6 34.3 32.9 38.4 47.3 46.1 66.0

0.7 2.2 1.1 5.2 1.8 2.5 2.7 3.0 1.2

20.5 17.8 20.9 22.9 29.2 33.3 41.5 39.8 63.8

1 2 2 6 7 16 32 9 18 162

29.0 20.5 30.0 30.3 28.9 33.4 38.0 40.0 50.7 66.4

0.5 7.0 3.8 1.9 2.3 1.4 1.2 2.5 1.1

1 5 7 13 19 44 58 25 36 313

29.0 22.2 25.6 26.7 32.3 33.1 38.2 44.7 48.4 66.2

0.8 2.4 2.0 3.3 1.4 1.3 1.9 2.0 0.8

Std Dev

age range

30.2

6.3

20-36

7.4 27.6 35.4 27.2 42.8 45.5 66.2

45.6 61.0 54.6 59.4 60.7 81.3 72.0

2.1 18.0 18.1 15.3 13.3 21.4 15.1

25-28 24-78 26-89 30-73 38-77 36-88 44-101

2.5

-9.3

54.3

3.5

20-25

0.9 7.6 3.5 3.0 2.4 2.2 1.6

21.8 17.2 33.1 38.2 40.7 54.6 65.3

26.6 56.4 47.5 50.5 50.5 63.3 71.5

1.9 18.7 18.1 15.2 10.8 15.5 15.2

21-26 21-68 22-90 26-83 27-68 26-94 35-97

23.0 33.7 36.0 41.3 52.3 50.5 62.2

2.0 8.7 11 1.7 4.5 3.8 2.7

-2.4 -4.0 -13.9 34.2 40.9 41.6 56.6

48.4 71.3 85.9 48.5 63.8 59.4 67.8

2.8 15.1 20.1 2.9 10.9 10.6 14.4

21-25 23-51 22-59 38-43 40-68 40-74 43-99

8 2 5 10 40 29 26 51 174

23.4 23.5 25.6 43.0 42.5 44.4 46.7 61.0 69.8

1.9 1.5 0.7 6.0 2.9 2.9 2.4 2.3 1.1

18.8 4.4 23.7 29.5 36.7 38.4 41.8 56.3 67.6

28.0 42.6 27.5 56.5 48.3 50.4 51.5 65.6 72.1

5.5 2.1 1.5 18.9 18.1 15.7 12.0 16.6 15.0

20-36 22-25 24-28 21-78 22-90 26-83 27-77 26-94 35-101

8 2 7 13 43 32 32 59 202

23.4 23.5 24.9 40.9 42.1 44.1 47.7 59.5 68.8

1.9 1.5 0.8 5.0 2.8 2.7 2.1 2.1 1.1

18.8 4.4 22.9 30.0 36.5 38.7 43.4 55.3 66.7

28.0 42.6 26.8 51.7 47.6 49.5 52.0 63.8 70.9

5.5 2.1 2.1 18.0 18.0 15.0 11.9 16.2 15.1

20-36 22-25 21-28 21-78 22-90 26-83 27-77 26-94 35-101

n

mean

SE

6 1 2 7 16 6 11 8 106

23.7 22.0 26.5 44.3 45.0 43.3 51.7 63.4 69.1

2.6

17.1

23-32 20-48 21-72 22-70 24-49 33-80 36-63 27-101

1.5 6.8 4.5 6.3 4.0 7.6 1.5

1.7 8.0 3.3 4.9 5.5 11.5 4.6 13.2 13.2

20-24 20-37 22-29 22-38 22-39 26-84 38-55 22-82 31-98

2 1 5 6 27 26 21 51 96

22.5 25.0 24.2 36.8 40.3 44.3 45.6 58.9 68.4

26.2 29.8 26.2 45.6 36.6 43.5 53.1 52.4 68.3

1.2 4.8 2.9 17.9 9.5 12.7 10.9 12.6 14.1

22-24 20-32 20-28 21-72 22-70 26-84 38-80 22-78 31-101

2 3 3 3 6 8 28

14.2 -58.9 20.6 24.3 28.5 35.2 37.2 45.5 64.2

26.9 118.9 40.1 33.5 38.4 40.9 42.8 55.9 68.6

0.7 9.9 9.3 5.0 9.2 7.9 3.7 10.5 14.0

20-21 23-37 22-48 22-38 27-62 24-63 33-46 32-82 27-94

20.0 19.7 22.3 25.3 30.3 35.5 40.7 44.5 64.7

24.4 31.5 31.1 39.3 35.9 40.9 48.6 52.4 67.8

1.8 6.4 7.2 14.5 9.3 10.2 9.6 11.7 14.0

20-24 20-37 20-48 21-72 22-70 24-84 33-80 22-82 27-101

356

95% CI

Pubic symphysis Suchey-Brooks REFERENCE AMERICAN S-B phase FEMALE I II III IV V VI MALE I II III IV V VI BLACK I II III IV V VI WHITE I II III IV V VI TOTAL I II III IV V VI

95% CI

RECENT AMERICAN Std Dev

age range

n

mean

SE

5 13 34 36 21 142

26.2 31.3 34.8 40.8 59.3 64.8

1.8 3.9 2.1 1.7 3.8 1.2

21.1 22.8 30.6 37.4 51.4 62.4

31.3 39.8 39.1 44.3 67.3 67.2

26.2 31.3 34.8 40.8 59.3 64.8

23-32 20-72 21-70 24-80 36-101 27-97

8 7 22 56 51 126

23.4 26.3 31.0 40.0 55.2 69.8

2.0 1.4 1.3 1.6 1.8 1.1

18.7 22.8 28.2 36.8 51.5 67.6

28.1 29.8 33.8 43.2 58.9 72.0

23.4 26.3 31.0 40.0 55.2 69.8

8 11 35 47 36 129

23.5 29.7 33.0 41.9 55.3 66.8

1.3 4.4 1.9 2.0 2.7 1.2

20.4 20.0 29.1 37.8 49.9 64.4

26.6 39.5 36.9 45.9 60.8 69.1

5 9 21 45 36 139

26.0 29.3 33.9 38.7 57.5 67.5

3.2 2.5 2.0 1.1 2.1 1.2

17.2 23.6 29.8 36.5 53.2 65.1

13 20 56 92 72 268

24.5 29.6 33.3 40.3 56.4 67.1

1.4 2.6 1.4 1.2 1.7 0.8

21.4 24.1 30.5 38.0 53.0 65.5

Std Dev

age range

24.6 38.3 52.9 57.8 65.5 74.0

1.9 10.3 15.3 17.3 15.8 14.5

20-24 20-54 26-78 30-89 36-88 44-101

20.9 29.2 43.6 52.3 66.2

25.1 42.1 51.3 61.5 72.4

2.2 13.7 16.0 15.4 15.0

20-25 22-68 22-90 29-94 35-97

2.0 6.8 3.0 1.8 2.8

-2.4 9.0 40.4 44.9 58.2

48.4 52.5 53.6 53.4 69.8

2.8 13.7 11.0 5.1 14.4

21-25 22-51 27-68 40-56 43-99

22.2 27.7 39.6 48.5 58.4 71.1

1.0 2.4 2.8 1.9 2.1 1.2

19.4 22.5 33.9 44.7 54.1 68.9

25.0 32.9 45.2 52.3 62.6 73.4

2.3 9.0 14.6 17.1 16.0 14.6

20-25 20-54 23-78 22-90 29-94 35-101

22.2 27.1 38.5 48.3 57.2 70.2

1.0 2.1 2.6 1.7 1.9 1.1

19.4 22.6 33.2 45.0 53.4 68.0

25.0 31.7 43.7 51.6 61.0 72.3

2.3 8.5 14.6 16.3 15.4 14.7

20-25 20-54 22-78 22-90 29-94 35-101

mean

SE

4 9 12 25 19 94

21.5 30.3 43.2 50.6 57.9 71.0

1.0 3.4 4.4 3.5 3.6 1.5

18.5 22.4 33.4 43.5 50.3 68.0

20-37 22-32 22-43 26-84 22-88 31-98

1 7 20 69 45 93

25.0 23.0 35.7 47.5 56.9 69.3

0.9 3.1 1.9 2.3 1.6

23.5 29.7 33.0 41.9 55.3 66.8

20-32 20-72 21-70 26-84 22-101 31-98

2 4 13 8 26

23.0 30.8 47.0 49.1 64.0

34.8 35.1 37.9 41.0 61.8 69.8

26.0 29.3 33.9 38.7 57.5 67.5

20-37 22-48 22-62 24-63 39-94 27-92

5 14 28 81 56 161

27.6 35.0 36.1 42.6 59.8 68.8

24.5 29.6 33.3 40.3 56.4 67.1

20-37 20-72 21-70 24-84 22-101 27-98

5 16 32 94 64 187

357

n

95% CI

Pubic symphysis Hartnett-Fulginiti REFERENCE AMERICAN H-F phase FEMALE I II III IV V VI VII MALE I II III IV V VI VII BLACK I II III IV V VI VII WHITE I II III IV V VI VII TOTAL I II III IV V VI VII

RECENT AMERICAN Std Dev

age range

31.3 40.7 38.2 44.9 65.9 65.7 77.0

4.1 15.2 10.8 10.4 17.0 13.6 15.4

23-32 20-72 22-70 24-80 36-101 27-91 35-97

18.7 23.8 29.3 37.2 51.5 67.1 73.2

28.1 28.4 34.7 43.9 58.9 71.5 94.3

5.7 3.3 6.2 12.0 13.1 12.3 6.7

1.3 3.8 1.7 2.1 2.6 1.2 3.3

20.4 23.5 29.0 38.5 50.1 63.4 72.4

26.6 39.5 35.7 46.9 60.8 68.3 87.4

26.0 29.3 34.7 38.9 57.0 67.0 70.4

3.2 2.0 1.9 1.2 2.1 1.3 3.1

17.2 24.8 30.8 36.5 52.8 64.5 64.1

24.5 30.6 33.3 40.8 56.2 66.4 72.8

1.4 2.4 1.2 1.2 1.7 0.9 2.5

21.4 25.7 30.8 38.4 52.9 64.7 67.7

n

mean

SE

95% CI

5 18 31 34 23 108 32

26.2 33.2 34.2 41.3 58.6 63.1 71.4

1.8 3.6 1.9 1.8 3.6 1.3 2.7

21.1 25.6 30.3 37.7 51.2 60.5 65.9

8 10 23 52 51 122 4

23.4 26.1 32.0 40.6 55.2 69.3 83.8

2.0 1.0 1.3 1.7 1.8 1.1 3.3

8 17 32 44 37 119 9

23.5 31.5 32.3 42.7 55.5 65.8 79.9

5 11 22 42 37 111 27 13 28 54 86 74 230 36

Std Dev

age range

27.0 38.1 53.8 58.5 65.2 74.2 80.3

2.0 10.5 16.6 17.4 15.1 14.5 14.6

20-24 20-54 24-78 30-89 36-88 44-101 47-99

21.5 33.3 42.9 54.6 65.0 72.7

26.5 46.6 51.1 62.7 71.9 93.8

3.4 15.7 16.0 15.3 15.0 11.4

20-32 23-80 22-90 29-94 35-97 67-95

1.2 8.0 3.3 1.6 3.1 16

17.5 13.3 39.3 47.7 57.6 -127

27.8 64.2 53.6 54.5 70.4 292.1

2.1 16.0 11.3 5.4 13.7 23.3

21-25 23-54 27-68 40-59 43-84 66-99

22.0 28.6 41.1 48.6 60.0 70.3 75.7

1.2 2.1 2.9 2.0 1.9 1.4 2.7

17.0 24.1 35.2 44.7 56.2 67.7 70.2

27.0 33.0 46.9 52.6 63.8 73.0 81.1

2.0 9.0 16.0 17.2 15.9 14.8 14.1

20-24 20-54 23-80 22-90 29-94 35-101 47-97

22.0 27.7 40.8 48.3 58.7 69.4 76.1

1.2 1.9 2.7 1.8 1.7 1.3 2.6

17.0 23.8 35.4 44.8 55.4 67.0 70.8

27.0 31.6 46.2 51.8 62.0 71.9 81.5

2.0 8.6 15.8 16.5 15.2 14.8 14.3

20-24 20-54 23-80 22-90 29-94 35-101 47-99

mean

SE

3 11 11 26 24 65 23

22.0 31.1 42.6 51.5 58.8 70.6 74.0

1.2 3.2 5.0 3.4 3.1 1.8 3.1

17.0 24.1 31.5 44.4 52.4 67.0 67.6

20-37 22-32 22-43 26-84 22-88 31-98 76-92

10 24 61 58 75 7

24.0 40.0 47.0 58.6 68.4 83.3

1.1 3.2 2.1 2.0 1.7 4.3

3.7 15.6 9.3 13.7 16.0 13.4 9.8

20-32 20-72 22-70 26-84 22-101 31-98 66-97

3 4 12 12 20 2

22.7 38.8 46.4 51.1 64.0 82.5

34.8 33.8 38.6 41.3 61.3 69.5 76.7

7.1 6.7 8.8 7.8 12.7 13.2 16.0

20-37 22-48 22-62 24-63 39-94 27-91 35-92

3 18 31 75 70 120 28

27.6 35.6 35.8 43.3 59.6 68.1 77.9

5.1 12.7 9.1 11.3 14.4 13.3 15.1

20-37 20-72 22-70 24-84 22-101 27-98 35-97

3 21 35 87 82 140 30

358

n

95% CI

Pubic symphysis Boldsen et al. symphyseal relief REFERENCE AMERICAN SR phase FEMALE I II III IV V VI MALE I II III IV V VI BLACK I II III IV V VI WHITE I II III IV V VI TOTAL I II III IV V VI

RECENT AMERICAN Std Dev

age range

33.6 54.9 54.2 61.8 75.8

5.1 20.9 19.2 16.6 21.0

23-38 21-93 22-102 26-94 26-97

18.8 35.5 43.7 58.7 50.3

36.1 52.6 52.7 64.1 73.7

11.2 19.8 18.2 17.0 18.5

1.6 3.9 2.5 1.4 6.2

22.7 36.6 41.9 57.1 50.6

29.6 52.7 51.8 62.6 77.4

32.2 48.0 50.3 60.8 65.6

6.3 3.6 2.2 1.5 4.0

14.8 40.7 45.8 57.9 57.2

28.1 46.4 48.7 60.3 64.9

2.2 2.6 1.7 1.0 3.4

23.4 41.2 45.5 58.3 57.9

n

mean

SE

95% CI

7 37 61 124 22

28.9 47.9 49.3 58.8 66.5

1.9 3.4 2.5 1.5 4.5

24.2 41.0 44.4 55.9 57.2

9 23 66 156 12

27.4 44.0 48.2 61.4 62.0

3.7 4.1 2.3 1.4 5.3

11 28 57 153 14

26.2 44.6 46.8 59.8 64.0

5 32 70 127 20

16 60 127 280 34

Std Dev

age range

58.1 54.6 61.8 68.2 78.9

23.1 19.3 18.3 16.9 14.0

20-81 20-93 26-90 24-101 49-99

-1.1 35.8 48.0 54.7 60.2

66.1 55.9 59.0 61.0 73.6

21.1 21.5 20.0 18.5 16.3

20-64 21-82 22-92 22-97 36-95

19 9.4 2.1 7.5

-24.1 19.8 49.8 45.9

144.1 67.9 58.5 82.4

33.9 22.9 13.0 19.7

21-82 22-72 27-84 43-99

25.0 36.2 45.2 54.6 61.7 71.6

7.0 3.0 2.2 1.4 2.1

20.3 39.2 50.2 59.0 67.4

52.1 51.2 59.0 64.5 75.8

22.3 19.0 18.9 18.8 14.6

20-81 20-93 24-92 22-101 36-98

25.0 35.2 46.2 53.8 60.4 70.7

6.5 3.0 2.2 1.2 2.1

20.8 40.1 49.5 58.0 66.6

49.6 52.3 58.1 62.9 74.8

21.4 20.1 19.3 18.1 15.3

20-81 20-93 22-92 22-101 36-99

mean

SE

7 24 26 83 31

36.7 46.5 54.4 64.5 73.7

8.7 3.9 3.6 1.9 2.5

15.4 38.3 47.0 60.9 68.6

20-54 22-84 22-89 26-98 36-85

1 4 20 53 134 25

25.0 32.5 45.9 53.5 57.9 66.9

10 4.8 2.7 1.6 3.3

5.1 20.8 18.7 17.1 23.3

20-38 21-81 22-102 26-98 26-97

1 3 6 37 7

25.0 60.0 43.8 54.1 64.1

49.6 55.3 54.7 63.7 74.0

14.0 20.3 18.6 16.7 18.0

20-54 27-93 22-87 27-94 27-90

1 10 41 73 180 49

32.8 51.7 52.0 62.3 71.9

8.8 20.4 18.7 16.9 20.0

20-54 21-93 22-102 26-98 26-97

1 11 44 79 217 56

359

n

95% CI

Pubic symphysis Boldsen et al. symphyseal texture REFERENCE AMERICAN ST phase FEMALE I II III IV MALE I II III IV BLACK I II III IV WHITE I II III IV TOTAL I II III IV

RECENT AMERICAN Std Dev

age range

53.9 60.2 65.4 64.8

19.7 17.8 20.0 18.3

21-101 22-91 23-102 27-72

47.8 53.6 50.6 49.3

56.2 61.1 59.8 82.0

19.3 20.2 17.4 19.6

2.2 1.8 2.6 5.8

43.7 53.6 50.9 49.5

52.4 60.8 61.3 79.5

53.4 56.8 59.0 54.0

2.0 1.9 2.4 6.7

49.4 53.1 54.1 39.2

50.8 57.0 57.5 57.7

1.5 1.3 1.8 4.8

47.8 54.5 53.9 47.5

n

mean

SE

95% CI

86 101 53 9

49.6 56.6 59.9 50.7

2.1 1.8 2.7 6.1

45.4 53.1 54.4 36.6

83 115 57 8

52.0 57.4 55.2 65.6

2.1 1.9 2.3 6.9

83 113 58 6

48.1 57.2 56.1 64.5

86 103 52 11 169 216 110 17

Std Dev

age range

57.6 61.0 74.5 74.4

20.8 19.4 18.0 11.0

20-101 20-94 27-98 50-99

46.0 50.3 52.2 58.1

59.1 58.0 62.5 70.3

21.0 20.1 18.7 16.3

20-91 21-97 25-95 35-94

2.8 3.9 3.9 7.4

38.9 42.3 46.6 46.1

54.7 58.3 63.3 84.2

6.3 18.5 16.2 18.1

42-57 21-82 27-84 49-99

51.8 55.5 64.8 67.0

2.6 1.7 2.1 2.0

46.7 52.2 60.7 63.1

57.0 58.8 69.0 71.0

21.5 20.0 19.5 14.0

20-101 20-97 25-98 35-94

51.5 54.8 63.2 66.8

2.4 1.5 1.9 1.9

46.7 51.8 59.4 63.0

56.3 57.8 67.0 70.7

20.8 19.8 19.3 14.3

20-101 20-97 25-98 35-99

mean

SE

32 60 50 26

50.1 55.9 69.4 69.9

3.7 2.5 2.6 2.2

42.6 50.9 64.3 65.5

20-86 22-98 26-84 36-85

42 106 53 30

52.6 54.1 57.3 64.2

3.3 2.0 2.6 3.0

19.9 19.1 19.8 14.3

20-101 22-98 26-102 43-80

5 23 17 6

46.8 50.3 54.9 65.2

57.5 60.6 63.9 68.8

18.8 19.1 17.5 22.1

20-94 22-92 23-88 27-85

69 143 86 50

53.8 59.6 61.0 67.9

19.5 19.1 18.7 19.9

20-101 22-98 23-102 27-85

74 166 103 56

360

n

95% CI

Pubic symphysis Boldsen et al. superior apex REFERENCE AMERICAN SA phase FEMALE I II III IV MALE I II III IV BLACK I II III IV WHITE I II III IV TOTAL I II III IV

RECENT AMERICAN Std Dev

age range

n

mean

SE

36.4 41.5 42.1 65.6

11.9 14.7 8.5 15.6

20-72 21-70 22-59 27-101

16 9 26 106

33.1 43.2 49.6 68.2

4.0 5.6 2.3 1.6

24.6 30.4 45.0 65.0

22.4 27.2 37.9 61.6

31.1 32.9 47.2 66.2

6.8 7.1 14.8 15.7

20-42 20-44 26-87 22-98

6 18 71 134

30.7 41.8 47.0 63.5

6.0 5.3 2.0 1.5

2.6 2.4 2.1 1.2

22.2 27.6 36.5 60.8

33.3 37.3 45.2 65.6

11.5 12.5 13.2 15.6

20-72 20-70 22-83 22-101

1 4 13 35

21.0 37.0 46.8 59.3

31.4 32.3 41.3 63.8

2.3 2.2 2.2 1.2

26.4 27.7 36.9 61.4

36.5 36.8 45.8 66.2

8.0 9.8 12.4 15.7

20-48 22-62 24-87 27-94

21 23 84 205

29.2 32.4 41.1 63.5

1.9 1.6 1.5 0.9

25.4 29.1 38.0 61.8

32.9 35.7 44.1 65.2

10.3 11.4 12.7 15.6

20-72 20-70 22-87 22-101

22 27 97 240

n

mean

SE

95% CI

19 22 28 152

30.7 35.1 38.8 63.1

2.7 3.1 1.6 1.3

24.9 28.6 35.5 60.6

12 26 42 180

26.8 30.1 42.6 63.9

2.0 1.4 2.3 1.2

19 28 38 164

27.7 32.4 40.8 63.2

12 20 32 168 31 48 70 332

361

Std Dev

age range

41.6 56.1 54.3 71.4

15.9 16.7 11.5 16.6

20-78 26-69 30-73 31-101

15.2 30.5 43.0 60.6

46.1 53.0 51.0 66.4

14.7 22.6 16.8 17.1

20-59 21-90 22-87 26-97

12 3.4 2.5

-2.4 39.3 54.2

76.4 54.2 64.4

24.8 12.4 14.9

21-21 22-74 23-68 38-99

33.0 43.2 47.9 66.7

3.4 4.2 1.8 1.2

26.0 34.5 44.4 64.3

40.0 51.9 51.3 69.0

15.5 20.2 16.0 17.2

20-78 21-90 22-87 26-101

32.5 42.3 47.7 65.6

3.3 4.0 1.6 1.1

25.7 34.2 44.6 63.4

39.2 50.4 50.8 67.7

15.3 20.5 15.5 17.0

20-78 21-90 22-87 26-101

95% CI

Pubic symphysis Boldsen et al. ventral symphyseal margin REFERENCE AMERICAN VSM phase FEMALE I II III IV V VI VII MALE I II III IV V VI VII BLACK I II III IV V VI VII WHITE I II III IV V VI VII TOTAL I II III IV V VI VII

RECENT AMERICAN Std Dev

age range

37.9 36.3 40.2 40.1 47.5 61.1 67.5

2.5 13.0 11.1 11.1 11.4 11.7 14.9

29-34 20-72 22-70 22-62 30-80 36-78 27-101

17.7 21.1 25.8 30.3 35.8 48.0 66.1

31.3 29.2 35.5 40.8 43.3 56.3 70.5

6.5 4.4 8.7 10.2 12.0 13.0 13.2

2.5 2.8 2.5 2.6 2.4 2.6 1.2

17.3 21.6 28.2 29.2 39.0 43.6 64.0

33.2 33.6 38.8 40.2 48.6 54.3 68.7

28.2 30.5 32.0 35.4 38.1 55.8 67.0

3.4 3.6 2.5 2.4 1.2 2.1 1.2

18.8 21.2 26.7 30.3 35.6 51.5 64.7

26.9 28.3 32.9 35.0 41.1 53.0 66.7

2.1 2.3 1.8 1.8 1.4 1.7 0.8

22.0 23.6 29.3 31.4 38.3 49.7 65.0

n

mean

SE

95% CI

3 17 18 18 29 17 146

31.7 29.6 34.7 34.6 43.2 55.1 65.1

1.5 3.1 2.6 2.6 2.1 2.8 1.2

25.4 22.9 29.1 29.0 38.9 49.1 62.7

6 7 15 17 42 40 143

24.5 25.1 30.7 35.5 39.6 52.2 68.3

2.6 1.7 2.3 2.5 1.9 2.1 1.1

4 18 19 19 37 23 144

25.3 27.6 33.5 34.7 43.8 49.0 66.3

5 6 14 16 34 34 145 9 24 33 35 71 57 289

Std Dev

age range

30.7 55.0 50.0 51.4 60.4 67.9 73.5

6.2 20.4 14.1 8.0 15.3 17.5 15.4

20-36 22-78 26-67 30-49 34-89 31-93 41-101

16.2 20.7 28.9 37.9 41.1 51.3 64.0

30.5 26.8 51.1 58.1 52.5 59.0 70.7

2.9 1.9 21.6 19.7 15.8 16.8 15.9

20-25 21-25 21-90 22-83 26-90 26-94 35-97

2.5 2.8 2.5 2.6 2.4 2.6 1.2

17.3 21.6 28.2 29.2 39.0 43.6 64.0

33.2 33.6 38.8 40.2 48.6 54.3 68.7

5.0 12.1 11.0 11.4 14.4 12.5 14.2

20-32 20-72 22-70 22-64 26-84 22-74 36-101

28.2 30.5 32.0 35.4 38.1 55.8 67.0

3.4 3.6 2.5 2.4 1.2 2.1 1.2

18.8 21.2 26.7 30.3 35.6 51.5 64.7

37.6 39.8 37.3 40.6 40.5 60.0 69.4

7.6 8.8 9.2 9.7 7.1 12.1 14.2

20-37 23-48 22-54 24-62 27-63 32-81 27-94

23.9 31.6 40.2 46.2 49.3 56.5 68.8

1.7 5.1 3.5 4.0 2.2 1.7 1.2

19.9 20.2 33.0 37.9 44.9 53.0 66.5

27.8 43.1 47.5 54.6 53.7 59.9 71.1

5.1 17.0 18.7 18.3 15.8 17.0 15.7

20-36 21-78 21-90 22-83 26-90 26-94 35-101

mean

SE

6 7 11 4 20 24 88

24.2 36.1 40.6 38.8 53.3 60.5 70.2

2.5 7.7 4.3 4.0 3.4 3.6 1.6

17.7 17.3 31.1 26.1 46.1 53.1 66.9

20-37 21-32 22-54 23-64 26-84 22-81 31-98

3 4 17 17 32 74 89

23.3 23.8 40.0 48.0 46.8 55.2 67.4

1.7 1.0 5.3 4.8 2.8 2.0 1.7

5.0 12.1 11.0 11.4 14.4 12.5 14.2

20-32 20-72 22-70 22-64 26-84 22-74 36-101

4 18 19 19 37 23 144

25.3 27.6 33.5 34.7 43.8 49.0 66.3

37.6 39.8 37.3 40.6 40.5 60.0 69.4

7.6 8.8 9.2 9.7 7.1 12.1 14.2

20-37 23-48 22-54 24-62 27-63 32-81 27-94

5 6 14 16 34 34 145

31.8 33.0 36.5 38.7 43.9 56.4 68.3

6.4 11.2 10.2 10.6 11.8 12.6 14.2

20-37 20-72 22-70 22-64 26-84 22-81 27-101

9 11 28 21 52 98 177

362

n

95% CI

Pubic symphysis Boldsen et al. dorsal symphyseal margin REFERENCE AMERICAN DSM phase FEMALE I II III IV V MALE I II III IV V BLACK I II III IV V WHITE I II III IV V TOTAL I II III IV V

RECENT AMERICAN Std Dev

age range

75.8 42.7 39.5 61.3 66.7

5.7 16.1 10.4 18.1 14.7

21-29 22-72 22-80 30-101 27-97

15.4 24.7 34.8 50.2 66.2

33.4 30.6 40.0 58.3 70.7

7.2 5.3 10.3 13.9 13.2

1.2 2.9 1.6 2.6 1.2

16.5 26.7 34.8 48.5 63.6

26.8 38.6 41.2 58.9 68.5

26.8 32.9 35.9 55.1 66.5

4.0 3.0 1.1 2.4 1.2

14.1 26.4 33.8 50.4 64.2

24.6 32.7 37.0 54.4 66.3

2.4 2.2 1.0 1.8 0.8

18.7 28.3 35.1 50.9 64.7

n

mean

SE

95% CI

2 24 49 30 144

25.0 35.9 36.5 54.5 64.3

4.0 3.3 1.5 3.3 1.2

-25.8 29.1 33.5 47.8 61.9

5 15 62 48 137

24.4 27.7 37.4 54.3 68.5

3.2 1.4 1.3 2.0 1.1

3 27 57 42 133

21.7 32.7 38.0 53.7 66.1

4 12 54 36 148 7 39 111 78 281

Std Dev

age range

27.1 50.6 57.1 64.6 74.0

2.3 18.5 16.1 19.5 14.3

20-24 20-81 24-78 27-101 44-99

-9.3 20.1 39.9 54.7 63.4

54.3 42.3 48.1 62.3 70.8

3.5 14.4 16.0 17.5 16.5

20-25 21-68 22-82 23-94 29-97

5.6 2.3 3.4

38.7 42.4 57.5

63.1 52.3 71.5

19.2 9.9 15.4

25-25 22-82 23-71 43-99

21.8 38.2 45.6 60.3 69.7

1.1 3.5 1.8 1.8 1.3

18.7 30.9 42.0 56.7 67.1

24.9 45.5 49.1 64.0 72.2

2.5 17.7 15.8 18.5 15.4

20-25 20-81 22-80 27-101 29-98

21.8 37.7 46.3 58.3 69.0

1.1 3.4 1.7 1.7 1.2

18.7 30.6 42.9 55.1 66.6

24.9 44.7 49.7 61.6 71.4

2.5 17.5 16.2 18.0 15.5

20-25 20-81 22-82 23-101 29-99

mean

SE

3 17 29 34 83

21.3 41.1 51.0 57.9 70.8

1.3 4.5 3.0 3.3 1.6

15.6 31.6 44.9 51.1 67.7

20-37 22-43 22-84 22-83 31-98

2 9 61 84 79

22.5 31.2 44.0 58.5 67.1

2.5 4.8 2.0 1.9 1.9

2.1 15.0 12.2 16.8 14.1

20-24 22-72 22-84 22-101 31-98

1 12 18 21

25.0 50.9 47.3 64.5

39.4 39.4 38.0 59.9 68.8

7.9 10.3 7.7 14.1 14.1

20-37 23-62 22-63 32-94 27-92

5 25 78 100 141

30.4 37.2 38.9 57.9 68.0

6.4 13.6 10.3 15.5 14.1

20-37 22-72 22-84 22-101 27-98

5 26 90 118 162

363

n

95% CI

Auricular surface Lovejoy et al. REFERENCE AMERICAN Lovejoy phase FEMALE I II III IV V VI VII VIII MALE I II III IV V VI VII VIII BLACK I II III IV V VI VII VIII WHITE I II III IV V VI VII VIII TOTAL I II III IV V VI VII VIII

n

RECENT AMERICAN Std Dev

age range

23.8 26.9 32.5 45.9 45.8 60.2 65.7 73.5

1.3 2.6 3.8 12.2 8.0 12.7 15.7 14.3

20-23 21-29 24-39 27-70 24-61 39-85 27-101 30-102

19.3 15.7 26.7 31.6 36.5 49.1 60.2 68.7

23.5 42.0 32.3 41.9 43.4 57.2 66.6 75.0

1.7 12.5 7.7 11.6 6.3 11.7 14.7 11.9

0.6 0.6 0.9 2.3 1.8 1.9 1.8 1.7

20.3 23.0 27.2 32.9 38.5 51.1 58.8 67.7

23.1 25.7 30.7 42.4 46.3 58.9 65.9 74.6

21.0 30.0 31.4 39.9 39.8 52.6 63.1 70.8

1.0 4.2 1.6 2.6 1.7 2.5 1.6 1.5

8.3 19.8 28.1 34.6 36.1 47.5 59.9 67.7

21.6 26.4 30.1 38.9 41.0 54.1 62.8 71.0

0.5 1.6 0.9 1.8 1.3 1.5 1.2 1.2

20.5 23.0 28.3 35.4 38.4 51.0 60.4 68.7

mean

SE

95% CI

4 13 22 25 18 28 75 77

21.8 25.3 30.9 40.8 41.8 55.3 62.1 70.3

0.6 0.7 0.8 2.4 1.9 2.4 1.8 1.6

19.8 23.7 29.2 35.8 37.8 50.4 58.5 67.0

5 6 32 22 15 35 84 58

21.4 28.8 29.5 36.7 39.9 53.1 63.4 71.9

0.8 5.1 1.4 2.5 1.6 2.0 1.6 1.6

7 12 30 21 15 38 73 63

21.7 24.3 29.0 37.7 42.4 55.0 62.4 71.2

2 7 24 26 18 25 86 72 9 19 54 47 33 63 159 135

Std Dev

age range

45.2 84.1 64.6 68.2 70.8 70.6 78.6

6.9 4.2 15.8 15.9 15.1 16.2 12.3

20-32 43-49 22-61 35-82 46-89 39-101 50-99

17.4 29.9 29.6 43.3 51.3 53.5 68.3

39.6 61.1 48.2 51.4 64.5 63.6 78.1

7.0 18.7 14.6 11.8 12.9 15.4 14.5

25-39 26-83 24-74 27-68 40-82 23-90 30-96

3.7 2.9 4.5 4.9

38.6 38.3 42.2 67.4

56.2 54.1 62.6 92.6

10.5 6.4 14.2 12.0

27-61 40-56 23-72 66-99

28.8 44.1 38.6 52.6 61.7 62.0 73.5

2.8 5.6 3.1 2.3 2.8 2.2 1.6

21.7 31.2 32.0 48.0 56.0 57.6 70.4

36.0 57.0 45.2 57.1 67.4 66.5 76.7

6.8 16.8 12.4 15.1 13.2 15.8 13.4

20-39 26-83 22-61 31-82 41-89 34-101 30-96

28.3 45.6 40.7 51.8 59.0 60.4 74.0

2.4 5.2 3.6 2.0 2.6 2.0 1.5

22.4 33.8 33.1 47.8 53.7 56.4 71.0

34.2 57.4 48.3 55.8 64.2 64.5 77.0

6.4 16.5 14.8 14.5 13.6 15.8 13.3

20-39 26-83 22-74 27-82 40-89 23-101 30-99

mean

SE

3 2 5 18 11 23 42

28.0 46.0 45.0 60.3 60.6 63.6 74.7

4.0 3.0 7.1 3.7 4.5 3.4 1.9

10.8 7.9 25.4 52.4 50.5 56.6 70.9

20-24 21-54 22-63 24-81 28-51 37-87 31-96 39-98

4 8 12 35 17 38 36

28.5 45.5 38.9 47.4 57.9 58.5 73.2

3.5 6.6 4.2 2.0 3.1 2.5 2.4

1.5 2.2 4.8 10.4 7.1 11.9 15.3 13.7

20-24 21-28 22-39 26-70 33-61 39-85 35-101 38-102

1 1 1 8 5 10 6

25.0 59.0 74.0 47.4 46.2 52.4 80.0

33.7 40.2 34.7 45.3 43.4 57.8 66.4 73.8

1.4 11.0 7.9 13.2 7.4 12.6 15.2 13.0

20-22 21-54 22-63 24-81 24-49 37-87 27-96 30-93

6 9 16 45 23 51 72

22.7 29.9 31.8 42.4 43.5 57.1 65.2 73.2

1.4 7.1 6.4 12.0 7.2 12.1 15.2 13.3

20-24 21-54 22-63 24-81 24-61 37-87 27-101 30-102

7 10 17 53 28 61 78

364

n

95% CI

Auricular surface Boldsen et al. superior demiface topography REFERENCE AMERICAN SDT phase FEMALE I II III MALE I II III BLACK I II III WHITE I II III TOTAL I II III

RECENT AMERICAN Std Dev

age range

36.4 55.6 69.6

11.9 18.2 17.5

21-70 20-94 29-102

23.1 50.2 56.2

27.1 57.0 62.7

3.9 19.2 17.8

2.2 1.8 1.6

23.4 45.9 58.5

32.6 52.9 64.6

27.4 55.3 63.0

1.2 1.4 2.2

24.6 52.6 58.7

27.8 53.2 62.1

1.6 1.1 1.3

24.7 51.0 59.5

n

mean

SE

95% CI

18 163 82

30.4 52.8 65.8

2.8 1.4 1.9

24.5 50.0 61.9

17 124 117

25.1 53.6 59.4

1.0 1.7 1.7

24 105 132

28.0 49.4 61.6

11 182 67 35 287 199

Std Dev

age range

61.0 66.4 74.5

19.8 16.3 15.0

20-72 32-95 39-101

32.2 51.1 55.6

48.1 60.6 63.5

14.4 17.6 17.9

25-78 25-96 23-94

9.8 3.7 4.1

0.1 42.3 50.3

84.6 59.5 67.6

17.0 10.3 18.9

25-59 40-68 23-99

39.8 58.9 64.9

3.7 1.8 1.6

31.9 55.2 61.7

47.7 62.6 68.1

15.9 17.6 17.3

20-78 25-96 24-101

40.1 58.2 64.0

3.4 1.7 1.5

33.0 54.8 61.0

47.3 61.7 67.0

15.6 17.2 17.6

20-78 25-96 23-101

mean

SE

6 43 55

40.2 61.4 70.5

8.1 2.5 2.0

19.4 56.3 66.4

20-32 20-87 26-98

15 56 81

40.1 55.9 59.6

3.7 2.4 2.0

10.9 18.2 18.0

20-70 20-89 26-102

3 8 21

42.3 50.9 59.0

30.1 58.1 67.4

4.1 18.5 18.0

21-35 20-94 29-96

18 91 115

31.0 55.3 64.6

9.2 18.6 18.0

20-70 20-94 26-102

21 99 136

365

n

95% CI

Auricular surface Boldsen et al. inferior demiface topography REFERENCE AMERICAN IDT phase FEMALE I II III MALE I II III BLACK I II III WHITE I II III TOTAL I II III

RECENT AMERICAN Std Dev

age range

42.7 53.2 68.3

16.2 18.1 17.5

21-70 20-86 29-102

23.4 49.2 57.0

34.3 56.1 63.6

11.4 18.8 18.4

2.6 1.9 1.5

24.3 44.8 58.3

35.0 52.2 64.4

34.3 52.9 64.3

5.5 1.5 1.9

21.3 50.0 60.6

30.8 51.3 62.5

2.4 1.2 1.2

25.9 49.0 60.2

n

mean

SE

95% CI

14 138 109

33.4 50.1 64.9

4.3 1.5 1.7

24.0 47.1 61.6

19 117 120

28.8 52.6 60.3

2.6 1.7 1.7

25 93 140

29.6 48.5 61.4

8 162 89 33 255 229

Std Dev

age range

62.8 66.8 72.7

8.6 17.9 16.0

32-49 20-101 38-99

27.7 52.9 53.6

49.8 62.2 61.5

16.4 17.6 17.8

25-78 26-94 23-96

9.8 4.7 4.0

0.1 43.8 48.9

84.6 66.8 65.5

17.0 12.4 18.7

25-59 43-72 23-99

38.5 59.3 63.2

4.5 1.9 1.6

28.4 55.6 59.9

48.5 63.1 66.4

15.0 18.1 17.6

25-78 20-101 24-98

39.3 59.0 62.2

4.0 1.8 1.5

30.7 55.5 59.2

47.9 62.6 65.2

14.9 17.7 17.9

25-78 20-101 23-99

mean

SE

3 41 59

41.3 61.1 68.5

5.0 2.8 2.1

19.9 55.5 64.4

20-67 20-98 22-96

11 58 80

38.7 57.6 57.5

5.0 2.3 2.0

13.1 18.1 18.2

20-70 20-98 22-102

3 7 22

42.3 55.3 57.2

47.2 55.7 68.1

15.5 18.4 17.8

21-67 20-87 27-96

11 92 117

35.6 53.5 64.9

13.6 18.4 18.1

20-70 20-98 22-102

14 99 139

366

n

95% CI

Auricular surface Boldsen et al. superior surface morphology REFERENCE AMERICAN SSM phase FEMALE I II III IV V MALE I II III IV V BLACK I II III IV V WHITE I II III IV V TOTAL I II III IV V

RECENT AMERICAN Std Dev

age range

38.2 43.4 52.8 61.9 74.7

9.8 13.1 18.6 18.9 13.4

20-49 21-69 23-97 22-102 49-91

26.2 22.6 36.6 57.6 56.3

51.7 36.7 45.4 63.0 71.3

20.1 10.5 15.9 17.7 15.5

4.2 2.6 2.6 1.5 2.6

22.0 26.5 36.6 56.1 59.6

40.7 37.6 47.1 61.9 70.5

40.2 37.1 46.3 60.3 67.9

6.6 4.2 2.3 1.4 4.4

24.9 27.8 41.7 57.6 58.5

35.4 34.1 44.2 59.7 66.2

3.8 2.3 1.7 1.0 2.3

27.4 29.4 40.7 57.7 61.5

n

mean

SE

95% CI

8 18 50 166 20

30.0 36.9 47.5 59.0 68.4

3.5 3.1 2.6 1.5 3.0

21.8 30.4 42.2 56.1 62.1

12 11 52 164 19

38.9 29.6 41.0 60.3 63.8

5.8 3.2 2.2 1.4 3.6

11 17 49 160 24

31.4 32.1 41.9 59.0 65.0

9 12 53 170 15 20 29 102 330 39

Std Dev

age range

60.7 70.1 70.6 82.2

18.5 19.4 15.3 16.9

20-72 32-95 35-101 44-98

17.5 15.9 37.8 54.5 60.6

44.5 52.6 52.0 61.5 72.7

8.5 11.5 14.7 17.9 13.6

25-43 25-51 26-83 23-96 47-94

4.0 3.5 8.8

41.4 47.3 34.6

67.1 62.0 110.6

8.1 17.4 15.3

43-61 23-99 55-82

33.0 40.2 50.3 62.8 68.3

5.3 4.9 3.5 1.4 2.7

10.2 29.2 43.1 60.0 62.8

55.8 51.2 57.6 65.6 73.7

9.2 16.4 19.0 17.2 15.1

25-43 20-72 26-95 24-101 44-98

31.0 40.2 50.8 61.6 68.6

4.2 4.9 3.1 1.3 2.5

17.5 29.2 44.5 59.0 63.5

44.5 51.2 57.2 64.3 73.7

8.5 16.4 18.0 17.4 14.9

25-43 20-72 26-95 23-101 44-98

mean

SE

7 14 70 13

43.6 58.9 67.0 72.0

7.0 5.2 1.8 4.7

26.4 47.7 63.4 61.8

20-81 21-52 22-81 22-98 37-86

4 4 19 103 22

31.0 34.3 44.9 58.0 66.6

4.2 5.8 3.4 1.8 2.9

13.9 10.8 18.3 18.6 12.9

20-63 21-52 22-97 22-102 41-89

1

25.0

4 24 3

54.3 54.7 72.7

55.5 46.4 50.9 63.1 77.4

19.9 14.6 16.6 18.1 17.0

20-81 21-69 22-85 23-96 37-91

3 11 29 149 32

43.3 38.9 47.6 61.7 70.8

17.0 12.5 17.5 18.3 14.5

20-81 21-69 22-97 22-102 37-91

4 11 33 173 35

367

n

95% CI

Auricular surface Boldsen et al. apical surface morphology REFERENCE AMERICAN APM phase FEMALE I II III IV V MALE I II III IV V BLACK I II III IV V WHITE I II III IV V TOTAL I II III IV V

RECENT AMERICAN Std Dev

age range

42.0 49.7 52.3 64.6 71.1

14.1 17.4 17.4 18.6 16.7

20-66 21-76 23-87 22-102 38-89

26.2 30.3 40.0 58.1 55.7

41.9 47.0 49.9 63.7 70.0

16.3 16.2 17.9 17.7 14.4

2.6 3.6 2.5 1.6 2.6

26.7 32.0 39.5 58.2 56.5

37.3 46.8 49.4 64.4 67.0

38.6 40.8 48.5 61.0 66.4

5.2 4.7 2.1 1.5 4.6

27.2 30.5 44.3 58.1 56.7

34.4 39.9 46.7 61.2 63.6

2.5 2.8 1.6 1.1 2.4

29.3 34.2 43.5 59.1 58.8

n

mean

SE

95% CI

17 18 67 136 25

34.8 41.0 48.1 61.5 64.2

3.4 4.1 2.1 1.6 3.3

27.5 32.4 43.8 58.3 57.3

19 17 52 152 18

34.0 38.7 44.9 60.9 62.8

3.7 3.9 2.5 1.4 3.4

23 23 53 137 26

32.0 39.4 44.4 61.3 61.8

13 12 66 151 17 36 35 119 288 43

Std Dev

age range

71.9 68.5 62.4 71.3 86.1

19.4 15.1 15.6 15.4 13.7

20-65 32-73 22-82 35-101 55-98

-3.6 32.4 33.7 55.9 56.2

71.0 42.4 49.2 62.9 69.7

15.0 7.0 14.0 17.4 16.8

25-51 27-52 25-61 24-96 23-94

5.7 3.3 14

29.8 50.9 -17.2

78.8 64.6 110.5

9.9 16.6 25.7

43-61 27-99 23-74

40.0 41.5 46.4 63.7 69.3

7.0 3.0 3.2 1.4 2.6

21.9 35.2 39.8 60.9 64.0

58.1 47.9 52.9 66.5 74.6

17.2 11.5 16.2 17.0 15.4

20-65 27-73 22-82 24-101 34-98

37.9 41.5 47.2 62.8 67.5

6.3 3.0 2.9 1.3 2.8

22.4 35.2 41.2 60.2 61.9

53.3 47.9 53.2 65.4 73.1

16.7 11.5 15.8 17.1 17.1

20-65 27-73 22-82 24-101 23-98

mean

SE

4 5 14 69 12

41.0 49.8 53.4 67.6 77.4

9.7 6.7 4.2 1.9 4.0

10.1 31.1 44.4 63.9 68.7

20-81 21-81 22-87 22-98 37-86

3 10 15 98 26

33.7 37.4 41.4 59.4 63.0

8.7 2.2 3.6 1.8 3.3

12.3 17.2 17.9 18.4 13.0

20-65 21-76 22-87 22-102 42-89

1

25.0

3 25 3

54.3 57.8 46.7

50.0 51.0 52.8 63.9 76.2

18.9 16.2 17.2 17.9 19.0

20-81 21-81 22-87 22-96 37-88

6 15 26 142 35

39.5 45.6 49.9 63.3 68.4

15.1 16.6 17.6 18.1 15.6

20-81 21-81 22-87 22-102 37-89

7 15 29 167 38

368

n

95% CI

Auricular surface Boldsen et al. inferior surface morphology REFERENCE AMERICAN ISM phase FEMALE I II III IV V MALE I II III IV V BLACK I II III IV V WHITE I II III IV V TOTAL I II III IV V

RECENT AMERICAN Std Dev

age range

43.5 46.9 53.8 62.7 74.4

13.8 17.2 18.2 18.7 12.8

20-66 21-91 23-87 23-102 49-91

23.3 30.9 36.9 57.8 56.8

46.4 52.4 45.5 63.5 70.9

19.1 16.1 15.8 18.0 15.9

3.8 3.6 2.7 1.4 3.2

31.6 26.3 42.2 61.0 62.5

50.1 43.5 53.2 66.5 75.9

28.1 40.1 45.3 59.5 63.4

2.5 4.8 2.4 1.5 3.0

22.8 29.9 40.5 56.5 57.2

35.0 40.0 45.0 60.2 66.3

3.2 2.9 1.7 1.1 2.2

28.4 34.0 41.6 58.1 61.9

n

mean

SE

95% CI

13 21 53 152 22

35.2 39.1 48.8 59.7 68.8

3.8 3.8 2.5 1.5 2.7

26.9 31.3 43.8 56.7 63.1

13 11 54 154 22

34.9 41.6 41.2 60.6 63.9

5.3 4.8 2.2 1.5 3.4

7 8 34 144 25

40.9 34.9 47.7 63.7 69.2

14 18 57 150 19 26 32 107 306 44

Std Dev

age range

78.4 75.4 59.0 72.2 85.4

2.8 15.3 15.5 14.9 17.8

51-55 20-49 22-82 35-101 44-98

26.3 24.5 35.4 55.9 55.9

42.0 50.8 49.5 62.7 71.7

7.5 12.6 13.7 17.3 14.8

25-43 25-59 26-71 23-96 38-90

2.0 3.6 0.0

36.4 50.7

53.6 65.7

3.5 18.1

43-49 23-99

40.9 34.9 47.7 63.7 69.2

3.8 3.6 2.7 1.4 3.2

31.6 26.3 42.2 61.0 62.5

50.1 43.5 53.2 66.5 75.9

10.0 10.3 15.9 16.7 16.2

25-55 20-49 22-82 24-101 38-98

38.9 37.6 47.5 62.9 67.7

3.8 4.2 2.5 1.3 3.2

29.8 27.9 42.4 60.3 61.2

47.9 47.2 52.6 65.5 74.2

10.8 12.5 15.2 17.0 16.5

25-55 20-59 22-82 23-101 38-98

mean

SE

2 3 20 66 11

53.0 37.3 51.8 68.6 73.5

2.0 8.8 3.5 1.8 5.4

27.6 -0.7 44.5 64.9 61.5

20-81 22-72 22-81 21-98 37-92

6 6 17 103 16

34.2 37.7 42.5 59.3 63.8

3.1 5.1 3.3 1.7 3.7

10.0 10.3 15.9 16.7 16.2

25-55 20-49 22-82 24-101 38-98

1 1 3 25 2

25.0 59.0 45.0 58.2 49.0

33.5 50.2 50.0 62.5 69.7

9.3 20.3 17.8 18.8 13.0

20-49 21-91 22-87 22-102 42-85

7 8 34 144 25

41.6 46.0 48.3 62.2 70.7

16.3 16.6 17.4 18.3 14.5

20-81 21-91 22-87 21-102 37-92

8 9 37 169 27

369

n

95% CI

Auricular surface Boldsen et al. inferior surface texture REFERENCE AMERICAN IST phase FEMALE I II III MALE I II III BLACK I II III WHITE I II III TOTAL I II III

RECENT AMERICAN Std Dev

age range

55.9 61.5 67.8

19.8 18.1 18.5

21-102 20-86 26-93

46.9 52.0 58.2

53.0 64.1 68.2

18.8 20.5 17.9

1.7 3.3 2.4

47.8 49.0 54.6

54.4 62.1 64.0

51.3 58.6 67.6

1.5 2.4 2.3

48.3 53.7 62.9

51.2 57.1 63.2

1.1 2.0 1.7

49.0 53.1 59.9

n

mean

SE

95% CI

142 45 65

52.6 56.1 63.2

1.7 2.7 2.3

49.3 50.7 58.7

149 47 51

49.9 58.0 63.2

1.5 3.0 2.5

147 45 61

51.1 55.6 59.3

144 47 55 291 92 116

Std Dev

age range

64.1 70.9 77.6

19.1 14.3 15.0

20-101 38-94 43-99

45.7 56.7 55.8

53.6 66.7 69.8

16.8 16.9 18.7

23-90 27-96 24-94

3.5 4.8 6.6

40.8 43.6 57.4

56.0 64.6 91.3

13.2 16.5 16.1

23-74 27-84 54-99

53.3 64.8 66.7

1.9 1.9 2.4

49.6 61.0 61.9

57.1 68.7 71.4

18.6 15.4 17.6

20-101 27-96 24-95

52.7 63.1 67.4

1.7 1.8 2.3

49.4 59.4 62.9

56.1 66.8 71.9

18.0 15.9 17.4

20-101 27-96 24-99

mean

SE

41 29 30

58.1 65.4 72.0

3.0 2.7 2.7

52.1 60.0 66.4

20-91 21-98 26-89

72 46 30

49.7 61.7 62.8

2.0 2.5 3.4

20.2 21.9 18.3

20-102 20-98 26-91

14 12 6

48.4 54.1 74.3

54.4 63.4 72.2

18.5 16.5 17.1

20-94 24-92 28-93

99 63 54

53.5 61.1 66.6

19.3 19.3 18.2

20-102 20-98 26-93

113 75 60

370

n

95% CI

Auricular surface Boldsen et al. superior posterior iliac exostoses REFERENCE AMERICAN SPE phase FEMALE I II III IV V VI MALE I II III IV V VI BLACK I II III IV V VI WHITE I II III IV V VI TOTAL I II III IV V VI

n

mean

SE

24 148 53 36

46.8 58.2 48.6 57.6

4.2 1.6 2.7 3.3

95% CI 38.0 55.1 43.3 50.9

55.5 61.3 54.0 64.4

RECENT AMERICAN Std Dev

age range

20.7 18.9 19.4 20.1

22-89 20-102 22-90 27-101

n

Std Dev

age range

87.5 69.8 71.3 72.6

27.3 18.9 11.6 15.8

22-95 32-101 47-88 39-99

-40.4

188.3

12.7

65-83

mean

SE

95% CI

6 27 19 47 1 2

58.8 62.3 65.7 68.0 40.0 74.0

11 3.6 2.7 2.3

30.2 54.8 60.2 63.3

9.0

27 175 7 34 2 26

37.7 55.3 33.6 60.9 63.0 67.7

3.3 1.4 5.6 3.2 13 2.9

30.8 52.5 20.0 54.4 -102 61.7

44.5 58.1 47.2 67.5 228.2 73.6

17.3 18.9 14.7 18.8 18.4 14.7

22-78 20-98 20-62 22-87 50-76 32-88

14 78 13 46 3 14

38.1 55.7 57.2 63.1 65.0 77.4

3.9 1.8 6.7 2.6 4.6 3.4

29.8 52.1 42.7 57.8 45.3 70.1

46.5 59.3 71.6 68.3 84.7 84.6

14.5 15.9 24.0 17.7 7.9 12.6

23-74 26-94 25-94 27-96 56-71 56-101

24 151 37 40 1 18

36.7 55.6 46.3 58.9 50.0 69.1

3.4 1.7 3.0 2.8

29.8 52.3 40.2 53.1

43.6 58.8 52.5 64.6

16.4 20.2 18.5 17.9

3.0

62.7

75.4

12.7

22-71 20-102 21-87 31-101 50-50 47-88

4 15 2 10 2 4

42.8 55.0 68.0 59.9 48.0 68.3

12 3.1 14 6.6 8.0 4.4

4.6 48.3 -109 45.1 -53.7 54.2

80.9 61.7 245.8 74.7 149.6 82.3

24.0 12.1 19.8 20.7 11.3 8.9

23-74 40-84 54-82 27-99 40-56 56-77

27 172 23 30 1 8

46.6 57.5 47.7 59.7 76.0 64.5

4.0 1.4 4.4 3.9

38.4 54.9 38.6 51.7

54.8 60.2 56.9 67.8

20.8 17.7 21.2 21.5

6.7

48.7

80.3

18.9

22-89 21-96 20-90 22-87 76-76 32-88

16 90 30 83 2 12

44.8 57.8 61.9 66.2 69.5 79.8

5.2 1.9 3.3 1.8 1.5 3.5

33.7 54.1 55.1 62.7 50.4 72.2

55.9 61.5 68.6 69.8 88.6 87.5

20.8 17.6 18.0 16.3 2.1 12.1

22-95 26-101 25-94 32-98 68-71 63-101

51 323 60 70 2 26

41.9 56.6 46.9 59.2 63.0 67.7

2.7 1.1 2.5 2.3 13 2.9

36.5 54.6 41.9 54.6 -102 61.7

47.4 58.7 51.9 63.9 228.2 73.6

19.3 18.9 19.4 19.4 18.4 14.7

22-89 20-102 20-90 22-101 50-76 32-88

20 105 32 93 4 16

44.4 57.4 62.3 65.5 58.8 76.9

4.7 1.7 3.2 1.7 7.0 3.1

34.6 54.1 55.8 62.1 36.4 70.4

54.1 60.7 68.7 69.0 81.2 83.5

20.8 16.9 17.9 16.8 14.1 12.2

22-95 26-101 25-94 27-99 40-71 56-101

371

Auricular surface Boldsen et al. inferior posterior iliac exostoses REFERENCE AMERICAN IPE phase FEMALE I II III IV V VI MALE I II III IV V VI BLACK I II III IV V VI WHITE I II III IV V VI TOTAL I II III IV V VI

n

RECENT AMERICAN Std Dev

age range

53.3 64.6 53.1 64.2 81.1

19.9 18.6 19.0 18.4 14.4

20-87 21-102 22-97 27-101 38-72

43.6 53.0 35.0 45.2 31.9 30.3

52.6 59.8 54.3 68.7 77.2 85.3

20.0 18.7 14.3 21.3 21.6 22.1

2.5 2.1 2.2 3.9 19 13

43.6 53.6 44.1 48.4 -202 -98.2

53.7 62.0 53.1 64.3 280.4 232.2

45.9 57.5 48.9 60.1 60.3 51.7

2.9 1.8 3.2 2.8 5.2 14

40.0 53.9 42.5 54.5 47.9 -12.7

47.5 57.6 48.7 58.7 56.0 57.8

1.9 1.4 1.8 2.3 5.8 9.9

43.7 54.9 45.1 54.2 43.0 30.3

mean

SE

95% CI

31 64 92 55 4

46.0 60.0 49.2 59.2 58.3

3.6 2.3 2.0 2.5 7.2

38.7 55.3 45.3 54.2 35.4

78 121 11 15 6 5

48.1 56.4 44.6 56.9 54.5 57.8

2.3 1.7 4.3 5.5 8.8 9.9

65 94 64 26 2 2

48.7 57.8 48.6 56.3 39.0 67.0

44 91 39 44 8 3 109 185 103 70 10 5

Std Dev

age range

71.9 75.6 62.3 77.0 84.8 188.3

16.9 18.5 12.8 19.2 9.9 12.7

22-82 44-98 35-88 32-101 57-81 65-83

45.7 53.9 51.4 45.8 -183 71.5

54.8 62.9 69.0 97.0 312.2 84.8

18.0 16.6 18.2 20.6 27.6 11.4

23-83 26-96 27-94 41-94 45-84 63-101

4.6 3.3 4.3 7.5

42.0 43.2 42.4 -3.8

61.7 59.4 63.3 186.8

17.8 8.8 11.3 10.6

23-81 43-68 40-72 84-99

72.3

2.4

62.0

82.7

4.2

69-77

62 66 40 30 6 13

52.6 61.5 59.5 68.3 67.5 78.9

2.4 2.2 2.5 3.4 6.0 3.4

47.9 57.2 54.5 61.3 52.1 71.5

57.3 65.9 64.5 75.3 83.0 86.2

18.5 17.8 15.6 18.8 14.7 12.1

22-83 26-98 27-94 32-101 45-84 63-101

77 73 47 32 6 16

52.4 60.6 58.5 69.8 67.5 77.6

2.1 2.0 2.2 3.4 6.0 2.8

48.3 56.5 54.1 62.9 52.1 71.6

56.6 64.6 62.9 76.6 83.0 83.6

18.3 17.3 15.1 19.1 14.7 11.2

22-83 26-98 27-94 32-101 45-84 63-101

mean

SE

14 19 28 27 4 2

62.1 66.7 57.4 69.4 69.0 74.0

4.5 4.2 2.4 3.7 5.0 9.0

52.4 57.8 52.4 61.8 53.2 -40.4

21-84 21-92 27-76 20-82 20-79 32-81

63 54 19 5 2 14

50.3 58.4 60.2 71.4 64.5 78.1

2.3 2.3 4.2 9.2 19 3.1

20.3 20.3 17.9 19.7 26.9 18.4

20-87 21-102 22-97 22-101 20-58 54-80

15 7 7 2

51.9 51.3 52.9 91.5

3

51.8 61.0 55.3 65.7 72.7 116.0

19.5 17.0 19.8 18.5 14.8 25.9

21-84 24-94 27-88 20-93 38-79 32-81

51.3 60.3 52.3 63.2 69.0 85.3

19.9 18.7 18.5 18.9 18.2 22.1

20-87 21-102 22-97 20-101 20-79 32-81

372

n

95% CI

Auricular surface Boldsen et al. posterior iliac exostoses REFERENCE AMERICAN PIE phase FEMALE I II III MALE I II III BLACK I II III WHITE I II III TOTAL I II III

RECENT AMERICAN Std Dev

age range

52.7 66.2 62.0

19.5 17.8 18.2

20-102 24-101 26-87

45.5 58.1 41.7

51.7 65.3 81.4

19.3 17.9 12.5

1.6 2.0 3.7

45.5 57.8 47.6

51.9 65.8 63.0

49.3 62.4 53.5

1.6 1.7 11

46.1 59.1 18.1

49.0 62.1 55.0

1.1 1.3 3.5

46.7 59.6 47.8

n

mean

SE

95% CI

139 94 21

49.4 62.6 53.8

1.7 1.8 4.0

46.1 59.0 45.5

149 99 4

48.6 61.7 61.5

1.6 1.8 6.2

155 83 21

48.7 61.8 55.3

133 110 4 288 193 25

Std Dev

age range

65.7 73.4 83.0

16.3 14.6 23.0

22-94 44-98 32-101

47.5 58.3 54.6

54.6 67.4 92.5

17.0 16.1 20.5

23-96 27-94 39-93

2.9 3.9 7.5

44.7 48.9 -3.8

56.4 69.1 186.8

14.3 9.7 10.6

23-81 47-72 84-99

55.4 65.8 66.5

1.7 1.8 5.5

52.1 62.4 54.8

58.7 69.3 78.2

17.9 15.9 21.1

22-96 27-98 32-101

54.5 65.4 69.5

1.5 1.7 5.2

51.6 62.1 58.4

57.4 68.7 80.6

17.4 15.7 21.6

22-96 27-98 32-101

mean

SE

49 39 10

61.0 68.6 66.6

2.3 2.3 7.3

56.3 63.9 50.2

20-89 23-98 50-78

91 50 7

51.0 62.9 73.6

1.8 2.3 7.7

20.3 18.3 17.0

20-102 23-101 26-87

25 6 2

50.6 59.0 91.5

52.4 65.7 88.9

18.4 17.5 22.3

20-90 24-94 27-78

115 83 15

51.2 64.7 62.2

19.4 17.8 17.4

20-102 23-101 26-87

140 89 17

373

n

95% CI

Sternal end of the fourth rib Iscan et al. REFERENCE AMERICAN Iscan phase FEMALE I II III IV V VI VII VIII MALE I II III IV V VI VII VIII BLACK I II III IV V VI VII VIII WHITE I II III IV V VI VII VIII TOTAL I II III IV V VI VII VIII

n

RECENT AMERICAN Std Dev

age range

41.3 39.6 44.7 54.7 67.6 112.7

13.1 11.0 11.2 12.4 14.2 10.1

22-62 22-65 22-75 27-78 35-102 77-97

20.4 24.1 30.4 40.4 51.1 65.1 67.9

23.8 37.2 41.4 53.3 57.8 72.7 80.5

2.0 13.2 11.3 18.8 11.8 10.7 14.2

1.0 0.7 3.0 2.0 1.9 1.8 3.8

19.9 24.5 31.0 36.6 50.6 62.3 68.6

24.8 27.6 43.4 44.6 58.4 69.4 85.4

21.0 35.0 31.9 47.2 49.7 65.3 74.5

0.6 4.8 1.6 2.9 1.9 2.5 4.4

18.5 24.6 28.4 41.2 45.9 60.2 64.8

21.9 30.5 34.9 43.8 52.8 65.7 75.8

0.7 2.5 1.9 1.8 1.4 1.5 2.9

20.3 25.3 31.1 40.2 50.0 62.8 69.9

mean

SE

95% CI

1 8 16 40 23 49 3

20.0 30.4 33.8 41.1 49.4 63.6 87.7

4.6 2.8 1.8 2.6 2.0 5.8

19.4 27.9 37.5 44.0 59.5 62.7

8 18 19 35 49 33 22

22.1 30.6 35.9 46.9 54.5 68.9 74.2

0.7 3.1 2.6 3.2 1.7 1.9 3.0

6 13 20 39 47 58 13

22.3 26.1 37.2 40.6 54.5 65.9 77.0

3 13 15 36 25 24 12

9 26 35 75 72 82 25

Std Dev

age range

35.7 37.9 57.2 59.0 76.9 88.0

1.4 7.2 19.4 9.8 12.1 10.7

22-24 22-45 25-98 38-77 46-97 58-99

20.7 22.8 35.4 46.8 50.7 58.7 71.5

26.7 32.0 52.1 62.5 59.1 69.6 80.2

2.9 5.5 15.6 19.4 13.8 15.0 12.3

20-28 21-36 23-71 27-88 27-94 34-94 49-96

1.5 1.0

4.4 9.3

42.6 34.7

2.1 1.4

4.5 2.5 4.4 7.4

40.2 49.5 53.5 53.5

60.2 60.1 74.2 91.5

14.9 9.6 12.4 18.1

22-25 21-23 27-27 27-81 42-71 51-84 49-99

22.5 27.6 39.9 52.5 55.1 69.1 78.2

1.4 1.9 2.7 3.2 1.7 1.9 1.7

19.0 23.2 34.3 46.1 51.6 65.3 74.7

26.0 32.0 45.4 58.9 58.5 72.9 81.7

3.3 5.3 13.8 20.5 13.0 14.4 11.0

20-28 21-36 22-71 25-98 27-94 34-97 53-97

22.8 26.5 39.4 52.0 55.0 68.5 77.5

1.1 1.7 2.7 2.7 1.5 1.8 1.8

20.3 22.8 33.9 46.7 52.1 64.9 73.9

25.2 30.2 44.8 57.4 57.9 72.0 81.0

3.0 5.2 13.8 19.4 12.3 14.2 12.0

20-28 21-36 22-71 25-98 27-94 34-97 49-99

mean

SE

2 2 11 27 28 33 13

20.0 23.0 33.0 49.5 55.2 72.6 81.5

0.0 1.0 2.2 3.7 1.9 2.1 3.0

10.3 28.2 41.8 51.4 68.4 75.1

20-26 22-81 22-64 25-86 37-88 42-85 44-96

6 8 16 26 44 32 33

23.7 27.4 43.8 54.6 54.9 64.1 75.9

1.2 2.0 3.9 3.8 2.1 2.7 2.1

2.3 2.6 13.3 12.3 13.2 13.6 13.9

20-26 22-31 22-65 22-80 30-88 35-102 54-97

2 2 1 11 15 8 6

23.5 22.0 27.0 50.2 54.8 63.9 72.5

23.5 45.4 35.3 53.2 53.5 70.5 84.2

1.0 17.2 6.3 17.7 9.3 12.2 15.2

20-22 22-81 22-41 27-86 27-69 38-85 44-96

6 8 26 42 57 57 40

23.5 35.7 38.7 47.3 55.7 68.6 81.7

2.0 12.9 11.1 15.4 12.1 13.1 14.3

20-26 22-81 22-65 22-86 27-88 35-102 44-97

8 10 27 53 72 65 46

374

n

95% CI

Cranial suture closure Meindl & Lovejoy vault system REFERENCE AMERICAN Vault phase FEMALE 0 I II III IV V VI MALE 0 I II III IV V VI BLACK 0 I II III IV V VI WHITE 0 I II III IV V VI TOTAL 0 I II III IV V VI

n

RECENT AMERICAN Std Dev

age range

56.0 54.4 54.7 64.2 66.5 69.7

21.0 20.2 17.2 21.8 18.1 15.7

22-78 21-91 22-89 20-102 22-97 24-94

20.6 32.2 49.3 54.5 51.4 60.2

36.0 43.9 59.5 63.0 62.2 68.7

8.3 15.0 21.5 18.8 18.1 13.2

6.0 2.7 2.5 2.7 2.8 2.5

24.3 39.9 47.9 51.1 51.3 59.4

51.2 50.7 58.0 61.8 62.7 69.4

35.0 28.4 44.9 52.0 60.4 60.5 65.2

2.9 3.6 2.4 2.4 2.6 2.0

20.4 37.6 47.3 55.6 55.4 61.3

35.0 34.8 45.1 52.5 58.4 59.0 64.8

4.3 2.1 1.7 1.8 1.9 1.6

25.6 40.9 49.1 54.8 55.2 61.8

mean

SE

95% CI

1 9 53 61 45 46 48

35.0 39.9 48.9 50.3 57.7 61.1 65.2

7.0 2.8 2.2 3.3 2.7 2.3

23.8 43.3 45.9 51.1 55.8 60.6

7 28 70 77 46 40

28.3 38.1 54.4 58.7 56.8 64.4

3.2 2.8 2.6 2.1 2.7 2.1

11 51 63 62 40 42

37.7 45.3 53.0 56.4 57.0 64.4

1 5 30 68 60 52 46 1 16 81 131 122 92 88

Std Dev

age range

76.7 63.3 71.6 72.2 79.3 78.7

15.0 20.3 17.6 15.0 13.6 15.3

22-49 20-94 36-99 32-95 47-98 40-101

11.3 34.2 52.6 58.8 53.1 54.0

36.7 52.0 62.8 69.2 66.9 68.0

1.4 14.8 18.3 18.1 17.4 15.8

23-25 24-83 25-94 26-96 25-88 38-101

1.0 1.9 7.1 3.9 5.8 5.2

11.3 38.3 47.2 53.5 50.2 42.9

36.7 50.7 83.5 71.3 79.8 67.3

1.4 3.9 17.3 12.5 14.1 14.6

23-25 40-49 54-99 43-82 43-81 40-84

32.0 39.3 45.8 59.8 65.4 64.7 67.6

8.7 4.3 2.1 2.1 2.9 2.9

2.0 36.7 55.5 61.2 58.9 61.8

76.7 54.9 64.0 69.6 70.5 73.5

15.0 18.9 18.4 17.5 17.6 15.7

22-49 20-94 25-94 26-96 25-98 38-101

32.0 33.2 45.6 60.2 65.0 64.8 65.0

6.1 3.6 2.0 1.9 2.6 2.6

16.4 38.1 56.1 61.2 59.6 59.7

50.1 53.0 64.2 68.8 69.9 70.3

13.6 17.2 18.2 16.9 17.0 16.1

22-49 20-94 25-99 26-96 25-98 38-101

mean

SE

3 10 28 31 17 16

39.3 48.8 64.8 66.7 72.3 70.5

8.7 6.4 3.3 2.7 3.3 3.8

2.0 34.3 58.0 61.1 65.3 62.3

20-42 22-75 22-98 20-86 26-92 29-91

1 2 13 52 49 27 22

32.0 24.0 43.1 57.7 64.0 60.0 61.0

1.0 4.1 2.5 2.6 3.4 3.4

20.0 19.2 20.0 21.0 17.9 16.0

21-78 21-87 22-98 20-102 22-97 24-91

2 4 6 10 6 8

24.0 44.5 65.3 62.4 65.0 55.1

36.4 52.2 56.7 65.2 65.6 69.2

6.4 19.5 19.5 18.6 18.4 13.3

20-38 23-91 22-96 24-93 28-92 29-94

1 3 19 74 70 38 30

44.0 49.4 55.9 61.9 62.7 67.9

17.3 19.2 19.6 19.9 18.1 14.5

20-78 21-91 22-98 20-102 22-97 24-94

1 5 23 80 80 44 38

375

n

95% CI

Cranial suture closure Meindl & Lovejoy lateral-anterior system REFERENCE AMERICAN Lat-ant phase FEMALE 0 I II III IV V VI VII MALE 0 I II III IV V VI VII BLACK 0 I II III IV V VI VII WHITE 0 I II III IV V VI VII TOTAL 0 I II III IV V VI VII

n

RECENT AMERICAN Std Dev

age range

82.4 61.1 50.9 54.1 62.9 64.3 66.7 69.1

17.1 20.8 18.6 19.7 23.5 17.4 17.6 14.8

26-59 22-76 20-82 23-101 22-102 30-90 22-89 28-91

-51.9 29.4 35.4 41.8 54.0 57.8 58.7

125.9 42.2 45.4 56.7 64.8 66.4 66.8

9.9 9.5 18.4 17.7 19.7 16.5 16.7

16 7.8 3.4 2.6 3.9 2.9 2.2 2.2

-167 15.6 27.7 38.8 42.6 51.8 57.3 58.3

252.2 58.8 42.6 49.0 58.6 63.3 66.2 67.0

35.0 45.3 41.6 47.5 53.0 60.6 62.1 64.5

10 5.1 2.3 4.5 2.7 2.4 2.0

11.0 30.4 43.0 43.7 55.2 57.3 60.4

40.0 40.8 38.2 45.9 51.5 59.0 61.9 63.6

9.9 6.2 3.0 1.7 2.9 2.0 1.6 1.5

-2.4 26.6 32.0 42.5 45.7 55.1 58.7 60.6

mean

SE

95% CI

3 7 14 78 27 36 51 47

40.0 41.9 40.1 49.7 53.6 58.4 61.7 64.7

9.9 7.9 5.0 2.2 4.5 2.9 2.5 2.2

-2.4 22.6 29.4 45.2 44.2 52.5 56.8 60.4

2 11 54 24 54 59 68

37.0 35.8 40.4 49.3 59.4 62.1 62.8

7.0 2.9 2.5 3.6 2.7 2.2 2.0

2 5 13 60 31 48 55 57

42.5 37.2 35.2 43.9 50.6 57.6 61.8 62.7

1 4 12 72 20 42 55 58 3 9 25 132 51 90 110 115

Std Dev

age range

72.3 68.1 68.7 74.1 82.5 83.2

7.9 20.0 16.4 12.7 14.0 15.5

49-67 20-94 36-94 40-93 53-99 53-101

-53.7 19.4 27.0 44.3 59.4 50.3 55.5 63.1

149.6 47.6 49.4 56.8 76.9 62.7 66.9 73.5

11.3 8.9 9.0 18.0 15.1 18.0 15.3 16.4

40-56 25-46 27-47 23-94 32-87 25-89 34-88 38-101

2.0 8.1 4.9 5.9 8.1 4.6

19.6 25.1 50.3 35.0 39.6 56.2

70.4 66.9 77.7 65.3 81.0 76.8

2.8 19.9 11.0 14.4 19.7 14.4

43-47 23-72 54-82 27-68 43-99 43-84

45.5 34.5 48.6 56.8 63.8 62.0 66.8 70.1

10 5.4 6.0 2.5 3.3 2.4 2.5 2.5

-87.9 17.3 33.9 51.8 57.1 57.1 61.8 65.0

178.9 51.7 63.3 61.7 70.5 66.9 71.8 75.3

14.9 10.8 15.9 19.5 17.0 17.1 15.7 16.7

35-56 25-50 27-67 20-94 32-94 25-93 34-98 38-101

43.7 36.8 47.8 55.8 63.8 60.7 66.0 69.5

6.3 4.8 4.6 2.4 2.8 2.3 2.4 2.2

16.4 23.6 37.1 51.1 58.1 56.1 61.2 65.0

70.9 50.0 58.5 60.6 69.6 65.3 70.8 73.9

11.0 10.7 13.9 19.6 16.1 17.1 16.2 16.2

35-56 25-50 27-67 20-94 32-94 25-93 34-99 38-101

mean

SE

1 1 4 34 18 20 16 12

35.0 50.0 59.8 61.1 60.5 68.2 75.0 73.3

3.9 3.4 3.9 2.9 3.5 4.5

47.2 54.1 52.3 62.2 67.5 63.5

30-44 22-59 20-87 22-80 22-98 28-96 26-92

2 4 5 34 14 35 30 41

48.0 33.5 38.2 50.6 68.1 56.5 61.2 68.3

8.0 4.4 4.0 3.1 4.0 3.0 2.8 2.6

23.3 17.4 12.3 19.7 21.8 19.9 16.4 16.5

26-59 22-67 20-59 20-101 22-102 22-98 22-89 26-91

1 1 2 6 5 6 6 10

40.0 46.0 45.0 46.0 64.0 50.2 60.3 66.5

79.5 52.8 52.1 62.3 66.0 66.9 68.6

21.5 17.6 19.5 19.9 17.3 17.6 15.4

35-35 29-76 22-82 20-93 27-94 22-90 27-96 29-92

2 4 7 62 27 49 40 43

82.4 55.0 44.5 49.3 57.4 62.9 65.1 66.5

17.1 18.5 15.1 19.6 20.9 18.7 17.0 15.9

26-59 22-76 20-82 20-101 22-102 22-98 22-96 26-92

3 5 9 68 32 55 46 53

376

n

95% CI

Cranial suture closure Boldsen et al. lambdoidal-asterica REFERENCE AMERICAN L-A phase FEMALE I II III IV V MALE I II III IV V BLACK I II III IV V WHITE I II III IV V TOTAL I II III IV V

n

RECENT AMERICAN Std Dev

age range

54.6 56.8 69.6 64.5 96.7

20.3 19.4 16.3 18.3 16.0

21-102 22-101 28-94 20-81 52-91

46.7 48.5 57.0 53.9 33.3

55.6 56.7 65.0 67.4 98.1

20.6 21.1 17.1 11.7 13.1

1.9 2.3 2.1 4.6 7.6

46.7 46.2 61.2 42.6 46.9

54.1 55.3 69.5 62.1 88.8

51.3 54.2 61.3 62.1 71.5

2.7 1.9 2.0 3.0 1.5

45.9 50.4 57.3 55.9 52.4

50.7 52.6 63.1 58.1 68.9

1.6 1.5 1.5 2.6 5.3

47.7 49.7 60.2 52.8 56.0

mean

SE

95% CI

90 84 62 23 4

50.3 52.6 65.4 56.6 71.3

2.1 2.1 2.1 3.8 8.0

46.1 48.4 61.3 48.7 45.8

84 102 71 14 3

51.1 52.6 61.0 60.6 65.7

2.3 2.1 2.0 3.1 7.5

107 88 57 15 5

50.4 50.8 65.4 52.3 67.8

67 98 76 22 2 174 186 133 37 7

Std Dev

age range

60.0 70.6 76.9 81.7

18.3 16.5 13.8 14.5

20-94 32-98 41-99 49-101

45.1 56.4 61.4 45.6

55.6 66.4 71.9 59.5

18.7 18.6 16.6 14.4

23-94 26-101 26-96 25-78

4.8 4.1 6.1 1.0

37.9 50.8 49.4 37.3

59.2 68.4 77.1 62.7

15.8 15.2 19.4 1.4

23-72 40-84 27-99 49-51

51.4 63.4 70.1 59.7 55.5

2.4 2.1 1.8 3.3 17

46.6 59.3 66.4 52.9 -166

56.1 67.5 73.7 66.5 277.8

19.0 18.3 14.8 17.3 24.8

20-94 26-101 26-96 25-101 38-73

51.0 62.9 69.2 59.0 55.5

2.1 1.9 1.8 3.1 17

46.7 59.2 65.6 52.6 -166

55.2 66.5 72.7 65.4 277.8

18.5 17.8 15.5 16.8 24.8

20-94 26-101 26-99 25-101 38-73

mean

SE

24 37 35 10 1

52.3 65.1 72.1 71.3 73.0

3.7 2.7 2.3 4.6

44.5 59.6 67.4 61.0

20-96 20-98 26-92 38-79 51-76

51 56 41 19 1

50.3 61.4 66.6 52.6 38.0

2.6 2.5 2.6 3.3

19.5 21.5 15.7 17.6 16.9

21-102 20-101 26-91 20-78 51-91

11 14 10 2

48.6 59.6 63.2 50.0

56.6 58.1 65.3 68.2 90.6

21.9 19.1 17.5 13.9 2.1

20-96 22-88 28-94 38-81 70-73

64 79 66 27 2

53.8 55.5 66.0 63.5 81.7

20.4 20.3 16.8 16.0 13.9

20-102 20-101 26-94 20-81 51-91

75 93 76 29 2

377

n

95% CI

Cranial suture closure Boldsen et al. sagittal-obelica REFERENCE AMERICAN S-O phase FEMALE I II III IV V MALE I II III IV V BLACK I II III IV V WHITE I II III IV V TOTAL I II III IV V

n

RECENT AMERICAN Std Dev

age range

56.0 52.9 60.4 68.0 64.8

22.1 18.9 17.1 17.8 21.9

23-91 22-89 21-88 28-101 20-102

21.2 34.4 53.2 52.5 55.6

37.7 47.4 62.9 60.5 63.7

10.8 16.7 18.8 20.1 17.7

5.2 2.8 2.2 2.6 2.3

33.3 41.6 49.1 53.0 53.5

55.0 52.9 57.9 63.4 62.8

39.0 43.9 61.2 59.4 60.7

5.1 2.8 2.0 2.0 2.6

28.0 38.2 57.2 55.5 55.5

42.0 45.7 57.2 58.9 59.2

3.7 2.0 1.5 1.6 1.7

34.5 41.8 54.2 55.8 55.7

mean

SE

95% CI

24 59 76 56 47

46.7 48.0 56.5 63.2 58.4

4.5 2.5 2.0 2.4 3.2

37.3 43.0 52.6 58.4 52.0

9 28 60 98 76

29.4 40.9 58.1 56.5 59.7

3.6 3.2 2.4 2.0 2.0

19 47 71 60 74

44.2 47.2 53.5 58.2 58.2

14 40 65 94 49 33 87 136 154 123

Std Dev

age range

71.3 70.9 72.8 73.2 77.2

25.2 19.9 13.1 16.6 14.6

20-95 35-99 41-93 32-101 43-89

23.0 43.6 55.2 54.4 55.4

42.3 60.9 69.2 62.9 66.1

11.5 12.9 19.8 17.8 18.1

23-59 33-73 26-94 24-96 25-101

14 10 4.0 3.0 6.3

-22.3 32.0 2.2 51.1 45.9

98.3 89.6 103.8 63.6 75.0

24.3 23.2 5.7 12.6 19.0

23-66 40-99 49-57 40-82 27-84

43.9 58.5 65.1 62.2 63.2

5.9 3.4 2.2 2.0 2.4

31.1 51.6 60.6 58.2 58.3

56.6 65.4 69.6 66.3 68.1

22.0 17.8 17.2 18.7 17.5

20-95 33-94 26-94 24-101 25-101

42.8 58.8 64.7 61.4 62.8

5.3 3.2 2.2 1.8 2.3

31.6 52.3 60.4 57.9 58.3

54.0 65.3 69.1 64.9 67.3

21.8 18.3 17.1 17.8 17.6

20-95 33-99 26-94 24-101 25-101

mean

SE

9 22 28 32 15

51.9 62.1 67.7 67.3 69.1

8.4 4.3 2.5 2.9 3.8

32.5 53.3 62.6 61.3 61.1

20-51 22-80 22-88 22-98 20-86

8 11 33 70 46

32.6 52.3 62.2 58.7 60.7

4.1 3.9 3.4 2.1 2.7

22.5 19.2 18.6 20.2 20.1

21-87 22-85 21-88 22-101 20-102

3 5 2 18 9

38.0 60.8 53.0 57.3 60.4

50.0 49.5 65.2 63.3 66.0

19.1 17.5 16.1 19.1 18.2

20-91 27-89 22-88 22-96 28-94

14 28 59 84 52

49.4 49.6 60.2 62.0 62.6

21.0 18.4 17.8 19.5 19.3

20-91 22-89 21-88 22-101 20-102

17 33 61 102 61

378

n

95% CI

Cranial suture closure Boldsen et al. coronal-pterica REFERENCE AMERICAN C-P phase FEMALE I II III IV V MALE I II III IV V BLACK I II III IV V WHITE I II III IV V TOTAL I II III IV V

n

RECENT AMERICAN Std Dev

age range

52.8 46.6 56.3 64.6 68.1

17.5 18.8 19.0 17.0 16.6

22-82 21-93 20-102 28-89 24-94

-29.2 31.1 49.2 53.7 60.9

110.5 39.3 58.8 62.5 67.6

28.1 14.1 19.2 17.5 16.3

7.4 2.3 2.5 2.2 1.8

21.0 33.0 47.0 56.3 59.4

59.0 42.1 56.9 65.2 66.5

39.3 38.8 53.7 57.7 65.6

8.9 2.7 2.3 2.2 1.7

16.4 33.5 49.0 53.4 62.3

39.7 38.1 52.7 59.1 64.4

5.5 1.7 1.7 1.6 1.2

27.5 34.7 49.3 56.0 62.0

mean

SE

95% CI

9 47 60 60 87

39.3 41.1 51.4 60.2 64.5

5.8 2.8 2.5 2.2 1.8

25.9 35.5 46.4 55.8 61.0

3 48 64 64 94

40.7 35.2 54.0 58.1 64.2

16 2.0 2.4 2.2 1.7

6 55 68 58 85

40.0 37.6 51.9 60.7 62.9

6 40 56 66 96 12 95 124 124 181

Std Dev

age range

52.6 63.1 68.8 73.1 76.0

2.1 20.9 15.1 14.6 14.8

32-35 20-94 36-88 46-99 32-101

11.5 35.1 50.1 54.2 61.8

81.1 49.0 63.7 63.1 71.2

14.0 16.2 19.2 15.7 17.5

32-60 23-83 26-88 25-96 33-101

5.4 6.6 5.0 4.4

28.3 45.6 47.9 51.6

54.5 82.4 69.9 71.0

14.2 14.9 17.3 15.3

23-64 43-82 27-99 40-84

39.8 46.4 58.9 61.0 69.9

6.8 3.5 2.5 2.0 1.7

18.2 39.2 54.0 57.0 66.4

61.4 53.6 63.9 65.1 73.3

13.6 19.3 17.9 15.5 16.4

32-60 20-94 26-88 25-96 32-101

41.2 45.5 59.4 60.7 68.9

5.5 3.0 2.3 1.9 1.6

26.1 39.3 54.7 56.9 65.7

56.3 51.6 64.0 64.4 72.1

12.2 18.4 17.6 15.7 16.5

32-60 20-94 26-88 25-99 32-101

mean

SE

2 14 25 19 47

33.5 51.1 62.6 66.1 71.7

1.5 5.6 3.0 3.4 2.2

14.4 39.0 56.4 59.1 67.3

22-73 20-85 22-89 26-88 26-98

3 23 33 51 56

46.3 42.0 56.9 58.6 66.5

8.1 3.4 3.3 2.2 2.3

18.1 17.0 20.4 16.9 16.6

22-73 20-85 20-102 26-88 24-98

1 7 5 12 12

47.0 41.4 64.0 58.9 61.3

62.3 44.2 58.3 62.0 68.9

21.8 16.8 17.5 17.5 16.2

22-82 20-93 22-87 28-89 27-96

4 30 53 58 91

51.8 41.5 56.1 62.2 66.8

19.1 16.8 19.1 17.2 16.4

22-82 20-93 20-102 26-89 24-98

5 37 58 70 103

379

n

95% CI

Cranial suture closure Boldsen et al. zygomaticomaxillary REFERENCE AMERICAN ZM phase FEMALE I II III IV V MALE I II III IV V BLACK I II III IV V WHITE I II III IV V TOTAL I II III IV V

RECENT AMERICAN Std Dev

age range

82.2 54.2 66.6 71.6 92.2

23.3 19.5 17.5 20.2 10.0

32-102 20-94 24-101 32-91 56-75

13.9 44.9 56.7 55.3 41.9

48.8 52.6 63.5 65.7 69.0

7.0 20.8 18.3 14.4 20.2

18 1.9 1.8 2.9 6.2

-151 43.6 55.6 54.7 41.4

318.6 51.0 62.6 66.6 69.8

54.9 52.4 63.9 56.4 62.4

6.9 1.5 1.9 4.6 9.3

39.7 49.4 60.1 46.1 36.5

59.0 50.2 61.1 59.5 58.0

6.8 1.2 1.3 2.5 5.0

44.3 47.8 58.5 54.6 47.1

n

mean

SE

95% CI

11 164 76 9 3

66.6 51.2 62.6 56.0 67.3

7.0 1.5 2.0 6.7 5.8

50.9 48.2 58.6 40.5 42.5

3 115 113 32 11

31.3 48.8 60.1 60.5 55.5

4.1 1.9 1.7 2.6 6.1

2 121 110 30 9

83.5 47.3 59.1 60.7 55.6

12 158 79 11 5 14 279 189 41 14

Std Dev

age range

77.2 66.4 83.1

16.3 15.3 19.4

40-93 22-94 32-101

22.9 46.6 55.1 60.9 64.2

104.1 56.7 64.0 71.5 80.5

25.5 19.7 16.9 15.8 7.7

32-94 23-90 32-101 38-96 61-84

4.4 5.9 4.7 3.2

36.6 42.7 52.1 64.2

55.7 69.7 73.7 80.5

15.1 17.6 14.0 7.7

23-68 43-99 43-82 61-84

68.7 58.7 63.9 68.3 66.0

4.0 1.7 2.3 3.2

60.2 55.3 59.4 61.7

77.2 62.0 68.4 74.9

17.1 18.3 18.5 16.9

32-94 22-94 32-101 38-96

67.2 57.5 63.0 67.0 71.4

4.1 1.6 2.1 2.7 2.8

58.6 54.3 58.8 61.6 64.5

75.8 60.7 67.2 72.4 78.3

17.9 18.3 18.5 16.3 7.5

32-94 22-94 32-101 38-96 61-84

mean

SE

15 69 19 1 1

68.1 62.7 73.7 95.0 66.0

4.2 1.8 4.5

59.1 59.1 64.4

24-38 20-96 22-98 32-88 29-89

4 61 58 36 6

63.5 51.6 59.5 66.2 72.3

12 2.5 2.2 2.6 3.2

26.2 20.6 18.5 16.0 18.5

65-102 20-89 22-101 32-91 32-89

1 12 9 9 6

40.0 46.2 56.2 62.9 72.3

70.2 55.5 67.7 66.6 88.3

24.0 19.4 17.0 15.2 20.8

24-90 20-96 22-92 37-81 29-81

18 118 68 28 1

73.7 52.6 63.7 64.5 68.9

25.5 20.1 18.0 15.7 18.8

24-102 20-96 22-101 32-91 29-89

19 130 77 37 7

380

n

95% CI

Cranial suture closure Boldsen et al. interpalatine REFERENCE AMERICAN IP phase FEMALE I II III IV V MALE I II III IV V BLACK I II III IV V WHITE I II III IV V TOTAL I II III IV V

n

RECENT AMERICAN Std Dev

age range

62.1 54.2 60.8 68.6 66.0

19.1 19.2 19.8 16.7 16.1

20-65 21-101 22-102 24-93 34-91

6.5 28.2 52.0 53.2 57.0

56.8 40.9 59.3 63.1 68.3

10.1 13.9 20.1 19.3 17.2

6.4 2.9 1.9 2.4 3.5

24.1 40.1 53.4 50.9 52.5

56.9 51.7 61.1 60.3 67.1

22.0 48.2 54.8 63.7 62.1

2.0 2.4 1.9 3.3 2.9

-3.4 43.4 51.0 57.1 56.3

35.9 47.0 56.1 59.3 61.2

5.6 1.9 1.4 2.0 2.2

22.7 43.3 53.4 55.3 56.8

mean

SE

95% CI

5 85 95 26 20

38.4 50.1 56.8 61.9 58.5

8.5 2.1 2.0 3.3 3.6

14.7 46.0 52.7 55.2 51.0

3 21 119 61 38

31.7 34.6 55.6 58.1 62.6

5.8 3.0 1.8 2.5 2.8

6 53 114 48 23

40.5 45.9 57.3 55.6 59.8

2 53 100 39 35 8 106 214 87 58

Std Dev

age range

79.3 65.6 75.5 75.1 112.6

7.0 17.4 16.5 14.2 23.7

61-76 22-94 35-101 40-94 41-93

37.7 51.0 60.0 59.3

53.3 59.6 69.6 73.2

18.6 17.6 17.2 16.5

24-94 23-93 31-96 33-101

7.3 4.7 5.0 4.7

25.6 48.9 46.6 47.5

71.9 68.9 69.4 71.8

14.5 18.8 15.9 11.6

27-57 23-99 40-84 43-74

68.3 55.6 60.6 66.9 69.6

3.5 2.4 2.0 2.1 3.9

57.2 50.8 56.6 62.6 61.5

79.3 60.4 64.6 71.2 77.8

7.0 19.3 18.5 16.3 18.3

61-76 22-94 26-101 31-96 33-101

59.6 55.2 60.3 65.6 67.5

9.1 2.3 1.8 2.0 3.3

34.5 50.6 56.7 61.6 60.8

84.8 59.8 64.0 69.5 74.2

20.3 19.0 18.5 16.4 17.4

25-76 22-94 23-101 31-96 33-101

mean

SE

4 45 35 17 4

68.3 60.3 69.8 67.8 75.0

3.5 2.6 2.8 3.4 11

57.2 55.1 64.1 60.5 37.3

20-38 20-77 20-98 22-92 22-86

1 24 66 51 24

25.0 45.5 55.3 64.8 66.3

3.8 2.2 2.4 3.4

15.6 21.1 20.7 16.3 16.9

20-65 20-101 22-102 24-88 22-86

1 4 16 10 6

25.0 48.8 58.9 58.0 59.7

47.4 53.0 58.6 70.3 67.9

2.8 17.4 19.0 20.4 16.9

20-24 27-88 20-96 22-93 29-91

4 65 85 58 22

49.1 50.7 58.8 63.2 65.6

15.8 19.3 19.9 18.6 16.8

20-65 20-101 20-102 22-93 22-91

5 69 101 68 28

381

n

95% CI

Appendix H: Age at transition data by group TOTAL Plots of ages at transition for the Todd method (Reference and Recent, respectively)

Ages at transition for the Todd method Transition (Todd Phase) I-II II-III III-IV IV-V V-VI VI-VII VII-VIII VIII-IX IX-X

Reference (age in years) 13.73281 16.94788 19.34549 22.34348 25.6066 31.47315 38.78029 41.9785 46.57818

Recent (age in years) -16.05893 17.05482 20.07991 24.1767 33.19188 38.88756 44.66757 55.54833

Difference in years -0.88895 2.29067 2.26357 1.4299 -1.71873 -0.10727 -2.68907 -8.97015

Plot of ages at transition for the Todd method

Plot of Ages at Transition (Todd) 60

Age in Years

50 40 Reference

30

Recent

20 10 0 I-II

II-III

III-IV

IV-V

V-VI

VI-VII

Transition

382

VII-VIII VIII-IX

IX-X

TOTAL Plots of ages at transition for the Suchey-Brooks method (Reference and Recent, respectively)

Ages at transition for the Suchey-Brooks method Transition (S-B Phase) I-II II-III III-IV IV-V V-VI

Reference (age in years) 18.60046 22.96379 31.04162 43.01072 52.43659

Recent (age in years) 14.86961 21.77266 29.89779 46.79661 58.22926

Difference in years 3.73085 1.19113 1.14383 -3.78589 -5.79267

Plot of ages at transition for the Suchey-Brooks method

Plot of Ages at Transition (Suchey-Brooks) 70

Age in Years

60 50 40

Reference

30

Recent

20 10 0 I-II

II-III

III-IV

IV-V

Transition

383

V-VI

TOTAL

Plots of ages at transition for the Hartnett-Fulginiti method (Reference and Recent, respectively)

Ages at transition for the Hartnett-Fulginiti method Transition (H-F Phase) I-II II-III III-IV IV-V V-VI VI-VII

Reference (age in years) 18.16482 23.93135 31.45078 42.90402 52.92645 101.0742

Recent (age in years) 12.85346 22.11262 30.53575 46.26405 61.81626 114.1458

Difference in years 5.31136 1.81873 0.91503 -3.36003 -8.88981 -13.0717

Plot of ages at transition for the Hartnett-Fulginiti method

Plot of Ages at Transition (Hartnett-Fulginiti) 120

Age in Years

100 80 Reference

60

Recent

40 20 0 I-II

II-III

III-IV

IV-V

Transition

384

V-VI

VI-VII

TOTAL Plots of ages at transition for the Boldsen et al. pubic symphyseal relief component (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. pubic symphyseal relief component Transition (SR Phase) I-II II-III III-IV IV-V V-VI

Reference (age in years) -9.602544 20.48587 40.9724 188.9067

Recent (age in years) 4.382643 10.39545 21.45007 37.91054 138.3142

Difference in years --0.79291 -0.9642 3.061865 50.59251

Highlighted cells indicate values that are either very low or very high, which is likely an artifact of regression Plot of ages at transition for the Boldsen et al. pubic symphyseal relief component

Plot of Ages at Transition (Boldsen symphyseal relief)

Age in Years

200 150 Reference

100

Recent

50 0 I-II

II-III

III-IV Transition

385

IV-V

V-VI

TOTAL Plots of ages at transition for the Boldsen et al. pubic symphyseal texture component (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. pubic symphyseal texture component Transition (ST Phase) I-II II-III III-IV

Reference (age in years) 17.922 265.622 4179.931

Recent (age in years) 17.47902 75.04312 213.0666

Difference in years 0.44298 190.5789 3966.865

Highlighted cells indicate values that are extremely high; it is unclear whether these values were influenced by the logit regression procedure, this component’s very low correlation with chronological age (0.205), or some other unidentified factor. Conclusions drawn from this component’s data should be made with caution.

Plot of ages at transition for the Boldsen et al. pubic symphyseal texture component

Age in Years

Plot of Ages at Transition (Boldsen symphyseal texture) 4500 4000 3500 3000 2500 2000 1500 1000 500 0

Reference Recent

I-II

II-III Transition

386

III-IV

TOTAL Plots of ages at transition for the Boldsen et al. pubic symphyseal superior apex component (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. pubic symphyseal superior apex component Transition (SA Phase) I-II II-III III-IV

Reference (age in years) 22.26988 29.93597 39.96178

Recent (age in years) 19.65555 26.30332 45.05228

Difference in years 2.61433 3.63265 -5.0905

Plot of ages at transition for the Boldsen et al. pubic symphyseal superior apex component

Plot of Ages at Transition (Boldsen superior apex) 50

Age in Years

40 30

Reference

20

Recent

10 0 I-II

II-III

III-IV

Transition

387

TOTAL Plots of ages at transition for the Boldsen et al. pubic symphyseal ventral margin component (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. pubic symphyseal ventral margin component Transition (VSM Phase) I-II II-III III-IV IV-V V-VI VI-VII

Reference (age in years) 17.6704 23.10754 28.0973 32.6794 41.93821 49.40798

Recent (age in years) 15.00416 19.39005 26.85064 31.21075 40.75405 59.88599

Difference in years 2.66624 3.71749 1.24666 1.46865 1.18416 -10.478

Plot of ages at transition for the Boldsen et al. pubic symphyseal ventral margin component

Plot of Ages at Transition (Boldsen ventral symphyseal margin) 70

Age in Years

60 50 40

Reference

30

Recent

20 10 0 I-II

II-III

III-IV

IV-V

Transition

388

V-VI

VI-VII

TOTAL Plots of ages at transition for the Boldsen et al. pubic symphyseal dorsal margin component (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. pubic symphyseal dorsal margin component Transition (DSM Phase) I-II II-III III-IV IV-V

Reference (age in years) 16.38498 25.19911 39.97079 50.42809

Recent (age in years) 12.30618 21.98876 39.91645 64.55839

Difference in years 4.0788 3.21035 0.05434 -14.1303

Plot of ages at transition for the Boldsen et al. pubic symphyseal dorsal margin component

Plot of Ages at Transition (Boldsen dorsal symphyseal margin) 70

Age in Years

60 50 40

Reference

30

Recent

20 10 0 I-II

II-III

III-IV Transition

389

IV-V

TOTAL Plots of ages at transition for the Lovejoy et al. auricular surface (Reference and Recent, respectively)

Ages at transition for the Lovejoy et al. auricular surface Transition (Lovejoy et al. Phase) I-II II-III III-IV IV-V V-VI VI-VII VII-VIII

Reference (age in years)

Recent (age in years)

Difference in years

16.93053 21.67875 30.12192 36.77465 41.39502 49.69603 71.51752

19.94954 25.39969 31.51755 46.52794 54.01152 74.67086

-1.72921 4.72223 5.2571 -5.13292 -4.31549 -3.15334

Plot of ages at transition for the Lovejoy et al. auricular surface

Plot of Ages at Transition (Lovejoy et al.) 80

Age in Years

70 60 50

Reference

40

Recent

30 20 10 0 I-II

II-III

III-IV

IV-V

V-VI

Transition

390

VI-VII

VII-VIII

TOTAL Plots of ages at transition for the Boldsen et al. auricular surface superior demiface topography component (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. auricular surface superior demiface topography component

Transition (SDT Phase) I-II II-III

Reference (age in years) 14.87479 65.79723

Recent (age in years) 15.92711 53.42181

Difference in years -1.05232 12.37542

Plot of ages at transition for the Boldsen et al. auricular surface superior demiface topography component

Plot of Ages at Transition (Boldsen superior demiface topography)

Age in Years

70 60 50 40

Reference

30 20

Recent

10 0 I-II

II-III Transition

391

TOTAL Plots of ages at transition for the Boldsen et al. auricular surface inferior demiface topography component (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. auricular surface inferior demiface topography component

Transition (IDT Phase) I-II II-III

Reference (age in years) 14.62314 58.09248

Recent (age in years) 7.735717 48.69423

Difference in years 6.887423 9.398251

Highlighted cells indicate values that are either very low or very high, which is likely an artifact of regression Plot of ages at transition for the Boldsen et al. auricular surface inferior demiface topography component

Plot of Ages at Transition (Boldsen inferior demiface topography)

Age in Years

70 60 50 40

Reference

30 20

Recent

10 0 I-II

II-III Transition

392

TOTAL Plots of ages at transition for the Boldsen et al. auricular surface superior surface morphology component (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. auricular surface superior surface morphology component Transition (SSM Phase) I-II II-III III-IV IV-V

Reference (age in years) 12.87563 18.29166 33.66438 148.7337

Recent (age in years) 10.52875 17.13829 29.37124 127.9324

Difference in years 2.34688 1.15337 4.29314 20.80133

Highlighted cells indicate values that are either very low or very high, which is likely an artifact of regression

Plot of ages at transition for the Boldsen et al. auricular surface superior surface morphology component

Age in Years

Plot of Ages at Transition (Boldsen superior surface morphology) 160 140 120 100 80 60 40 20 0

Reference Recent

I-II

II-III

III-IV Transition

393

IV-V

TOTAL Plots of ages at transition for the Boldsen et al. auricular surface apical surface morphology component (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. auricular surface apical surface morphology component Transition (APM Phase) I-II II-III III-IV IV-V

Reference (age in years) 15.52976 21.26418 39.33115 147.8246

Recent (age in years) 13.1069 20.15564 30.43596 121.6224

Difference in years 2.42286 1.10854 8.89519 26.20224

Highlighted cells indicate values that are either very low or very high, which is likely an artifact of regression

Plot of ages at transition for the Boldsen et al. auricular surface apical surface morphology component

Plot of Ages at Transition (Boldsen apical surface morphology) 160

Age in Years

140 120 100

Reference

80

Recent

60 40 20 0 I-II

II-III

III-IV Transition

394

IV-V

TOTAL Plots of ages at transition for the Boldsen et al. auricular surface inferior surface morphology component (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. auricular surface inferior surface morphology component Transition (ISM Phase) I-II II-III III-IV IV-V

Reference (age in years) 13.91757 19.56034 35.7462 146.8287

Recent (age in years) 14.70842 19.16419 32.55145 132.7205

Difference in years -0.79085 0.39615 3.19475 14.10814

Highlighted cells indicate values that are either very low or very high, which is likely an artifact of regression

Plot of ages at transition for the Boldsen et al. auricular surface inferior surface morphology component

Age in Years

Plot of Ages at Transition (Boldsen inferior surface morphology) 160 140 120 100 80 60 40 20 0

Reference Recent

I-II

II-III

III-IV Transition

395

IV-V

TOTAL Plots of ages at transition for the Boldsen et al. auricular surface inferior surface texture component (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. auricular surface inferior surface texture component Transition (IST Phase) I-II II-III

Reference (age in years) 67.69064 134.7684

Recent (age in years) 51.6079 103.5059

Difference in years 16.08274 31.26252

Highlighted cells indicate values that are very high, which is likely an artifact of regression

Plot of ages at transition for the Boldsen et al. auricular surface inferior surface texture component

Plot of Ages at Transition (Boldsen inferior surface texture) 160

Age in Years

140 120 100

Reference

80 60

Recent

40 20 0 I-II

II-III Transition

396

TOTAL Plots of ages at transition for the Boldsen et al. auricular surface superior posterior iliac exostoses (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. auricular surface superior posterior iliac exostoses component Transition (SPE Phase) I-II II-III III-IV IV-V V-VI

Reference (age in years) 4.46052 136.9176 268.8021 1047.06 1121.368

Recent (age in years) 17.4833 54.18206 69.04485 188.4999 207.1741

Difference in years -13.0228 82.73549 199.7573 858.5598 914.1939

Highlighted cells indicate values that are either very low or extremely high; it is unclear whether these values were influenced by the logit regression procedure, this component’s very low correlation with chronological age (0.245), or some other unidentified factor. Conclusions drawn from this component’s data should be made with caution.

Plot of ages at transition for the Boldsen et al. auricular surface superior posterior iliac exostoses

Plot of Age s at Transition (Boldsen superior posterior iliac exostoses) 1200 Age in Years

1000 800 Reference

600

Recent

400 200 0 I-II

II-III

III-IV

IV-V

Transition

397

V-VI

TOTAL Plots of ages at transition for the Boldsen et al. auricular surface inferior posterior iliac exostoses (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. auricular surface inferior posterior iliac exostoses component Transition (IPE Phase) I-II II-III III-IV IV-V V-VI

Reference (age in years) 5.472713 113.5112 762.6176 12041.07 44760.54

Recent (age in years) 37.66459 69.97634 109.704 177.5114 204.9264

Difference in years -32.1919 43.53488 652.9137 11863.55 44555.61

Highlighted cells indicate values that are either very low or extremely high; it is unclear whether these values were influenced by the logit regression procedure, this component’s very low correlation with chronological age (0.173), or some other unidentified factor. Conclusions drawn from this component’s data should be made with caution.

Plot of ages at transition for the Boldsen et al. auricular surface inferior posterior iliac exostoses

Plot of Ages at Transition (Boldsen inferior posterior iliac exostoses)

Age in Years

50000 40000 30000

Reference

20000

Recent

10000 0 I-II

II-III

III-IV

IV-V

Transition

398

V-VI

TOTAL Plots of ages at transition for the Boldsen et al. auricular surface posterior iliac exostoses component (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. auricular surface posterior iliac exostoses component Transition (PIE Phase) I-II II-III

Reference (age in years) 62.37937 325.5391

Recent (age in years) 66.25233 201.1942

Difference in years -3.87296 124.3449

Highlighted cells indicate values that are very high, which is likely an artifact of regression

Plot of ages at transition for the Boldsen et al. auricular surface posterior iliac exostoses component

Plot of Ages at Transition (Boldsen posterior iliac exostoses) 350

Age in Years

300 250 200

Reference

150

Recent

100 50 0 I-II

II-III Transition

399

TOTAL Plots of ages at transition for the İşcan et al method (Reference and Recent, respectively)

Ages at transition for the İşcan et al method Transition (İşcan Phase) I-II II-III III-IV IV-V V-VI VI-VII VII-VIII

Reference (age in years) -17.26198 24.77808 31.53157 45.20274 60.83032 96.05648

Recent (age in years) -17.83161 22.9366 32.39673 45.51855 63.31529 89.76016

Difference in years --0.56963 1.84148 -0.86516 -0.31581 -2.48497 6.29632

Plot of ages at transition for the İşcan et al method

Plot of Ages at Transition (İşcan et al.) 120

Age in Years

100 80 Reference

60

Recent

40 20 0 I-II

II-III

III-IV

IV-V Transition

400

V-VI

VI-VII

VII-VIII

TOTAL Plots of ages at transition for the Boldsen et al. lambdoidal-asterion suture score (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. lambdoidal-asterion suture score Transition (LA Phase) I-II II-III III-IV IV-V

Reference (age in years) 25.42824 100.909 430.8739 1511.707

Recent (age in years) 28.97751 81.71106 229.8981 862.2052

Difference in years -3.54927 19.19796 200.9758 649.5017

Highlighted cells indicate values that are very high, which is likely an artifact of regression

Plot of ages at transition for the Boldsen et al. lambdoidal-asterion suture score

Plot of Ages at Transition (Boldsen lambdoidal-asterion) 1600

Age in Years

1400 1200 1000

Reference

800

Recent

600 400 200 0 I-II

II-III

III-IV Transition

401

IV-V

TOTAL Plots of ages at transition for the Boldsen et al. sagittal-obelica suture score (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. sagittal-obelica suture score Transition (SO Phase) I-II II-III III-IV IV-V

Reference (age in years) 5.485111 17.49031 48.83953 148.4441

Recent (age in years) 3.790463 11.99364 38.62208 219.9487

Difference in years 1.694648 5.496676 10.21745 -71.5046

Highlighted cells indicate values that are either very low or very high, which is likely an artifact of regression

Plot of ages at transition for the Boldsen et al. sagittal-obelica suture score

Plot of Ages at Transition (Boldsen sagittal-obelica) 250

Age in Years

200 150

Reference Recent

100 50 0 I-II

II-III

III-IV Transition

402

IV-V

TOTAL Plots of ages at transition for the Boldsen et al. coronal-pterica suture score (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. coronal-pterica suture score Transition (CP Phase) I-II II-III III-IV IV-V

Reference (age in years) 10.49457 26.48582 45.80383 72.30263

Recent (age in years) 11.61731 26.95243 45.48986 73.79757

Difference in years -1.12274 -0.46661 0.31397 -1.49494

Plot of ages at transition for the Boldsen et al. coronal-pterica suture score

Plot of Ages at Transition (Boldsen coronal-pterica) 80

Age in Years

70 60 50

Reference

40

Recent

30 20 10 0 I-II

II-III

III-IV Transition

403

IV-V

TOTAL Plots of ages at transition for the Boldsen et al. zygomaticomaxillary suture score (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. zygomaticomaxillary suture score Transition (ZM Phase) I-II II-III III-IV IV-V

Reference (age in years) 2.302015 61.96126 395.4753 1136.044

Recent (age in years) 2.178999 77.51585 531.8462 4675.19

Difference in years 0.123016 -15.5546 -136.371 -3539.15

Highlighted cells indicate values that are either very low or extremely high; it is unclear whether these values were influenced by the logit regression procedure, this component’s very low correlation with chronological age (0.186), or some other unidentified factor. Conclusions drawn from this component’s data should be made with caution. Plot of ages at transition for the Boldsen et al. zygomaticomaxillary suture score

Plot of Ages at Transition (Boldsen zygomaticomaxillary) 5000 Age in Years

4000 3000

Reference

2000

Recent

1000 0 I-II

II-III

III-IV Transition

404

IV-V

TOTAL Plots of ages at transition for the Boldsen et al. interpalatine suture score (Reference and Recent, respectively)

Ages at transition for the Boldsen et al. interpalatine suture score Transition (IP Phase) I-II II-III III-IV IV-V

Reference (age in years) 2.708003 19.55317 102.6821 249.1754

Recent (age in years) 3.222112 24.97212 97.78129 335.5281

Difference in years -0.51411 -5.41895 4.900784 -86.3527

Highlighted cells indicate values that are either very low or very high, which is likely an artifact of regression

Plot of ages at transition for the Boldsen et al. interpalatine suture score

Plot of Ages at Transition (Boldsen interpalatine) 400

Age in Years

350 300 250

Reference

200

Recent

150 100 50 0 I-II

II-III

III-IV Transition

405

IV-V

TOTAL Plots of ages at transition for the Meindl & Lovejoy cranial vault suture score (Reference and Recent, respectively)

Ages at transition for the Meindl & Lovejoy cranial vault suture score Transition (CS V Phase) 0-I I-II II-III III-IV IV-V V-VI

Reference (age in years) 2.549133 7.527341 20.58498 43.5742 79.20498 138.6643

Recent (age in years) 3.707066 7.537938 17.14602 46.73218 97.07602 164.2397

Difference in years -1.15793 -0.0106 3.438953 -3.15798 -17.871 -25.5754

Highlighted cells indicate values that are either very low or very high, which is likely an artifact of regression Plot of ages at transition for the Meindl & Lovejoy cranial vault suture score

Age in Years

Plot of Age s at Transition (M eindl & Lovejoy vault sutures) 180 160 140 120 100 80 60 40 20 0

Reference Recent

0-I

I-II

II-III

III-IV

Transition

406

IV-V

V-VI

TOTAL Plots of ages at transition for the Meindl & Lovejoy cranial lateral-anterior suture score (Reference and Recent, respectively)

Ages at transition for the Meindl & Lovejoy cranial lateral-anterior suture score Transition (CS LA Phase) 0-I I-II II-III III-IV IV-V V-VI VI-VII

Reference (age in years) 5.035675 7.996052 12.83603 33.08888 42.23217 62.97036 107.2077

Recent (age in years) 7.201017 10.40485 14.54628 38.18444 50.53948 79.89906 126.0059

Difference in years -2.16534 -2.4088 -1.71025 -5.09556 -8.3073 -16.9287 -18.7982

Highlighted cells indicate values that are either very low or very high, which is likely an artifact of regression Plot of ages at transition for the Meindl & Lovejoy cranial lateral-anterior suture score

Plot of Ages at Transition (Meindl & Lovejoy lateralanterior sutures) 140 Age in Years

120 100 80

Reference

60

Recent

40 20 0 0-I

I-II

II-III

III-IV

IV-V

Transition

407

V-VI

VI-VII

Appendix I: Graphs of the number of individuals per observed phase, by Reference and Recent groups Number of Individuals by Observed Phase 350 300 250 200

Reference

150

Recent

100 50 0 1

2

3

4

5

6

7

8

9

10

.

Todd Phase

Number of Individuals by Observed Phase 300 250 200 Reference

150

Recent

100 50 0 1

2

3

4

5

Suchey-Brooks Phase

408

6

.

Number of Individuals by Observed Phase 250 200 150

Reference Recent

100 50 0 1

2

3

4

5

6

7

.

Hartnett-Fulginiti Phase

Number of Individuals by Observed Phase 300 250 200 Reference

150

Recent

100 50 0 1

2

3

4

5

Boldsen et al. Symphyseal Relief Phase

409

6

.

Number of Individuals by Observed Phase 250 200 150

Reference Recent

100 50 0 1

2

3

4

.

Boldsen et al. Symphyseal Texture Phase

Number of Individuals by Observed Phase 350 300 250 200

Reference

150

Recent

100 50 0 1

2

3

4

Boldsen et al. Superior Apex Phase

410

.

Number of Individuals by Observed Phase 350 300 250 200

Reference

150

Recent

100 50 0 1

2

3

4

5

6

7

.

Boldsen et al. Ventral Symphyseal Margin Phase

Number of Individuals by Observed Phase 300 250 200 Reference

150

Recent

100 50 0 1

2

3

4

5

Boldsen et al. Dorsal Symphyseal Margin Phase

411

.

Number of Individuals by Observed Phase 200 150 Reference

100

Recent

50 0 1

2

3

4

5

6

7

8

.

Lovejoy et al. Auricular Surface Phase

Number of Individuals by Observed Phase 350 300 250 200

Reference

150

Recent

100 50 0 1

2

3

Boldsen et al. Superior Demiface Topography Phase

412

.

Number of Individuals by Observed Phase 300 250 200 Reference

150

Recent

100 50 0 1

2

3

.

Boldsen et al. Inferior Demiface Topography Phase

Number of Individuals by Observed Phase 350 300 250 200

Reference

150

Recent

100 50 0 1

2

3

4

5

Boldsen et al. Superior Surface Morphology Phase

413

.

Number of Individuals by Observed Phase 350 300 250 200

Reference

150

Recent

100 50 0 1

2

3

4

5

.

Boldsen et al. Apical Surface Morphology Phase

Number of Individuals by Observed Phase 350 300 250 200

Reference

150

Recent

100 50 0 1

2

3

4

5

Boldsen et al. Inferior Surface Morphology Phase

414

.

Number of Individuals by Observed Phase 350 300 250 200

Reference

150

Recent

100 50 0 1

2

3

.

Boldsen et al. Inferior Surface Texture Phase

Number of Individuals by Observed Phase 350 300 250 200

Reference

150

Recent

100 50 0 1

2

3

4

5

6

Boldsen et al. Superior Posterior Iliac Exostoses Phase

415

.

Number of Individuals by Observed Phase 200 150 Reference

100

Recent

50 0 1

2

3

4

5

6

.

Boldsen et al. Inferior Posterior Iliac Exostoses Phase

Number of Individuals by Observed Phase 350 300 250 200

Reference

150

Recent

100 50 0 1

2

3

Boldsen et al. Posterior Iliac Exostoses Phase

416

.

Number of Individuals by Observed Phase 250 200 150

Reference Recent

100 50 0 1

2

3

4

5

6

7

8

.

Iscan 4th Rib Phase

Number of Individuals by Observed Phase 180 160 140 120 100 80 60 40 20 0

Reference Recent

0

1

2

3

4

5

6

Meindl & Lovejoy Cranial Vault Sutures Phase

417

.

Number of Individuals by Observed Phase 250 200 150

Reference Recent

100 50 0 0

1

2

3

4

5

6

7

Meindl & Lovejoy Lateral-Anterior Sutures Phase

418

.

Appendix J: Age at transition data by sex FEMALES VS. MALES Plots of ages at transition for the Todd method: Reference (left) and Recent (right), Females (above) and Males (below)

Ages at transition for the Todd method Transition (Todd Phase) I-II II-III III-IV IV-V V-VI VI-VII VII-VIII VIII-IX IX-X

Reference females 14.83554 16.11090 18.47105 22.70708 26.02127 33.00416 37.89109 41.89265 44.52543

Recent females

Reference males

Recent males

20.13112 21.21890 23.36920 28.83492 37.25851 40.11607 45.30296 48.88052

17.79977 20.15313 21.94079 25.13575 29.93387 39.78940 42.18791 48.65376

12.76256 13.94538 17.98595 21.33253 31.04983 38.50588 44.65046 61.15104

419

Difference: females 14.8355 -4.02022 -2.74785 -0.66212 -2.81365 -4.25435 -2.22498 -3.41031 -4.35509

Difference: males 0 5.03721 6.20775 3.95484 3.80322 -1.116 1.28352 -2.4626 -12.497

Plot of Ages at Transition (Todd) 70

Age in Years

60 50

Reference Females

40

Recent Females

30

Reference Males Recent Males

20 10 0 I-II

II-III

III-IV

IV-V

V-VI

VI-VII VII-VIII VIII-IX IX-X

Transition

420

FEMALES VS. MALES Plots of ages at transition for the Suchey-Brooks method: Reference (left) and Recent (right), Females (above) and Males (below)

Ages at transition for the Suchey-Brooks method Transition (S-B Phase) I-II II-III III-IV IV-V V-VI

Reference females 17.23422 23.02294 32.73945 42.56877 48.34084

Recent females 18.52084 27.15085 34.70037 46.31197 54.29112

Reference males 19.89560 22.86526 29.43274 43.63547 56.36371

421

Recent males 11.55149 18.12815 27.26925 47.60746 61.80413

Difference: females -1.28662 -4.12791 -1.96092 -3.7432 -5.95028

Difference: males 8.34411 4.73711 2.16349 -3.972 -5.4404

Plot of Ages at Transition (Suchey-Brooks) 70

Age in Years

60 50

Reference Females

40

Recent Females

30

Reference Males Recent Males

20 10 0 I-II

II-III

III-IV

IV-V

Transition

422

V-VI

FEMALES VS. MALES Plots of ages at transition for the Hartnett-Fulginiti method: Reference (left) and Recent (right), Females (above) and Males (below)

Ages at transition for the Hartnett-Fulginiti method Transition (H-F Phase) I-II II-III III-IV IV-V V-VI VI-VII

Reference females 16.69839 24.19815 32.76929 42.37966 48.97877 90.46006

Recent females 16.39881 26.52163 33.34868 45.97083 56.83674 98.07331

Reference males 19.91638 23.93506 30.56965 43.72930 56.36045 120.84557

423

Recent males 19.09914 29.04570 46.74212 66.12455 140.85118

Difference: females 0.29958 -2.32348 -0.57939 -3.59117 -7.85797 -7.61325

Difference: males 19.9164 4.83592 1.52395 -3.0128 -9.7641 -20.006

Plot of Ages at Transition (Hartnett-Fulginiti) 160 140 Age in Years

120 Reference Females

100

Recent Females

80

Reference Males

60

Recent Males

40 20 0 I-II

II-III

III-IV

IV-V

Transition

424

V-VI

VI-VII

FEMALES VS. MALES Plots of ages at transition for the Boldsen et al. pubic symphyseal superior apex component: Reference (left) and Recent (right), Females (above) and Males (below)

Ages at transition for the Boldsen et al. pubic symphyseal superior apex component Transition (SA Phase) I-II II-III III-IV

Reference females 24.38955 31.30791 39.65904

Recent females 28.74339 33.89426 45.85135

Reference males 20.27273 28.82763 40.36091

425

Recent males 13.18103 21.63055 45.31454

Difference: females -4.35384 -2.58635 -6.19231

Difference: males 7.0917 7.19708 -4.9536

Plot of Ages at Transition (Boldsen et al. superior apex)

Age in Years

50 45 40 35 30 25

Reference Females Recent Females Reference Males

20 15 10 5

Recent Males

0 I-II

II-III

III-IV

Transition

426

FEMALES VS. MALES Plots of ages at transition for the Boldsen et al. pubic symphyseal ventral margin component: Reference (left) and Recent (right), Females (above) and Males (below)

Ages at transition for the Boldsen et al. pubic symphyseal ventral margin component Transition (VSM Phase) I-II II-III III-IV IV-V V-VI VI-VII

Reference females 16.72280 24.36796 29.52452 34.38176 42.47402 47.05985

Recent females 19.98982 26.06566 32.87623 35.11315 44.93551 55.50390

Reference males 18.46527 21.72181 26.80175 31.26019 41.53497 51.73892

427

Recent males 11.15909 14.51651 23.01798 28.80685 38.41930 64.92177

Difference: females -3.26702 -1.6977 -3.35171 -0.73139 -2.46149 -8.44405

Difference: males 7.30618 7.2053 3.78377 2.45334 3.11567 -13.18285

Plot of Ages at Transition (Boldsen et al. ventral symphyseal margin) 70

Age in Years

60 50

Reference Females

40

Recent Females

30

Reference Males

20

Recent Males

10 0 I-II

II-III

III-IV

IV-V

Transition

428

V-VI

VI-VII

FEMALES VS. MALES Plots of ages at transition for the Boldsen et al. pubic symphyseal dorsal margin component: Reference (left) and Recent (right), Females (above) and Males (below)

Ages at transition for the Boldsen et al. pubic symphyseal dorsal margin component Transition (DSM Phase) I-II II-III III-IV IV-V

Reference females 13.75774 25.23861 38.52360 47.06054

Recent females 15.09150 29.04080 42.70441 58.60442

Reference males 18.52232 24.88892 41.43302 53.58477

429

Recent males 10.42699 17.25821 38.67739 70.00125

Difference: females -1.33376 -3.80219 -4.18081 -11.5439

Difference: males 8.09533 7.63071 2.75563 -16.41648

Plot of Ages at Transition (Boldsen et al. dorsal symphyseal margin) 80

Age in Years

70 60

Reference Females

50

Recent Females

40

Reference Males

30

Recent Males

20 10 0 I-II

II-III

III-IV Transition

430

IV-V

FEMALES VS. MALES Plots of ages at transition for the Lovejoy et al. auricular surface method: Reference (left) and Recent (right), Females (above) and Males (below)

Ages at transition for the Lovejoy et al. auricular surface Transition (Lovejoy et al. Phase) I-II II-III III-IV IV-V V-VI VI-VII VII-VIII

Reference females 16.48314 22.88002 30.13900 37.24381 42.09980 49.26380 68.84010

Recent females 22.93637 26.73566 33.20313 47.52863 54.51706 71.02673

Reference males 17.50398 20.59205 30.15780 36.31234 40.68360 50.14833 74.40417

431

Recent males 18.50192 24.73606 30.76845 46.20352 54.04271 78.49252

Difference: females 16.4831 -0.05635 3.40334 4.04068 -5.42883 -5.25326 -2.18663

Difference: males 17.50398 2.09013 5.42174 5.54389 -5.51992 -3.89438 -4.08835

Plot of Ages at Transition (Lovejoy et al. auricular surface) 90 80 Age in Years

70 60

Reference Females

50

Recent Females

40

Reference Males

30

Recent Males

20 10 0 I-II

II-III

III-IV

IV-V

V-VI

Transition

432

VI-VII

VII-VIII

FEMALES VS. MALES Plots of ages at transition for the Boldsen et al. auricular surface superior demiface topography component: Reference (left) and Recent (right), Females (above) and Males (below)

Ages at transition for the Boldsen et al. auricular surface superior demiface topography component

Transition (SDT Phase) I-II II-III

Reference females 17.55048 73.65375

Recent females 23.85311 60.76883

Reference males 12.91968 57.07686

433

Recent males 12.54629 48.93588

Difference: females -6.30263 12.8849

Difference: males 0.37339 8.14098

Plot of Ages at Transition (Boldsen superior demiface topography) 80

Age in Years

70 60

Reference Females

50

Recent Females

40

Reference Males

30

Recent Males

20 10 0 I-II

II-III Transition

434

Appendix K: Age at transition data by race BLACK vs. WHITE Plots of ages at transition for the Todd method: Reference (left) and Recent (right), Black (above) and White (below)

Ages at transition for the Todd method Transition (Todd Phase) I-II II-III III-IV IV-V V-VI VI-VII VII-VIII VIII-IX IX-X

Reference Black 15.49434 18.38217 21.30931 25.19838 32.03883 38.45822 42.41271 46.98058

Recent Black

20.43088 26.84640 31.76374 36.06444 42.75381 50.54699

Reference White 16.68064 19.11284 20.82413 23.82982 26.32823 31.06783 39.25963 41.65939 46.28511

435

Recent White 16.60367 17.71661 20.21210 23.90545 33.56647 39.54626 45.12842 56.44575

Difference: Black

Difference: White

0.87843 -1.64802 0.27509 2.39378 -0.3411 -3.56641

2.50917 3.10752 3.61772 2.42278 -2.49864 -0.28663 -3.46903 -10.16064

Plot of Ages at Transition (Todd) 60

Age in Years

50 40

Reference black Recent black

30

Reference white

20

Recent white

10 0 I-II

II-III

III-IV

IV-V

V-VI

VI-VII VII-VIII VIII-IX

Transition

436

IX-X

BLACK VS. WHITE Plots of ages at transition for the Suchey-Brooks method: Reference (left) and Recent (right), Black (above) and White (below)

Ages at transition for the Suchey-Brooks method Transition (S-B Phase) I-II II-III III-IV IV-V V-VI

Reference Black 17.72604 22.01005 31.39603 43.28880 52.86617

Recent Black 21.03560 30.25999 45.15983 52.29291

Reference White 19.84546 24.30076 30.85090 42.91090 52.15843

437

Recent White 15.18435 22.02309 30.11566 47.22131 59.23584

Difference: Black 0.97445 1.13604 -1.87103 0.57326

Difference: White 4.66111 2.27767 0.73524 -4.31041 -7.07741

Plot of Ages at Transition (Suchey-Brooks) 70

Age in Years

60 50

Reference black

40

Recent black

30

Reference white Recent white

20 10 0 I-II

II-III

III-IV

IV-V

Transition

438

V-VI

BLACK VS. WHITE Plots of ages at transition for the Hartnett-Fulginiti method: Reference (left) and Recent (right), Black (above) and White (below)

Table 4.7: Ages at transition for the Hartnett-Fulginiti method Transition (H-F Phase) I-II II-III III-IV IV-V V-VI VI-VII

Reference Black 17.86189 23.94823 32.06917 43.36205 53.12099 117.67544

Recent Black 23.54014 32.05475 44.85491 56.15689 103.08404

Reference White 18.73314 24.09220 30.92196 42.52361 52.79802 91.63546

439

Recent White 12.97561 21.96175 30.51541 46.62018 62.64299 115.30614

Difference: Black 0.40809 0.01442 -1.49286 -3.0359 14.5914

Difference: White 5.75753 2.13045 0.40655 -4.09657 -9.84497 -23.67068

Plot of Ages at Transition (Hartnett-Fulginiti) 140

Age in Years

120 100

Reference black

80

Recent black

60

Reference white Recent white

40 20 0 I-II

II-III

III-IV

IV-V

Transition

440

V-VI

VI-VII

BLACK VS. WHITE Plots of ages at transition for the Boldsen et al. pubic symphyseal superior apex component: Reference (left) and Recent (right), Black (above) and White (below)

Table 4.7: Ages at transition for the Boldsen et al. pubic symphyseal superior apex component Transition (SA Phase) I-II II-III III-IV

Reference Black 22.07044 30.22813 40.58404

Recent Black 14.47561 24.71243 41.69374

Reference White 22.57361 29.64584 39.32298

441

Recent White 20.42410 26.82486 45.82091

Difference: Black 7.59483 5.5157 -1.1097

Difference: White 2.14951 2.82098 -6.49793

Plot of Ages at Transition (Boldsen et al. superior apex)

Age in Years

50 45 40 35 30 25

Reference black Recent black Reference white

20 15 10 5

Recent white

0 I-II

II-III

III-IV

Transition

442

BLACK VS. WHITE Plots of ages at transition for the Boldsen et al. pubic symphyseal ventral margin component: Reference (left) and Recent (right), Black (above) and White (below)

Table 4.7: Ages at transition for the Boldsen et al. pubic symphyseal ventral margin component Transition (VSM Phase) I-II II-III III-IV IV-V V-VI VI-VII

Reference Black 15.97181 23.41687 28.59612 33.33232 42.58485 48.55522

Recent Black 15.64650 18.60487 28.26345 30.05642 40.37320 54.32826

Reference White 19.94089 22.93761 27.70548 32.12221 41.37351 50.33442

443

Recent White 15.01328 19.61335 26.87587 31.62681 40.95767 60.82158

Difference: Black 0.32531 4.812 0.33267 3.2759 2.21165 -5.77304

Difference: White 4.92761 3.32426 0.82961 0.4954 0.41584 -10.48716

Plot of Ages at Transition (Boldsen et al. ventral symphyseal margin) 70

Age in Years

60 50

Reference black

40

Recent black

30

Reference white

20

Recent white

10 0 I-II

II-III

III-IV

IV-V

Transition

444

V-VI

VI-VII

BLACK VS. WHITE Plots of ages at transition for the Boldsen et al. pubic symphyseal dorsal margin component: Reference (left) and Recent (right), Black (above) and White (below)

Table 4.7: Ages at transition for the Boldsen et al. pubic symphyseal dorsal margin component Transition (DSM Phase) I-II II-III III-IV IV-V

Reference Black 14.23602 25.62402 40.08564 51.46299

Recent Black 12.92395 33.77409 60.40143

Reference White 19.08364 24.89483 40.12191 49.67204

445

Recent White 12.94899 23.07870 40.92716 65.23970

Difference: Black 14.23602 12.70007 6.31155 -8.93844

Difference: White 6.13465 1.81613 -0.80525 -15.56766

Plot of Ages at Transition (Boldsen et al. dorsal symphyseal margin) 70

Age in Years

60 50

Reference black

40

Recent black

30

Reference white

20

Recent white

10 0 I-II

II-III

III-IV Transition

446

IV-V

BLACK VS. WHITE Plots of ages at transition for the Lovejoy et al. auricular surface method: Reference (left) and Recent (right), Black (above) and White (below)

Table 4.7: Ages at transition for the Lovejoy et al. auricular surface Transition (Lovejoy et al. Phase) I-II II-III III-IV IV-V V-VI VI-VII VII-VIII

Reference Black 18.06830 22.50255 31.09778 37.36747 41.82028 51.50804 70.79144

Recent Black 17.77968 22.40757 25.13337 40.52443 50.76097 90.21724

Reference White 15.06530 20.39629 28.78596 35.96705 40.85045 47.68181 72.26541

447

Recent White

Difference: Black

Difference: White

20.35962 25.87970 32.53487 47.66251 54.75813 73.47209

4.72287 8.69021 12.2341 1.29585 0.74707 -19.4258

0.03667 2.90626 3.43218 -6.81206 -7.07632 -1.20668

Plot of Ages at Transition (Lovejoy et al. auricular surface)

Age in Years

100 90 80 70 60 50

Reference black Recent black Reference white

40 30 20 10

Recent white

0 I-II

II-III

III-IV

IV-V

V-VI

Transition

448

VI-VII

VII-VIII

BLACK VS. WHITE Plots of ages at transition for the Boldsen et al. auricular surface superior demiface topography component: Reference (left) and Recent (right), Black (above) and White (below)

Table 4.7: Ages at transition for the Boldsen et al. auricular surface superior demiface topography component Transition (SDT Phase) I-II II-III

Reference Black 18.89160 50.21393

Recent Black 15.85968 36.51878

Reference White 11.01028 93.31381

449

Recent White 16.73811 56.36392

Difference: Black 3.03192 13.69515

Difference: White -5.72783 36.94989

Age in Years

Plot of Ages at Transition (Boldsen superior demiface topography) 100 90 80 70 60 50 40 30 20 10 0

Reference black Recent black Reference white Recent white

I-II

II-III Transition

450

Appendix L: Age at transition data by sex-race category SEX-RACE CATEGORIES Plots of ages at transition for the Todd method

Reference

Recent

Black females

Insufficient sample size

Black males

White females

White males

451

Ages at transition for the Todd method Transition Ref Rec Ref Rec Ref Rec Ref Rec (Todd BF BF BM BM WF WF WM WM Phase) I-II 19.47 II-III 14.34 16.64 20.18 19.60 13.32 III-IV 17.05 19.60 21.37 21.28 21.01 14.68 IV-V 21.81 20.50 20.36 24.60 23.46 23.60 17.75 V-VI 26.65 23.44 26.98 25.97 29.02 27.10 20.02 VI-VII 34.28 29.97 32.19 32.25 37.69 30.06 31.06 VII-VIII 38.97 38.24 36.88 37.13 40.67 41.62 39.21 VIII-IX 43.77 41.35 43.05 40.25 45.58 43.20 45.26 IX-X 46.39 47.81 51.78 42.91 49.30 49.56 64.11 Highlighted cell indicates a transition from 1 to 3; no phase 2 was scored for that grouping

diff: BF

diff: BM

diff: WF

diff: WM

----------

---0.14 -3.54 -2.22 1.35 -1.69 -3.97

--0.09 1.14 -3.05 -5.44 -3.53 -5.33 -6.39

-6.28 6.32 5.85 7.08 -1.00 2.40 -2.06 -14.55

Plot of Ages at Transition (Todd) 70.00 60.00

Age in Years

50.00 40.00 30.00 20.00 10.00 0.00 I-II

II-III

III-IV

IV-V

V-VI

VI-VII

VII-VIII

VIII-IX

IX-X

Transition

Ref BF

Rec BF

Ref BM

Rec BM

Ref WF

Rec WF

Ref WM

Rec WM

note the significant difference in slope between the Reference and Recent white males

452

SEX-RACE CATEGORIES Plots of ages at transition for the Suchey-Brooks method

Reference

Recent

Black females

Insufficient sample size

Black males

White females

White males

453

Ages at transition for the Suchey-Brooks method Transition (S-B Phase) I-II II-III III-IV IV-V V-VI

Ref BF 16.01 22.11 33.82 44.10 50.31

Rec BF

Ref BM 19.37 21.78 29.08 42.93 55.46

Rec BM 20.74 30.26 44.46 52.56

Ref WF 19.25 24.56 31.94 41.38 46.80

Rec WF 18.42 27.16 34.95 46.11 54.41

Ref WM 20.65 24.21 29.95 44.45 57.26

Rec WM 11.82 17.79 26.95 48.80 64.67

diff: BF ------

diff: BM -1.05 -1.18 -1.53 2.90

diff: WF 0.82 -2.60 -3.01 -4.73 -7.61

diff: WM 8.83 6.43 3.00 -4.35 -7.41

Plot of Ages at Transition (Suchey-Brooks) 70.00 60.00

Age in Years

50.00 40.00 30.00 20.00 10.00 0.00 I-II

II-III

III-IV

IV-V

V-VI

Transition

Ref BF

Rec BF

Ref BM

Rec BM

Ref WF

Rec WF

Ref WM

Rec WM

note the significant difference in slope between the Reference and Recent white males

454

SEX-RACE CATEGORIES Plots of ages at transition for the Hartnett-Fulginiti method

Reference

Recent

Black females

Insufficient sample size

Black males

White females

White males

455

Ages at transition for the Hartnett-Fulginiti method Transition (H-F Phase) I-II II-III III-IV IV-V V-VI VI-VII

Ref BF 16.37 24.71 34.19 44.05 50.70 108.25

Rec BF

Ref BM 19.29 23.04 30.19 43.08 55.51 134.29

Rec BM 23.29 32.10 44.12 55.91

Ref WF 17.44 23.74 31.33 40.76 47.39 80.72

Rec WF 16.20 26.46 33.51 45.72 56.63 100.13

Ref WM 20.80 25.10 31.08 44.46 57.26 111.52

Rec WM -18.22 28.63 47.74 69.15 141.17

diff BF -------

diff: BM --0.24 -1.91 -1.04 -0.40 --

diff: WF 1.24 -2.71 -2.18 -4.96 -9.23 -19.41

Plot of Ages at Transition (Hartnett-Fulginiti) 160.00 140.00

Age in Years

120.00 100.00 80.00 60.00 40.00 20.00 0.00 I-II

II-III

III-IV

IV-V

V-VI

VI-VII

Transition

Ref BF

Rec BF

Ref BM

Rec BM

Ref WF

Rec WF

Ref WM

Rec WM

note the significant difference in slope between the Reference and Recent white males

456

diff: WM -6.87 2.45 -3.28 -11.89 -29.65

SEX-RACE CATEGORIES Plots of ages at transition for the Boldsen et al. pubic symphyseal superior apex component

Reference

Recent

Black females

Insufficient sample size

Black males

White females

White males

457

Ages at transition for the Boldsen et al. pubic symphyseal superior apex component Transition (SA Phase) I-II II-III III-IV

Ref BF 24.56 32.56 39.96

Rec BF

Ref BM 19.49 28.33 41.46

Rec BM 14.25 24.82 42.12

Ref WF 24.31 29.99 39.41

Rec WF 28.92 34.21 46.16

Ref WM 21.21 29.50 39.27

Rec WM 13.05 21.13 46.58

diff: BF ----

diff: BM 5.23 3.51 -0.66

diff: WF -4.61 -4.21 -6.75

diff: WM 8.16 8.37 -7.31

Plot of Ages at Transition (Boldsen et al. superior apex) 50.00 45.00

Age in Years

40.00 35.00 30.00 25.00 20.00 15.00 10.00 5.00 0.00 I-II

II-III

III-IV

Transition

Ref BF

Rec BF

Ref BM

Rec BM

Ref WF

Rec WF

Ref WM

Rec WM

note the significant difference in slope between the Reference and Recent white males, and the Reference and Recent black males

458

SEX-RACE CATEGORIES Plots of ages at transition for the Boldsen et al. pubic symphyseal ventral margin component

Reference

Recent

Black females

Insufficient sample size

Black males

White females

White males

459

Ages at transition for the Boldsen et al. pubic symphyseal ventral margin component Transition (VSM Phase) I-II II-III III-IV IV-V V-VI VI-VII

Ref BF

Rec BF

14.08 24.27 30.60 35.86 44.20 49.07

Ref BM

Rec BM

Ref WF

Rec WF

Ref WM

Rec WM

17.52 22.42 26.66 31.23 41.29 48.29

15.00 18.05 28.05 29.99 39.12 55.20

20.33 25.03 28.76 33.23 41.05 45.39

19.88 26.05 33.08 35.42 44.68 55.71

19.99 21.07 27.19 31.45 41.72 55.04

10.32 13.79 22.05 28.76 38.42 68.03

diff: BF

diff: BM

diff: WF

diff: WM

2.52 4.37 -1.39 1.24 2.17 -6.92

0.45 -1.03 -4.32 -2.18 -3.63 -10.32

9.67 7.28 5.15 2.69 3.29 -12.99

Plot of Ages at Transition (Boldsen et al. ventral symphseal margin) 80.00 70.00

Age in Years

60.00 50.00 40.00 30.00 20.00 10.00 0.00 I-II

II-III

III-IV

IV-V

V-VI

VI-VII

Transition

Ref BF

Rec BF

Ref BM

Rec BM

Ref WF

Rec WF

Ref WM

Rec WM

note the significant difference in slope between the Reference and Recent white males

460

SEX-RACE CATEGORIES Plots of ages at transition for the Boldsen et al. pubic symphyseal dorsal margin component

Reference

Recent

Black females

Insufficient sample size

Black males

White females

White males

461

Ages at transition for the Boldsen et al. pubic symphyseal dorsal margin component Transition (DSM Phase) I-II II-III III-IV IV-V

Ref BF

Rec BF

11.71 26.51 39.52 48.35

Ref BM

Rec BM

Ref WF

Rec WF

Ref WM

Rec WM

16.47 24.20 41.00 54.46

11.45 34.07 64.25

16.63 23.73 38.03 46.36

15.05 29.20 43.40 59.11

21.04 25.91 42.07 52.81

11.51 18.46 39.69 71.51

diff: BF

diff: BM

diff: WF

diff: WM

12.75 6.93 -9.79

1.58 -5.48 -5.37 -12.75

9.53 7.45 2.38 -18.70

Plot of Ages at Transition (Boldsen et al. dorsal symphseal margin) 80.00 70.00

Age in Years

60.00 50.00 40.00 30.00 20.00 10.00 0.00 I-II

II-III

III-IV

IV-V

Transition

Ref BF

Rec BF

Ref BM

Rec BM

Ref WF

Rec WF

Ref WM

Rec WM

note the significant difference in slope between the Reference and Recent white males, and the Reference and Recent black males

462

SEX-RACE CATEGORIES Plots of ages at transition for the Lovejoy et al. auricular surface method

Reference

Recent

Black females

Insufficient sample size

Black males

White females

White males

463

Ages at transition for the Lovejoy et al. auricular surface Transition (Lovejoy et al. Phase) I-II II-III III-IV IV-V V-VI VI-VII VII-VIII

Ref BF

Rec BF

18.12 23.99 31.34 38.37 43.29 51.59 71.03

Ref BM

Rec BM

Ref WF

Rec WF

Ref WM

Rec WM

diff: BF

diff: BM

diff: WF

diff: WM

18.15 21.22 30.84 36.22 40.24 51.56 70.46

15.16 19.89 22.94 39.39 52.91 108.38

21.10 28.51 35.87 40.71 46.66 66.80

22.82 26.70 33.40 47.51 54.83 71.48

17.04 19.80 29.12 36.13 41.01 48.68 78.85

19.08 25.49 32.08 47.83 54.75 75.55

--------

-6.06 10.95 13.28 0.85 -1.35 -37.92

0.00 -1.72 1.81 2.47 -6.80 -8.17 -4.68

-0.72 3.63 4.05 -6.82 -6.07 3.31

Plot of Ages at Transition (Lovejoy et al.) 120.00

Age in Years

100.00 80.00 60.00 40.00 20.00 0.00 I-II

II-III

III-IV

IV-V

V-VI

VI-VII

VII-VIII

Transition

Ref BF

Rec BF

Ref BM

Rec BM

Ref WF

Rec WF

Ref WM

Rec WM

note the significant difference in slope between the Reference and Recent black males

464

SEX-RACE CATEGORIES Plots of ages at transition for the Boldsen et al. auricular surface superior demiface topography component

Reference

Recent

Black females

Insufficient sample size

Black males

White females

White males

465

Ages at transition for the Boldsen et al. auricular surface superior demiface topography component Transition (SDT Phase) I-II II-III

Ref BF 20.40 59.01

Rec BF

Ref BM 17.91 41.80

Rec BM 12.42 33.98

Ref WF 14.93 95.04

Rec WF 23.83 61.55

Ref WM 8.23 88.01

Rec WM 13.11 52.93

diff: BF ---

diff: BM 5.49 7.82

diff: WF -8.90 33.49

diff: WM -4.87 35.07

Plot of Ages at Transition (Boldsen et al. superior demiface topography) 100.00 90.00

Age in Years

80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00 I-II

II-III Transition

Ref BF

Rec BF

Ref BM

Rec BM

Ref WF

Rec WF

Ref WM

Rec WM

note the significant difference in slope between the Reference and Recent white males, and the Reference and Recent white females

466

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Evidence for a change in the rate of aging of osteological indicators in

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