Overcoming inconsistency in woodland bird classification [PDF]

Overcoming inconsistency in woodland bird classification Hannah Fraser

Submitted in total fulfilment of the requirements of the degree of Doctor of Philosophy

December 2017

School of BioSciences Faculty of Science The University of Melbourne

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Abstract Ecologists (and other scientists) rely on terms to convey complex ideas, however researchers use these terms differently. The need for ecologists to use terms consistently is debated. On one hand, if all researchers subscribed to a single use of each term, it would be easier to synthesize results between studies and communicate findings between researchers. On the other hand, there is no proof that standardizing terminology is necessary and it could inhibit researchers from answering certain research questions. In this thesis, I investigated, and attempted to address, inconsistent terminology in ecology using ‘woodland birds’ as a case study. I examined how consistently researchers use the term ‘woodland bird’ in Australia (Chapter 2, 3) and Europe (Chapter 3) and whether using the term differently impacts on ecological inference. I found that the term ‘woodland birds’ is used to refer to different groups of species by researchers in both Australia and Europe. In both regions, the differences have a profound impact on our understanding of the ecology of woodland birds; influencing whether we believe they are declining and how we understand their response to habitat fragmentation. Researchers believed that it is reasonable to include different species in a definition of ‘woodland birds’ depending on the aim and location of their study (Chapter 2). My investigation confirmed that study aims, methods of classification and study location may influence the species included as woodland birds in individual studies (Chapter 4), but these factors did not explain all of the variation in which species were included. I sought to resolve inconsistency in woodland bird classification using two methods. First, I built a model linking species’ occurrence in woodland and other habitats to their traits (Chapter 5). Second, I elicited expert opinion on which species should be included in a woodland bird community to be listed as a Threatened Ecological Community under the Environment Protection and Biodiversity Conservation (EPBC) Act (Chapter 6). There was a high level of agreement between the two methods about which species should be considered woodland birds (Chapter 7), but they serve different purposes. The first method provided valuable insight into what makes a species a ‘woodland bird’. The second method devises lists of ‘woodland birds’ that, if the listing application is successful, would provide national level protection to the woodland bird community. I believe that the existence of a nationally listed ‘woodland bird threatened ecological community’ will motivate people to study that exact group of species if they are interested in woodland birds (rather than including different species) because it will enhance the conservation significance of their research. I hope that the evidence in this thesis

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along with the identification and introduction of a ‘woodland bird threatened ecological community’ will resolve the ongoing terminological inconsistency around ‘woodland birds’.

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Declaration

This is to certify that i. ii. iii.

the thesis comprises only my original work towards the PhD except where indicated in the preface, due acknowledgement has been made in the text to all other material used, the thesis is less than 100,000 words in length, exclusive of tables, maps, bibliographies and appendices

Signed……………………..

Date………………………..

Hannah Fraser

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Preface The work presented here was completed for this thesis and is predominantly my own work. It was conducted under the supervision of Professor Michael McCarthy (School of BioSciences, University of Melbourne), Dr Libby Rumpff (School of BioSciences, University of Melbourne), Dr Cindy Hauser (School of BioSciences, University of Melbourne), and Dr Georgia Garrard (School of Global, Urban and Social Studies, RMIT University). Publications and contributions from others are detailed below. The work in Chapter 2 is based on the paper: Fraser H, Garrard GE, Rumpff L, Hauser CE, McCarthy MA. 2015. Consequences of inconsistently classifying woodland birds. Frontiers in Ecology and Evolution: 3. Co-author Georgia Garrard assisted in interpreting and altering R code used to analyse the impact of inconsistently classifying woodland birds on estimates of sensitivity to fragmentation. All coauthors assisted with study design and provided guidance and feedback on the written manuscript. The work in Chapter 3 is based on the paper: Fraser H, Pichancourt J-B, Butet A. 2017. Tiny terminological disagreements with far reaching consequences for global bird trends. Ecological Indicators: 79–87. Co-author Alain Butet assisted in conducting the systematic review. Both co-authors assisted with study design and provided guidance and feedback on the written manuscript. The work in Chapter 4 is based on the unpublished manuscript: Fraser H, Rumpff L, Hauser CE, Garrard GE, Gould E, Warton D, McCarthy MA. unpublished manucript. Is there an ecological basis for the inconsistent classification of woodland birds? Co-author David Warton wrote code to facilitate analysis using the mvabund R package. Coauthor Elise Gould wrote code to assist in running simultaneous generalised linear models and creating resultant graphs as well as assisting with the qualitative analysis of articles. Co-authors Libby Rumpff, Cindy Hauser, Georgia Garrard and Michael McCarthy assisted with study design and provided guidance and feedback on the written manuscript.

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The work in Chapter 5 is based on the submitted article: Fraser H, Hauser CE, Garrard GE, Rumpff L, McCarthy MA. submitted article. Classifying animals into ecologically meaningful groups: a case study on woodland birds. Co-author Cindy Hauser assisted with re-analysing published case study data. All co-authors assisted with study design and provided guidance and feedback on the written manuscript. The work in Chapter 6 forms part of a forthcoming application to list a Woodland Bird Threatened Ecological Community under the Environment Protection and Biodiversity Conservation Act. The work was conducted with collaborators Martine Maron, Jeremy Simmonds and Alex Kutt. Collaborator Jeremy Simmonds assisted with data collection at the workshop. All collaborators assisted with study design and helped develop tools for the workshop and subsequent expert surveys as well as providing feedback on the final written chapter.

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Acknowledgements I have learnt a lot over the course of my PhD and enjoyed (almost) every minute of it. I would like to take a brief moment to thank the people who made this possible. Firstly, thanks go to my fantastic supervisors Libby Rumpff, Cindy Hauser and Georgia Garrard and Michael McCarthy; I couldn’t have asked for a better team. Those people who said “four supervisors is too many” were dead wrong; you each brought your own strengths that shone at different times. Libby, your confidence in me kept me positive throughout and helped me feel like a ‘real’ researcher well before my first article made it through the publication pipeline. I was also always astonished by your supernatural ability to untangle and restructure my arguments. The confidence and skills you have helped me develop will stay with me always. Cindy, your mathematical prowess was immensely useful and your ability to explain complex concepts in a way I understood could not be undervalued. Perhaps your biggest contribution was in keeping us all on track. I think you were the reason that having four supervisors was viable. You made sure that none of my questions or drafts fell through the gaps. Georgia, your help constructing and understanding the Bayesian Hierarchical Model saved me months of unproductive confusion. You were also extremely helpful in refining the course of my research and the content of my manuscripts. You were always able to cut through the details to see the practical implications and helped keep my research grounded and relevant. Mick, your help understanding my results has been invaluable. Without you I may never have spotted some fundamental modelling errors that would have rendered my results void and my description of my Chapter 5 model results would have been complete gibberish. Your enthusiasm and ability to see the broader picture have inspired me and the lab environment you have helped build here has spoiled me for all future employers. Next, I would like to thank my thesis committee members Mark Burgman and Peter Vesk. Mark, your input on study design and expert elicitation was fantastic and helped develop my thesis into a cohesive and interesting story. Pete, your contribution transcends your role as committee chair. You provided excellent feedback and helped guide the progress of my PhD in progress reviews but your help didn’t stop there. You have always spared time from your busy schedule to talk through my ideas and help me think through the ecological relevance of

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my findings. I’ve also really loved teaching with you and feel like I’ve learned so much from the experience. Thank you for the opportunities. Thanks are also due to my co-authors who worked on chapters 3, 4 and 6 with me. Jean-Baptiste Pichancourt and Alain Butet allowed me to extend my woodland bird story to an international scale. Gary Luck saved me weeks of work extracting traits from the Handbook of Australian New Zealand and Antarctic Birds. David Warton re-wrote the mvabund package to work for my strange data. Elise Gould spent an extreme amount of time reading woodland bird articles and grouping them according to their aims etc. as well as helping me run and graph hundreds of simultaneous glms. Martine Maron, Alex Kutt and Jeremy Simmonds provided fantastic insights and assistance along the journey to listing woodland birds as a Threatened Ecological Community. I’d also like to thank all of the other woodland bird experts who have given me advice and feedback along the way. My PhD was funded by the Centre of Excellence for Environmental Decisions (CEED). Being part of CEED provided me with some truly fantastic opportunities. I was able to take part in CEED events including internal conferences and an excellent leadership course and made many valuable contacts. Their funding also allowed me to travel to a series of conferences and workshops that have helped me develop as a researcher. I have been in the Quantitative and Applied Ecology group (QAECO) since I began my Masters in 2011 and I couldn’t imagine a better place to work. Taking part in lab activities such as lab meetings, reading group, maths group and lab retreats has taught me a lot and helped me build strong relationships with some fantastic like-minded researchers. I’d like to thank everyone in QAECO for making my PhD experience a happy one. I would also like to specifically recognise Pauline Byron. She is the linchpin of the lab and makes sure that everything runs smoothly. She has helped me with a million things along the way and I’m not sure how we ever coped without her. Last but not least I would like to thank my family. To all the Pearsons, Deeleys, Frasers and FitzGeralds thank you so much for the love, confidence and support. You’ve always believed that I could achieve anything I wanted, even when I didn’t believe it myself. Without your support I would be a different person and I certainly would not be handing in a PhD. Dad, your advice about students, theses and research in general has been really helpful and helped keep my eye on the prize. Mum, it’s been fantastic sharing the PhD experience with you. I learned a lot from your approach and feel that it has brought us even closer together. Max, you are my

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rock. You were there to buoy my spirits each time things went awry and for every sleepless night before conferences or workshops. You’ve always had complete confidence in my abilities and faith that I will have the strength to face every challenge. I’m not as certain as you that people will be fighting to employ me but I am very proud of what I have achieved and am excited to move on to the next phase of life with you. Thank you so much everyone, I would do it all over again in a second.

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Table of Contents Abstract ................................................................................................................................................ iii Declaration ............................................................................................................................................ v Preface .................................................................................................................................................. vi Acknowledgements ............................................................................................................................ viii Table of Contents .................................................................................................................................. xi List of Tables ....................................................................................................................................... xvi List of Figures .................................................................................................................................... xviii Chapter 1 General Introduction .............................................................................................................1 1.1.

Why study inconsistency in woodland bird research? ...........................................................2

1.2.

Widespread inconsistency in terminology .............................................................................2

1.3.

Inconsistent terms in ecology ................................................................................................3

1.4.

From linguistics to terminology ..............................................................................................4

1.5.

Why do researchers use terms inconsistently? ......................................................................6

1.6.

The implications of terminological inconsistency in ecology..................................................7

1.7.

What are woodland birds and what is happening to them? ..................................................8

1.8.

Summary ..............................................................................................................................10

Chapter 2 Consequences of Inconsistently Classifying Woodland Birds ..............................................11 2.1.

Abstract ................................................................................................................................12

2.2.

Introduction .........................................................................................................................13

2.3.

Material and methods ..........................................................................................................15

2.3.1.

Investigating inconsistency in woodland bird classification .........................................15

2.3.2.

Testing the influence of inconsistent classification ......................................................17

2.3.3.

Reasons for inconsistency in woodland bird classification ...........................................18

2.4.

Results ..................................................................................................................................19

2.4.1.

Systematic review ........................................................................................................19

2.4.2.

Effects of inconsistency ................................................................................................20

2.4.3.

Survey results ...............................................................................................................20

2.5. Discussion .................................................................................................................................23 Chapter 3 Tiny terminological disagreements with far reaching consequences for global bird trends27 3.1.

Abstract ................................................................................................................................28

3.2.

Introduction .........................................................................................................................29

3.3.

Materials and Methods ........................................................................................................31

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3.3.1. Data sourcing .....................................................................................................................32 3.3.2. Analysis of classification inconsistency ..............................................................................33 3.3.3. Impact of classification inconsistencies on bird trends ......................................................34 3.4.

Results ..................................................................................................................................36

3.4.1. Classification inconsistencies .............................................................................................36 3.4.2. Impact of classification inconsistencies on bird trends ......................................................38 3.4.3. Differences in index values ................................................................................................40 3.5.

Discussion ............................................................................................................................41

Chapter 4 Is there an ecological basis for the inconsistent classification of woodland birds? .............47 4.1. Abstract .....................................................................................................................................48 4.2. Introduction ..............................................................................................................................49 4.3. Methods ....................................................................................................................................50 4.3.1. Systematic Review .............................................................................................................50 4.3.2. Thematic analysis of articles ..............................................................................................51 4.3.3. Multispecies analysis using the mvabund package ............................................................53 4.3.4. Multispecies analysis using the glm2 package ...................................................................55 4.4. Results.......................................................................................................................................56 4.4.1. Australian multispecies analysis .........................................................................................56 4.4.2. Europe ................................................................................................................................57 4.5. Discussion .....................................................................................................................................59 Chapter 5 Towards consistency: identifying an ecologically meaningful group of Australian woodland birds .....................................................................................................................................................65 5.1. Abstract .....................................................................................................................................66 5.2. Introduction ..............................................................................................................................67 5.3. Methods ....................................................................................................................................68 5.3.1. Hierarchical Models ...........................................................................................................68 5.3.2. Data ....................................................................................................................................72 5.3.3. Examination of Model Output ............................................................................................73 5.3.4. Woodland bird groups .......................................................................................................74 5.3.5. Hypothesis 1: woodland birds will be negatively impacted by clearing and livestock grazing .........................................................................................................................................75 5.3.6. Hypothesis 2: Woodland birds will be more prevalent where there is more greenspace and tree and shrub cover .............................................................................................................76 5.3.7. Hypothesis 3: Woodland birds are more likely to be declining than other species ............76 5.4. Results.......................................................................................................................................77

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5.4.1. Hierarchical models ............................................................................................................77 5.4.2. Regional analyses ...............................................................................................................78 5.4.3. Variation in species lists .....................................................................................................80 5.4.4. Model Validation ................................................................................................................81 5.4.5. Hypothesis 1: woodland birds will be negatively impacted by clearing and livestock grazing .........................................................................................................................................81 5.4.6. Hypothesis 2: Woodland birds will be more prevalent where there is more greenspace and tree and shrub cover .............................................................................................................82 5.4.7. Hypothesis 3: Woodland birds are more likely to be declining than other species ............84 5.5. Discussion .................................................................................................................................84 Chapter 6 Towards protecting the woodland bird community under the EPBC act ............................88 6.1. Abstract .....................................................................................................................................89 6.2. Introduction ..............................................................................................................................90 6.3. Methods ....................................................................................................................................91 6.3.1. Methodological overview...................................................................................................91 6.3.2. Workshop...........................................................................................................................92 6.3.3. Which species comprise the woodland bird community? ..................................................94 6.3.4. When does a bird assemblage qualify as a woodland bird community? ............................94 6.3.5. How to measure community condition? ............................................................................95 6.3.6. Sensitivity Analysis .............................................................................................................97 6.3.7. Criteria for determining woodland bird community condition ..........................................97 6.4. Results.......................................................................................................................................98 6.4.1. Workshop...........................................................................................................................98 6.4.2. Which species comprise the woodland bird community? ..................................................99 6.4.3. When does a bird assemblage qualify as a woodland bird community? ............................99 6.4.4. How to measure community condition? ..........................................................................101 6.4.5. Sensitivity Analysis ...........................................................................................................104 6.4.6. Assessing the Woodland Bird Threatened Ecological Community ...................................105 6.5. Discussion ...............................................................................................................................106 Chapter 7 Comparing the species considered ‘woodland birds’ based on ecological models and expert opinion ...................................................................................................................................110 7.1. Abstract ...................................................................................................................................111 7.2. Introduction ............................................................................................................................112 7.3. Methods ..................................................................................................................................112 7.4. Results.....................................................................................................................................113

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7.5. Discussion ...............................................................................................................................115 Chapter 8 General Discussion ............................................................................................................118 8.1. Introduction ............................................................................................................................119 8.2. How consistently are woodland birds classified? ....................................................................119 8.3. Does inconsistently using key terms influence ecological inference? .....................................120 8.4. Why are woodland birds classified differently? ......................................................................121 8.5. Resolving the inconsistent classification of woodland birds ...................................................122 8.6. Future research directions ......................................................................................................123 8.6.1. Representativeness of the single case study ....................................................................123 8.6.2. Excluding Western Australia and Tasmania .....................................................................124 8.6.3. Resolving past inconsistency ............................................................................................124 8.7. In closing .................................................................................................................................125 Chapter 9 References.........................................................................................................................126 Chapter 10 Appendices ......................................................................................................................142 Appendix 2.1. Article Selection Schematic .....................................................................................143 Appendix 2.2. Articles evaluated for chapter 2 ..............................................................................144 Appendix 2.3. Details of the Garrard et al. 2012 model as used in this thesis ...............................161 Appendix 2.4. Moving Window Analysis ........................................................................................165 Appendix 2.5. Expert Survey ..........................................................................................................167 Appendix 2.6. Species list and check for biases .............................................................................174 Appendix 2.7. Comparison of the FFGA listed woodland bird community and my findings ..........190 Appendix 3.1. International Article Selection Protocol ..................................................................192 Appendix 3.2. Proportion of studies classifying species as woodland, farmland and generalist species. ..........................................................................................................................................193 Appendix 3.3. Targetted classifications used in chapter 3 analyses ...............................................198 Appendix 4.1. mvabund model code .............................................................................................220 Appendix 4.2. glm2 model code.....................................................................................................227 Appendix 4.3. Australian glm2 full model coefficients and deviance .............................................232 Appendix 4.4. European glm2 classification model coefficients and deviance ..............................261 Appendix 5.1. Bayesian hierarchical model code ...........................................................................267 Appendix 5.2. Chapter 5 lists of species by group ..........................................................................269 Appendix 6.1. Woodland bird workshop spreadsheet ...................................................................280 Appendix 6.2. Online bird community condition survey ................................................................290 Appendix 6.3. Regional lists for the Woodland Bird Threatened Ecological Community ...............302

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Appendix 6.4. Sensitivity analysis of community condition models ...............................................307

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List of Tables Table Caption

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Table 2.1.: The number of respondents (out of 69) selecting different factors as influencing the way they classify vegetation as woodland. These factors are broken into shrub, tree and other categories to give a clear indication of how important they each were for determining whether vegetation is ‘woodland’.

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Table 2.2.: The number of respondents (out of 69) selecting different factors as influencing the way they classify species as woodland birds. These factors are classed by underlying orientation into 5 classes: occurrence based, authorized classification, trait based, based on habitat associations and on exclusion criteria. As in table 2.1, the number of respondents listing each factor is given as well as the number of respondents listing any factor within the 5 classes.

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Table 3.1: Articles included in analysis of the effect of different classification on bird trends

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Table 4.1: Classes chosen to represent Australian and European woodland bird study attributes

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Table 4.2: Mean percentage deviance explained by the each of the modelled hypotheses for the Australian dataset: the full model which includes all objectives, classifications and spatial context, the classification model includes all the different classification classes, the objective model includes all the objective classes. The explained deviance for each classification and aim class is also included.

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Table 4.3: Percentage deviance explained by the each of the modelled hypotheses for the European dataset: the full model which includes all objectives, classifications, spatial contexts and whether forest or woodland birds were being considered; the classification model includes all the different classification classes modelled for; the objective model includes all the objective classes modelled for. The explained deviance for each classification and aim class is also included

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Table 5.1: Definition of model terms

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Table 5.2: Best ranked models (∆ AICc ≤ 2) for each bird group showing number of parameters, differences in Akaike Information Criterion corrected for small sample bias (AICc) compared with the model with the lowest AICc, Akaike weights, percentage deviance explained and model coefficients.

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Table 6.1: Generalised linear model coefficients and AIC scores, explaining expert-judged community condition for the Australia-wide dataset and South Australia, sub-tropical Queensland and temperate south-east regions

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Table 6.2: Criteria for determining woodland bird community condition (low, 0 – 35%; medium, 35 – 65%; high, 65 – 85%; pristine, 85% +) based on species richness and the proportion of small species

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Table 7.1: Distribution of the chapter 6 woodland bird community species (Appendix 6.3) between the chapter 5 modelled species groups (Appendix 5.2). Columns marked ‘N’ represent the northern region, ‘SE’ the south eastern regions and ‘SW’ the south western region.

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Table A.2.2.1: Artciles evaluated in chapter 2

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Table A.2.6.1: Species present in 10 or more lists

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Table A.2.6.2: Species present in fewer than 10 lists

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Table A.2.6.3: Analysis of whether certain orders of species are more or less likely to be excluded from my analyses

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Table A.2.6.4: Geographic distribution of studies included in chapter 2 analyses

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Table A.3.2.1: The proportion of studies used in chapter 3 analyses specifying each species as a farmland rather than woodland specialist and as a generalist as opposed to a specialist in either farmland or woodland habitats

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Table A.3.3.1: Species classed as farmland, generalist and woodland species in each of the Australian and European targeted classification used in chpter 3 analyses

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Table A.4.3.1: Null, residual and % deviance explained by the full glm2 model including all objectives, spatial contexts and classification strategies.

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Table A.4.3.2: Estimates and standard errors for intercept and objective classes in the full Australian model based on glm2 analyses

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Table A.4.3.3: Estimates and standard errors for classification strategy classes in the full Australian glm2 model.

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Table A.4.3.4: Estimates and standard errors for spatial context in the full Australian glm2 model.

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Table A.4.4.1: Null, residual and % deviance explained by the classification glm2 model including all classification strategy classes.

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Table A.4.4.2: Coefficient estimates and standard error for the European classification glm2 model including all classification strategy classes.

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Table A.5.2.1: Species classified into 5 bird groups (Intact Woodland, Degraded Woodland, Forest, Open Habitat and Uncertain) based on the 6 model fits described in chapter 5.

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Table A.6.1.1: Spreadsheet filled in by woodland bird experts at the workshop aimed at defining the woodland bird community for eastern mainland Australia

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Table A.6.3.1: List of species in the woodland bird community by region based on expert opinion

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Table A.6.3.2: List of species that are not in the woodland bird community but are associated with degraded woodland bird communities based on expert opinion

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Table A.6.4.1: Sensitivity analysis using the Australia-wide model showing changes in predicted community condition according to different values of species richness and proportion small species

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Table A.6.4.2: Sensitivity analysis using the South Australian model excluding small birds showing changes in predicted community condition according to different values of species richness and proportion small species

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Table A.6.4.3: Sensitivity analysis using the South Australian model including small birds showing changes in predicted community condition according to different values of species richness and proportion small species

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Table A.6.4.4: Sensitivity analysis using the temperate south eastern Australia model showing changes in predicted community condition according to different values of species richness and proportion small species

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Table A.6.4.5: Sensitivity analysis using the temperate sub-tropical Queensland model showing changes in predicted community condition according to different values of species richness and proportion small species

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Table A.6.4.6: R2 values for correlations between regional and Australia-wide models

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List of Figures Figure Legend

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Figure 2.1.: Frequency distribution of the percentage of studies in which individual species are classified as a woodland bird (total number of species = 165). Complete consistency in classification would appear as a binary distribution, where species are either regarded as woodland species 100% of the time or 0% of the time. Maximum inconsistency would occur if all species were classified as woodland birds in 50% of lists.

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Figure 2.2: The predicted effect of tree cover aggregation on species prevalence, for different subsets of species representing frequency thresholds of 10, 20, 30,…80%. At 80 on the horizontal axis, only species which are regarded as woodland birds in 80% or more of studies are included in the model. Error bars represent 95% credible intervals. Mean estimate from the original Garrard et al. (2012) model is represented by the line and the 95% credible intervals by the grey shaded area.

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Figure 2.3.: Ratings assigned to each of 7 reasons that researchers use different lists to classify woodland birds: A) different ideas about how to determine whether species rely on woodland vegetation, B) different aims of research, C) regional differences in the distribution of species in woodland and non-woodland areas, D) regional differences in the behavior or habitat requirements of species, E) different ideas about what constitutes woodland vegetation, F) uncertainty about the distribution of the species in woodland and non-woodland areas, G) uncertainty about the behavior or habitat requirements of different species. Error bars represent the 95% confidence intervals of the estimates.

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Figure 3.1.: Diagram showing the structure of this article beginning with sourcing data and ending with analysing the effect of inconsistent classification on an index of bird trends

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Figure 3.2.: Classification consistency of species where the y axis shows the proportion of studies (n ranges from 3 to 26) in which a species is regarded as a generalist as opposed to a specialist (in either woodland or farmland habitats) and the x axis shows the proportion of studies in which the same species are regarded as a farmland specialist as opposed to a woodland specialist: a) shows 112 Australian species, b) shows 71 European species. Each point represents one species, though some species overlap. Lines show second order polynomial relationships between the two variables.

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Figure 3.3: Trends in indices of species groups from 1998 to 2012 for a) European farmland birds, b) Australian farmland birds, c) European generalist birds, d) Australian generalist birds, e) European woodland birds, and f) Australian woodland birds. Lines represent trends in indices obtained using species lists from five European articles and four Australian articles.

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Figure 3.4.: Yearly multiplicative trend (EBCC 2001) for European and Australian farmland, generalist and woodland birds. Black points and error bars represent the mean and range achieved using assessed above. Grey points and error bars represent the median minimum and maximum achieved when alternately considering the 10 most declining or 10 most increasing species conceivable from the complete dataset.

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Figure 4.2: European model coefficients ± standard error for the influence classification strategy class (everything seen in a woodland, exclusion criteria, existing classification, expert opinion, metric, occurrence and traits) on the classification of 5 selected species of 96 total. For table of all coefficients and standard errors see supplementary material

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Figure 5.1: The distribution of the three studied Ecoregions, from the World Wildlife Fund ecoregions map (Olson et al. 2001)

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Figure 5.2: Standardised coefficients for Australia-wide models predicting the ‘woodland’ dependence of birds when considering ‘woodlands’ as: any woodland vegetation (orange), eucalypt woodland vegetation (blue), or any treed habitat type (excluding rainforest) (brown). Coefficients representing associations between woodland vegetation cover and species traits are denoted by  and those representing associations between tree cover and species traits are denoted by 

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Figure 5.3: Standardised coefficients for models predicting eucalypt woodland dependence in Ecoregions 12 (green), 4 (blue) and 7 (red).

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Figure 5.4: percentage change in probability of occurrence associated with a bird having a particular trait relative to a ‘null species’ in open habitat (triangle), woodland (square) and forest (circle) habitats in A) ecoregion 7, B) ecoregion 4 and C) ecoregion 12. The ‘null species’ has a median dispersal distance and does not have any of the other traits. Graphs to the left of the panel express change associated with species having high and low dispersal abilities. Graphs to the right of the panel express changes associated with foraging (e.g. F. bark) and nesting traits (e.g. N.hollow). The scale of the y axis is different for dispersal vs nesting and foraging trait graphs.

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Figure 5.5: Mean and 95% confidence intervals of species richness by bird group for pasture and woodland habitat types.

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Figure 5.6: Mean and 95% confidence intervals of species richness by bird group for different levels of grazing

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Figure 5.7: Percentage of species that declined between Birds Australia’s first and second atlases broken into categories based on Barrett et al.’s (2004) definition, and the four bird groups from out model analysis.

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Figure 6.1: Schematic of methodology used in this chapter. Arrows show where data from one task was directly applied to another task

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Figure 6.2: Regional differences between degraded and intact woodland bird communities: a. bird species richness, b. proportion of intact associated species, c. proportion of degraded associated species, and d. proportion of small species <50g body mass. Markers give mean values and error bars give 95% confidence intervals.

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Figure 6.3: Sensitivity analysis of modelled South Australian community condition to whether the model includes (grey) or excludes (black) the proportion of small birds. Dashed lines represent different proportions of small birds

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Figure 7.1: Mean and 95% confidence intervals of standardised coefficients of woodland association (chapter 5: xi ) and tree cover association (chapter 5: zi) for species included in the woodland bird community (chapter 6) in the a) northern region, b) south western region, and c) south eastern region

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Figure A.2.1.1: The process of article selection used in chapter 2 to understand ecologists’ categorisation of woodland bird species and non-woodland bird species.

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Figure A.2.2.1: Moving window analysis of estimated effect of tree cover aggregation on bird prevalence. The points to the left of the graph represent windows which include species that are most consistently regarded as woodland birds; those to the right are regarded as woodland birds infrequently. The mean estimate from the original Garrard et al. (2012) model is represented by the line and the 95% credible intervals by the grey shaded area

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Figure A.3.1.1: The process of article selection used to collect data on European birds and expand the Australian analyses in chapter 3 to understand ecologists’ categorisation of woodland, farmland and generalist bird species.

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Chapter 1 General Introduction

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1.1. Why study inconsistency in woodland bird research? As a Masters student I wanted to investigate the effect of different management actions on woodland birds but was stymied by my inability to determine which species to include in this group. I didn’t have the expertise to determine which species to include myself and trawling the literature unearthed a multitude of non-identical lists of woodland birds. As a result, my Masters project ended up examining how management actions affected birds living in woodlands. This list included such diverse species as the aquatic White-faced Heron, open associated Nankeen Kestrel, urban Common Starling, widespread Australian Magpie and the endangered, woodland associated Grey-crowned Babbler. Given the range of species I grouped together, it is probably unsurprising that I found no strong effect of management on birds. At the end of my project I wasn’t satisfied with how I’d treated this obstacle and was full of questions. Did other people have trouble classifying woodland birds? Were the differences in the lists I noticed just due to which species researchers found in their surveys? Were these differences based on different heuristics for what a ‘woodland bird’ is? What impact does the use of different species lists have on the results of studies, and how they can be interpreted? With encouragement from my supervisors, Drs Libby Rumpff, Georgia Garrard, Cindy Hauser, and Professor Mick McCarthy, answering these questions became the focus for my PhD. In this chapter, I provide background on the issues of inconsistent terminology across disciplines and look at specific cases and consequences within the field of ecology. I then discuss the implications of this problem for my case study on woodland birds.

1.2. Widespread inconsistency in terminology Concern about inconsistently using terms like ‘woodland bird’ is not recent. A shared understanding of the meaning of words is central to communicating ideas, so it is not surprising that articles citing the need to develop consistent terms are common to many fields including law (Coughlan 2011), archaeology (Whittaker et al. 1998), biological science (Seberg et al. 2003, Tautz et al. 2003), physiotherapy (Tol et al. 2013), psychology (American Psychiatric Association 2000) and medicine (Reeves et al. 1994, Weise et al. 2011). There is particular pressure on fields which directly affect human health to develop consistent terminology as miscommunication between practitioners can have profound medical consequences (Lingard et al. 2004) . The development of texts such as the diagnostic and statistical manuals of mental (American Psychiatric Association 2000) and physiological (Gray 2000) disorders is indicative of the importance placed on consistently using terms in medical fields. Though it is more

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difficult to quantify the risk to human lives, the persistence of many species may depend on the efficiency of the research and communication of ecologists.

1.3. Inconsistent terms in ecology In 1931, the Ecological Society of America (ESA) formed the Committee on Ecological Nomenclature which aimed to promote suitable and consistent use of terminology throughout the membership of the ESA (Hanson et al. 1931, McGinnies et al. 1931). This came after a symposium on environmental units and their terminology held by the ESA in 1931 discussed dissatisfaction with the condition of ecological nomenclature. The committee decided to improve the state of nomenclature by assembling and summarising data on how terms are currently used and suggesting how they should be used in the future. In 1952, the committee published a monograph which presented a glossary of 789 terms. Unfortunately, when a new committee was appointed in 1956, the original committee refused to have the glossary evaluated, which resulted in the committee disbanding (Herrando-Perez et al. 2014b, 2014c). Since the disbanding of the Committee on Ecological Nomenclature there has been no unifying effort to resolve the inconsistent use of ecological terms. Nevertheless, a number of studies have highlighted the inconsistent use of specific terms in ecology with concern. Mason and Langenheim (1957) wrote an article regarding the use of the term ‘environment’. They emphasise that everyone is permitted to ascribe their own meaning to a word but that through usage, words gain a meaning which is, at least temporarily, stable. However, sometimes this process results in terms that are so inconsistently used that dissatisfaction results. According to Mason and Langenheim (1957) these inconsistently used terms must be either clarified or avoided. Peet (1974) investigated usage of the term ‘species diversity’ and argued that “If diversity is to continue to play a productive role in ecological investigations, agreement is needed on the definitions of the many constituent concepts included in its current application” and went on to state that “progress in ecology, as in all science, depends upon precise and unambiguous definition of terms and concepts” (Peet 1974, p285). Hall et al. (1997) found that 82% of articles in the fields of wildlife and ecology use the term ‘habitat’ vaguely; either by not defining it, defining it incompletely, or confusing it with vegetation association. They recommended that as long as ecologists use terms inconsistently they should: 1) define terms in ways which are accurate and measurable, 2) cite references to

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the first use of the term or to an article which explicitly defines the term, and 3) make a commitment to standardising terminology in the future. Jax (2006) discussed the problem of inconsistently defining ecological units such as communities or populations. Different understandings of the concepts they represent cause researchers to delimit them in different ways, engendering confusion. Jax argued that despite continued complaints about the inconsistent use of terminology, no standard linguistic conventions have been defined and awareness of the equivocalness of terms has been lost. According to Jax, the most serious problems arise when “the different possibilities of defining ecological units are not recognized and the concepts are believed to be self-evident” (Jax 2006, p247). Other studies have called for consistency in the use of particular terms, including ‘biodiversity’ (Kaennel 1998), ‘alien plants’ (Richardson et al. 2000), ‘invasive species’ (Catford et al. 2016), ‘urban’, ‘suburban’ (MacGregor-Fors 2011), ‘influence’ and ‘information’ in animal communication (Ruxton and Schaefer 2011), ‘supralittoral’ in benthic ecology (Dauvin et al. 2008), geographic areas (Ebach et al. 2008), ‘territoriality’ (Maher and Lott 1995), ‘immunocompetence’ (Vinkler and Albrecht 2011), ‘heterogeneity’ (Li and Reynolds 1995), ‘landscape connectivity’ (Tischendorf and Fahrig 2000), ‘ecosystem services’ (Fisher et al. 2009) and ‘indicator’ (Heink and Kowarik 2010). These studies demonstrate that, since at least 1957 and continuing to the present, some ecologists are concerned that the inconsistent use of ecological terms is harming scientific progress and communication. Despite this widespread and long-enduring concern, ecological terms are still being used inconsistently, so it is important to understand why this is the case. Perhaps one contributing factor is the view, held by many linguists, that language evolves constantly and attempts to stop this are pointless (Aitchison 1981).

1.4. From linguistics to terminology Considering a linguistic perspective may be useful when attempting to understand and address the occurrence of homonyms (words with multiple definitions) within ecology. Language is constantly evolving, driven by the necessity of describing new phenomena and of communicating things more efficiently or emotively (Aitchison 1981). Linguists see their role as describing the behaviour of language rather than trying to alter it. Nevertheless, many influential figures throughout history have criticized change in languages and attempted to standardise the use or addition of words to a language (Aitchison 1981). Additionally, there is

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a hypothesis in linguistics called the one-form-one-meaning hypothesis which refers to the “preference for languages to avoid homonymy and synonymy” (Bauer 2007, p155). Furthermore, linguistics text books are peppered with complaints about the inconsistent use of terms, for example: “There are, unfortunately, many terms in linguistics which mean one thing in one place and another in another... In principle, these terms are rendered unambiguous by the sub-area of linguistics in which they are used, but in practice the use of the same term can cause transient or even long-term problems of understanding” (Bauer 2007, p107). There is a tension in linguistics between the desire to allow language to develop naturally and the need for the major terms used in the field to be used consistently. This tension is exemplified by a quote from Aitchison (1981, p234) “Language change is in no sense wrong, but it may, in certain circumstances be socially undesirable. Minor variations in pronunciation from region to region are unimportant, but change which disrupts the mutual intelligibility of a community can be socially and politically inconvenient. If this happens, it may be useful to encourage standardization – the adoption of a standard variety of one particular language which everybody will be able to use, alongside existing regional dialects or languages.” The field of terminology research sprang from this tension and emphasises the importance of using terms clearly and consistently in specialised language such as in the sciences (L’Homme et al. 2003). Wüster (1898-1977) is seen as the father of terminology. He was an engineer and made it his life’s work to eliminate ambiguity from technical language (including science and engineering) and convince users of technical language that having a standardised terminology would be beneficial (Cabré Castellví 2003). The field of terminology differs from linguistics in that it prescribes the development of (a portion of) language, discouraging the creation of homonyms (single words referring to multiple concepts) and synonyms (multiple words for the same concept). In its Wüsterian form, the field does not allow terms to develop over time, although many modern terminologists see this development as necessary (Cabré Castellví 2003, Rey 2005, Araúz et al. 2013, Pecman 2014). The conflict between traditional linguistic (let language develop organically) and terminological (standardise and control the use of terms) points of view may be partly responsible for the continued inconsistent use of terms in ecology. Finding such, often adamant, arguments for and against standardisation of terminology may make it difficult for ecologists to justify the need for consistent terminology. However, there are also more proximal reasons for continuing to use terms inconsistently.

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1.5. Why do researchers use terms inconsistently? There are a range of reasons for the persistence of inconsistent use of terms in ecology (and other fields) including: 1) linguistic uncertainty (Regan et al. 2002); 2) the push for novelty in science (Arnqvist 2013) and limited funding opportunities (Adams et al. 1997); 3) the ‘silent rule’ which allows scientists to use terms without defining them (Herrando-Perez et al. 2014c); and 4) the belief that multiple definitions can be beneficial (Hodges 2008). Linguistic uncertainty occurs when different authors or readers of papers consider the same term to have different meanings. It comes in various forms (Regan et al. 2002): Ambiguity occurs where one term may mean multiple things and it is unclear which meaning is intended. For example, ‘species diversity’ may refer to the number of species, the taxonomic distance between the species, alpha, beta or gamma diversity or Shannons diversity, among other measures (Peet 1974, Moreno and Rodríguez 2010, Tuomisto 2011). Context dependence arises when terms are not contextualised, such as ‘small’, ‘large’, ‘old’, and ‘young’. For example, a small human is much larger than even a large frog and without the context the terms small and large are meaningless. Indeterminacy occurs when a term is understood in a particular way now but there is potential for it to be interpreted differently in the future. For example, before the discovery of DNA the term ‘gene’ was understood differently than it is today. Underspecificity is when a statement provides insufficient detail to be useful. For example, a study which reports that species richness increases with tree cover is of limited use if it does not give information about the gradient of the relationship or the magnitude of the two variables. Vagueness arises when a term allows borderline cases where it is unclear whether to include something in the category. For example, consider the categories ‘migratory’ and ‘non-migratory’, assigning a species to either of these groups may be difficult if only some populations migrate. Some types of linguistic uncertainty are difficult to avoid or study, particularly indeterminacy, but others could be minimised by including detailed definitions in studies or referring to articles which define the term (Hall et al. 1997). This is effective for single studies but, unless these

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definitions are the same as those used in other studies, linguistic uncertainty impairs our ability to compare and generalise results. The ability to publish articles, particularly in high impact journals, is fundamentally important to securing the limited funding available for research. Researchers’ publication and funding records determine their ability to secure employment and promotion (Clapham 2005, Barbour 2015). Arnqvist (2013) critiques the use of novelty as a criterion for publishing research in high impact journals. He contends that aiming for novelty promotes poor scientific practice for two reasons, firstly it leads authors to delineate their own unique scientific territory, and secondly, it encourages researchers to downplay the importance of previous work or ignore it completely. Both of these considerations could lead to inconsistency in the use of terms: new terms may be created to fit to a researcher’s ‘scientific territory’ or to obscure a relationship with earlier work, making the concept seem more novel. When a scientist uses a term in a publication they have no obligation to define it. Often when terms are defined no corroborating reference is provided. Herrando-Perez et al. (2014b) refer to this as “the silent rule”. Essentially there is no imperative for scientists to use definitions which are consistent with each other. This results in the proliferation of different definitions of terms. Furthermore, because there are so many definitions for many terms, those authors who would like to use the term in consistent ways find this difficult. Lastly, it is proposed that having vague or multiple definitions of terms can accelerate progress within a field in its early stages and avoids stunting its growth by limiting the available fields of inquiry (Hodges 2008). Definitions created early in the development of a field may include or exclude elements that later research finds to be important. By leaving definitions more flexible, the usage of the term is able to adapt as understanding of the field improves. For example, once the term ‘gene’ referred to a discrete heritable unit but, since the discovery of DNA, it specifically refers to a sequence of nucleotides that control the production of certain proteins that can be inherited (Hodges 2008).

1.6. The implications of terminological inconsistency in ecology Inconsistent terminology can hinder the progress of ecology in a number of ways. Firstly, using different terms for the same thing can lead to redundant investigations and make it difficult for researchers to find all relevant past studies (Herrando-Perez et al. 2014c). Comparing studies

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or combining them via meta-analysis can be difficult, and disagreement in the findings of studies may result (MacGregor-Fors 2011, Herrando-Perez et al. 2014c). Inconsistent use of terminology can also cause problems in communication among scientists, policy makers and the public (Peters 1991, MacGregor-Fors 2011, Herrando-Perez et al. 2014c). Arguments against developing consistent nomenclature in ecology cite a lack of evidence of inconsistent use of terms affecting the conclusions of studies. However, articles by Tischendorf and Fahrig (2000) on ‘connectivity’ and Pimm (1984) on ‘ecosystem stability’ discuss the profound impact that differing definitions have on results. Unfortunately, neither paper investigates the effects on a real world system. To the best of my knowledge, no study has quantitatively investigated the effects of uncertainty in ecological terms using a real world example and very few studies attempt to overtly relate differences in terminology to results. In the case of researching ‘woodland birds’, inconsistency potentially arises through grouping species. Many researchers publish articles regarding ‘woodland birds’ but, based on my investigations during my Masters research, it appears that authors use the term ‘woodland bird’ to refer to different groups of species. This observation is supported by Jax (2006), who investigated the usage of terms for ecological units such as ‘woodland birds’. He found that the terms describing ecological units may be used differently depending on i) whether they are statistically defined, ii) whether their boundaries are defined by process-related criteria, iii) how high the required internal relationships are and, iv) whether they are perceived as real entities or abstractions. When it comes to the term ‘woodland birds’ this would mean that the species referred to as ‘woodland birds’ could differ depending on i) whether the group is chosen based on statistics or expert opinion, ii) whether authors are using process-related criteria such as nesting and foraging requirements, iii) how strongly related to woodland vegetation species need to be to qualify as woodland birds, and iv) whether researchers consider ‘woodland birds’ to be a true representation of birds’ habitat dependence. Jax (2006) proposed that inconsistent use of such terms and concepts often leads to “contradictory statements about the same object, consequently leading to disparate proposals for measures in the fields of environmental protection, management, and conservation” (p283).

1.7. What are woodland birds and what is happening to them? Research regarding ‘woodland birds’ primarily occurs in Australia and Europe, although a small number of studies from North America, South American and Africa also concern ‘woodland birds’. Woodland birds (sometimes called woodland-dependent birds) are bird

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species which depend on native woodlands for their survival. In Australia and some parts of Europe woodlands are considered a different vegetation type to forests. Although both are wooded, woodlands generally have a more open structure and lower tree density than forests. As a result of this low tree density, woodlands in Europe and Australia have been subject to widespread clearing for agriculture and urban development, (Kaplan et al. 2009, Bradshaw 2012) resulting in a pervasive belief that the abundance and diversity of woodland birds populations must also be in decline (Watson et al. 2003, Gregory et al. 2007a, Ford 2011a, Rayner et al. 2014a). Concern over the decline of Australian woodland birds has prevailed since the 1920s and has only increased over recent years, with more than half of all woodland bird articles referring to their decline (Ford et al. 2001, Barrett et al. 2004, Watson 2011). A recent study by Rayner et al. (2014) reviewed and evaluated the evidence that Australian woodland birds are in decline. They found that, while the decline of woodland birds was well accepted in the literature, the evidence is not sufficient to prove a decline. There is also concern about the decline of woodland birds in Europe, with many articles citing or measuring the decline (Vickery et al. 2004, Hewson et al. 2007, Gregory et al. 2007a, GilTena et al. 2009). Unlike in the Australian context, no concerted effort has been allocated to evaluating these studies so the quality of the evidence is uncertain. Despite this, it appears likely that European woodland bird populations are also in decline (Gregory et al. 2007a, Vorisek et al. 2010). Many studies have attempted to disentangle the relationship between woodland birds and their habitats and threats. Several factors are frequently reported in the literature to be related to woodland bird occurrence: high tree cover, low fragmentation, low levels of grazing and the presence of site characteristics such as the species, diameter, height and density of trees and shrubs and the cover of understorey elements such as herbs and leaf litter (Donald et al. 1997, 1998, Santos et al. 2002, Holt et al. 2011, Yen et al. 2011, Newson et al. 2012). However, the evidence for these relationships is not consistent. Take for example, the most obvious relationship – that woodland birds depend on tree cover. Studies on the influence of tree cover on woodland birds report vastly different findings. In Australia, studies variously record the minimum desired patch size (before a dramatic drop off in richness) as 10 ha (Mac Nally and Horrocks 2002), 20 ha (Bennett and Ford 1997, Reid 1999) and 100 ha (Major et al. 2001), and one study considers a parabolic relationship whereby having patches smaller than 20 ha or

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larger than 400 ha is detrimental (Barrett et al. 1994). Internationally, studies variously consider the minimum patch size to be 2 ha (Bellamy et al. 1995), 3 ha (Dolman et al. 2007),10 ha (Fernández-Juricic and Jokimäki 2001, Mason 2001), and 20 ha (Fernández-Juricic and Jokimäki 2001). I propose that this inconsistency may in part be attributed to differences in the species referred to as ‘woodland birds’ in different studies.

1.8. Summary Ecologists use terms differently despite numerous calls for increased consistency. This may be driven by linguistic uncertainty, pressure to publish novel material, a lack of accountability, and/or the belief that it is unnecessary to define or classify terms consistently. In this thesis I aim to i)

quantify inconsistency in the use of the term ‘woodland bird’

ii)

examine the influence of inconsistent use of the term on ecological inference

iii)

understand why woodland birds are being classified differently

iv)

resolve the inconsistent classification of woodland birds

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Chapter 2 Consequences of Inconsistently Classifying Woodland Birds Based on published article Fraser, H., G. E. Garrard, L. Rumpff, C. E. Hauser, and M. A. McCarthy. 2015. Consequences of inconsistently classifying woodland birds. Frontiers in Ecology and Evolution 3: 83.

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2.1. Abstract There is a longstanding debate regarding the need for ecology to develop consistent terminology. On one hand, consistent terminology would aid in synthesizing results between studies and ease communication of results. On the other hand, there is no proof that standardizing terminology is necessary and it could limit the scope of research in certain fields. This chapter provides the first evidence that terminology can influence results of ecological studies. I find that researchers are classifying “woodland birds” inconsistently because of their research aims and linguistic uncertainty. Importantly, I show that these inconsistencies introduce a systematic bias to results. I argue that using inconsistent terms can bias the results of studies, to the detriment of the field of ecology, because scientific progress relies on the ability to synthesize information from multiple studies.

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2.2. Introduction Interpreting language is subjective and inexact. Language can be flexible and meaning is typically attributed to words on the basis of how people use them (Rey 2005, Temmerman and Van Campenhoudt 2011). However, sometimes this process results in omnibus terms which have too many meanings to be useful (Peters 1991, Lindenmayer and Fischer 2007). Using terms inconsistently can be problematic in scientific papers which must: a) be replicable; and b) convey a particular message as intended by the authors (Peters 1991). From this need for exact terms comes the field of ‘terminology’, concerned with developing specific, universallyacknowledged terms (Pecman 2014). Terminologists propose that specialized fields like science, engineering and medicine require consistent language for clear and specific communication of findings (Cabré Castellví 2003, L’Homme et al. 2003). This need for consistency is particularly pronounced in the medical field, due to the potentially high costs of miscommunication. This has led to the development of manuals guiding the use of medical terms (American Psychiatric Association 2000, Gray 2000). In ecology, there have been sporadic efforts to promote consistency in terminology. In 1931, the Ecological Society of America formed the Committee on Ecological Nomenclature to promote consistent use of terminology by their members (Hanson et al. 1931). Since it disbanded in 1956, there has been little progress towards a consistent terminology in ecology (contra Laporte and Pey, 2014; Laporte and Garnier, 2012). Nevertheless, debate persists in the literature, with some arguing that inconsistent terminology is a problem in ecology (e.g. Peet, 1974; Herrando-Perez et al., 2014), and others suggesting that consistent terminology is unnecessary (Jax and Hodges 2008, Hodges 2008, 2014). Calls for the development of a consistent terminology in ecology focus on two primary issues (Mason and Langenheim 1957, Hall et al. 1997, MacGregor-Fors 2011). First, ambiguous terms are rife in ecology. Ambiguous terms are those which may be defined in several, often similar, ways (Regan et al. 2002). For example the ‘cover’ of vegetation might be defined as foliage projective cover or crown cover, which are different in practice. Precise definition can reduce ambiguity. The second issue is one of vagueness in classification. Vague terms contain borderline cases in which it is hard to know whether something belongs in one category or another (Regan et al. 2002). For example, discrete categories such as “rare” or “widespread” are commonly used in ecology and related disciplines, but they group complex concepts into

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restrictive, often arbitrarily divided alternatives. The terms will be used inconsistently if researchers have different understandings of how categories should be distinguished (Jax 2006). Inconsistent terminology can slow scientific progress (Herrando-Perez et al. 2014c). It can lead to difficulties in finding and compiling relevant studies when reviewing the literature, which may cause redundant scientific investigations, or the inclusion of incomparable studies in a research synthesis (e.g. a meta-analysis) (MacGregor-Fors 2011, Herrando-Perez et al. 2014c). Inconsistent use of terminology can also cause problems when communicating findings to other scientists, policy makers and the public, as results may be misinterpreted or misrepresented due to ambiguous language (MacGregor-Fors 2011, Herrando-Perez et al. 2014c). On the flipside, some researchers argue against the need for consistent terminology. Hodges (2008) suggests that there is no empirical proof that using terms inconsistently causes meaningful miscommunication in ecology. Furthermore, many authors define key terms in their articles, which minimises ambiguity. Each individual article can be exact and unambiguous despite the lack of a consistent terminology between studies (Hodges 2008). Another line of argument proposes that consistent terminology is unnecessary because the intended meaning is clarified by a term’s context (Hodges 2008, Araúz et al. 2013). Lastly, multiple definitions of a term can open a number of fields of enquiry which would have been proscribed by using one concrete definition. Therefore, a greater understanding of a problem might be achieved if it is less precisely defined (Hodges 2008). Regardless of arguments for and against a consistent terminology in ecology, there is a lack of empirical evidence to inform this debate. I address this gap by examining the classification of Australian woodland birds. Currently, no strong evidence exists that this group is being classified inconsistently (but see Reid 1999; Kavanagh et al. 2007 and Kinross & Nicol 2008 for examples of studies which consider multiple classifications). Nevertheless, inconsistency in findings suggests that some unstudied variation might exist. The majority of woodland bird articles cite a decline in woodland birds due to habitat destruction and degradation (Rayner et al. 2014a); however, the evidence of this is equivocal. Some studies show evidence of a decline in woodland birds (e.g. Barrett et al., 2004) while others do not (e.g. Rayner et al., 2014a). There is similar disagreement about whether woodland bird prevalence relates to vegetation extent (Major et al. 2001, Mac Nally and Horrocks 2002) or fragmentation (Radford et al. 2005, Amos et al. 2013). The disagreement between these

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studies could be attributed to regional differences (Polyakov et al. 2013), or differences in temporal (Yen et al. 2011) or spatial scale (Lindenmayer et al. 2010), but may also be symptomatic of underlying disagreement about exactly what constitutes a ‘woodland bird’. Deciding how to classify woodland birds raises many questions including: Does this term refer to birds that occur in woodlands and how often do they have to reside in woodlands to count? Do we only include species that nest in woodlands or also those that forage there? What if species only need woodlands for part of their life cycle? Exactly what vegetation does ‘woodland’ refer to? The way authors address these questions is often unclear and the species they include as ‘woodland birds’ will differ depending on their answers. I chose to study inconsistency in the classification of Australian woodland birds for three reasons. First, inconsistency seemed plausible given the lack of classification guidelines and contradictory findings about how woodland birds respond to their habitat. Second, a sufficient body of research existed to obtain meaningful results. Third, any inconsistency has potential policy consequences. Here, I investigate i) how consistently researchers are classifying woodland birds, ii) why any inconsistencies are occurring and, most importantly, iii) how inconsistencies are affecting conclusions about woodland bird ecology and management. I expected that researchers would classify most of the same species as woodland birds, but disagree about a few species that are difficult to classify because they partially depend on woodland vegetation, and are therefore subject to vagueness, or their biology or behavior is poorly understood. I was uncertain whether I would find evidence of substantial differences in the results of studies attributable to the use of different classifications. If so, it would demonstrate that inconsistent classification is impeding the capacity for researchers to fully understand the ecology and management of woodland birds and that there may be benefit in developing a consistent definition of what constitutes a woodland bird.

2.3. Material and methods 2.3.1. Investigating inconsistency in woodland bird classification I conducted a systematic review of research about Australian woodland birds using two concurrent methodologies in August 2014. I performed a Google Scholar search using the search terms “woodland birds” and “Australia” and recorded the digital databases that host the first 100 papers. I then performed a thorough search of these databases (Elsevier, Wiley online library, Taylor and Francis, CSIRO, Springer, Royal Society Publishing, PLoS one and

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JSTOR). To complement this, I also searched the online repositories Scopus and Web of Science. The search terms used in both instances were: ‘Australia’ AND any of 1) ‘woodland bird’ OR 2) ‘woodland dependent’ AND ‘bird’ OR 3) ‘woodland’ AND ‘bird’. Articles not based in Australia and those focused on non-avian species or communities, non-woodland habitats or single or pairs of species were removed. Information about the number of articles found and excluded at each step of this process is presented in Appendix 2.1 and a list of articles reviewed is included in Appendix 2.2. The articles obtained through this search were broken into 5 categories for articles that: 1) mentioned but did not study woodland birds; 2) studied woodland birds but did not specify the species; 3) specified the species they regarded as woodland birds but gave no information about the species which were classed as non-woodland birds; 4) were about a specific group of woodland birds such as ‘declining woodland birds’; and 5) those which specified both woodland and non-woodland species. I was only interested in lists which included both woodland and non-woodland species to ensure that common birds were classified as woodland species no more frequently than less common species (as would be the case if I included studies which only specified woodland species). When multiple articles used the same dataset and method of classifying woodland species (e.g. Radford et al. 2005; Radford & Bennett 2007; Garrard et al. 2012), only one list was retained. This search process yielded 38 lists of woodland birds. I recorded the authors of each study so that I could control for the fact that I would expect the classifications used to be more similar when they have an author in common. However, given my small sample size and the difficulty of determining which author was responsible for the classification this was discarded. However, it is possible that correlation between these articles will cause classification to appear more consistent than it would if I only retained one article per author. For each species I calculated the number of lists containing the species and the percentage of these that classified the species as a woodland bird. This avoids confounding rare or patchily distributed species with species that are rarely considered woodland birds. This method is subject to variation when there are few records for a particular species. For example, if a species was reported in only one list, it can only have been classified as a woodland species in either 0% or 100% of studies, which is unlikely to represent the value that might be achieved if more authors considered the species. In order to reduce this variation, those species that were present in 10 or fewer species lists were excluded from further analysis. This left me with data on the classification of 165 species. The frequency distribution across species of the percentage of

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studies making a ‘woodland bird’ classification was then plotted to illustrate consistency (or inconsistency) in woodland bird classification. 2.3.2. Testing the influence of inconsistent classification To assess the consequences of inconsistent use of the term ‘woodland bird’, I investigated how variation in woodland bird classification affected the findings and interpretation of a published ecological study (Garrard et al. 2012). This study investigates the vulnerability of woodland bird species to habitat fragmentation. The response of woodland birds to habitat fragmentation was the subject of 33% of the articles returned in my systematic review and, as such, I expect that my investigation is broadly applicable to woodland bird research in Australia. The methods used by Garrard et al. (2012) and the alterations made to investigate the effect of inconsistent classification are described in Appendix 2.3. In essence, Garrard et al.'s (2012) study involved two steps. First, they estimated the natal dispersal ability of a particular set of woodland bird species based on their traits. They then investigated the relationship between this estimate of dispersal ability and the probability of species occurring in landscapes with varying levels of tree cover aggregation, a measure of how clumped together trees are in the landscape; this is roughly the inverse of habitat fragmentation (Radford and Bennett 2007). I investigated the sensitivity of Garrard et al.'s findings about the relationship between dispersal ability and response to tree cover aggregation by simulating the effect of choosing different sets species to represent woodland birds, based on the percentage of studies in which species were classified as woodland birds. Some authors consider all species present in woodlands to be woodland birds, while others have more stringent criteria, resulting in shorter lists. I analyzed the simulated effect of applying increasingly stringent classification criteria to identifying woodland birds on the response of the Garrard et al. (2012) model (Appendix 2.3). I fit the model 9 times on different sets of species, one which included all species, and 8 representing frequency thresholds of 10, 20, 30…80% to simulate the effect of becoming more selective about which species are included as woodland birds. So, the list based on the 80% frequency threshold only included those species that are classified as woodland birds in 80% or more of lists obtained from the process described in Section 2.3.1. The list based on the 70% frequency threshold included the same species, but also those that classified as woodland birds in 70-80% of lists, and so on. The results of each model run are not independent but further analyses show that this does not affect my overall result (Appendix 2.4).

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I examined how the estimate of the mean species response to tree cover aggregation for each of these nested subsets compared with the original estimate from Garrard et al (2012) to determine how classification inconsistency might influence ecological inference. Garrard et al.’s study used a subset of data collected in an earlier study (Radford et al. 2005). In my study, I included all species found during the original survey; I estimated the median dispersal distance of the species not included by Garrard et al. (2012) using the dispersal model presented in their study (Appendix 2.3). 2.3.3. Reasons for inconsistency in woodland bird classification I was interested in understanding the reasons for inconsistency in classification. In particular, I was interested in: 1) how well recognized lack of consistency was in the research community; 2) why researchers were classifying species differently; and 3) whether researchers thought inconsistent classification was problematic. I considered all authors of articles on woodland birds to be experts. I emailed 131 of these experts by finding the contact details of the authors of the 109 woodland bird research papers identified in the systematic review (i.e. not those that mentioned but did not study woodland birds) to invite them to participate in a survey. They were presented with the findings of the systematic review and a value representing how consistent the lists they used in their studies were with other research. They were then asked a series of questions via SurveyMonkey (SurveyMonkey Inc. 1999) regarding how they classify woodland birds and woodlands, and their views on why researchers may be classifying species differently (a copy of the survey is available in Appendix 2.5). Authors received up to 4 emailed reminders to prompt them to fill in the survey. Of the 131 authors I contacted, 69 completed the survey, 31 responded to say that they were not involved in the woodland bird aspects of the study, and 31 did not respond. Survey questions came under four main headings: experience; beliefs about classification consistency; how woodland vegetation and birds are classified; and why classifications may be different between studies. The experience section was intended to allow me to exclude answers given by people who had never been involved in classifying woodland birds and therefore were unable to meaningfully answer some of the questions. In the section on beliefs about classification consistency, experts were asked whether they agreed that some research questions required different lists of woodland birds, and whether using a standardized list would be detrimental to answering certain research questions. The next section asked them to select the criteria that they used to distinguish woodland vegetation, and woodland birds. In the section about why classifications differ between studies, experts were asked to rate, on a scale

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of 1 to 10, how much they believed each of a number of options contributed to differences in the classification of woodland birds. In order to collect quantitative information, answers to the majority of questions were given via multiple choice options (n=6) or Likert scales (n=2). The options posed in the multiple choice questions were drawn from definitions of woodlands and woodland birds found in the systematic review articles.

2.4. Results 2.4.1. Systematic review Of the articles reviewed, 7 mentioned but did not study woodland birds, 32 studied woodland birds but did not specify the species, 28 specified the species they regarded as woodland birds but gave no information about the species which were classed as non-woodland birds, 15 were about a specific group of woodland birds such as ‘declining woodland birds’, and 38 specified both woodland and non-woodland species. These 38 lists formed the basis of the analyses. Only including studies specifying woodland and non-woodland species avoids confounding species that are ‘non-woodland birds’ with those ‘woodland birds’ which were absent from the survey. In total, 165 bird species were recorded in at least 10 of the lists examined by this study. Excluding species that were in fewer than 10 lists tended to exclude more species from waterdependent and uncommon orders of birds (details about the classification of species are supplied in Appendix 2.6). Of the 165 species, 8 were recorded as woodland birds in every list and 13 species were always classified as non-woodland birds. The remaining 144 species were inconsistently classified at least once (Figure 2.1).

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Figure 2.1. Frequency distribution of the percentage of studies in which individual species are classified as a woodland bird (total number of species = 165). Complete consistency in classification would appear as a binary distribution, where species are either regarded as woodland species 100% of the time or 0% of the time. Maximum inconsistency would occur if all species were classified as woodland birds in 50% of lists.

The bimodal frequency distribution represented in Figure 2.1. indicates that there is agreement regarding the classification of a substantial proportion of species, but that this is not unanimous and there is little certainty in the classification of many species. 2.4.2. Effects of inconsistency The data collected by Radford, Bennett and Cheers (2005) and used for Garrard et al (2012) comprised 126 species. When all species found during the original field surveys were included in the analysis, the predicted effect of landscape aggregation on prevalence was 3.0 (Fig. 2.2; 95% credible interval 2.1- 3.9). This is substantially smaller than the effect size estimated by Garrard et al. (2012), who estimated the mean effect of habitat aggregation (variable γi and br in Appendix 2.3) to be 5.9 with 95% credible intervals of 4.2-8.2. The effect sizes estimated from the other subsets of data increased with increasing frequency thresholds such that the 80% threshold yielded an effect of 6.1 (95% credible interval 4.5- 7.6; Figure 2.2).

Figure 2.2: The predicted effect of tree cover aggregation on species prevalence, for different subsets of species representing frequency thresholds of 10, 20, 30,…80%. At 80 on the horizontal axis, only species which are regarded as woodland birds in 80% or more of studies are included in the model. Error bars represent 95% credible intervals. Mean estimate from the original Garrard et al. (2012) model is represented by the line and the 95% credible intervals by the grey shaded area.

2.4.3. Survey results In total, 69 woodland bird experts filled in the survey. All researchers acknowledged that they list woodland birds differently to each other but the vast majority (n=55, 80%) believe that using different lists of woodland birds is not problematic for ecological research (Appendix 2.5, item 9). In fact, 39 (of 68, 57%) experts believed that it would be problematic to have a

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unified list of woodland species because it would hinder the investigation of certain research questions (item 4). The survey identified vagaries in the definition of woodland vegetation as a key reason for inconsistency of woodland bird classification (items 6 and 7). Researchers variously listed between 1 and 9 factors that affected the way they classified woodland vegetation (Table 2.1). The majority of responses (n=67, 97%) included a characteristic of trees, with a lesser number specifying that their classification was based on ecological vegetation classes or other similar systems (n=23, 33%) or characteristics of shrubs. Very few experts (n=2, 3%) considered soil properties when classifying woodlands. Table 2.1. The number of respondents (out of 69) selecting different factors as influencing the way they classify vegetation as woodland. These factors are broken into shrub, tree and other categories to give a clear indication of how important they each were for determining whether vegetation is ‘woodland’. Option Tree characteristics

Shrub characteristics

Number of responses (n=69)

Presence of trees

54

Tree density

50

Canopy cover

46

Tree height

36

Tree species

35

Any tree characteristic

67

Shrub species

7

Shrub cover

6

Presence of shrubs

9

Any shrub characteristic

15

Soil properties

2

Ecological Vegetation Class: woodland

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Researchers listed between 1 and 9 factors as contributing to their classification of species as woodland birds (item 5; Table 2.2). Of the 14 options, all were selected at least once. The majority (n=60, 87%) of experts considered occurrence when classifying woodland birds, and a substantial number of experts based their classification on traits (n=57, 83%) or used an authorized classification such as one used in a field guide or journal article (n=39, 57%). There was also widespread use of exclusion criteria such as whether a species was nocturnal or a water bird (n=25, 36%).

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Table 2.2. The number of respondents (out of 69) that identified individual factors as influencing the way they classify species as woodland birds. These factors are classed by underlying orientation into 5 classes: occurrence based, authorized classification, trait based, based on habitat associations and on exclusion criteria. As in Table 2.1, the number of respondents listing each factor is given as well as the number of respondents listing any factor within the 5 classes. Option Occurrence based

Authorized classification

Trait based

Habitat associations

Exclusion criteria

Number of responses (n=69)

Present in woodlands

43

Occurs more frequently in woodlands than in other habitats

55

Any occurrence based metric

60

Classified as a woodland bird by another author

38

Classified as a woodland bird in a field guide/bird handbook

18

Any authorized classification

39

Nests in woodlands

55

Forages in woodlands

50

Shelters in woodlands

41

Any trait based metric

57

Intolerant of degraded sites (e.g. grazed sites)

4

Intolerant of fragmented sites

1

Prefers large areas of vegetation

6

Any habitat association option

8

Not wetland

16

Not an introduced species

11

Not nocturnal

3

Not a raptor

4

Any exclusion criteria

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Authors were asked to rate possible reasons for inconsistencies between researchers (item 7). Those that ranked the highest were A) ‘different ideas about how to determine whether species rely on woodland vegetation’, B) ‘different aims of research’ , C) ‘regional differences in the behavior or habitat requirements’, and D) ‘regional differences in the distribution of species’ (Figure 2.3). Uncertainty about the behavior or habitat requirements (G) and distribution of species (F) ranked relatively low.

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Figure 2.3. Ratings assigned to each of 7 reasons that researchers use different lists to classify woodland birds: A) different ideas about how to determine whether species rely on woodland vegetation, B) different aims of research, C) regional differences in the distribution of species in woodland and non-woodland areas, D) regional differences in the behavior or habitat requirements of species, E) different ideas about what constitutes woodland vegetation, F) uncertainty about the distribution of the species in woodland and non-woodland areas, G) uncertainty about the behavior or habitat requirements of different species. Error bars represent the 95% confidence intervals of the estimates.

2.5. Discussion I have shown that there is substantial inconsistency around how woodland birds are classified, stemming from different difinitions of ‘woodlands’ as well as differing ideas about how to tell whether birds depend on them. In Australia, botanists typically distinguish between vegetation types according to the canopy cover of the vegetation’s tallest (dominant) strata (Specht 1970). In this context, vegetation is classified as ‘woodland’ if it has a trees over 10m tall and a foliage projective cover less than 30%. Vegetation is classified as ‘forest’ if it has trees over 10m tall and a foliage projective cover more than 30%. However, bird researchers do not always subscribe to these classifications, often not assessing the structure of the vegetation, and may classify habitat based on coarse scale maps or on the presence of trees. Further, habitat preference is a continuum from species that only occur in one habitat type (e.g. woodlands) to species that are equally prevalent in several habitats. Some species may be easy to classify but many are difficult to reliably assign to a single category (Regan et al., 2002). Classification imprecisely simplifies the habitat preference continuum that depends on a range of different factors, including where and for how long the species occurs in other habitats, whether it depends on woodlands for critical life stages, and so on.

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Despite these clear inconsistencies, the experts I surveyed expressed ambivalence about the need to develop a consistent classification. This reflects the disagreement with this idea in the broader literature. Although the surveyed experts were aware that the term ‘woodland bird’ was being used to represent different sets of species, many thought the inconsistency was not problematic (n=55 of 69). Over half of experts (n=39 of 68) felt that conforming to a consistent definition would inhibit their ability to answer certain research questions, although many (n=29 of 68) did not. This belief is consistent with Hodges’ (2008) assertion that retaining flexibility in definitions of terms can allow a subject to be more fully explored. The results of my study highlight the trade-off between using flexible terminology for woodland birds, allowing researchers to fully explore nuanced research questions, and using standardized terminology, which facilitates generalizing across studies and improves clarity in communication. Given the lack of evidence for standardization and the benefits of retaining flexibility, it is unsurprising to find inconsistency in ecological terms such as ‘woodland bird’. Prior to my study, Hodges (2008) suggested there was no compelling evidence that consistent terminology is necessary in ecology. However, my study provides evidence that using terms inconsistently can be problematic, both for ecological inference and conservation. In the case of woodland birds, inference changed depending on the way the term ‘woodland birds’ is defined, hindering comparison or synthesis of findings from different studies (Peters 1991, MacGregor-Fors 2011, Herrando-Perez et al. 2014c). I have demonstrated that results could vary substantially depending on which definition of woodland bird the author considers. Using the model developed by Garrard et al. (2012), I found that the effect of tree aggregation on the occurrence of woodland birds varies substantially depending on the species included in the definition of a ‘woodland bird’. This is a clear demonstration that results from different studies are not necessarily comparable. Importantly, the variation in findings attributed to inconsistent classification may also have direct management implications. For example, a finding that indicated that tree cover aggregation has little effect on ‘woodland bird’ occurrence may have implications for where revegetation or reserves are located in the landscape. Furthermore, the Victorian temperatewoodland bird community is protected under the Flora and Fauna Guarantee Act (Victorian Government 2013) but the species included in this list are only a subset of the species which authors frequently consider as woodland birds (Appendix 2.7). My results demonstrate that researchers use the term ‘woodland bird’ to refer to substantially different sets of birds, and

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have variable ideas about how this group of species should be identified. Researchers variously categorized ‘woodland birds’ depending on their occurrence, their traits, their habitat associations, on authorized classifications or by using exclusion criteria. This inconsistent classification is a result of both linguistic vagueness (it is unclear where on the continuum of dependence on woodlands that a species becomes a ‘woodland bird’) and ambiguity (it may have more than one meaning, such as ‘birds that occur in woodlands’, ‘birds that do not occur outside woodlands’ or ‘birds that nest in woodlands’) (Regan et al. 2002). This variation is unrecognized in literature reviews (Bennett and Watson 2011, Ford 2011b), meta-analyses (Maron et al. 2011, Rayner et al. 2014b) and management recommendations (Paton and O’Connor 2010). Researchers are thus combining information from studies that consider widely different sets of species, confusing the understanding of woodland bird ecology and management, and inhibiting the generalizability of their results. This brings me to my core questions. Does the uncertainty surrounding the term ‘woodland bird’ actually matter? In this case, and in ecology more broadly, is it necessary to consistently define terms to understand ecological relationships and avoid misunderstandings and difficulties in the development of meta-analyses, literature reviews and management recommendations? Is the trade-off between flexible and standardized definitions of terms worth making? My findings demonstrate that the magnitude of an effect can depend on how the term ‘woodland bird’ is defined. If two different researchers conducted the same study using the same data but different definitions of ‘woodland bird’, they might develop incongruent or even contradictory results. This is problematic when attempting to understand woodland bird ecology or predict how they will respond to land management. Only a small subset of woodland bird research uses identical lists of ‘woodland birds’, so researchers must choose between including information from many studies (which risks terminological differences confounding results) or only including studies which use the same definition and list of ‘woodland birds’ (which risks excluding valuable insights from other studies). However, it is possible that standardizing the definition of ‘woodland birds’ would make it more difficult to answer important ecological questions, as reflected in the responses of 39 (57%) respondents. The empirical results of this study, which show that classifying woodland birds consistently is important, conflict with the opinions of those experts who believe that

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standardizing the term ‘woodland bird’ is unnecessary and possibly detrimental. Clearly, there is a trade-off between using flexible definitions of terms and standardizing terms. My research provides some of the first empirical evidence that using terms inconsistently in ecology is problematic and needs to be resolved. More research is required to examine whether this effect is widespread but I propose that ecologists need to carefully consider whether they are using terms consistently. Contrary to the argument that inconsistency in classification is not an issue in ecology because terms are context-specific and well-defined by authors (Hodges 2008), I found that many studies either do not define the term ‘woodland birds’, or define it incompletely. I support an approach which increases the transparency of woodland bird research by making overt the species which are considered ‘woodland birds’, and detailing why they were classified accordingly. Beyond this, I believe that developing standardized definitions of key terms is vital to ecological research and management. When it comes to classifying ecological groups (such as woodland birds) we need to develop terms that have management relevance and, ideally, are based on ecological theory and empirical data. However, I recognize that standardizing terminology may come at a cost in terms of the flexibility of research. Therefore, I propose that, when the research question would be impeded by a standardized terminology, researchers either avoid using the term by studying species individually or using quantitative estimates of traits and habitat associations, or present their results alongside results achieved with the standardized terminology. This would allow flexibility to be maintained but also retain generalizability between studies.

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Chapter 3 Tiny terminological disagreements with far reaching consequences for global bird trends Based on published article: Fraser, H., J.-B. Pichancourt, and A. Butet. 2017. Tiny terminological disagreements with far reaching consequences for global bird trends. Ecological Indicators 73: 79–87.

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3.1. Abstract Various combinations of data and expert opinion have been used to select species for indices of bird trends. Commonly these indices break species into groups based on their habitat preference such as woodland specialist, farmland specialist and generalist birds. It is unclear what influence differences in how species are allocated to these groups might have on trends in these indices. There is uncertainty surrounding reported trends in these bird groups with studies variously showing declines or increases in prevalence. This is usually attributed to ecological factors but if studies classify bird groups differently this variation may be due to inconsistency in classification. Disagreement about whether these bird groups are stable, increasing or declining has the potential to obscure important changes in bird prevalence and impede appropriate, timely conservation. I examined how consistently European and Australian researchers classified woodland, farmland and generalist birds, and whether this affected the trends in indices of these groups. Researchers from both regions classified species differently, and the population trends seen in these groups were strongly affected by differences in classification. All classifications I studied suggest that populations are consistently declining for Australian woodland and European farmland birds, and increasing for European woodland birds. European generalist and Australian farmland and generalist bird populations have been recorded to be increasing or decreasing in prevalence depending on classification. My results question the current practice of idiosyncratically classifying indicators in scientific research and conservation. Current practice is making it more difficult to infer whether, when and how to conserve bird groups in Europe and Australia, potentially leading to sub-optimal biodiversity outcomes. I offer suggestions for building consensus on how to classify these bird groups in order to provide more reliable evidence to support conservation decisions.

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3.2. Introduction Globally, governments are united in the desire to preserve Earth’s remaining biodiversity, as evidenced by the creation of the Convention on Biological Diversity and Aichi targets (CBD 2006), along with other commitments made at national and regional scales. Attempts to gain an understanding of global biodiversity trends have inspired the creation of numerous biodiversity indicators (Tittensor et al. 2014). These indicators are restricted to specific taxa, areas, or aspects of biodiversity loss (Cairns et al. 1993, IUCN 2000, Gottschalk et al. 2010, Scholefield et al. 2011, EBCC 2014). Birds are particularly charismatic and diverse and as a result have been extensively studied and monitored, eventuating in the development of multiple indicators of their trends through time (Olsen et al. 2005, Gregory and Strien 2010, DEFRA 2013, EBCC 2014, Rayner et al. 2014a). These indicators typically group birds by their primary habitat association to pick up changes associated with habitat modifications. However, Fraser et al. (2015) indicated that the species included in these groups differ between studies. The study, based in Australia, found that the species experts classify as woodland birds differ substantially and may lead to meaningful differences in results. However, an English study found that the indicators of bird trends derived from the Breeding Bird Survey were robust to changes in species classification (Renwick et al. 2012). My study aims to compare how sensitive bird indices in Europe and Australia are to differences in species classification demonstrated in the literature. I examine how indices of trends in bird groups differ under published classifications of farmland, woodland, and generalist bird groups. If indices of trends in these bird groups differ substantially, then the results from different studies are not comparable. This impedes scientific progress because researchers can no longer justify drawing conclusions from the broader body of research on farmland, woodland or generalist birds; rather, they can draw only from the minority of articles which classify species identically. This is problematic in all research but especially in conducting systematic reviews and meta-analyses as it thwarts the direct comparison of results between studies. Between-study disagreement about trends in a bird group may obscure declines causing necessary conservation efforts to be delayed or deemed unnecessary. Researchers and conservationists throughout Australia and Europe are concerned that forest and woodland birds are declining due to deforestation, fragmentation and degradation of forests and woodlands (Ford et al. 2001, Vickery et al. 2004, Hewson et al. 2007, Gregory et al. 2007a,

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Gil-Tena et al. 2009, Watson 2011). Reductions in the amount of available habitat combined with the increased need to disperse through hostile environments are thought to lead to declines in forest and woodland birds (Deconchat et al. 2009, Garrard et al. 2012, Gil-tena et al. 2014). The relationship is complicated in Europe because in areas such as Britain woodland habitat is receding while in some Mediterranean countries it is expanding. In Europe there is also concern about a possible decline in farmland birds as a result of reductions in the extent and quality of traditional farmland habitats. This decline in farmland habitats is thought to have been triggered by the European Union’s Common Agricultural Policy, which had a two-fold effect: intensifying agricultural practices and indirectly increasing afforestation (Vanhinsbergh et al. 2002, Vickery et al. 2004, Pithon et al. 2005). In 2003, the European Common Agricultural Policy was revised (Butler et al. 2010) and, along with the promotion of agri-environmental schemes, this may have redressed both issues, but declines in farmland birds continue to be reported (Aviron et al. 2009). This concern about a decline in farmland birds in Europe contrasts with the Australian context where there are few birds that are truly adapted to farmlands. This is a result of vastly different agricultural histories in the two regions. Australia has only had western agriculture for around 200 years, not enough time for species to evolve substantially to these conditions whereas Europe has been farming using similar methods for over 7000 (Martin et al. 2012). Declines in farmland and forest/woodland birds are thought to be accompanied by increases in species which have broader habitat requirements (i.e. the generalists) and are able to persist in modified areas (McKinney and Lockwood 1999). It is hypothesised that there is a global rise in the population of generalist birds as a result of biotic homogenization caused by extinctions, habitat degradation, urbanization and introduced species (McKinney 2006, Rooney et al. 2007, Croci et al. 2008, Gregory and Strien 2010, Robertson et al. 2013). However, there is also evidence that common bird species (mainly generalists) are declining in Australia (Birdlife Australia 2015). More research is required to determine whether generalist birds are declining or increasing in either region. In this study, I examine whether the proposed trends in three bird groups (woodland specialist, farmland specialist and generalist) are robust to classification according to different published sources. Fraser et al. (2015) demonstrated that the classification of Australian woodland birds is inconsistent, and in this study I aimed to extend this research by investigating whether: i) the inconsistency found in the classification of Australian woodland birds is true of other well-

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studied bird groups; ii) whether this is a phenomenon that is restricted to Australian researchers or is indicative of a broader trend; and iii) inconsistent classification of species substantially impacts the interpretation of indices of trends in bird groups.

3.3. Materials and Methods The terminology used to identify groups of bird species varied between and within regions. I accounted for this difference by defining each group explicitly: Farmland specialists: These species are thought to specialise in agricultural areas with no trees or low density tree cover and an abundances of grasses, forbs or crops. Typical environments may include agricultural areas where trees are only present in shelterbelts or hedgerows. Common terms used to describe these species in the literature were ‘farmland’, ‘open country’, ‘hedgerow’ and ‘savannah’. Woodland specialists: These species are thought to specialise in areas with a treed overstorey. Terms often used to describe these species in the literature were ‘woodland’, ‘woodland-dependent’, ‘forest’ and ‘woodland/forest’. Generalists: These species are characterized by a lack of dependence on a particular habitat type. In the Australian bird literature studies often consider ‘woodland’ and ‘open country’ specialist species and ‘open tolerant’ species which inhabit both habitats. In that context, I consider the term ‘open tolerant’ to refer to generalists. Other terms used to describe this group were ‘generalist’ and ‘ubiquitous’. Hereafter I refer to the terms ‘farmland’, ‘woodland’ and ‘generalist’ species for the sake of simplicity. I determine how consistently birds are classified as woodland and farmland specialist and generalist species and investigate the influence any inconsistency has on the trends in indices of abundance and reporting rate of these groups (Figure 3.1). To do this I use the index of yearly multiplicative trend which is used to report bird trends by the European Bird Census Council (EBCC) (EBCC 2014).

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Data sourcing

I collected data via a systematic review regarding how consistently farmland, woodland and generalist birds were classified in the literature

Analysis of classification inconsistency I plotted the probability of being classified as a generalist against being either a farmland or woodland specialist

Impact of classification inconsistency on indices of bird trends I calculated indices of bird trends based on classifications from 9 studies to evaluate whether differences impact ecological inference

Figure 3.1.: The structure of this chapter beginning with sourcing data and ending with analysing the effect of inconsistent classification on an index of bird trends.

3.3.1. Data sourcing The results from two systematic reviews were combined for this study. One collected data on woodland and farmland specialist and generalist birds internationally. The other augmented the data with additional records from Australia, which was poorly represented in the initial search. Previous research (Fraser et al., 2015; Chapter 2) identified a body of research regarding Australian woodland birds. I used this data to augment a review of several databases (Elsevier, JSTOR and Wiley online library, SCOPUS and Web of Science), for articles including the terms ‘woodland bird’; ‘woodland’ and ‘bird; ‘forest bird’; forest’ and ‘bird’; ‘farmland bird’; ‘farmland’ and ‘bird’; ‘open country bird’; ‘open country’ and ‘bird’; ‘generalist’ and ‘bird; and ‘ubiquitous’ and ‘bird’. The search returned 2593 articles. As for the previous review (Fraser et al., 2015; Chapter 2), articles that focused on non-avian species or communities, single or pairs of species were removed, after which 439 articles remained. I recorded the authors of each study so that I could control for the fact that I would expect the classifications used to be more similar when they have an author in common. However, given my small sample size and the difficulty of determining which author was responsible for the classification this was discarded. However, it is possible that correlation between these articles will cause classification to appear more consistent than it would if I only retained one article per author. The articles which specified at least two groups of bird species were retained for further analysis (e.g. generalist and farmland birds). Studies which only considered a single category were excluded to avoid confounding the species that did not fall into the category of interest with those that were not seen during the study. This search yielded 37 articles from Europe, one article from Australia (not previously included in Fraser et al. 2015 or Chapter 2; Appendix 3.1), four from Africa, three from Asia, five from South America and four from North America. Articles from Africa, Asia, North America and South America were discarded due to low sample size.

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The data were analysed in two ways (Figure 3.1). First, I analysed the level of inconsistency in the classification of bird species on two axes using the full range of articles gathered in the systematic review. This was completed for the two possible bird group comparisons: farmland specialist – woodland specialist; and generalist – specialist (where ‘specialist’ includes both woodland and farmland habitat specialists). Second, I took nine of the articles (4 from Australia and 5 from Europe) from the systematic review and used them to analyse the effect of classifying species differently on indices of trends in woodland specialist, farmland specialist and generalist species. The four Australian articles I selected were the only ones that classified species into all three categories. I chose to evaluate the 4 most recent European articles as well as including the species list used by the Eurpean Bird Census Council. 3.3.2. Analysis of classification inconsistency The papers sourced in the systematic review variously classified birds into two or three of the categories, i.e. woodland specialist, farmland specialist and generalist species. I coded the data such that when a species was included in a study’s list it was given a 1 in the appropriate category and 0s in the other two categories. Where a species is not included in a study’s list, it is coded as NA. This means avoids confounding species that were not present in the study area with those that were present but not classified into a particular category. Each category was considered by a different number of articles, with the majority of articles considering woodland birds (n=58), and the fewest articles considering generalist birds (n=35). Therefore, the percentage of studies classifying species into each category could be biased towards certain classifications (i.e. if more studies concern woodland birds these might appear to be more or less consistently classified) and confused by missing data (e.g. a study that considers farmland specialist and woodland specialist groupings might find a Magpie and classify it as a woodland specialist but, if that study did not include a woodland specialist grouping the Magpie might be left off the study’s species list. Looking at this study it would be impossible to know whether the Magpie was excluded because it didn’t fit the categories or because it was not seen during the study. for more detailed explanations, see Fraser et al., 2015). To avoid this bias, I considered the data on two axes: i) the proportion of studies which considered woodland vs farmland specialists and ii) the proportion of studies which considered specialists (in either woodland or farmland habitats) vs generalists. Only studies that classified species into both woodland and farmland categories were used to determine the position of species (n=49) on the woodland – farmland specialist axis. Then I grouped farmland and woodland species into one ‘specialist’ group and used studies which considered generalists and

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(at least one type of) specialist species to calculate the position of species (n=35) on the generalist – specialist axis (Appendix 3.2). By doing this, the position of species on the generalist/specialist axis may be less certain than their position on the farmland/woodland axis (Figure 3.2). To minimise this effect I excluded species which were included in fewer than three studies that considered woodland and farmland categories and fewer than three studies that considered specialist and generalist categories. The classification of species was then plotted onto the two axes (farmland specialist – woodland specialist and specialist – generalist). It was expected that species that were less consistently classified on the farmland/woodland axis (i.e. are nearer the 0.5 mark) would be more likely to be regarded as generalist species (i.e. would have higher values on the Y axis than other species), which could be modelled as a second order polynomial equation. This relationship was examined by using regression analysis and the r 2 value of the relationship was calculated to determine whether it meaningfully explained variation in classification. A high r 2 value would support this hypothesis, a low r2 value would suggest that species that are consistently classified as woodland or farmland birds are just as likely to be classed as generalists as species that are classified as woodland birds 50% of the time and farmland birds 50% of the time. 3.3.3. Impact of classification inconsistencies on bird trends The purpose of this study was to evaluate the impact that classifying bird groups differently has upon inference about their trends through time. Therefore, I have endeavoured to present my results in a similar format to those presented by the EBCC and BirdLife Australia, which typically present smoothed year-by-year trend graphs and a single estimate of the slope of the trend. To illustrate the impact of different classifications of woodland, farmland and generalist birds, I selected four studies from the systematic review which separated species into all three categories for each region (Australia and Europe) and their species lists used to delineate different possible sets of woodland specialist, farmland specialist and generalist birds. In Australia only four studies classified species into all three categories. More studies were available for Europe so the four most recent studies were selected and a fifth study was added for farmland and woodland birds to represent the EBCC classification of these groups (EBCC 2014) (Table 3.1, see Appendix 3.3. for species lists). In total, nine different lists of woodland, farmland and generalist birds were examined. Data on the abundance of 163 European species (EBCC 2014) and the reporting rate of 516 Australian species (BirdLife International and NatureServe 2016) were available. The

34

European Bird Census Council (EBCC) has devised a method for measuring trends in groups of birds (including farmland and woodland species) over time (EBCC 2014). The abundance of each species at year one is used as a reference point for the index of bird abundance (i.e. at year 1 the index is 100, subsequent values represent percentage difference from year 1). Using this as a reference point, the log of the indices and the slope of the regression line are calculated. Back-transforming this slope gives the ‘yearly multiplicative trend’, which provides the average percentage change in the index (of abundance) per year (EBCC 2001). The EBCC then takes the geometric mean of the abundance of species within a group at each time-step to calculate trends in the various bird groups. This method was used to calculate the multiplicative trend in abundance of 163 European bird species and reporting rate of 516 Australian bird species, over the 15-year period between 1998 and 2012 using data provided by Birdlife Australia and the European Bird Census Council. I replicated the global tendency of researchers to idiosyncratically re-combine species-level indices by using 4 published bird lists of farmland, woodland and generalist bird indices from Australia and 5 from Europe to predict the trends in these indices. Some birds were added to the EBCC dataset after 1997 and so are missing in earlier years; also surveys of some species were not implemented in all years. When there were missing data in the European dataset log-linear models were used, as implemented in TRIM software, to fill the gaps (EBCC 2001). There were no missing values in the Australian dataset but sampling effort varied and sites were selected for sampling haphazardly (compared to the European data set which collects data at standard sites), so zero values of reporting rate were recorded in some years for species which were rare, cryptic or range restricted. As proportional decreases in each species are weighted equally in the multiplicative trend index, a 100% decrease in one of these species disproportionately influences the index. To mitigate this effect, I excluded species that had zero reporting rates in any year (86 species). These data were used to calculate Pearson correlation coefficients between abundance/reporting rate indices (geometric mean of species’ abundance/reporting rate at year 1 =100, subsequent years’ values represent percentage change from year 1) under 9 classifications (Table 3.1) from different articles. I propose that, given these trends are calculated from the same data using the same method, an r 2 value below 0.2 should be considered very poor agreement and 0.2-0.5 poor agreement. An r2 value between 0.5 and 0.8 would represent an acceptable level of agreement and an r 2 value greater than 0.8 would be evidence of strong agreement.

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Table 3.1: Articles included in analysis of the effect of different classification on bird trends. Europe CalviñoCancela (2013)

Gregory et al. (2007)

Guilherme & Miguel Pereira (2013)

Mimet et al. (2014)

EBCC (2014)

Spain

Europe

Portugal

France

Europe

No. woodland specialists

19

33

16

9

33

No. farmland specialists

7

19

10

13

37

No. generalists

19

21

10

14

NA

Source

Geographic extent

Australia Barrett et al. (1994)

Haslem & Bennett (2008)

Radford, Bennett & Cheers (2005)

Silcocks et al. (2005)

NSW

Victoria

Victoria

Australia

No. woodland specialists

75

56

77

206

No. farmland specialists

16

17

24

64

No. generalists

17

17

34

54

Source Geographic extent

The data also allowed me to determine the slope of trends in these groups through time by using Ordinary Least Squares regression on the abundance/reporting rate indices. This yielded the average yearly change in abundance/reporting rate indices under each studied classification of farmland, generalist and woodland birds in Australia and Europe (Figure 3.4). To demonstrate the full possible range of variability in these indices I also calculated the multiplicative trend index for each bird group by sub setting the species which had been classified into each bird group to only include i) the ten most steeply declining species and ii) the ten most steeply increasing species, to provided minimum and maximum conceivable trends for these groups (Figure 3.4).

3.4. Results 3.4.1. Classification inconsistencies Figure 3.2. illustrates the proportion of studies in which birds are classified as woodland specialists as opposed to farmland specialists, against the proportion of studies in which species are classified as a generalist as opposed to a (either a farmland or woodland) specialist species

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(see Appendix 3.1 for species list and classification proportions). Australian birds are classified less consistently on the farmland-woodland axis, with birds spanning the full range of values from consistently classed as farmland specialist to consistently classed as woodland specialist species (Figure 3.2.a). In Europe, classification of birds as farmland or woodland specialists is more consistent, with no species classified in between ¼ and ⅔ of studies as farmland specialists (Figure 3.2.b). The number of species assigned to each category in more than 50% of studies varied substantially between the two regions. The majority of Australian researchers classified 24 of the 112 birds (21%) as farmland specialists and 86 (77%) as woodland specialists. Of these birds 22 (20%) were more likely to be considered generalist than specialist species. In comparison, the majority of European researchers classified 36 of 71 (51%) birds as farmland specialists and 35 (49%) as woodland specialists while 24 (34%) species were more likely to be considered generalist than specialist species.

Figure 3.2.: Classification consistency of species where the y axis shows the proportion of studies (n ranges from 3 to 26) in which a species is regarded as a generalist as opposed to a specialist (in either woodland or farmland habitats) and the x axis shows the proportion of studies in which the same species are regarded as a farmland specialist as opposed to a woodland specialist: a) shows 112 Australian species, b) shows 71 European species. Each point represents one species, though some species overlap. Lines show second order polynomial relationships between the two variables.

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It was initially expected that species that were less consistently classified on the farmlandwoodland specialist axis would be more likely to be regarded as generalist species, but evidence for a second order polynomial relationship, as displayed in Figure 3.2., is not compelling (r 2 values of 0.1 and 0.3, for Australian and European birds respectively). It appears that, particularly in the case of Australian species, the proportion of studies in which a species is classified as a farmland or woodland specialist is unrelated to the proportion of studies in which it is regarded as a generalist. 3.4.2. Impact of classification inconsistencies on bird trends Although fluctuating through time, the trends in the European farmland specialist birds are relatively stable between 1998 and 2012, regardless of which article’s classification was used to delineate the group (Figure 3.3.a). Indices of trends in European farmland birds were poorly correlated under some classifications and strongly correlated under others with r 2 values ranging from 0.36 (between articles by Guillherme and Miguel Pereira 2013 and Mimet et al. 2014) and 0.92 (between articles by Gregory et al. 2007 and Mimet et al. 2014). By contrast, there was a big difference in the trends in Australian farmland specialist birds depending on classification, with the classification from the report by Silcocks et al 2005 showing a steep increase in farmland specialist prevalence compared to the other three classifications (Figure 3.3.b). The trend index from the Silcocks et al 2005 classification was not strongly correlated with the other trends with r2 values ranging from 0.73 to 0.84. However, the other trends were well correlated with r2 values ranging from 0.82 to 0.91.

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a

b

c

d

e

f

Figure 3.3: Trends in indices of species groups from 1998 to 2012 for a) European farmland birds, b) Australian farmland birds, c) European generalist birds, d) Australian generalist birds, e) European woodland birds, and f) Australian woodland birds. Lines represent trends in indices obtained using species lists from five European articles and four Australian articles.

European generalist species show a steady increase in index value (of abundance) except when using the classification from the article by Calvino-Cancela (2013), when their trend is fairly stable through time. The index from Calvino-Cancela (2013) is poorly correlated with the other indices with r2 values ranging from -0.18 to 0.17, and the other trends have correlation coefficients ranging from 0.72 to 0.91. Australian generalist species showed greater differences, with the classification from the article by Silcocks et al. (2005) again showing a greater increase than the other classifications (r 2 values for correlations with other articles ranging from 0.41 to 0.78), and classifications from articles by Barrett et al. (1994) and Haslem and Bennett (2008) revealing the possibility of a

39

decline in index value (of reporting rate) (Figure 3.3.d). Trends from classifications by Barrett et al. (1994), Haslem and Bennett (2008) and Radford, Bennett and Cheers (2005) were well correlated with r2 values ranging from 0.78 to 0.93. There is a general trend towards an increase in European woodland specialist birds although this is less clear under the classifications from Gregory et al (2007) and Mimet et al. (2014) (Figure 3.3.e). Trends calculated from Gregory et al (2007) correlate poorly with those from Guilherme and Miguel Pereira (2013) and Mimet et al (2014) (r 2 values 0.31 and 0.34) but the other trends are better correlated (r2 values from 0.68 to 0.98). The index values for Australian woodland specialist birds fluctuate so widely (Figure 3.3.f) that it is difficult to discern the downward trend (Figure 3.4). Nevertheless, indices from all articles’ classifications are reasonably well correlated, with r 2 values ranging from 0.81 to 0.94. 3.4.3. Differences in index values The EBCC, among other organisations, present a single value for the trend in bird groups through time. Figure 3.4. shows the variation in these values that is achieved using the classifications from articles studied above, as well as the maximum conceivable variation achieved by alternately calculating trends in declining and increasing woodland and farmland specialist, and generalist birds.

Figure 3.4.: Yearly multiplicative trend (EBCC 2001) for European and Australian farmland, generalist and woodland birds. Black points and error bars represent the mean and range achieved using the data assessed above.

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Grey points and error bars represent the median minimum and maximum achieved when alternately considering the 10 most declining or 10 most increasing species conceivable from the complete dataset.

Figure 3.4. demonstrates the high uncertainty that differences in classification bring to these indices of bird trends. Based on published classifications, Australian generalist and farmland specialist birds and European generalist birds may be increasing or decreasing over time. The direction of trends (increase or decrease) for Australian woodland and European farmland and woodland specialists was robust to differences between these classifications, with indices from all articles showing a decrease in Australian woodland specialist birds and European farmland specialist birds and increases in European woodland specialist birds. However, when considering the maximum conceivable variation (grey error bars in Figure 3.4) it is evident that all of these bird groups may be considered to be either increasing or decreasing depending on which species are included in the index.

3.5. Discussion This study highlights the global tendency of researchers to classify bird groups associated with the same habitats differently, even when there are organisationally endorsed indices available (see Gregory & Strien, 2010; EBCC, 2014). I found that classifying woodland, farmland and generalist birds in different ways substantially changed the trends in the indices of these groups. This has profound implications for the research and conservation of these bird groups. Many studies build on existing research into farmland, woodland and generalist species. However, my research suggests that the results are likely to differ from study to study purely because the authors do not classify the same species as being part of the same group. This is detrimental to all types of ecological research but makes it particularly difficult to conduct structured comparisons between articles using meta-analyses or systematic reviews. Unless each article classifies the species within the group of interest identically, it is impossible to know whether differences between studies are ecologically important or due to inconsistent classification. This inconsistent classification is likely perpetuated because there is currently no standard protocol to help decide which published classification to use over another. As a consequence it is not uncommon to see authors use the classification that best fit the objective of the their own study (Fraser et al. 2015). If there is no clear ‘best’ classification and evidence from a suite of studies variously report that a bird group is declining, increasing or remaining stable, it will be more difficult to justify, find funding for and implement conservation actions. Further, evidence suggests that the way bird groups respond to habitat variables such as fragmentation also varies

41

according to their classification (Fraser et al. 2015). Therefore, bird groups may respond differently to management interventions depending on how they have been classified. My study showed that, depending on which classification is used, trends in farmland, generalist and woodland birds vary substantially. The direction of trends in Australian woodland and European farmland and woodland birds remains the same regardless of which published classification is used but the slope of the trend varies. If you consider the trends found when, either by chance or design, only the species considered are those which are increasing or only those which are declining (Figure 3.4) the variation is even more pronounced. For example, depending on whether the declining or increasing species are included in the trend index, Australian woodland birds may be decreasing at a rate of -4.1% or increasing at a rate of 10.9% per year. Although my evidence shows that, depending on which conceptualisations of ‘woodland’ and ‘farmland’ specialist, and ‘generalist’ birds a study is using, indices may suggest either a decline or increase in population (grey error bars in Figure 3.4). My work supports proposed declines in Australian woodland birds (Ford 2011a, Watson 2011) and European farmland birds (Butler et al. 2010, Sanderson et al. 2013), based on the published classifications I studied. I found evidence of an increase in European woodland birds, which contradicts the literature expectation of a decline in these groups (Gregory et al. 2007a, Gil-Tena et al. 2009). However, given the variability in trends using different published classifications, it is not possible to conclude with certainty whether Australian farmland and generalist birds or European generalist birds are declining, increasing or stable. The results provide no strong evidence regarding the hypothesised global increase in generalist species; depending on how they are classified their populations may be increasing, decreasing or remaining stable through time. My findings show that researchers are classifying species inconsistently in both Europe and Australia. However, European researchers classify woodland and farmland species more consistently than Australian researchers, and this is likely to be (in part) related to the existence or organisationally supported indices for these bird groups. For instance, European organizations such as the British Trust for Ornithology (DEFRA 2014) and the European Bird Census Council (EBCC 2014) have indices that give researchers guidance about how to define and classify these groups. The difference in consistency between the regions could also be related to the agricultural histories in the two areas. In Europe, farmland has been around for over 7000 years which means that birds have evolved to inhabit these areas, the vegetation that

42

consititues farmland is more homogeneous, and that people may have a more stable concept of what a farmland is as compared with a woodland (Martin et al. 2012). In contrast, agriculture in Australia is recent and some of the land that is now farmland still maintains some elements of the original woodland vegetation (e.g. scattered trees, native grass species etc.). Therefore, woodland birds in Australia are more likely to use farmland habitats than they might be in Europe, making the distinction between the two groups understandably less certain. This study demonstrates that regardless of Europe’s longer history of agriculture potentially allowing for improved classification consistency, the existence of classification guidelines does not completely eliminate inconsistencies, as studies continue to classify species idiosyncratically. Comparatively, Australia is lacking widely available indices and guidelines for studying farmland and woodland bird groups. A few attempts at producing authoritative classifications of woodland and farmland species have been made (Silcocks et al., 2005; DEPI, 2013) but these have not yet been widely accessed and applied; the report by Silcocks et al. (2005) is only available by contacting the author, and the Victorian Temperate Woodland Bird Community is listed under the Flora and Fauna Guarantee Act (DEPI, 2013), but is only relevant to one state of Australia and is limited to a small set of species. Unlike farmland and woodland bird groups, there is no authorized list of generalist species in either Europe or Australia. Researchers use a range of methods to determine whether a species is a specialist or generalist. These approaches combine expert opinion and data on the occurrence or abundance of species and may calculate habitat specialization by evaluating the relative occurrence or abundance of a species in different habitat types (Julliard et al. 2006, Devictor et al. 2008). However, these studies often only consider a limited selection of habitat types, and consider birds that do not depend on any of these habitats to be generalist species. Therefore, specialists in unstudied habitats may be regarded as generalists by default. For example, a study that considers farmland and edge birds may call those which are equally prevalent in edge and farmland habitats generalists, but these birds may not have been called generalist species if the study considered farmland, edge and woodland habitats. Given the different methods of calculating habitat specialization and the lack of an authorized list, the inconsistent classification of generalist species is understandable. The confusion around the classification of generalist, woodland and farmland birds leads to conflicting results and conclusions, as evidenced by the lack of robust trends for some groups

43

found in this study. This has the potential to lead to spurious conclusions about the correct way to implement actions to conserve these bird groups. My research suggests that having available, organisationally endorsed, bird prevalence indices (as is the case for woodland and farmland birds in Europe) might improve the shared understanding of the species belonging to these groups. However, even in Europe woodland and farmland birds are still being classified inconsistently. This may be due to the existence of multiple endorsed farmland and woodland bird indices (Gregory et al. 2005, DEFRA 2014, EBCC 2014) or may reflect researchers’ unwillingness to use standard indices. I hope that, by providing evidence that it directly affects findings and inhibits the generalizability of studies, this study will increase researcher willingness to build consensus around standard indices. My methods are sufficient to demonstrate the amount of inconsistency in the classification of farmland, generalist and woodland birds and the impact of this inconsistency on inference about trends in bird groups. But the multiplicative trend index is more suitable to the European than the Australian data. The European data is collected at standard sites at the same time each year and only relatively common species are included, reducing the between-year variation in species abundance in the dataset. In contrast, the Australian reporting rate data is collected on all species by citizen scientists at sites of their choosing which vary year to year. As a result, there is an elevated probability of achieving a zero value in the Australian data particularly for species that are rare or occur in poorly sampled areas. These zero values can strongly influence the index because each species is weighted equally and a zero value means 100% decrease. To overcome this, I have removed all of the Australian species which have a reporting rate of zero in any year. I acknowledge that by doing so I am preferentially excluding species which are rare or range restricted and may be of particular interest when constructing an index of bird trends. It should be noted that ‘farmland specialist’, ‘woodland specialist’ and ‘generalist’ birds represent very simplistic categories; and classifying birds like this reduces my ability to make relevant inference on how species depend on subtle habitat variables. However, this approximation is commonly used in research and conservation management. In this context, my results provide a useful insight into how sensitive findings about these bird groups may be to how they are classified. I provide a number of recommendations that may allow these groups to be classified more consistently and minimize the chance of obtaining inconsistent results.

44

Firstly, I propose that researchers systematically present species lists and group classifications to increase the falsifiability of any statement inferred from their analyses. Next, I propose that researchers view the degree to which species are generalists or specialists as a life history trait (similar to dispersal ability), rather than as a classification for species which do not fit into a pre-defined category. Agreed upon lists of generalist bird species could be developed for Europe and Australia in collaboration with well-regarded ornithological organizations such as Birdlife or the European Bird Census Council. I recommend that research organizations in Europe examine the need for classifying farmland and woodland birds differently in their indices and look toward developing a shared understanding and shared index for these bird groups. In some circumstances, it may be justifiable to classify indices differently, especially if there are regional differences in habitat availability or the occurrence of birds. For instance, some species may use a wider variety of habitats in the centre of their range than at the range edges, due to greater habitat availability or increased competition. However, this should be objectively evaluated and the indices should be accompanied by advice regarding when one would be superior to another. It may also be meaningful to include information on a regional index as well as one which is designed to be used consistently across regions. This would balance the generalisability of the information against regional specificity. In cases where differences in classification are largely due to diverse opinions of experts, I propose unifying behind a single index (and classification). This may involve discussion of how life history traits factor in to determining whether species are farmland or woodland specialists or generalists as well as the implementation of more nuanced categories (e.g. farmland birds may be broken into field and hedgerow categories to better discern the effects of changes in farmland management). Unifying bird indices or clearly stating why bird indices differ would hopefully increase the consistency with which European researchers classify farmland and woodland birds. Australian researchers should develop a standard list of woodland and farmland birds that is easily available and endorsed by an organization such as BirdLife Australia. There are a number of strategies for developing indicators of bird groups, based on expert opinion (Gregory and Strien 2010), subjective measures of resource requirements (Butler et al. 2012), or objective measures such as relative habitat use (Larsen et al. 2011). Any of these methods could be applied to the problem of delineating these groups provided that they are

45

representative, falsifiable, sensitive to changes over a short timeframe (Gregory and Strien 2010) and supported by the research and conservation community. This last criterion is crucial and undervalued, and without it another under-used indicator is added to the field. It is also very difficult to achieve. I propose that an approach which involves the participation of stakeholders and researchers is more likely to be supported (and used) by the research and conservation community. Participation in this kind of decision process increases transparency and benefits from a diverse range of experience and perspectives as well as increasing the trust in and ownership over the final index (Reed 2008). This study is the first to demonstrate the influence of inconsistent classification on trends in indices of biodiversity at an international scale. I have shown that the fluctuations and trends in indices of bird groups in Europe and Australia differ substantially depending on which published classification was used to determine the species included in each group, with different classifications of the same groups sometimes finding opposite trends. I suggest that, where possible, researchers and institutions unify behind a single classification and index of woodland, farmland and generalist birds for each region.

46

Chapter 4 Is there an ecological basis for the inconsistent classification of woodland birds?

47

4.1. Abstract Many terms are used inconsistently in ecology and the need to develop more consistent definitions for these terms has been debated in recent years. Literature from linguistics and philosophy of science suggests that flexible definition of terms is necessary to allow researchers to persue a broad range of research questions. Research described in this thesis has shown that there are inconsistencies in how researchers define woodland birds that have implications for the generalisability of the results. At the same time, many researchers believe that using a standardized list would make their research harder. Australian bird researchers report that they classify woodland birds differently according to their research question. However, unless they are doig so consistently classifying species differently in different studies is likely to impede scientific progress. Therefore, in this chapter I investigate whether the apparent inconsistency in woodland bird classification can be attributed to differences in the objectives and spatial context of studies and the strategies used to classify woodland- vs other- birds. My research indicates that there is a systematic difference in the way bird are classifed based on where the study took place. Some study objectives and classification strategies also result in systematic differences in classification but the evidence is equivocal.

48

4.2. Introduction Terminology is used inconsistently in ecology and the necessity of developing a standardised terminology has been discussed in recent years (Herrando-Perez et al. 2014a, 2014c, Hodges 2014). A body of literature from linguistics and philosophy of science supports a belief that flexibility in the definition of terms is necessary and beneficial (Aitchison 1981, Hodges 2008). The theory behind this is that having a specific and inflexible definition or classification scheme precludes certain important lines of enquiry (Hodges 2008). In contrast, terminologists suggest that consistently defining and classifying key concepts is important to the progress of science because it allows for future researchers to build on existing knowledge (Cabré Castellví 2003, L’Homme et al. 2003, Pecman 2014). Both sides of the argument have merits in ecology. Concern about the possible implications of using ecological terms inconsistently has persisted since 1931 when the Ecological Society of Australia formed the Committee on Ecological Nomenclature to promote suitable and consistent use of terminology (Hanson et al. 1931, McGinnies et al. 1931). Since then, a multitude of ecologists have called for more consistent use of key terms such as ‘environment’ (Mason and Langenheim 1957), ‘species diversity’ (Peet 1974) and ‘habitat’ (Hall et al. 1997). More recently, Jax (2006) drew attention to the tendency for ecologists to group things into ecological units such as populations or communities. These units tend to be inconsistently defined, and ecologists often use several terms for the same unit or the same term for several non-identical units (Jax 2006). Recent research on the ecological unit ‘woodland birds’ has analysed this inconsistency further, finding substantial inconsistencies in the way ‘woodland birds’ are defined and which species comprise the group across both Europe and Australia (Fraser et al. 2015, 2017). This inconsistency is problematic because it can influence research findings even in cases where the hypotheses, data and analyses are identical (Fraser et al. 2015, 2017). Yet, despite this, the majority of ecologists studying woodland birds believe that using a standardized list would be detrimental to their research (Fraser et al. 2015). Australian ecologists report that they feel justified in classifying woodland birds differently in order to fit the context of their study. Specifically, they report that it is reasonable to classify woodland birds differently according to the objectives of the study, where it is located and the strategy used to classify woodlandversus other- birds (Fraser et al. 2015). While it may be justifiable to classify woodland birds differently depending on study context, unless these differences are systematic (e.g. everyone studying woodland birds in a particular region includes and excludes the same species), this

49

can make it hard to compare or build upon results from multiple studies. Here, I aim to investigate whether the apparent inconsistency in woodland bird classification (Fraser et al. 2015, 2017) can be attributed to differences in the objectives and spatial context of studies and the strategies used to classify woodland- vs other- birds.

4.3. Methods My analysis comprised three distinct components. First, I systematically reviewed literature from Australia and Europe to collate as many examples of woodland bird classification as possible (see chapters 2 and 3). Next, I grouped studies according to their attributes (objectives, spatial contexts and strategies of classifying woodland birds) with a thematic analysis. Finally, I conducted multispecies, multivariate occurrence analyses (Wang et al. 2012) to analyse whether the likelihood of bird species being included in a study’s classification of woodland birds depended on the study objectives, spatial contexts or classification strategies. 4.3.1. Systematic Review The classification of woodland birds is a case study of a broader terminological discussion within ecology. The usage of the term is widespread in both Australia and Europe, providing the opportunity to study trans-continental similarities and differences in the correlates and magnitude of terminological consistency. Previous research suggest that European ecologists are more consistent than Australian ecologists when classifying woodland birds (Fraser et al. 2017); here I investigate whether this is because European ecologists do not change their classification according to study attributes, or because they change their classifications to match their study context more consistently than Australian ecologists. Therefore, the results from two systematic reviews were combined for this study; one focused on forest/woodland, farmland and generalist bird species in Europe and Australia (Fraser et al. 2017; chapter 3), the other was used to augment this data with more information from Australia (Fraser et al. 2015; chapter 2). Both of these searches were updated in 2016 to account for new publications. These systematic reviews excluded studies that considered other regions (outside Australia and Europe) or taxonomic groups, or focussed on small numbers of indicator species. Studies that did not provide a full list of their study species were also excluded. For this study I analysed all articles that classified woodland birds. Studies considering ‘forest birds’ were also included in the European dataset because the terms ‘woodland bird’ and ‘forest bird’ are used interchangeably across the continent. In contrast, in Australia most studies specifically consider woodland birds and there is often an overt distinction between woodland

50

and forest birds. The systematic review yielded 75 studies from Europe and 83 studies from Australia. 4.3.2. Thematic analysis of articles I thoroughly reviewed all articles collected in the systematic review and, for both European and Australian sets, devised classes to represent common attributes of study context: the term used (i.e. forest or woodland bird), objectives, spatial context and strategy used to classify woodlandvs other- birds (Table 4.1).

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Table 4.1: Classes chosen to represent Australian and European woodland bird study attributes.

Classification Strategy2 Spatial context

Study Attribute

Objectivess1

Term

Australia

Europe

Class

Number of articles

Woodland

83

Woodland

29

Forest Investigate response of birds to components of the vegetation Investigate response of birds to habitat fragmentation Describe the assemblage of the bird community Investigate the impact of restoration Investigate trends through time Use woodland birds as a methodological case study Expert opinion

0

45

4

Forest Investigate response of birds to components of the vegetation Investigate response of birds to habitat fragmentation Investigate response of birds to agriculture Investigate response of birds to forestry Investigate trends through time Use woodland birds as a methodological case study Expert opinion

Occurrence

4

Occurrence

16

Existing classifications

30

Existing classifications

34

Traits All species seen in woodlands Exclusion criteria

8

Traits

23

4

All species seen in woodlands

9

23

Exclusion criteria

9

Metric

9

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North (Finland, Russia etc.)

11

9

Central etc.)

23

3

South (Spain, Italy etc.)

20

5

United Kingdom Cross-regional (more than one of the above)

17

South East (Victoria, New South Wales, Australian Capital Territory) North (Queensland, Northern Territory and northern Western Australia) West (southern Western Australia) South Australia Cross-regional (more than one of the above)

31 29 25 9 10 8

7

Number of articles

Class

(Germany,

France

23 19 11 10 14 7 7

4

1

Vegetation components refers to site-scale aspects of the vegetation such as shrub density, a methodological case study might investigate a model for determining dispersal distance using convenient woodland bird data (e.g. Garrard et al. 2012). 2

Existing classification is where woodland birds are classified according to an existing, published classification, studies using exclusion criteria might exclude species known to be water birds etc. from the woodland bird group; studies using a metric to classify use a mathematical construct often based on several lines of evidence (e.g. Renwick et al. 2012)

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Each article was then grouped into classes for each of these attributes by myself and another independent assessor. The classes for objectives, classification strategy and whether the study included forest or woodland birds were binary variables, and spatial context was a categorical variable. Often studies stated several objectives or classification strategies that used multiple classes listed above. For example, Bennett & Ford (1997) examined bird responses to vegetation components and habitat fragmentation. Where multiple classes were relevant to a single article, all relevant classes were recorded. Where assessors disagreed on the classification of studies, they conferred and came to a consensus based on their shared understanding of the paper (see Higgins and Green 2011). 4.3.3. Multispecies analysis using the mvabund package I conducted a multispecies, multivariate occurrence analysis using the mvabund package (Wang et al. 2012) in R (R Core Development Team 2017). The mvabund package allows quantitative hypothesis testing using multivariate abundance or occurrence data. For example, it might be used to analyse the environmental correlates of changes in plant community assemblage instead of using ordination. Essentially it does this by running many simultaneous generalised linear models (GLMs) and controlling for the possibility of inflated p-values, resulting in an overall type I error rate of 5%. My data most closely resemble multi-species occurrence data; however, I was interested in whether the species were present (occurring in the list of woodland birds) or absent (occurring in the list of non-woodland birds) in each study rather than in a traditional survey site. Therefore, instead of testing for the effect of environmental context, I was interested in the study attributes; specifically the objectives, classification strategy and spatial context of the study and, in the case of European studies, whether the study considered ‘forest birds’ or ‘woodland birds’. Unlike traditional occurrence data where species are recorded as either present or absent at a site, I have three possible states for each species in each study, species occurring in a study’s list of woodland birds were coded as 1, species occurring in a study’s list of non-woodland birds were coded as 0, and species that were not mentioned in a study received an NA value. It was important to retain these NA values instead of assigning a 0 value in this case because if a species was not included in a study’s list seen during a study it is uncertain whether or not that species would have been considered a woodland bird in that study had it been present. Unfortunately, this meant that the majority (78%) of the data comprised NA values, which interfered with the functioning of the mvabund package. Therefore, I excluded species that had fewer than 10 non-NA records and used a modified version of the manyany

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function in the mvabund package to determine whether study attributes (woodland vs forest bird terminology, objectives, spatial context, and classification strategy) related to systematic differences in the probability of species occurring in a study’s classification of woodland (see Appendix 4.1 for model code). Using the Australian dataset, I tested four hypotheses to explain the composition of woodland bird species lists. Namely, that the species considered woodland birds by a particular study would depend on study attributes: 1) study objectives, classification strategies and spatial contexts (i.e. the full model in Table 4.2); 2) study objectives; 3) classification strategies; or 4) spatial context. For example, to test Hypothesis 2, I investigated the relationship between occurrence and the attribute ‘classification of species’ as a function of the classes of this attribute (whether the study was classed as a methodological case study, as linking bird presence to vegetation components, fragmentation, restoration, or investigating population trends or community assemblage). Using the European dataset, I also tested a fifth hypothesis; that there was a systematic difference in the bird lists according to whether they referred to ‘forest birds’ or ‘woodland birds’. GLM Equations Full model Australia: C ~ CS + VC + F + PT + CA + Res + EO + O + EC + T + Cr + EW + R Europe: C ~ CS + VC + F + Ag + Fo + PT + ND + EO + O + EC + T + Cr + EW + M + R + WF

Study Objectives Model Australia: C ~ CS + VC + F + PT + CA + Res Europe: C ~ CS + VC + F + Ag + Fo + PT + ND

Classification Strategy Model Australia: C ~ EO + O + EC + T + Cr + EW Europe: C ~ EO + O + EC + T + Cr + EW + M

Spatial Context Model Australia and Europe C ~ R

Forest / Woodland model Europe: C ~ WF

Where Ag = Agriculture, C = Classification, CA = Community Assemblage, Cr = Exclusion Criteria, CS = Case Study, EC = Existing Classification, EO = Expert Opinion, EW = Everything seen in Woodlands, Fo = Forestry, F = Fragmentation, M = Metric, ND = Natural

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Disturbance, O = Occurrence, PT = Population Trends, R = Region, Res = Restoration, T = Traits, VC = Vegetation Condition, WF = Woodland or Forest. All of these variables except region are binary. Region is a categorical variable. The mvabund package provides information about the model coefficients and which relationships were statistically significant. Where I found a significant effect of one of the above hypotheses, I conducted post-hoc testing using the mvabund package to determine which classes of the attributes were driving the relationship. For example, if the model for the attribute ‘objectives’ was found to be significantly related to species occurring in lists of woodland birds, post-hoc analyses would determine whether this was driven by studies in the objective classes for investigating the response of birds to vegetation components, fragmentation or restoration etc. (Table 4.1). 4.3.4. Multispecies analysis using the glm2 package Because the modified manyany function did not provide estimates of uncertainty or explained deviance around model coefficients, I fit binomial generalised linear models (GLMs) for each species individually using the package glm2 (Marschner 2014). Although these models are able to perform a significance test, they do not control for the effect of over inflated p-values caused by conducting hypothesis tests on large numbers of species. I fit these models for each of the hypotheses about study attributes as well as for each class of aim and classification strategy (a total of 16 models for the Australian dataset and 19 for the European dataset; see Tables 4.2 and 4.3 for results from each model). I recorded the mean percentage deviance explained by these models across all species with more than 10 non-NA values (Tables 4.2 & 4.3). I also recorded the model coefficients and standard errors of significant models (according to the mvabund analysis) and, in cases where no model was significant, I recorded the coefficients and standard errors of the full model (corresponding to hypothesis 1) (see Appendices 4.1 & 4.2 for model code and Appendices 4.3 & 4.4 for full list of deviance, model coefficients and standard errors). To illustrate any significant relationships found during the mvabund analysis, I selected five species with narrow confidence intervals. This allowed me to graphically represent the kind of relationship that a significant finding represents. However, it does not present a full picture of the relationships of all species I investigated in this chapter; this can be found in Appendices 4.3 & 4.4.

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4.4. Results 4.4.1. Australian multispecies analysis 4.4.1.1. mvabund package Using the mvabund package, I was unable to reject the null hypothesis for the Australian dataset; that a study’s attributes: objectives, spatial context or classification strategies (either separately or together), had no significant influence on which species were included in a study’s list of woodland birds (p>0.05 for all hypotheses). 4.4.1.2. glm2 package The glm2 analysis indicated that the full model explained a mean of 97.8% of the deviance in the classification of birds, the majority of which came from the attribute spatial context, classification classes ‘existing classification’ and ‘everything seen in a woodland’, and aim classes ‘fragmentation’ and ‘community assemblage’ (Table 4.2).

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Table 4.2: Mean percentage deviance explained by the each of the glm2 modelled hypotheses for the Australian dataset: the full model which includes all objectives, classifications and spatial context, the classification model includes all the different classification classes, the objective model includes all the objective classes. The explained deviance for each classification and aim class is also included. Model

Mean % explained deviance

Full Model

97.8

Classification Model

62.7

classification: expert opinion

3.4

classification: occurrence

7.7

classification: existing classification

11.0

classification: traits

7.8

classification: exclusion criteria

5.6

classification: everything seen in a woodland

45.9

Objective Model

64.8

objective: case study

3.9

objective: vegetation components

2.6

objective: fragmentation

24.4

objective: population trend

3.4

objective: community assemblage

22.2

objective: restoration

5.4

Spatial Context Model

29.7

4.4.2. Europe 4.3.2.1. mvabund package The mvabund analyses showed that the classification of European birds was significantly (p<0.05) predicted by classification strategy (Hypothesis 3). Post-hoc mvabund analyses showed that the main significant relationship this represents is between classification and considering every species seen in a woodland as a woodland bird (p<0.01). 4.4.2.2. glm2 package The glm2 analysis indicated that the classification strategy explained on average 37% of the deviance around species occurrence in lists of woodland birds. The glm2 analysis showed that whether or not a study’s classification strategy was classed as ‘considering everything in a woodland a woodland bird’ explained on average 13.7% of the deviance around species occurrence in lists of woodland birds. None of the other methods of classification had a significant influence (p>0.05 according to mvabund analysis) on the assemblage of species and each individual class of objective or classification method on average only explained a small amount of variance in how woodland birds are classified (Table 4.3). Although the spatial context of the study had no significant impact on which species were included in a list of

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woodland birds (according to the mvabund analysis), spatial context explained 14.9% of the total variation in classification (Table 4.3). Table 4.3: Percentage deviance explained by the each of the glm2 modelled hypotheses for the European dataset: the full model which includes all objectives, classifications, spatial contexts and whether forest or woodland birds were being considered; the classification model includes all the different classification classes modelled for; the objective model includes all the objective classes modelled for. The explained deviance for each classification and aim class is also included.

Model

Mean % explained deviance

Full Model

81.6

Classification Model

36.7

classification: expert opinion

3.5

classification: occurrence

5.2

classification: existing classification

4.8

classification: traits

3.9

classification: exclusion criteria

3.8

classification: everything seen in woodland

13.7

classification: metric

3.6

Objective Model

37.8

objective: case study

2.4

objective: vegetation components

3.9

objective: fragmentation

6.5

objective: population trend

6.1

objective: agriculture

7.5

objective: forestry

3.7

objective: natural disturbance

3.7

Spatial Context Model

14.9

Distinction between forest and woodland birds

4.9

Each of the five illustrative species in Figure 4.2 was more or less likely to be classified as a woodland bird under different classification classes. For example, the Dunnock (Prunella modularis) was more likely to be classified as a woodland bird in studies that base their classification on exclusion criteria and/or existing classifications than in studies that used a different strategy. In contrast, the Woodlark (Lullula arborea) is most likely to be classified as a woodland bird in studies that base their classification on traits or occurrence or use a mathematical metric to classify species, and less likely to be classified as woodland in studies that use expert opinion or existing classifications.

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Figure 4.2: European model coefficients ± standard error for the influence of classification strategy (everything seen in a woodland, exclusion criteria, existing classification, expert opinion, metric, occurrence and traits) on the classification of 5 selected species of 96 total. For table of all coefficients and standard errors see Appendices 4.3 & 4.4 for full list of deviance, model coefficients and standard errors

4.5. Discussion Researchers classify woodland birds differently (Fraser et al. 2015, 2017). Australian woodland bird experts suggest that these differences are necessary because changing the classification according to study objectives, spatial context or classification strategy allows for a diverse range of research questions to be answered precisely (Fraser et al. 2015). However, this has the potential to impede the comparison and combination of study results unless the classification of species is changed systematically according to these attributes. In this study I investigated whether the species classified as woodland birds changed systematically according to study objectives, classification strategy or spatial context. I found equivocal evidence of this in both European and Australian datasets. In the Australian dataset I found no significant evidence that the classification of woodland birds was altered systematically according to study objectives, spatial contexts or classification strategies based on the mvabund analysis. However, according to the glm2 analysis, the spatial context and some classes of objective and classification strategy explained a substantial percentage of the deviance around whether the average species is classified as a woodland bird (Table 4.2). This suggests that the non-significant relationship found in the mvabund analysis might be due to the small sample size relative to the variance in whether species were considered woodland or non-woodland birds, rather than due to a lack of response. The results for the European dataset were reversed. I found a significant effect of classification strategy (mvabund analysis), but this only reflected the effect of including everything seen in a

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woodland as a woodland bird and only explained a small portion of model deviance. With the exception of spatial context (which explained 14.9% of deviance in classifications), all other attributes and their classes showed a non-significant effect (mvabund analysis) and explained very little difference in whether the average species was classified as a woodland bird (glm2 analysis; Table 4.3). In the European dataset, I included studies which classified ‘forest birds’ as well as ‘woodland birds’ so I also wanted to test whether I was right in assuming that studies of ‘forest birds’ and ‘woodland birds’ were interchangeable in a European context. As my results showed no significant effect of classifying ‘forest’ vs ‘woodland’ birds in Europe and the relationship explained little of the overall deviance, I believe that grouping the two classifications is justified. The contrasting results between the Australian and European analyses suggest two different approaches to classifying woodland birds. In Australia, woodland birds are classified inconsistently (Fraser et al. 2015, 2017) but there is some evidence that the species being classified as woodland birds may be modified systematically, at least by a subset of studies (based on glm2 analyses). Studies classifying woodland birds according to existing classifications and by including everything they see in a woodland tend to consistently include and/or exclude species from their ‘woodland bird’ classification. Similarly, the species included/excluded from a ‘woodland bird’ classification tended to differ systematically between studies in different spatial contexts and with objectives relating to habitat fragmentation or community assemblage. However, even accounting for these effects, there is still such a high degree of variance in how species are classified that no significant effects were found using the mvabund analysis. This suggests that, in Australia, researchers have several different concepts of ‘woodland birds’ in each spatial context but that none of these are applied consistently across all species in all studies. In contrast, European woodland birds are classified more consistently (Fraser et al. 2017). This is evidenced by the significant effect of classification strategy (specifically the class ‘everything seen in woodland’) even though it explained a far lower percentage of deviance in the classification of woodland birds on average than in Australian dataset (Tables 4.2, 4.3). The only other influential attribute in the European dataset was spatial context which explained a substantial percentage of model deviance in the glm2 analyses. This suggests that, in Europe, the classification of woodland birds differs by region but is being applied across situations with a greater degree of consistency.

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Some of this difference in the way Australian and European articles classify species may be attributed to the relative variabilities in their landscapes. In Europe there is a long history of agriculture and forestry which may have resulted in woodlands/forests being more homogeneous and also more distinct from other habitats such as farmland (Attwood et al. 2009). Australia has only been subject to European-style forestry and agriculture for around 200 years and there is still a substantial amount of variation in the structure and floristics of woodlands (Specht 1970). Therefore, you might expect more variation whether birds are considered dependent on woodlands in Australia than you might in Europe. Of course, in both regions, there was evidence that studies classifying ‘everything seen in a woodland’ as woodland birds more consistently classified different species as woodland birds than other classification strategies. There are advantages and disadvantages to this approach. On one hand including everything seen in a woodland is unambiguous; two different researchers using the same data and using this method would come up with identical lists of woodland birds. Other classification strategies are more ambiguous because different researchers will apply different thresholds to the classification. For example, if the classification strategy is based on traits one researcher might propose that birds must nest in woodlands 50% or more of the time to be considered a woodland bird and another may suggest that they should nest in woodland 70% of the time to qualify. When classification strategies are less ambiguous there is a higher chance that they are being used consistently by different researchers. However, classifying everything seen in a woodland as a woodland bird leads to the inclusion of opportunistic or transient species and, in the case of riparian woodlands, water birds. For example, my results show that the Fairy Martin (Petrochelidon ariel) and Eurasian Skylark (Alauda arvensis) (typically associated with open habitats) and the Eurasian Coot (Cygnus atratus) and Black Swan (Cygnus atratus) (water birds) are more likely to be considered woodland birds in studies that classify all species seen in woodlands to be woodland birds than in studies using different classification strategies. Depending on the research question it may not make sense to group such diverse species as ‘woodland birds’. Further, evidence suggests that including all birds seen in woodlands in an analysis may mask the impact of important environmental gradients (Fraser et al. 2015). In light of this, it seems unlikely that this is the optimal way of classifying woodland birds even though the birds included and excluded from the classification of woodland birds differs systematically from other methods of classification.

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Prior research suggested that researchers classify woodland birds differently depending on the study objectives (Hodges 2008, Fraser et al. 2015). Interestingly, although I found no significant effect of study objectives, some classes of study objective explained a high mean percentage deviance in the Australian dataset (based on glm2 analyses). There was no evidence of a similar effect in the European dataset. Studies with objectives relating to the composition of the woodland bird community tended to include/exclude the same species in the classification of woodland birds. The same is true for studies investigating the influence of habitat fragmentation on woodland birds. I expected studies focussing on the composition of the woodland bird community to be more selective about which species were included in woodland bird lists (i.e. excluding more of the species that are less strongly associated with woodlands). There was too much uncertainty in the model estimates to concretely evaluate this (see Appendix 4.3), but for those studies who include objectives relating to community composition, species such as the Grey Falcon (Falco hypoleucos) and Letter-winged Kite (Elanus scriptus) (which require open habitats for hunting) were less likely to be included and species such as the Rufous Treecreeper (Climacteris rufus) and Inland Thornbill (Climacteris rufus) were more likely to be. It should also be noted that, contrary to my expectations, some typically non-woodland species (like the Great Cormorant: Phalacrocorax carbo) also had high coefficients (indicating a higher likelihood of being included as a woodland bird) while some ‘woodland’ species (like the Scaly-breasted Lorikeet: Trichoglossus chlorolepidotus) had low coefficients (indicating a lower likelihood of being included as a woodland bird). Which may suggest there is some correlation between a studies choice of objectives (i.e. studies on community composition may be more likely to include all species seen in a woodland). When it comes to studies investigating habitat fragmentation, I expected species with long natal dispersal distances would be less likely to be included in a classification of woodland birds. These species are thought to respond less strongly to habitat fragmentation because they are better able to move between habitat patches and recolonise sites if they go locally extinct (Henle et al. 2004). Therefore, excluding them might allow researchers to get a better understanding of how fragmentation is influencing the more sensitive species. My results show that Australian studies with objectives related to habitat fragmentation are less likely to include the Whistling Kite (Haliastur sphenurus) and Tawny Frogmouth (Podargus strigoides) (both able to disperse long distances) than studies that are not about fragmentation. However, the Gilbert’s Whistler (Pachycephala inornata) and White-naped Honeyeater (Melithreptus lunatus) are less likely to be classified as woodland birds in studies about fragmentation and neither of those species is

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especially associated with having long dispersal distances. It is possible that these species are excluded from fragmentation studies for other reasons such as population size or annual survival, both of which influence sensitivity to fragmentation (Henle et al. 2004). The systematic difference in the species classified as woodland birds in Australian studies depending on whether the study is concerned with habitat fragmentation or with understanding the composition of the woodland bird community suggests a shared understanding between researchers about a legitimate reason for classifying woodland birds differently. However, I argue that articles excluding species from the ‘woodland bird’ category on this basis should note in their study 1) why they did this and 2) which species they included and excluded to increase the transparency and replicability of their research. It was interesting there was little evidence that other study objectives influenced the species classified as woodland birds. There are three possible reasons that study objectives were not more influential in determining which species would be considered woodland birds. First, my sample of studies was relatively small with only 75 articles from Europe and 83 from Australia, which may limit my ability to pick up subtle trends. Second, it is possible that the objective classes I used did not correspond to, or pick up the nuances in, the study objectives that dictate how species are classified. Third, researchers classify woodland birds differently depending on study objectives but each researcher does so differently. My evidence is insufficient to distinguish between these possibilities but, if the latter reason is responsible for these differences, it may not be scientifically supportable to classify species differently according to the study objectives found to be less influential in our analyses. I found no significant effect of spatial context on the classification of woodland birds in Australia or Europe. This is surprising because some species depend more or less on woodland vegetation depending on the relative habitat availability (see chapters 5 & 7). However, the lack of significant effect can most likely be attributed to sample size because spatial context explained a high mean percentage of the deviance in woodland bird classification in both Europe and Australia. The high percentage deviance explained by spatial context in both regions suggests that this is influential in deciding which species to include as woodland birds. The sample size in my study is limited to the number of studies on woodland birds that include a list of the species they classified as woodland birds. Unfortunately, a large proportion of woodland bird studies do not contain such a list (Fraser et al. 2015), limiting the sample size for this study and the power of the study to detect significant relationships. My study potentially

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lacks the power to detect significant effects of classification strategy, spatial contexts or objectives that influence a small subset of species; this is a consequence of the modelling methods used. As I was investigating such a large number of species simultaneously I used the mvabund (Wang et al. 2012) package which controls for over-inflated p-values caused by conducting large numbers of significance tests. This means I was unlikely to find a significant difference in woodland bird classification where there isn’t one (low false positive rate), but that there is an elevated likelihood of missing significant relationships (relatively high false negative rate). The purpose of this chapter was to determine whether there are systematic differences in the classification of species according to the objectives, classification strategies and spatial contexts of studies, as suggested by Australian woodland bird experts (Fraser et al. 2015). When there are systematic differences in the species classified as woodland birds in different articles, it suggests that there is a shared understanding of which species to include in these different circumstances. Therefore, classifying woodland birds differently based on region, whether all species seen in woodlands are classified as woodland bird, or (in Australia) whether the study objectives relate to investigating the community composition or the effect of fragmentation may be (at least partially) replicable and scientifically supportable. More research would be required to prove this; I failed to find significant effects of many of these attributes. Even if these study attributes and classes significantly affected species classification, replicability is limited because the reasons for species classification aren’t transparently described in articles and only account for a small percentage of the deviance in woodland bird classification (Tables 4.2, 4.3). Therefore, I propose that woodland bird researchers should investigate whether one classification method is superior to other methods in terms of the species coverage and sensitivity to change (Pearson 1994, Heink and Kowarik 2010). If there is one superior classification strategy this should be used in all studies, and if not, it is important that studies are explicit about why a specific strategy is being used to classify woodland birds.

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Chapter 5 Towards consistency: identifying an ecologically meaningful group of Australian woodland birds

65

5.1. Abstract Ecologists often classify animals into binary groupings such as woodland vs non-woodland birds. However, different studies classify these animal groups differently, which might impede progress towards understanding and conservation. This study describes and tests a method for deriving empirically-based, ecologically relevant animal groups, using a case study on Australian woodland birds. I use a Bayesian hierarchical model to understand the ecological drivers of woodland dependence in birds, assessing the bird traits associated with habitat dependence. I use this model to classify birds into intact woodland, degraded woodland, forest and open country groups. I then apply these groupings to published case studies, validating my model. Interestingly, no traits are strongly associated with species occurrence in woodland habitats, but occurrence in open habitats and forests differ depending on dispersal ability and foraging habits. My results suggest that woodland birds may be united by an inability to tolerate open or forested habitats, rather than by shared traits. I find that classifying species according to my groupings provides results consistent with literature on how woodland birds respond to clearing and grazing (Martin and McIntyre 2007) and urbanisation (Ikin et al. 2012). Despite strong sentiment in the literature that Australian woodland birds are declining, I find no convincing evidence that woodland birds fared worse than forest or open habitat groups between the early 1980s and 2000s. However, the percentage of woodland birds I found to have declined over this time period is consistent with results from Barrett et al. (2004). Validation of my model using published case studies shows strong agreement with current ecological understanding regarding woodland birds. Furthermore, by classifying species into four groups I was able to provide more nuanced understanding than is possible using the standard binary classifications. I propose that our modelling approach could be used to class animal species in other locations and taxa, providing transparent, ecologically relevant animal groupings.

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5.2. Introduction Destruction and degradation of suitable habitat is thought to threaten woodland birds worldwide (Gregory et al. 2007b, Rayner et al. 2014a). As a result, considerable resources are spent on both managing and monitoring these bird assemblages (Forestry Commission England 2009, Ingwersen and Tzaros 2011, EBCC 2014, Birdlife Australia 2015, Douglas and Fox 2015).

However, decisions about how to manage woodland birds are complicated by

conflicting evidence about the relationship between woodland birds and their habitat, and the nature and magnitude of any decline in woodland birds (Rayner et al. 2014a). For instance, there is disagreement about how Australian woodland birds respond to vegetation extent (Major et al. 2001, Mac Nally and Horrocks 2002) and fragmentation (Radford et al. 2005, Amos et al. 2013). The disagreement among these studies could be attributed to regional composition differences (Polyakov et al. 2013), or differences in temporal (Yen et al. 2011) or spatial scale (Lindenmayer et al. 2010) of sampling. However, it may also be symptomatic of underlying disagreement about exactly what constitutes a ‘woodland bird’. Fraser et al. (2017) demonstrate that inconsistency in classifying species as ‘woodland birds’ can change the direction and magnitude of relationships in indices of ‘woodland bird’ prevalence, even when the data and analyses are the same. Inconsistent classification of woodland birds persists for two main reasons. Firstly, classification of vegetation can be inconsistent (Bruelheide and Chytrý 2000, Čarni et al. 2011, de Cáceres and Wiser 2012). Bird researchers variously classify a woodland as anything with a treed overstorey, or according to different vegetation classification schemes (Fraser et al. 2015). These schemes can differ in the combination of species composition, tree height and canopy cover (e.g. Specht vegetation classes, National Vegetation Information System, Ecological Vegetation Classes) (Lewis et al. 2008). Furthermore, dedicated species lists or guidelines do not exist to assist researchers and managers when determining which species they should classify as woodland birds. Woodland birds may be variously considered as: any species that occurs in a woodland; any species that is more common in woodlands than other habitats; or, species that possess particular life history traits that mean they depend on woodlands (e.g. nesting and foraging requirements) (Fraser et al. 2015). This spectrum of classification schemes means that researchers classify woodland birds idiosyncratically, potentially impeding understanding of the ecology, status and subsequent management of woodland birds.

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Here I explore the definition of ‘woodland bird’ by examining the relationship between species traits and relative occurrence in woodlands and other habitat types. By considering traits and relative occurrence of birds I hope to identify an ecologically-founded list of bird species that depend on woodland habitats, which are likely to be negatively impacted by the destruction and degradation of woodlands in Australia (Ford 2011a, Bradshaw 2012). I use a hierarchical model to examine which traits are associated with woodland dependence, which species demonstrate a preference for woodlands, and how these change depending on where a study occurs and how ‘woodland’ vegetation is defined. I aim to understand the traits associated with woodland dependence and develop a justifiable and objective classification of woodland birds. This study has broad relevance despite the Australian case study. For example, woodland, farmland and generalist bird groups are inconsistently classified across Europe (Fraser et al. 2017). This inconsistent classification can inhibit the acquisition of important ecological knowledge by introducing uncertain terminology and precluding direct comparison between studies (Herrando-Perez et al. 2014c). The approach used in this chapter could be applied to other inconsistently classified groups worldwide to provide scientific, objective classification schemes.

5.3. Methods I compiled information on the occurrence of all Australian bird species (excluding waterbirds) and their traits, as well as the distribution of ‘woodlands’ as determined using three different definitions. I used these data to fit a hierarchical model of species occurrence (see Pollock et al. 2012) in which the relationship between species occurrence and vegetation is mediated by species traits. Based on the results of this model, I developed a classification in which species are grouped according to habitat preference. Finally, I applied my classification to three existing woodland bird ecology case studies to examine whether the inference based on my classifications was consistent with the original case studies. Each of these aspects of my methods is described below. 5.3.1. Hierarchical Models I used hierarchical generalised linear models to examine the determinants of woodland dependence in birds in four datasets; one for the whole of Australia and one for each of the three ecoregions in which woodland vegetation occurs - ecoregion 7, tropical and subtropical grasslands, savannas and shrublands; ecoregion 4, temperate broadleaf and mixed forests; and ecoregion 12, Mediterranean forests, woodlands and shrublands (Figure 5.1). These models

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suit datasets with a hierarchical structure, partitioning explained and unexplained variation between different levels of a dataset (Gelman and Hill 2007).

Figure 5.1: The distribution of the three studied Ecoregions, from the World Wildlife Fund ecoregions map (Olson et al. 2001).

I primarily aimed to investigate the woodland dependence of birds, and how the strength and nature of that dependence for each species is mediated by species’ traits (full model code available in Appendix 5.1). For each of the four datasets, I modelled the observed presence or absence of each species i (n=458, 308, 298 and 234 respectively for Australia, Ecoregion 7, Ecoregion 4 and Ecoregion 12) at each site j (n=5891, 632, 2640 and 1314 respectively) as a random sample from a Bernoulli distribution: Yi,j ~ Bernoulli(pi,j) where pi,j is the predicted probability that species i is present at site j. The logit of the predicted probability pi,j is a species-specific function of woodland dependence which is comprised of reliance on ‘woodland’ habitat (w) and reliance on tree cover (t).

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logit(pi, j)= i + xi log(wj+1)+zi log(tj+1) where i is the intercept term for each bird species i and xi and zi are the parameter estimates explaining the influence of wj (percentage woodland vegetation) and tj (percentage tree cover), respectively, on the probability of occurrence of species i. The values for wj and tj the logarithms of percentage cover values were then centred and standardised by subtracting the mean and dividing by the standard deviation. It was necessary to add 1 to wj and tj before taking the logarithms because both variables had instances of 0 values. The intercept term i was assigned an uninformed Normal prior distribution with a mean of 0 and a standard deviation of 100. Species reliance on woodland habitat, xi, was modelled as a function of eight bird trait variables thought to influence whether a species is dependent on woodlands (see Table 5.1): xi = x +  hhi +  nni + ggi + ssi +  bbi +  aai + cci + ddi + xi where x is the intercept term, h, n, g, s, b, a,  c and d are coefficients explaining the influence of each bird trait on xi (see Table 5.1 for traits), and xi accounts for extra variation between species. The  and  coefficients are all assigned uninformed Normal prior distributions with means of 0 and standard deviations of 100. xi is drawn from a Normal (0,

Zi2) distribution with Zi2 assigned a uniform prior (0,100) Species reliance on tree cover, zi, is also modelled as a function of eight bird trait variables zi = z + hhi + nni + ggi + ssi + bbi + aai + cci + ddi + zi where z is the intercept term, h, n, g, s, b, a, c, d are coefficients explaining the influence of each bird trait on zi, all assigned uninformed normal prior distributions with means of 0 and standard deviations of 100.Between-species variation zi is drawn from a Normal(0, Zi2) distribution with Zi2 assigned a uniform prior (0,100) (Table 5.1).

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Table 5.1: Definition of model terms

Variable

Symbol

Coefficient

Hollow Nesting

hi

h h

Tree Nesting

ni

n n

Ground Foraging

gi

g g

Shrub Foraging

si

s s

Bark Foraging

bi

b b

Aerial Foraging

ai

a a

Canopy Foraging

ci

c c

Natal Dispersal Distance

di

(centred and standardised)

Error term

d d

xi zi

In total, I fit six models to explore whether variation in the definition of woodland or regions would influence the traits associated with woodland dependence or the birds regarded as ‘woodland birds’. Three models use the Australia-wide dataset and investigate the impact of defining woodlands differently by considering woodlands to be: any area with a treed overstorey; areas classed as woodlands under the National Vegetation Information System (NVIS); and areas classed as eucalypt woodland under the NVIS. Operationally, this means that the values of wj are altered to reflect different definitions of woodlands in each of the three models. Another three models used the most commonly studied interpretation of woodlands

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(eucalypt woodlands) to investigate regional differences between three ecoregions: ecoregion 7, tropical and subtropical grasslands, savannas and shrublands; ecoregion 4, temperate broadleaf and mixed forests; and ecoregion 12, Mediterranean forests, woodlands and shrublands (Model code in Figure 5.1). 5.3.2. Data I obtained species occurrence data for 468 Australian bird species from the BirdLife Australia Atlas (BirdLife International and NatureServe, 2014). The BirdLife Australia Atlas comprises bird survey data collected by volunteers. Unlike many bird atlases that collect data in specified grid cells (Gibbons et al., 2007), Birdlife Australia’s atlas data is collected by volunteers at sites they choose, with exact coordinates recorded for each survey. This allows for fine resolution analyses and close links between the bird survey and landscape-scale data, but means that data are biased toward areas that are more attractive or close to major settlements. I use data gathered using 500 m2 area searches, where the volunteer records the geographical reference at the centre of their site and all birds seen or heard in their survey area, including those flying overhead. Volunteers can choose the shape of the 500m2 area they survey and how long they survey it for, provided that they survey for more than 20 minutes and less than one week. The data are validated by Birdlife, ensuring that the geographical references are sensible and that the species are within their known range. I selected a subset of the available Birdlife data, only including data from 2011 (corresponding with the year that the tree cover layer I use was developed) and excluding 10 species that were recorded fewer than ten times each (to avoid creating uninformative models). Species trait data were compiled using the Handbook of Australian, New Zealand and Antarctic Birds (HANZAB) and an unpublished traits database derived from HANZAB (Luck, unpublished data). Traits extracted for each species were: wingspan, mass, diet, feeding guild, foraging location and nest location and type. Data on wingspan, mass and diet were then used to calculate the dispersal potential of birds following Garrard et al.’s (2012) model.Vegetation data were extracted from three spatial layers using R packages ‘dismo’ (Hijmans et al. 2016) and ‘raster’ (Hijmans 2015). A tree cover layer was derived from the Global Land Cover Facility map (resolution 30 m). Vegetation type was derived from NVIS (National Vegetation Information System) version 4.1 (resolution 100m). In order to discover how robust ‘woodland dependence’ is to how ‘woodlands’ are classified, I modelled woodland dependence across the entire extent of Australia under three interpretations of ‘woodland’ (from Fraser et al 2015): i) any vegetation where the dominant stratum is trees, excluding rainforest; ii) any wooded

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vegetation with an open canopy structure (up to 30% canopy cover) (Specht 1970); and iii) eucalypt dominated vegetation with an open canopy structure (up to 30% canopy cover). To explore the relationship between bird occurrence and vegetation, I calculated the percentage of the landscape within a 500m radius of bird occurrence data points comprised of tree cover or woodland vegetation (under the three above definitions) from the vegetation layers (sensu Montague-Drake et al. 2011). The resulting dataset included species occurrence data and proportion of vegetation cover for 458 species and 5891 sites across Australia. However, bird species’ relationships to habitat may vary across regions (Polyakov et al. 2013, Fraser et al. 2015). As such, I further divided my data into three ecoregions (Olson et al. 2001) representing the areas in which the majority of woodland bird research takes place in Australia (Rayner et al. 2014; Figure 5.1): ecoregion 7, tropical and subtropical grasslands, savannas and shrublands, which included data for 308 species across 632 sites; ecoregion 4, temperate broadleaf and mixed forests, which included data for 298 species across 2640 sites; and ecoregion 12, Mediterranean forests, woodlands and shrublands, which included data for 234 species across 1314 sites. 5.3.3. Examination of Model Output I studied the model coefficients (andof all six models to determine whether dependence on woodland vegetation (xi) or tree cover (zi) are significantly related to bird trait variables. Next, I conducted a sensitivity analysis to determine how each trait impacts on bird species’ probability of occurrence, pi. To do this I calculated the mean probability of occurrence of a ‘null’ species. A ‘null’ species is one which has a median dispersal ability and does not nest in trees or tree hollows, or forage on the ground, in shrubs, on bark, in the air, or in the canopy. I then compared this to nine ‘hypothetical’ species which vary from the null species in one trait. Relative to the ‘null’ species, each hypothetical species possesses one of: high dispersal distance; low dispersal distance; nests in trees; nests in tree hollows; forages aerially; forages on the ground; forages on bark; forages in shrubs; or forages in the canopy. I calculated the percentage change in probability of occurrence of the hypothetical species compared to the null species for three vegetation types: open habitat (10% eucalypt woodland vegetation cover, 15% tree cover within a 500m radius), woodland (90% eucalypt woodland vegetation cover, 80% tree cover within a 500m radius) and forest (10% eucalypt woodland vegetation cover, 80% tree cover within a 500m radius). These vegetation types are designed to be indicative of differences between open, woodland and forest habitats, not to divite the full habitat continuum

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into three groups. Therefore, some many types of vegetation are not covered by these ‘vegetation types’. 5.3.4. Woodland bird groups The majority of bird conservation efforts are concerned with providing additional habitat for birds (Thomson et al. 2007, Lindenmayer et al. 2012). However, each species has slightly different requirements which complicates conservation decision making. Grouping species according to their habitat association may simplify this problem and allow researchers and conservationists to target habitat management to a particular group of birds. As species have varying requirements, several bird groups are necessary. Using the responses to the proportion of the habitat considered woodland vegetation based on the NVIS and tree cover from the hierarchical models described above, I delineated species into five groups according to their habitat preference (full species lists for each group in each region are provided in the Appendix 5.2): Intact or closed woodland species. The occurrence of species i is positively related to woodland vegetation cover and tree cover. These species are mathematically defined as those where the lower credible intervals of the estimated association with woodland vegetation (xi) and with tree cover (zi) are positive. In other words, for these species I am confident that their occurrence is positively related to woodland vegetation and tree cover; Degraded or open woodland species. The occurrence of species i is positively related to woodland vegetation cover, but is not demonstrably associated with increased tree cover. These species are mathematically defined as those where the lower credible interval of the estimated association with woodland vegetation (xi) is positive, but the lower credible interval for association with tree cover (zi) is negative. In other words, it is likely that their occurrence is positively related to woodland vegetation but they are not strongly related to tree cover. This group includes species with positive (but uncertain) coefficients for tree cover and species that respond negatively to tree cover; Forest species: The occurrence of species i is positively related to tree cover but is not demonstrably related to increased woodland vegetation cover. These species are mathematically defined as those where the lower credible interval of the estimated association with tree cover (zi) is positive but the lower credible interval for (xi) woodland vegetation type is negative. In other words, it is likely that their occurrence is positively related to tree cover, but they are not strongly related to woodland vegetation. This group includes species with

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positive (but uncertain) coefficients for woodland vegetation and species that respond negatively to woodland vegetation; Open habitat species: The occurrence of species i is negatively related to both woodland vegetation cover and tree cover. These species are mathematically defined as those where the upper credible intervals of the estimated association with woodland vegetation (xi) and for association with tree cover (zi) are negative. In other words, it is likely that their occurrence is negatively associated with both woodland vegetation and tree cover; and Uncertain species: based on occurrence data, species i does not fit into any of the above groupings (either both credible intervals span zero, or one credible interval spans zero and it does not qualify for the other four categories listed above). In order to validate the ecological significance of these groups, I tested them against three case studies which investigate key hypotheses relating to woodland birds: 1) that birds in groups 1 and 2 (considered woodland birds) are more abundant in woodlands than other habitat types, and less abundant in more heavily grazed landscapes (Martin and McIntyre 2007); 2) that they occur less frequently in more urbanised areas (Ikin et al. 2012); and 3) that they are declining, especially in ecoregion 4 (Barrett et al. 2004). 5.3.5. Hypothesis 1: woodland birds will be negatively impacted by clearing and livestock grazing Martin and McIntyre (2007) investigated the impact of habitat type (pasture, riparian or woodland) and grazing level (low, moderate or high) on the abundance of birds in Queensland. They found that woodlands were more species rich and had a greater abundance of birds than cleared habitats, and that the species richness and abundance of birds in treed habitat types decreased with increasing grazing intensity. They also studied the effect of habitat type and grazing level on bird communities with ordination, and on species-specific responses using Bayesian Generalised Linear Models. This showed that species respond to habitat type and grazing differently, corresponding to different bird communities in sites with woodland vs cleared habitats and high vs low levels of grazing. They included riparian habitats in their analysis but I exclude them because they are outside the scope of this paper. For cleared and woodland habitat types and low, medium and high grazing levels, I used raw data provided by the authors to estimate species richness (mean and 95% CIs) for each of the four informative bird groups (intact woodland, degraded woodland, forest and open habitat) we derived for Ecoregion 7. I expected that species richness of woodland-dependent birds (i.e. groups intact

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woodland and degraded woodland) to be higher in woodland habitats than in cleared, pasture habitats, and to be lower in areas subject to higher intensity grazing. 5.3.6. Hypothesis 2: Woodland birds will be more prevalent where there is more greenspace and tree and shrub cover Ikin et al. (2012) studied the relationship between bird richness and different elements of the urban matrix in Canberra: number of trees per hectare, number of large eucalypts per hectare, percentage of the landscape used as greenspace, greenspace patch size, number of greenspace patches, proximity to greenspaces, residential block size, number of residential blocks, and percentage cover of trees and shrubs. Using an information theoretic approach, they compared alternative hypotheses to determine which most closely matched the data. They found that woodland bird richness was most likely to be positively associated with number of large eucalypts, percentage of the landscape used for greenspace, and percentage tree and shrub cover. They found likely negative associations with number of trees per hectare, residential block size and number of residential blocks. I used their data and models to replicate this approach for each of the four informative bird groups (intact woodland, degraded woodland, forest and open habitat) I derived for Ecoregion 4 (temperate broadleaf and mixed forests). I expected to find that richness of woodland-dependent birds (i.e. groups intact woodland and degraded woodland) would relate positively to the same habitat components as in the original study of Ikin et al. (2012). 5.3.7. Hypothesis 3: Woodland birds are more likely to be declining than other species Barrett et al. (2004) reviewed changes to the distribution and number of bird species in New South Wales over the 20-year period between Birds Australia Atlases 1 and 2. Their report estimated the percentage change in reporting rate of New South Wales bird species, including a summary which reveals that 24% of woodland bird species declined between Atlases. I use these data to calculate percentage of species declining between the two Atlases for each of the four informative bird groups (intact woodland, degraded woodland, forest and open habitat) I derived for Ecoregion 4 (temperate broadleaf and mixed forests). Based on prevalent concern about a decline in woodland birds (Rayner et al. 2014a), I expect to see a greater number of declining species in intact and degraded woodland bird communities than other bird communities.

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5.4. Results 5.4.1. Hierarchical models Regardless of which definition of ‘woodland’ vegetation I used, the relationship between bird occurrence and percentage tree cover (z) is significantly more positive for tree nesting (n is positive) species and more negative for ground foraging (g is negative) and aerial foraging (a is negative) species when considering the full Australian dataset (Figure 5.2). There was also some evidence that the relationship between occurrence and tree cover was more positive for bark foraging species (b is positive) and more negative for species with higher dispersal ability (h is negative), although the 95% credible intervals for these estimates include zero. Evidence of traits influencing the relationship between bird occurrence and woodland vegetation (x) was less definitive, as indicated by mean estimates of  falling closer to zero (Figure 5.2). However, the relationship between bird occurrence and woodland vegetation is more positive for tree nesting and aerial- and canopy-foraging species. When considering ‘woodland’ to refer to all treed habitats excluding rainforests, the mean estimates for the coefficients of ‘woodland’ dependence (x) were closer to zero and had narrower confidence intervals than the estimates for the other woodland types.

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Figure 5.2: Standardised coefficients for Australia-wide models predicting the ‘woodland’ dependence of birds when considering ‘woodlands’ as: any woodland vegetation (orange), eucalypt woodland vegetation (blue), or any treed habitat type (excluding rainforest) (brown). Coefficients representing associations between woodland vegetation cover and species traits are denoted by  and those representing associations between tree cover and species traits are denoted by 

5.4.2. Regional analyses As all treed vegetation does not fit will with the concept of a botanical woodland and there was little difference in model coefficients between ‘all woodlands’ and ‘eucalypt woodlands’, and the majority of existing studies consider birds of eucalypt woodlands, I only used data from eucalypt woodlands for my regional analyses (Figure 5.3).

Figure 5.3: Standardised coefficients for models predicting eucalypt woodland dependence in Ecoregions 12 (green), 4 (blue) and 7 (red).

5.4.2.1. Ecoregion 7: Tropical and subtropical grasslands, savannas and shrublands In ecoregion 7, the relationship between bird occurrence and percentage tree cover (z) is significantly more positive for tree nesting species (n is positive) and more negative for ground foraging (g is negative) and aerial foraging (a is negative) species. The relationship between occurrence and percentage tree cover may also be more positive for bark foraging (b is positive) species but the 95% credible interval overlaps zero (Figure 5.3). The relationship between the percentage of habitat within a 500m radius that is classed as Eucalypt woodland

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(x) and bird occurrence was significantly more positive for bark foraging species (b is positive) and negatively related to dispersal ability ( d is negative). Using trait data extracted from Luck et al (unplublished dataset), it was evident that some life history traits were more influential in determining which habitat species occurred in than others. In Figure 5.4, I compare the change in a hypothetical species probability of occurrence in different habitats if they had vs did not have a particular life history trait. In ecoregion 7 (Figure 5.4a), traits have little influence on the probability of species occurring in woodland vegetation, as indicated by the squares being situated close to zero for all traits. This is also true in ecoregions 4 and 12 (Figures 5.4b and c). In contrast, some traits were influential in determining whether a species was likely to occur in other habitats, both forest and open country. In ecoregion 7, species responded similarly in woodland, open and forest habitats (Figure 5.4a) except that species are more likely to be found in forest habitats if they have high dispersal ability. These long-distance dispersers are even more likely to occur in pasture habitats along with aerial and ground foraging species. A

B

C

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Figure 5.4: Percentage change in probability of occurrence associated with a bird having a particular trait relative to a ‘null species’ in open habitat (triangle), woodland (square) and forest (circle) habitats in A) ecoregion 7, B) ecoregion 4 and C) ecoregion 12. The ‘null species’ has a median dispersal distance and does not have any of the other traits. Graphs to the left of the panel express change associated with species having high and low dispersal abilities. Graphs to the right of the panel express changes associated with foraging (e.g. F. bark) and nesting traits (e.g. N.hollow). The scale of the y axis is different for dispersal vs nesting and foraging trait graphs.

5.4.2.2. Ecoregion 4: temperate broadleaf and mixed forests In ecoregion 4 the only trait to significantly influence the relationship between occurrence and tree cover (z) was dispersal ability (d) which made the relationship more negative (Figure 5.3). The lack of other significant relationships may be attributable to high variability in the data, as indicated by wide 95% confidence intervals (Figure 5.3). Bark foraging (b) and ground foraging (g) traits may make the relationship between tree cover and occurrence more positive, though the relationships are uncertain. The relationship between occurrence and woodland habitat (x) was more positive for shrub foragers (s) and potentially ground foragers ( g), although the latter is less certain. Figure 5.4b shows that, despite these apparent differences, traits have little influence on how likely species are to occur in woodland habitats. In contrast, species with high dispersal ability (Figure 5.4b) are more likely to occur in open country sites and species with low dispersal ability are more likely to occur at forest sites. No other traits substantially influenced the probability of occurrence in any habitat. 5.4.2.3. Ecoregion 12: Mediterranean forests, woodlands and shrublands In ecoregion 12, traits had no significant impact on the relationship between bird occurrence and tree cover (z) but the relationship between occurrence and percentage woodland vegetation (x) is significantly more positive for tree nesting (  n) species and more negative for species with high dispersal ability ( d) (Figure 5.3). The canopy foraging trait ( c) may also make the relationship more positive but is uncertain. Figure 5.4c shows that, again traits have little influence on whether species are likely to occur in woodland habitats but that species with high dispersal ability are strongly more likely to occur in pastures and forests. 5.4.3. Variation in species lists For the Australia-wide dataset, habitat-dependent bird lists differed only slightly depending on the type of vegetation considered ‘woodland vegetation’. Of the 458 species included in the model, 412 were classified into the same group when considering eucalypt vs all woodland vegetation (an additional 33 species were classed as uncertain under one of these two vegetation classes). Of these species, 73 were classified differently if the model considered ‘woodlands’ to include all treed vegetation types except rainforest (see Appendix 5.2 for species lists).

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Bird lists differed substantially between ecoregions. Of the 420 species which occurred in Ecoregions 4, 7 or 12, 155 occurred in all three ecoregions and 154 only occurred in one ecoregion. Of the 266 species which occurred in more than one region, my classification scheme classed 111 species into the same group in all regions. Only one species was consistently categorised as an intact woodland bird across regions but 12 species were consistently degraded woodland birds, 23 forest birds and 12 open country birds. A total of 26 species were always classed as belonging to a woodland category (i.e. intact or degraded woodland) and only 12 species were sometimes classed as open country and sometimes in one of the forest and woodland associated groups. 5.4.4. Model Validation 5.4.5. Hypothesis 1: woodland birds will be negatively impacted by clearing and livestock grazing Few species in the lists used by Martin and McIntyre (2007) were classed as intact woodland or open country species, leaving little opportunity to detect an effect of different habitat types in these bird groups. However, there were more degraded woodland and forest birds in woodland than pasture habitats (Figure 5.5).

Figure 5.5: Mean and 95% confidence intervals of species richness by bird group in Martin and McIntyre’s (2007) dataset for pasture and woodland habitat types.

The richness of degraded woodland and forest birds was also higher at sites that were grazed less intensely (Figure 5.6). The opposite relationship was found for the richness of open country species which increased with grazing intensity.

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Figure 5.6: Mean and 95% confidence intervals of species richness by bird group in Martin and McIntyre’s (2007) dataset for different levels of grazing.

5.4.6. Hypothesis 2: Woodland birds will be more prevalent where there is more greenspace and tree and shrub cover Table 5.2 shows model outcomes when using the methodology used in Ikin et al. (2012). In order of descending frequency, models variously included the percentage cover of trees and shrubs (n=10), percentage greenspace land use (n=8), number of trees per hectare (n=8), number of large eucalypts per hectare (n=8), residential block size (n=2), number of residential blocks (n=2), proximity to greenspace (n=2) and greenspace patch size (n=1). All models for intact woodland species, three models for degraded woodland species and two models for forest species show positive relationships between richness and the percentage cover of shrubs and trees. In contrast, open habitat species responded negatively to percentage cover of shrubs and trees in two of three models. Tree density seems to have a negative effect on intact woodland, degraded woodland and forest species although density of large eucalypts has a positive effect on those groups. Intact woodland, degraded woodland and open habitat species richness also responded positively to percentage greenspace land use. However, examination of the confidence intervals surrounding model coefficients suggests substantial uncertainty around these responses, most likely due to low sample size.

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83

Model Summary

Model Terms

Greenspace patch size

Proximity of greenspace

0.02

-0.12

-0.12

0.07

Residential blocks #

0.26

-0.04

0.38 -0.05

0.30

Residential block size

% Tree and shrub cover

% Greenspace land use

0.05

0.09

0.02

0.03

0.18

0.17

-0.09

0.06

-0.08

0.03

0.04 0.07

0.11

0.12

0.12

0.12

0.12

0.24

# Large eucalypts/ha 0.11

-0.31 -0.30

-0.07

-0.07

-0.06

-0.06

-0.07

-0.07

# Trees/ha

0.10

1.12 1.12 1.12 -0.54 -0.58 -0.57 0.90

0.90

0.89

0.89

0.89

0.90

0.90

0.90

0.90

0.89

-0.86

-0.85

0.11

105 106 106 106 104 105

106

105

103

103

103

105

105

106

105

104

104

105

Residual Degrees of Freedom 106

0.09

5.52 2.12 5.00 1.97 7.44 5.43

0.46

4.15

11.35

11.48

11.57

4.65

5.47

3.46

6.88

10.48

7.68

7.01

4.92

% Deviance Explained

0.09

0.11 0.14 0.29 0.12 0.19

0.21

0.05

0.05

0.06

0.06

0.06

0.06

0.08

0.12

0.12

0.13

0.09

0.21

0.24

Akaike Weight

0.20

1.85 1.47 0 1.16 0.26

0.00

1.95

1.77

1.66

1.58

1.52

1.47

0.95

0.09

0.08

0.00

1.91

0.33

0.00

∆ AICc

-0.83

2 1 1 1

3

2

1

2

4

4

4

2

2

1

2

3

3

2

1

Number of parameters

Intercept

3 2 1

3

2

1

10

9

8

7

6

5

4

3

2

1

3

Open Habitat Species

2

Forest Species

1

Degraded Woodland Species

Model #

Intact Woodland Species

Table 5.2: Best ranked models (∆ AICc ≤ 2) for each bird group showing number of parameters, differences in Akaike Information Criterion corrected for small sample bias (AICc) compared with the model with the lowest AICc, Akaike weights, percentage deviance explained and model coefficients5.

5.4.7. Hypothesis 3: Woodland birds are more likely to be declining than other species In original analysis using their own classification of ‘woodland dependent’, Barrett et al. (2004) found that 23% of all birds and 40% of woodland birds in New South Wales declined between Birdlife Atlases 1 (1977-1981) and 2 (1998-2001) (Figure 5.7). In contrast, I find that 32% of intact woodland species and 50% of degraded woodland species showed evidence of decline in New South Wales between the two atlases. A high percentage (48%) of open habitat species had declines but not so many forest species (40%).

Percentage declined

50 40 30 20 10 0 Barrett woodland

Intact woodland

Degraded woodland

Forest

Open habitat

Figure 5.7: Percentage of species that declined between Birds Australia’s first and second atlases broken into categories based on Barrett et al.’s (2004) definition, and the four bird groups from out model analysis.

5.5. Discussion There is a pressing need to determine exactly which bird species are ‘woodland birds’ because of widespread concern over their decline, and uncertainty about how to best manage them. In this research I provide a transparent, logical and ecologically-founded method for classifying woodland birds and describe the traits associated with birds’ woodland dependence. In order to provide ecologically meaningful insights, I classified birds into five groups based on their traits and relative occurrence in different habitat types: ‘intact woodland’, ‘degraded woodland’, ‘forest’, ‘open habitat’ and ‘uncertain’ species. I found that the traits associated with woodland dependence were very similar regardless of whether ‘woodlands’ included all woodland vegetation or just eucalypt woodlands, but the relationships did not hold if I considered woodlands as ‘all treed’ vegetation. This remains true for the species classified into my five bird groups (see Appendix 5.2). The classification of woodland birds is relatively robust to the difference between all woodlands and eucalypt

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woodlands, but researchers should consider a different list of species if defining woodlands as any vegetation community with trees. The composition of the five bird groups and the traits associated with woodland dependence varied more substantially across regions, indicating that it may be meaningful to classify woodland birds differently based on broad study location. For example, the white-breasted woodswallow is classed as an open country species in south eastern (ecoregion 4) and northern (ecoregion 7) Australia but is associated with degraded woodlands in western and southern Australia (ecoregion 12), perhaps because other habitat types are too arid for their persistence (Simson and Day 1999). Interestingly, the ‘null species’ analysis, which examined the importance of different traits on species occurrence, showed that all species (species with any traits) were equally likely to occur in woodland vegetation. However, dispersal distance and, in the case of ecoregion 7, aerial and ground foraging preferences influenced the probability of birds occurring in forest and open country habitats. This could be because woodlands have a sufficiently open structure to satisfy birds that prefer open habitats as well as enough trees to provide nesting and foraging habitat for birds that rely on treed areas. The result suggests that ‘woodland birds’ may be a collection of species that are relegated to woodlands because they struggle to occur in open country or forest habitats rather than due to any specific combination of traits that determines a preference for woodlands. Dispersal ability and (aerial and ground) foraging traits had the strongest influence on the probability of bird occurrence when accounting for habitat. In ecoregion 7 (tropical and subtropical grasslands, savannas and shrublands), ground foraging and aerial foraging birds were more likely to occur in pasture habitats than species without those traits but this effect was not strongly evident in ecoregions 4 (temperate broadleaf and mixed forests) or 12 (Mediterranean forests, woodlands and shrublands). This may be due to differences in the habitat availability and climate between the relatively intact, sub-tropical ecoregion 7 and the degraded, drier ecoregions 4 and 12. The effect of dispersal ability was consistently important between regions. In ecoregions 7 (tropical and subtropical grasslands, savannas and shrublands) and 12 (Mediterranean forests, woodlands and shrublands), species were more likely to occur in open country and forest sites if they had a higher dispersal ability. In ecoregion 4, although species with high dispersal distances were still more likely to occur in open habitats, species with low dispersal abilities were more likely to be found in forest habitats. It is intuitive that species with high dispersal

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ability are more likely to occur in open habitats because, if they require trees for roosting or nesting, they are able to access them easily while also utilising open areas. The reason that species with high dispersal capabilities might be more likely to occur in forest habitats (with high tree cover and low woodland cover), as they do in ecoregions 7 and 12 is less intuitive. I attribute this to two factors. Firstly, what little forest habitat remains in ecoregions 7 and 12 is highly fragmented, so species requiring forests will need to disperse long distances to move between patches of suitable habitat (Bradshaw 2012). Secondly, very few bird records from these regions come from within forest habitats, meaning that the model is predicting outside of the majority of the training data, where it is more uncertain. Despite this confusing artefact, my model allowed me to group species as ‘intact woodland’, ‘degraded woodland’, ‘forest’ and ‘open habitat’ species and I found that, using these groups, my results emulated the current understanding of woodland birds and provided further insights into some key hypotheses about woodland, and other, bird groups. The results of the first hypothesis test are consistent with the current understanding of how woodland birds respond to clearing and grazing (Maron and Lill 2005) and provide additional insights to the work by Martin and McIntyre (2007). Had they used my four bird groups (not including ‘uncertain’ species), they would have concluded that the main difference in bird assemblage between woodland and cleared sites is that cleared sites comprised fewer degraded woodland and forest associated bird species. This analysis suggest that this is because woodlands provide habitat for a wide range of species but that species must have particular adaptive traits to persist in open habitats. The second hypothesis was upheld as the analysis showed that richness of intact- and degradedwoodland bird groups had very similar responses to urbanisation to the woodland birds investigated by Ikin et al. (2012). Yet this analysis also extended Ikin et al.’s (2012) findings. For example, they found a possible negative relationship between woodland species richness and the number and size of residential blocks but, using the bird groups identified in this study, it is clear that this relationship is relevant to degraded woodland birds only. This may suggest that intact woodland birds are very sensitive to the composition of the habitat, primarily requiring high cover of trees and shrubs to persist. Consistent with the findings of Barrett et al. (2004), I did not find convincing support for hypothesis 3, that woodland birds are more likely to be declining than other bird groups. However, by analysing the four bird groups separately, I show that a greater percentage of

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degraded woodland and open country birds are declining than any of the other bird groups. Both of these groups are associated with areas with lower levels of tree cover which may suggest that degradation of these areas through, for example, agricultural intensification is driving the majority of bird declines in the region (Attwood et al. 2009). This research provides new insights into the drivers of woodland dependence in birds as well as proposing an empirically-based and tested classification of woodland birds. The methods used here produce lists of intact woodland, degraded woodland, forest and open country species which respond to drivers in a way that is consistent with current ecological understanding. This approach also allows a more nuanced interpretation of data than would be achieved by grouping species as ‘woodland’ and ‘non-woodland’ birds, as is common practice. Globally, researchers face similar problems regarding inconsistent classification (Fraser et al. 2017) and the model presented here could be easily adapted to provide similar groupings for different taxonomic groups provided that there is sufficient occurrence data available. An important suggestion arising from this study is that woodland birds are species which are unable to tolerate open or forested habitats, rather than a group of species with shared traits. I also found that the species classified as ‘intact woodland’ or ‘degraded woodland’ birds depended on the region and whether the density of canopy cover was taken into account when determining what counts as a ‘woodland’ (i.e. ‘woodland’ NVIS categories were used rather than all those with trees). I propose that woodland bird researchers and managers may only wish to consider birds which are associated with woody vegetation with up to 30% canopy cover (Specht 1970) in order to ensure that their conclusions are representative of woodland birds. Further, I suggest that it is important to account for regional differences when studying woodland birds rather than using one classification of woodland birds for all regions. The approach introduced here could be applied to create ecologically meaningful groups of species across a wide range of biomes provided that sufficient occurrence and trait data are available. The method illustrated in this chapter would allow managers and researchers to understand the reasons that species belong to different groups as well as highlighting groups of species that are likely to respond similarly to habitat alteration or destruction. These groups may also provide a transparent, ecologically-sound basis for delineating animal communities that may then obtain a national (e.g. Australian Government EPBC Act Threatened Ecological Communities) or global (e.g. IUCN threatened ecosystems) threatened assessment.

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Chapter 6 Towards protecting the woodland bird community under the EPBC act

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6.1. Abstract Despite decades of concern over the decline of Australian woodland birds, little has been done to protect them. A number of individual species have been provided with legislative protection, but studies continue to report a decline in woodland birds, suggesting that this is insufficient to protect the community as a whole. In Australia, the Environment Protection and Biodiversity Conservation Act provides protection for threatened ecological communities but, despite widespread concern about their decline, the woodland bird community is not protected under this legislation. One of the main roadblocks to obtaining legislative protection for the woodland bird community is the lack of consensus which species it includes and how you might evaluate its condition. Here I worked with woodland bird experts to redress this problem. With the help of these experts I develop consensus around regional lists of the species that comprise the woodland bird community. Further, using their input I develop region-specific metrics for determining the condition of the community. This work represents the first steps in a process towards nominating the woodland bird community for legislative protection.

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6.2. Introduction The condition and amount of woodland habitat has drastically reduced since European settlement of Australia (Halton et al. 2011, Bradshaw 2012), with woodland birds often confined to small habitat fragments and dispersing through a resource-depleted agricultural matrix (Radford et al. 2005, Szabo et al. 2011). These fragmented habitats also attract high densities of aggressive Manorina species such as the Noisy Miner and Yellow-throated Miner, which suppress the richness of woodland bird assemblages by excluding small-bodied birds (Mac Nally et al. 2012, Robertson et al. 2013). However, the universality of a decline in woodland bird populations is equivocal, with many studies examining short term trends, and providing only weak empirical evidence of a decline (Rayner et al. 2014a). Given the reduction in suitable habitat and the Manorina threat, the woodland bird community should be declining in distribution and condition, but clear ongoing negative trends have been elusive. It is possible that some of the uncertainty around population trends for woodland birds is due to a lack of consistency in which birds are specified as ‘woodland birds’ (Fraser et al. 2015, 2017). Evidence of any trends may be clearer if researchers study a single group of woodland birds (see chapters 2 and 3) rather than developing a different list in each study. The Environment Protection and Biodiversity Conservation (EPBC) Act 1999 provides nationwide protection to Australian threatened species and threatened ecological communities. Currently, mainly plant communities are listed as threatened ecological communities and there are no vertebrate communities listed at all. This may correspond to the relative difficulty of describing vertebrate communities as compared with plant communities. The EPBC Act does protect many individual vertebrate threatened species, including a number of species of woodland bird (e.g. Swift Parrot, Regent Honeyeater and Painted Honeyeater). However, this is not sufficient to cover all of the woodland bird species that may be declining. There is potential to enact broader protection by listing a Woodland Bird Threatened Ecological Community under the EPBC Act, hopefully protecting the community from further decline. For protection under the EPBC Act, the species composition of the community needs to be defined unambiguously. Currently, the absence of an agreed definition of the woodland bird community precludes federal protection. I hope to address this shortcoming in this chapter. This chapter begins an investigation into whether the woodland bird community warrants listing as a Threatened Ecological Community under the EPBC Act. A community must meet at least one of 6 threat criteria to be listed as a Threatened Ecological Community: (1) declining

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geographic distribution; (2) small geographic distribution and evidence of an immediate threat; (3) loss or decline of functionally important species; (4) reduction in community integrity; (5) rate of continuing detrimental change; or (6) quantitative analysis showing probability of extinction. In order to assess whether a community is threatened under these criteria, indicators of the presence and condition of the community must be devised. In this chapter I will describe the first steps towards developing these indicators. The aim is to develop indicators that will: i) determine whether the woodland bird community is present at a site; and ii) describe the condition of the woodland bird community. Useful indicators will be: relevant to ecological function and community assessment; feasible to implement; responsive to changes in community composition but otherwise resistant to variation between assessments; and easily used and interpreted by researchers and managers (Jackson et al. 2000). To develop the indicators and subsequent woodland bird list, I elicited information from woodland bird experts from across Australia during a two-day workshop, and accompanying online survey, which aimed to: i) examine which species are thought to comprise the woodland bird community; and ii) develop indicators that can provide appropriate information about whether the community is present at a specific site, and what the condition of the community is in.

6.3. Methods 6.3.1. Methodological overview I conducted a two-day expert workshop on 27th-28th November 2015, in Adelaide, to determine which species to include in the woodland bird community in different regions (Figure 6.1). As in chapter 2, I considered anyone who has authored an article on woodland birds to be a woodland bird expert. I also asked these authors to recommend other woodland bird experts including people from government and non-government agencies and PhD students. Lastly, I advertised the first expert workshop at the Australasian Ornithology conference and considered anyone who responded to that invitation to be a woodland bird expert. Experts first provided data from bird surveys from woodland and other habitat types. I distinguished between the woodland bird community and other bird communities by analysing differences in bird assemblage between habitats. Feedback from experts allowed me to ensure that the resulting species lists were representative of expert knowledge. Experts also provided data from several surveys that they assigned as examples of either an ‘intact’ or ‘degraded’ woodland bird community. I used these data to develop an online survey regarding the condition of the

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woodland bird community, and this data allowed me to develop an index of woodland bird community condition. The details of the approach are outlined further below (Figure 6.1).

Figure 6.1: The methodology used in this chapter. Arrows show where data from one task was directly applied to another task

6.3.2. Workshop I invited 38 experts in Australian wooland birds to attend a workshop over two days in November 2015. I also advertised the workshop at the Australasian Ornithological Conference which was held in the week of the workshop. A total of 27 experts from universities, government and non-government agencies attended one or more workshop sessions. The aims of the workshop were to determine which species comprise the woodland bird community and to elicit expert opinions regarding the drivers of community condition. On the first day of the workshop, I had a 1-hour open discussion held during the Australasian Ornithological Conference. During this time, I aimed to elicit a basic understanding of what distinguishes intact from degraded woodland bird communities in terms of the representation and species richness of various bird trait guilds. I proposed a number of guilds: small insectivores (<50g), large insectivores (>50g), granivores, frugivores, nectarivores, carnivores (Howes et al. 2014), and generalist feeders. I asked researchers to break into small groups and discuss the suitability of these bird guilds and then report back to the larger group.

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The aim of the second day of the workshop was to form a consensus on which species should be included in the woodland bird community and, time permitting, further our understanding of the determinants and drivers of community condition. An open discussion about suitable regional divisions was the first task on day 2 of the workshop. This was followed by a session where the whole group brainstormed which species to consider woodland birds, which allowed us to resolve linguistic uncertainty surrounding exactly what was meant by ‘woodland bird’ (Fraser et al. 2015) and identify any possible regional differences in species’ dependence on woodland habitat. Experts were presented with a list of 369 eastern Australian land birds that had been found in at least 10 birdlife Australia surveys in 2011 and asked to individually fill in a spreadsheet based on the shared understanding developed in previous conversations (see Appendix 6.1). The spreadsheet asked of each bird species: “Would you expect to see this species in most woodland bird communities (degraded or intact)”, “Would you expect to only (or mostly) see this species in intact communities”, and “Would you expect to see this species more often in degraded than intact communities”. For the purposes of this task I defined an ‘intact’ woodland bird community as “a community in which most of the important woodland bird families and/or functional groups are well represented”. The intention of this was to distinguish between communities that for example, might be lacking ground foraging species which are considered an important group for woodland habitats and those only lacking in water-foraging species or similar which are seen as less important in the woodland bird community. The condition of the community would be very different if ground foragers were absent as opposed to waterforagers. While the constituent species of the community will vary geographically, an intact community will always comprise the same characteristic families or functional groups of woodland birds. A ‘Degraded’ woodland bird community was defined as a community in which certain woodland bird families or functional groups are not present, or are underrepresented in terms of number of species, because of threats associated with anthropogenic habitat modification and land use. This task ensured that all experts got an equal say in which species should be included in the community and that no species were excluded because they did not immediately come to mind. I compiled this information and reported to the group on the species that were included by 70% of more of experts under each question. Looking over the species lists, experts believed that the 70% cut off ensured a good balance between including the most woodland dependent species while excluding the species only tenuously woodland related. I then encouraged an

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open discussion about these findings and how they compared with the results of the brainstorm. Last, I asked experts to break into small groups and qualitatively describe what they expect to see in a high condition woodland bird community. 6.3.3. Which species comprise the woodland bird community? Eleven experts filled in the spreadsheet at the workshop, indicating which species they associated with the woodland bird community. These data were augmented by results from another 9 experts after the workshop. I created an Australia-wide list of woodland birds by calculating the proportion of experts associating each species with woodland bird communities and the proportion of experts considered each species to be particularly associated with intact or degraded bird communities. Based on discussions at the workshop, it was decided that 70% agreement on a species was sufficient to support including it in the woodland bird community. Using this criterion, I compiled a list of woodland birds across all of Australia, as well as lists of birds associated with ‘intact’ and ‘degraded’ woodland bird communities. Next, I split the expert responses into the three regions decided upon during the workshop (subtropical Queensland -5 responses-, temperate south-eastern mainland Australia -11 responsesand South Australia -4 responses-). I compared the proportion of experts agreeing that each species was a woodland bird between the regional lists and the original, Australia-wide list. I created regional lists of woodland birds by adapting the Australia-wide list such that if the proportion agreement in any regional list was 50% more or less than the proportion in the Australia-wide list, species were added to or removed from the regional lists. For example, if 80% of all experts agreed that the Apostlebird was a woodland bird but only 30% of subtropical Queensland experts thought it was a woodland bird, the Apostlebird would be removed from the sub-tropical Queensland species list. This minimised the possibility of including or excluding species from a regional list based on chance differences in expert responses. I excluded species from each regional list if they did not occur in that region. The same process was applied to determine which species are particularly associated with intact or degraded woodland bird communities. Finally, experts were asked to review the regional lists and comment about any counterintuitive results; the lists were adapted accordingly. The lists were circulated iteratively until no further comments were received; at this point I assumed a consensus had been reached. 6.3.4. When does a bird assemblage qualify as a woodland bird community? Experts volunteered bird survey data that they had collected in woodland, forest, heathland, and grassland vegetation types. For each survey I recorded the bird species richness and the

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proportion of species that were: woodland species, associated with intact woodlands or associated with degraded woodlands (according to the regional lists developed above) as well as each of the trait guilds identified by experts on day 1. Species could be included in several categories. I recorded the mean and 95% confidence intervals around species richness or proportions of species in each vegetation type. This allowed me to determine whether the bird assemblage in woodland vegetation was different to that in other vegetation types. 6.3.5. How to measure community condition? 6.3.5.1. Pilot data Experts provided data from surveys that they identified as representing intact and degraded woodland bird communities. As above, for each site, I calculated species richness and the proportion of species that were: woodland species, associated with intact woodlands, associated with degraded woodlands, and belonging to each of the trait guilds identified by experts on day 1. I recorded the mean and 95% confidence intervals of the proportion of species belonging to each guild in intact and degraded communities. The variables that differed substantially between ‘intact’ and ‘degraded’ communities in these pilot data were used in subsequent analyses of community condition. 6.3.5.2. Online survey As only a subset of experts was able to provide me with data, I was concerned that these results would not represent the full range of expert understanding regarding the drivers of woodland bird community condition. The majority of experts volunteering these data were from the subtropical Queensland and temperate south-eastern Australia and experts agreed that there may be regional differences in species’ association with the woodland bird community. To explicitly account for potential regional bias, I constructed an online survey to elicit expert judgement about the condition of woodland bird communities (survey questions available in Appendix 6.2). The online survey was adapted from Sinclair et al. (2015) to allow for the online format while preserving the integrity of the swing-weighting system (Von Winterfeldt and Edwards 1986). Sinclair et al. (2015) implemented an in-person protocol to elicit expert judgement about the condition of grassland vegetation. Experts were given cards representing hypothetical grassland sites with different levels of known indicators of condition (e.g. Themeda basal area, exotic annual cover etc.). The suite of sites they were presented with included both very low quality and very high quality sites to capture the full range of vegetation conditions. Experts ranked sites from lowest to highest quality, they were required to assign a value of 100 to the

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best ranked site and a value of 0 to the worst ranked site unless they could envision a higher or lower quality site - in which case they were required to describe that site and include it in their set. They then used a swing-weighting protocol to assign relative weights to the sites (Sinclair et al. 2015). In this study, I wanted to present experts with data that was analogous to what they usually work with. Therefore, instead of providing information on metrics such as the proportion of small species (analogous to a measure of Themeda basal area used by Sinclair et al. 2015), I provided species lists. These 23 lists (see Appendix 6.2) were taken from data provided by experts and chosen to cover the full range of variable combinations that were reliably different between ‘intact’ and ‘degraded’ sites in the pilot data discussed above: species richness and proportions of small, intact and degraded species. I obtained human research ethics approval and sent the online survey to 58 experts and received 29 responses; 7 from sub-tropical Queensland, 18 from temperate south-eastern Australia and 4 from South Australia. Because the woodland bird experts were distributed across Australia, I was unable to use the in-person elicitation protocol adopted by Sinclair et al. (2015) and was restricted to asking questions that fit with formats provided by the online survey site SurveyMonkey (SurveyMonkey Inc. 1999). This precluded me from requesting that experts rank all sites for a swing weighting exercise. Instead, experts were asked to assign absolute condition values to five woodland bird community calibration sites based on species lists. Condition was assigned on a scale of 0 to 100, where 0 represents the worst possible condition and 100 represents the best possible condition of the woodland bird community. These five calibration sites were chosen to include sites that I expected to be in very good or very bad condition based on their species richness and proportions of small, intact and degraded specialist species. Next, experts were asked to rate the value of a number of sites relative to each other using swing weighting (Von Winterfeldt and Edwards 1986). In four successive questions, experts were provided with a baseline site chosen because it had low species richness, proportion of small and intact specialist species and high proportions of degraded species. Experts were asked to rate how much they would prefer to switch this baseline site for each of four other sites on a scale of 0 (neutral to switching) to 100 (out of the available options this would be the best). Sites were scored a value of 50 if it was considered half as preferable to switch to that site as opposed to the best site. Experts evaluated a total of 23 sites. In each of these four questions, at least one of the sites was from the set of five calibration sites to allow the responses to be scaled.

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Using the information from the absolute rating and swing weighting tasks, I estimated the condition (0-100) of each bird community under each expert’s judgement. As each of the four swing-weighting questions included one calibration site (assigned an absolute rating), I was able to substitute this value into an equation with the relative weights of the sites to calculate the absolute condition of each site. For example, if an expert gave site 19 an absolute condition score of 80 and, during swing weighting, site 1 was valued half as much as site 19, the expert is assumed to be assign the condition at site 1 a score of 40. I took these values for expert-judged community condition and investigated how they related to the variables that the pilot data indicated were predictors of woodland bird community condition: species richness and the proportion of small, intact associated, and degraded associated species. I divided the expert-judged condition score by its maximum because the above calculations yielded several values over 100, the highest of which was 105. It was necessary to rescale the variables on a scale of 0 to 1 in order to construct a generalised linear model with a binomial distribution. Using the package glm2 (Marschner 2014) in R (R Core Development Team 2017), I constructed binomially distributed generalised linear models for the whole Australia-wide dataset, sub-tropical Queensland, South Australia and the temperate south-east. The dependent variable in each of these equations was the expert-judged condition score of the site (0-1). The independent variables included in this analysis were species richness, and the proportions of intact associated species, degraded associated species, and small species. I conducted backwards model selection based on Akaike Information Criterion (AIC) values (Akaike 1973) and the significance of model coefficients to select the best model for explaining woodland bird community condition. I compared the model coefficients and significance of relationships between the four models to determine whether there are regional differences in the way that experts perceive bird community condition. 6.3.6. Sensitivity Analysis I took the variables present in the lowest AIC models from the glm2 analysis described above (species richness and proportion of small species) and conducted a sensitivity analysis for the Australia-wide and regional equations; using the model coefficients to predict the condition of sites with every species richness value between 1 and 40 with different proportion small species increasing from 0 to 1 by increments of 0.1. 6.3.7. Criteria for determining woodland bird community condition I used the condition estimates from this sensitivity analysis to divide community condition into four categories: low (condition 0-35%), medium (condition 35-65%), high (condition 65-85%),

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and pristine (condition 85%+). In order to compare the regional models, I evaluated the proportion of sensitivity analysis ‘sites’ that were classed into the same condition category using the regional coefficients as using the Australia wide coefficients. I also calculated the r 2 value for the relationship between the estimates of community condition between the Australiawide model and the regional models. For the South Australian data I also compared r2 values for models including and excluding the proportion of small species. The condition categories above allowed me to develop some simple rules describing the condition of woodland bird community based on species richness and the proportion of small species. These can be used to assess the condition of the woodland bird community for the purposes of the EPBC Act listing process and for ease of assessing community condition.

6.4. Results 6.4.1. Workshop Experts deemed the guilds I proposed (small insectivores (<50g), large insectivores (>50g), granivores, frugivores, nectarivores, carnivores, and generalist feeders) to be insufficient. Consensus was that all birds should be broken into size categories (rather than just insectivores as I had proposed), hollow nesting behaviour should be included, and insectivores should be broken into further categories with particular importance placed on bark insectivores. These elements were included in analyses to determine when a bird assemblage qualifies as a woodland bird community, and in the analysis of community condition. Experts also raised the concern that defining and listing the woodland bird community over the whole of eastern mainland Australia reduces the level of protection it would receive. Clearing a single site inhabited by the woodland bird community would result in a tiny reduction in the nation-wide extent but could represent a more substantial reduction at a regional scale. According to the woodland bird experts, small proportional changes in extent or condition of a community are unlikely to receive protection under the EPBC Act because they will not be deemed to have a ‘significant impact’ on the community as a whole. Furthermore, there are substantial differences in the species pool, land-use history, and level of degradation between northern and southern regions. The northern extent of the woodland bird community is considered relatively intact because a lesser extent of the available habitat has been cleared while, in South Australia, even the best examples of the woodland bird community may be considered degraded. Experts were concerned that this might mean that important populations in southern Australia may be de-prioritised because their condition is much worse than those

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in northern Australia. As a result, the experts agreed to break the listing into three sub-regions: sub-tropical Queensland; temperate south-eastern Australia (Victoria, New South Wales and the Australian Capital Territory), and South Australia. 6.4.2. Which species comprise the woodland bird community? A full list of species comprising the woodland bird community for each region is presented in Appendix 6.3, the Australia-wide list includes all species considered woodland birds in regional lists. The woodland bird community lists for sub-tropical Queensland, includes 128 species, temperate south-eastern Australia includes 136 species, and South Australia includes 115 species. Appendix 6.3 also includes regional lists of species that are particularly associated with intact woodland bird communities and lists of species that are not part of the woodland bird community but are often present in degraded woodland bird communities. The list of nonwoodland species associated with degraded woodlands is common to all regions with the exception of species that have restricted ranges. It comprises 15 species, of which five are introduced: Rock Dove (Columba livia), Spotted Dove (Spilopelia chinensis), House Sparrow (Passer domesticus), Common Mynah (Sturnus vulgaris) and Common Starling (Sturnus vulgaris). 6.4.3. When does a bird assemblage qualify as a woodland bird community? Species richness of the woodland bird community did not differ substantially among habitat types although forest habitats appear to have a lower species richness (10.0 ± 0.2, 95% confidence interval) than grassy (11.0 ± 0.7) or woodland (12.0 ± 0.2) habitats. It was also difficult to distinguish the woodland bird community based on the proportion of intact and degraded specialist species. Heathland surveys and forest surveys had the highest proportion of species associated with intact communities at 0.47 (± 0.02) and 0.55 (± 0.01), respectively. In comparison, only 0.40 (± 0.01) of species in woodland vegetation were considered associated with intact woodland bird communities. The proportion of the community that was included on the Australia-wide list of woodland birds (Appendix 6.3) was quite different among vegetation types. The highest proportion of woodland bird species were found in forest surveys (0.77 ± 0.01). Woodland birds comprised 0.67 (± 0.01) of birds in woodland surveys, while the proportion in heathland habitat was slightly, but significantly lower (0.62 ± 0.02), and the proportion in grassy habitats was much lower again (0.27 ± 0.03).

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I was unable to distinguish the woodland bird community from bird communities associated with other vegetation types based on the representation of the different trait guilds (Figure 6.2). In surveys of grassy areas there was a higher proportion of water birds and carnivores, and a lower proportion of small birds than in woodlands. In forest surveys there was a higher proportion of hollow nesters, bark insectivores, foliage insectivores and small birds, and a lower proportion of ground insectivores than in woodlands. Forest

Grassy

Heathland

Woodland

1 0.9

Proportion of species

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

0

Figure 6.2: Mean proportion of species recorded in surveys in different habitat types belonging to various bird trait guilds. Error bars represent 95% confidence intervals.

Based on these results, the main factor distinguishing the woodland bird community from other bird communities is the percentage of species included on the list of woodland birds (Appendix 6.3). Less than 65% of species in heathland and open country bird communities belong to the woodland bird community, whereas the percentage is higher for woodland and forest bird communities. Overall, my results suggest no strong difference in the composition of forest and woodland bird communities. The logical next step is to define the woodland bird community based on its habitat association. However, experts were reluctant to define the woodland bird community based on its association with vegetation. They believed that there is only a loose relationship

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between woodland vegetation and the woodland bird community. For example, it is possible to find the woodland bird community in non-woodland habitats such as golf courses and farmlands. Furthermore, intact woodland bird communities are sometimes found at sites where the vegetation community is considered to be in poor condition because vegetation condition metrics do not match the habitat requirements of woodland birds. Despite this, experts expressed that the woodland bird community occurs in temperate and subtropical regions with low moisture, low nutrient soils. These woodlands are united by a similar open treed vegetation structure and shared threats, especially clearing and fragmentation associated with conversion to agriculture. The species associated with these habitats may differ regionally due to species’ ranges, habitat availability, and climatic differences, but the bird community will respond similarly to threats across its range. In contrast, experts expected bird communities in wetter, high nutrient areas to respond differently to these drivers as well as being more susceptible to other threats (e.g. forestry). In order to conform with Australia’s vegetation type conventions I set a threshold value for canopy cover at 70%, which relates to closed forest vegetation (Specht 1970); bird communities at sites with canopy covers exceeding 70% should not be considered woodland bird communities. Therefore, I characterise the woodland bird community as a community that occurs in vegetation with less than 70% tree canopy cover where 65% or more of species belong to the appropriate regional list of woodland birds found in Appendix 6.3. 6.4.4. How to measure community condition? 6.4.4.1. Pilot data At the workshop, experts were asked to describe an intact woodland bird community. During this exercise, they brought up a number of common themes – a high number of small bodied birds and a range of species representing the variety of ecological guilds. The presence of particular guilds such as bark insectivores, a mixture of both uncommon and common species, and the absence of competitively dominant species such as the Noisy Miner were emphasized. When examining pilot data from bird surveys identified as examples of intact and degraded woodland bird communities I found that, in all regions, intact communities had higher species richness than degraded communities (Figure 6.3a). However, species richness also differed among regions, with South Australia having a substantially lower richness in communities designated as intact than the other two regions.

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As expected, the proportion of intact associated species was lower in bird surveys where the community was deemed degraded than those where it was deemed intact by experts (Figure 6.3b). Conversely, the proportion of degraded associated species was higher where the community was deemed degraded than intact (Figure 6.3c). There was no difference in the proportion of species belonging to hollow nesting, waterbird, ground insectivore, bark insectivore, foliage insectivore, carnivore, frugivore, or nectarivore guilds between surveys deemed intact and degraded. However, in each region, there was a substantially higher proportion of small birds in surveys where the community was deemed intact than in those deemed degraded (Figure 6.3d).

Figure 6.3: Regional differences between degraded and intact woodland bird communities: a. bird species richness, b. proportion of intact associated species, c. proportion of degraded associated species, and d. proportion of small species <50g body mass. Markers give mean values and error bars give 95% confidence intervals.

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6.4.4.2. Online survey I tested 4 potential models to predict community condition: 1) intercept, species richness, proportion of small species, proportion of degraded species, and proportion of intact species, 2) intercept, species richness, proportion of small species, and proportion of degraded species, 3) intercept, species richness, and proportion of small species, and 4) intercept and species richness. Models 1 to 3 performed similarly in terms of AIC, yielding values within 2 of each other for Australia-wide, sub-tropical Queensland and the temperate south-east (Table 6.1). However, in each case only the intercept, species richness and proportion of small species were found to be significantly (p<0.05) related to expert-judged community condition. Model 4 yielded higher AIC values for all regions apart from South Australia where it was within 2 AIC of the model with the lowest AIC score. Interestingly, in South Australia, only the intercept term and species richness were found to be significantly (p<0.05) related to expertjudged community condition. Table 6.1: Generalised linear model coefficients and AIC scores, explaining expert-judged community condition for the Australia-wide dataset and South Australia, sub-tropical Queensland and temperate south-east regions*

intercept -1.73

species richness 0.10

-1.70

0.10

1.23

-2.03

0.10

1.54

-1.33

0.10

-1.81

0.12

1.00

-1.14

-1.93

0.13

0.99

-0.98

-2.50

0.12

1.57

-1.68

0.13

-1.68

0.11

1.18

-0.54

-1.64

0.11

1.21

-0.60

-1.97

0.11

1.51

-1.28

0.11

-1.74

0.09

1.26

-0.56

-1.72

0.09

1.28

-0.60

-2.02

0.09

1.56

-1.32

0.10

temperate southeast

sub-tropical Queensland

Region

South Australia Australia-wide

Model Coefficients ppn. small ppn. degraded species species 1.22 -0.59

ppn. intact species 0.05

-0.63

-0.18

0.09

0.06

634.98

explained deviance % 51.43

633.03

51.42

634.64

50.34

680.79

38.83

43.64

64.00

41.77

63.86

39.88

61.65

40.37

50.66

161.07

59.92

159.44

59.88

159.35

58.87

172.62

47.25

445.36

48.05

443.23

48.03

442.67

47.03

471.44

35.34

AIC

*Bold coefficients signify relationships that are significant at p<0.05

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6.4.5. Sensitivity Analysis When the proportion of small species is excluded from the South Australian condition model, it produces substantially different estimates of condition (Figure 6.4). When compared to the model that includes an intercept term, species richness and the proportion of small birds, the model may over-estimate the community condition by up to 22 percentage points or underestimate it by up to 18 percentage points. Furthermore, the correlation between the South Australian model and the Australia-wide model is much lower when excluding the proportion of small species (r2 values drop fro 0.99 to 0.75). In contrast, the other regional models correlate well to the Australia-wide model with r2 values of 0.99, and 1.0 for sub-tropical Queensland, South Australia and the temperate south-east respectively.

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community condition (%)

90 70

Model without ppn small birds

60

ppn small birds 0.0

80

50 ppn small birds 0.2

40 30

ppn small birds 0.8

20 10

ppn small birds 1.0

0 0

5

10

15

20

25

30

35

species richness

Figure 6.4: Sensitivity analysis of modelled South Australian community condition to whether the model includes (grey) or excludes (black) the proportion of small birds. Dashed lines represent different proportions of small birds (from 0-1).

I chose to break community condition into four categorical states: low (0 – 35% condition), medium (35 – 65%), high (65 – 85%), and pristine (85% +), which comprise roughly equal parts of the spectrum between completely degraded and completely intact condition according to the sensitivity analysis (Appendix 6.4). These discrete categories should be useful for onground assessments because they are simple and easy to apply. They will also insulate the measure against predicting differences in condition based on differences between surveyors; small differences in detection rate between experienced surveyors are unlikely to push the community into a different category.

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Modelled community condition was relatively stable across regional models that included species richness and proportion of small birds. In 90% of cases, the regional models predicted the same condition state as the Australia-wide model. Given this similarity, I used the condition equation from the Australia-wide dataset for the remainder of analyses. Woodland bird community condition was predicted by the following equation where C is community condition (scaled to between 0 and 1), R is bird species richness and S is proportion of small species. Logit(C) = -2.03 + 0.10R +1.54S 6.4.6. Assessing the Woodland Bird Threatened Ecological Community If the community obtains protection under the EPBC Act, it will be necessary for surveyors to determine whether any given site is home to the Woodland Bird Threatened Ecological Community and what condition that community is in. This can be achieved according to the two steps outlined below. First, determine whether the community qualifies as a ‘woodland bird community’. As stated above, I consider a woodland bird community (sampled using a 2ha, 20-minute bird count) to only occur in vegetation types with less than 70% tree canopy cover, have a species richness greater than 5, and more than 65% of these species must be included on the appropriate regional list of woodland birds (Appendix 6.3). If these conditions are not met, stop the assessment herethe site does not house the Woodland Bird Threatened Ecological Community. Second, determine the condition state of the community according to the data outlined in Table 6.2. For example, a site with 14 species, two of which (proportion 0.12) are small bodied, would be in low condition but the same site would be in medium condition if it had three small bodied species (proportion 0.21). Table 6.2: Criteria for determining woodland bird community condition (low, 0 – 35%; medium, 35 – 65%; high, 65 – 85%; pristine, 85% +) based on species richness and the proportion of small species

Species richness

Proportion small species 0 to 0.19

0.20 to 0.49

0.50 to 0.79

0.80 to 1

5 to 9

low

low

medium

medium

10 to 14

low

medium

medium

medium

15 to 19

medium

medium

medium

high

20 to 24

medium

medium

high

high

25 to 29

high

high

high

pristine

30 to 34

high

high

pristine

pristine

34 to 39

high

pristine

pristine

pristine

105

40 +

pristine

pristine

pristine

pristine

6.5. Discussion In this chapter I have identified and defined what constitutes a woodland bird community in eastern mainland Australia. I have also explored the need for addressing geographical variation in a community that is distributed over a continental scale. With the support of experts in Australia’s woodland birds, I have compiled three regional species lists to represent the woodland bird community in sub-tropical Queensland, temperate south-eastern Australia, and South Australia (Appendix 6.3). To complement these lists, I used a combination of data from bird surveys and expert opinion to determine what distinguishes intact and degraded woodland bird communities to develop an index of community condition. My index of woodland bird community condition (Table 6.2) meets key indicator assessment criteria for: conceptual relevance, feasibility, response variability, interpretation, and utility (Jackson et al. 2000). I ensured conceptual relevance (Jackson et al. 2000) by using a combination of expert opinion and bird survey data to consider the elements of the woodland bird community believed to be ecologically relevant and short-list those that were reliably different in intact vs degraded communities. This also improved the utility and interpretation of the indicator (Jackson et al. 2000) by simplifying it to three easily measured and understood attributes: species richness, proportion of small birds, and proportion of species specialising in degraded habitats. The attributes in my model are calibrated for use with bird data collected from 2ha, 20-minute bird surveys conducted at dawn. This survey methodology is the standard bird survey technique used by BirdLife Australia and, as it is not time or resource intensive, is feasible (Jackson et al. 2000) in an assessment context. I was able to balance the response variability (Jackson et al. 2000) of the indicator by ensuring that it was sensitive to changes in community condition and relatively insensitive to temporal changes and differences between observers. The sensitivity analysis showed that community condition scores varied with changes in species richness and proportion of small species. The index assigns a value of 18 (low condition) to a site with 5 species, none of which are small (<50 g), and a value of 86 (pristine condition) to a site with 25 species and 23 of which are small. Categorising condition values into four broad classes (low, medium, high and pristine) improves the utility and interpretation of the indicator (Jackson et al. 2000) as well as reducing the likelihood that assessments by different people or at different times will find a community

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to be in different conditions. While developing the indicator I also endeavoured to ensure that it was applicable across the geographic range of the woodland bird community. There are two main reasons to break the community down into regions. Firstly, experts suggested that some species would be more or less associated with the woodland bird community in different regions due to differences in climate and habitat availability. This is supported by results from Chapter 5 which showed that different types of species depend more on woodlands in different regions. Further, the species lists I derived in this chapter differ regionally, with some species like the Little Button-quail or Australian Ringneck only considered woodland birds in some parts of their range (Appendix 6.3) Secondly, a number of the woodland bird experts belonged to state government agencies and had been involved in assessing development sites to determine whether they met criteria for protection under the EPBC Act. Through this process, they had often been frustrated to find that a site does not trigger EPBC Act protection because the area of the site is too small relative to the area of the Threatened Ecological Community. Though greatly diminished in extent, there is still a large enough expanse of woodlands in Australia that destroying any given site would only slightly reduce the community as a whole. By dividing Australia into three geographic regions I reduce this effect, allowing greater priority to be placed on each site housing the woodland bird community in regions where the community is less widespread. With the assistance of woodland bird experts, I then developed three regional lists representing the woodland bird community. These are able to account for differences in the occurrence and habitat dependence of bird species between regions. The regions I use closely resemble the regions used in chapter 5, which were based on the World Wide Fund for Nature Ecoregions. However, the ecoregions used in chapter 5 included south-west Western Australia with South Australia and Tasmania with Victoria, the Australian Capital Territory and New South Wales. Given the prevalence of woodland vegetation in these two regions, I plan to include Tasmania and Western Australia in the EPBC Act listing process for the woodland bird community using a similar method to that outlined here. Unfortunately, when the decision was made to break the woodland bird community into sub-regions, I only had access to experts specialising in eastern mainland Australia. Therefore, my species lists and condition criteria do not consider woodland bird communities in Tasmania or Western Australia. Sensitivity analyses found only minor regional differences in condition scores, so it seems likely that my indicator of community condition will be relevant to Tasmania and Western

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Australia. However, due to differing species richness and histories of degradation it will be necessary to test the applicability of the condition metric in those regions. Although the sensitivity analysis found little difference in condition estimates depending on region, close examination reveals that experts from South Australia view condition differently. In this state woodland birds have suffered the greatest declines (Ford et al. 2001, Szabo et al. 2011). South Australian experts expect a lower species richness in intact sites than experts in other regions (Figure 6.3a) and my generalised linear models found no significant relationship between the condition estimates of South Australian experts and the proportion of small species. The difference in findings between South Australia and the other regions may reflect the small sample of South Australian experts or be a true regional difference in based on climatic differences (resulting in naturally more depauperate communities). However, it may also be evidence of shifting baselines in the understanding of woodland bird community condition (Papworth et al. 2009). Expectations of what constitutes an intact woodland bird community in South Australia may have lowered over time in response to a long history of habitat destruction, with experts now perceiving partially degraded sites as intact (Papworth et al. 2009). Due to the aridity of the landscape, South Australia never had as much woodland habitat as sub-tropical Queensland or temperate south-eastern Australia, and the woodland vegetation they had has been severely depleted (Halton et al. 2011, Bradshaw 2012). This has left very little suitable habitat for South Australia’s woodland bird community. As a result, the woodland bird community in South Australia is in poor condition and continues to decline (Westphal et al. 2003, Paton and O’Connor 2010). The majority of South Australia’s woodlands were cleared prior to 1980, and so it is reasonable to assume that the woodland bird community had suffered significant degradation by that date. As a result, there are few active ecologists with a clear memory of what an intact woodland bird community would look like in South Australia. This kind of shifting baseline could be problematic because it makes declines in the South Australian woodland bird community seem less severe, under-emphasising the importance of protecting the relictual woodland vegetation. To counter this issue, I chose to use the same equation and criteria for determining woodland bird community condition across the whole of Australia, despite apparent differences in South Australia. In this chapter I have developed regional lists describing the species composition of woodland bird communities and indicators to describe its occurrence and condition. These are applicable across a broad ecological range and may combat the shifting baseline of community condition in South Australia. The work in this chapter has outlined a process for using expert opinion,

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augmented with field data, to arrive at a consensus about which birds comprise the woodland bird community. The list developed through this process has the potential to resolve the ongoing and detrimental inconsistency in the use of the term ‘woodland bird’ (Fraser et al. 2015, 2017). By involving woodland bird experts in the development of these lists, I have given them buy-in and maximized the chance that they will feel comfortable about the contents of the list, which may encourage uptake (Reed 2008). The lists and condition index developed here will allow me to progress towards listing the woodland bird community as a Threatened Ecological Community under the EPBC Act. If the listing process is successful, the threatened status of the community will provide incentive for researchers to consistently define ‘woodland birds’ according to the Threatened Ecological Community described here. The next phase of the EPBC listing process is to determine how threatened the woodland bird community is. The indicators developed in this chapter can be used to address the EPBC Act threat criteria, specifically: (1) declining geographic distribution, (4) reduction in community integrity, and (5) rate of continuing detrimental change. My information will allow me to address these criteria by measuring the decline in geographic range and the reduction in community condition as well as assessing the future impacts of drivers of decline such as the spread of Noisy Miners and ongoing habitat destruction. This work forms the first step towards obtaining protection for the woodland bird community and provides a sound basis for future work towards listing the Woodland Bird Threatened Ecological Community under the EPBC Act.

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Chapter 7 Comparing the species considered ‘woodland birds’ based on ecological models and expert opinion

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7.1. Abstract This chapter is essentially an addendum to chapters 5 and 6 and is designed to fill a gap in the narrative of this thesis rather than act as an independent chapter. In both chapter 5 and chapter 6, I developed lists of woodland bird species. Although these lists were developed for different purposes, they characterise the same community and I felt that it would be neglectful not to compare them. In this chapter I show that, although the lists differ to some extent, there are marked similarities between them. The majority of species included in one chapter’s list are included in the other. Further, those species that were included as woodland birds in chapter 6 had higher associations with woodland cover and, in most cases, tree cover according to chapter 5 model coefficients than those not included in the chapter 6 list. The similarity between the two lists suggests that both may be representing an ecological truth about which birds are woodland dependent.

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7.2. Introduction There is marked uncertainty about which species comprise the woodland bird community (chapters 2, 3). In this thesis I attempted to describe (chapters 2, 3) and understand (chapter 2, 4) this uncertainty as well as working towards developing an understanding of which species should be included in the woodland bird community (chapters 5, 6). In chapters 5 and 6, I described two methods for determining which species belong to the woodland bird community but there was no space in either chapter to discuss how these two methods compare. In chapter 5, I used a computationally intensive, quantitative modelling approach to determine which species depend on woodland habitats, and in chapter 6 I used a combination of a workshop, questionnaires, and feedback to elicit expert opinion on which species comprise the woodland bird community. The aims for these chapters were slightly different. In chapter 5 I was primarily concerned with understanding the drivers of woodland dependence in birds, whereas in chapter 6 I hoped to compile a list of species that would form a ‘Woodland Bird Threatened Ecological Community’ to be listed under the EPBC Act. However, in both chapters I devised three lists of woodland birds for approximately equivalent regions: ecoregion 7, tropical and subtropical grasslands, savannas and shrublands (chapter 5), which includes the sub-tropical Queensland region (chapter 6); ecoregion 4, temperate broadleaf and mixed forests (chapter 5) which corresponds to the temperate south-eastern mainland Australia region (chapter 6); and ecoregion 12, Mediterranean forests, woodlands and shrublands (chapter 5) which includes the South Australian region (chapter 6) (hereafter referred to as ‘northern’, ‘south eastern’ and ‘south western’ regions respectively). Given the overlap in the scope of these two chapters, this short addendum will briefly discuss the similarities and differences in results yielded by the two methodologies.

7.3. Methods In chapter 6, I produced lists of birds belonging to the woodland bird community for northern, south eastern and south western regions of Australia. In chapter 5, I created a model that produces coefficients corresponding to species’ relationships with eucalypt woodland vegetation cover and tree cover and used this information to divide species into 5 groups: Intact Woodland, Degraded Woodland, Forest, Open Habitat and Uncertain. In this chapter I investigated the similarities in the results of these chapters in two ways. First, I looked at how the birds in different subsections (intact associated, degraded associated and woodland birds not particularly associated with intact or degraded communities) of the chapter 6 woodland bird

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community were divided into the five groups presented in chapter 5, for each of the three geographic regions (Table 7.1). Second, I took the species included (woodland birds) vs excluded (non-woodland birds) in regional lists of the woodland bird community from chapter 6 and collated the coefficients for their relationship with woodland vegetation and tree cover according to the chapter 5 analyses (Figure 7.1). I took the mean and 95% confidence intervals of the coefficients for woodland birds and compared it to that of non-woodland birds for each of the three regions.

7.4. Results The majority of species included in the chapter 6 regional woodland bird communities were designated into Intact Woodland or Degraded Woodland bird groups in chapter 5 (Table 7.1). Species considered to be particularly indicative on intact woodland bird communities in chapter 6 were most likely to be placed into the Intact Woodland or Degraded Woodland groups in chapter 5. In contrast, species included in the chapter 6 woodland bird community but not particularly associated with intact bird communities were most likely to be placed into the Degraded Woodland group in chapter 5. Not all species included in the chapter 6 regional woodland bird communities were included in either Intact or Degraded woodland groups in chapter 5 with 22, 14, and 18 species included in the Forest group and 17, 17 and 9 species included in the Open Habitat group in the northern, south eastern and south western regions respectively. Table 7.1. Distribution of the chapter 6 woodland bird community species (Appendix 6.3) between the chapter 5 modelled species groups (Appendix 5.2). Columns marked ‘N’ represent the northern region, ‘SE’ the south eastern region and ‘SW’ the south western region. Chapter 5 group

Chapter 6 community

Intact Woodland Intact woodland bird community Core woodland bird community Degraded woodland bird community

Degraded Woodland

Forest

Open Habitat

Uncertain

N

SE

SW

N

SE

SW

N

SE

SW

N

SE

SW

N

SE

SW

41

24

29

14

32

14

10

9

11

6

7

2

7

5

6

5

5

8

15

18

16

6

4

5

8

6

4

2

2

1

1

0

2

1

8

4

6

1

2

3

4

3

0

0

0

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In the northern and south western regions, woodland birds (those species included in the Australia-wide bird list from chapter 6) had higher coefficients for woodland and tree cover association than non-woodland birds (Figure 7.1a, b). Confidence intervals for woodland and non-woodland birds do not overlap in the northern region (Figure 7.1a) or for woodland association in the south western region (Figure 7.1b) suggesting that there is a significant difference between woodland and non-woodland birds according to inference by eye (Cumming and Finch 2005). However, the confidence intervals for tree cover association in the south western region overlap by more than 50%, suggesting that there is no significant difference between groups. The pattern in the south eastern region is different. Woodland birds had a more positive association with woodland vegetation than non-woodland birds as in the other regions. However, non-woodland birds had significantly higher coefficients for association with tree cover than woodland birds.

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Axis Title

a

Standardised coefficient

b

Axis Title

c

1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1 -1.2 -1.4 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1 -1.2 -1.4 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1 -1.2 -1.4 woodland association woodland birds

tree cover association non-woodland birds

Figure 7.1: Mean and 95% confidence intervals of standardised coefficients of woodland association (chapter 5: xi ) and tree cover association (chapter 5: zi) for species included in the woodland bird community (chapter 6) in the a) northern region, b) south western region, and c) south eastern region.

7.5. Discussion There was a significant difference between the habitat preferences (woodland and tree cover association) of woodland vs non-woodland birds (those species included in the Australia-wide

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bird list from chapter 6). Further, the majority of species included in the regional woodland bird communities in chapter 6 were grouped into either Intact Woodland or Degraded Woodland groups in chapter 5. This indicates broad agreement in the findings of chapters 5 and 6. In general terms, the species considered woodland birds in chapter 6 had more strongly positive coefficients for association with woodland habitat than the species that weren’t included in the community in chapter 6. Interestingly, although woodland birds were more related to woodland vegetation than non-woodland birds in all regions, this was not true of their association with tree cover. Woodland birds had a strong positive association with tree cover compared with non-woodland birds in the south western region. In the northern region there was no significant difference in coefficients of tree cover association between woodland and non-woodland birds though woodland birds had a slightly higher mean coefficient. However, in the south eastern region non-woodland birds had positive coefficients for tree cover association while woodland birds had negative coefficients. This apparent anomaly is likely due to the differing availability of woodland (specifically eucalypt woodland as per the calculations in chapter 5) and other treed habitats in the three regions. In the south western region, the vegetation comprises mainly eucalypt woodlands and acacia woodlands and shrublands. Therefore, dependence on woodland vegetation is likely to be strongly correlated with dependence on tree cover in the south west. This is also true, to a lesser extent, in the northern region (which excludes rainforest areas), the vegetation comprises mainly eucalypt woodlands and tussock grasslands. In these two regions, species that require trees will be strongly associated with eucalypt woodland vegetation and tree cover because other suitable treed habitats are lacking. In contrast, a substantial portion of the south eastern region is comprised of forest vegetation (NLWRA 2001) which has a greater tree cover and has been subject to less clearing and degradation than woodland vegetation. Therefore, in the south eastern region the model in chapter 5 is able to distinguish between species that require a habitat with trees (which will be almost exclusively found in woodlands in the northern and south western regions) and those that specifically prefer woodland habitats. This emphasises the influence of regional context in defining the woodland bird community. Although in all regions woodland birds had a stronger positive association with woodland vegetation than non-woodland birds, these results suggest that using one, continental-scale, list

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of woodland birds determined by coefficients for woodland association might exclude species that only depend on woodland vegetation in regions where other vegetation types are scarce. Excluding species that live in forests in the south east but depend on woodlands in the northern region, for example, might provide an incomplete assessment of the impact of future woodland clearing and degradation in Queensland. This could have even more detrimental impacts if my colleagues and I succeed in listing the Woodland Bird Threatened Ecological Community under the EPBC Act. If I fail to account for regional differences in which bird species comprise the Woodland Bird Threatened Ecological Community, some forest-preferring species might be excluded from the listing, even though those species are truly woodland dependent in some regions.

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Chapter 8 General Discussion

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8.1. Introduction I began studying woodland bird classification because I was frustrated by being unable work out which species to include as woodland birds during my Masters research. In the course of my investigations I discovered that inconsistent terminology was common in ecology, not just restricted to woodland bird research. To that end, I had four main objectives for this thesis: 1) quantify how consistently woodland birds are classified; 2) use woodland birds as a case study to examine the influence of using key terms inconsistently on ecological inference; 3) understand why woodland birds are being classified differently; and 4) resolve the inconsistent classification of woodland birds. I addressed these objectives in five data chapters using a combination of systematic reviews, questionnaires, statistical models and expert workshops and will discuss my findings and conclusions related to each of these objectives below.

8.2. How consistently are woodland birds classified? Chapters 2 and 3 of this thesis examined how consistently woodland birds are classified, finding that in both Australia and across Europe, researchers use the term ‘woodland bird’ to refer to substantially different sets of species. I was surprised by the magnitude of this effect. I had expected that the majority of species would be classified into the same group (woodland, farmland, generalist etc.) by most articles, with a minority of species that have uncertain ranges and habitat requirements classified differently between articles. However, my research shows that the majority of species are classified into a different group by at least one article. For example, 54% of species considered woodland birds in Australia were sometimes considered farmland or generalist species. The same is true of 73% of birds considered woodland birds in Europe. Given that woodland birds (and in fact farmland and generalist birds) are classified so inconsistently, it is difficult to justify comparing or combining results from multiple studies; two studies on ‘woodland birds’ may actually be about different suites of species. Therefore, it is difficult to know to what extent any differences in the results of two studies are due to ecological factors as opposed to terminological disagreement. This is compounded by the fact that some articles study ‘woodland birds’ without specifying the species they saw or studied. In order to be sure that differences in results are attributable to ecological factors it is necessary to use the same bird list across all studies in the comparison (impossible in the case of studies that do not specify their study species), unless previous research has established that using different lists of woodland birds has little impact on the findings or conclusions of ecological studies.

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8.3. Does inconsistently using key terms influence ecological inference? In this thesis I focussed on woodland birds because of my previous frustration in classifying them for my own research, but it was my intention to use them as a case study to demonstrate the potential impact of using key terms inconsistently on ecological inference. As I discuss in chapter 1, ecologists have long been concerned that their inconsistent use of terms might be having a negative effect on their research. The term ‘woodland bird’ is not one that has been directly questioned before but it is an ‘ecological unit’ that suffers from various sources of uncertainty and can cause serious interpretation errors if the concepts (i.e. the concept of a ‘woodland bird’) are considered self-evident (Jax 2006) as they are in woodland bird research. Therefore, I consider my woodland bird case study to be a good example of how inconsistent use of terminology for ecological units can result in problems for ecological inference. In chapters 2 and 3, I focussed on two examples of this by looking at how using different interpretations of the terms ‘woodland-’, ‘farmland-’ and ‘generalist birds’ influences ecological findings. In both chapters I took one dataset, one question and one method of analysis to ensure that any differences in my findings were attributable to differences in the birds included as ‘woodland-’, ‘farmland-’ and ‘generalist birds’. In chapter 2, I showed that being more selective about which species are considered to be woodland birds (i.e. only include species that most articles consider woodland birds increases the apparent strength of the relationship between ‘woodland bird’ ocurrence and habitat fragmentation. In chapter 3, I showed that different studies’ interpretations of ‘woodland-’, ‘farmland-’ and ‘generalist birds’ can result in different assessments of their trends through time with some classifications of Australian farmland and generalist species showing increasing or decreasing population through time. These findings underline the importance of being consistent about how ecological units such as ‘woodland birds’ are defined. For woodland bird research, this means that comparisons of results from multiple studies that use different classifications must be undertaken with extreme caution even though the differences in ecological interpretation may not always be as significant as those found in this thesis. I propose that much could be gained for scientific progress and conservation if researchers classified ecological units such as ‘woodland birds’ more consistently.

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8.4. Why are woodland birds classified differently? Given the many calls for using ecological terms consistently (see chapter 1) and the potential impact of inconsistently using terms as demonstrated in this thesis, it is surprising that so much inconsistency exists around a seemingly straight forward term like ‘woodland bird’. I explored the reasons for the existence and persistence of terminological inconsistency in a few different ways in chapters 1, 2, 4, and 5. My research in chapter 1 gave me insights into the reasons for terminological inconsistency in ecology. This research also provided the insights necessary to devise an appropriate questionnaire with which to survey woodland bird experts about their opinions on why woodland birds might be classified differently for chapter 2. Woodland bird experts were asked to rate how various reasons contributed to researchers classifying woodland birds differently. The four reasons that rated most highly were: different ideas about how to determine whether a species relies on woodland vegetation; different study aims; regional differences; and differences in the definition of woodland habitat. In chapter 4 I investigated the influence of study aim, method of selecting ‘woodland bird’ species for a study and study location on which birds were classified as woodland birds. I found that, in the Australian context, none of these factors were significantly related to differences in woodland bird classification. However, there is some evidence to suggest that a study with larger sample sizes might find an effect of aims and classification methods. In the European context, classification method significantly influenced woodland bird classification. Interestingly, the location of the study did not come out as a significant predictor of which species were considered woodland birds by different articles. However, location explained a large percentage of the deviance in species classification so this lack of effect is likely due to low sample sizes. In chapter 5, I employed a different approach which allowed me to further investigate the potential impact of location on the classification of woodland birds. Instead of investigating how woodland birds are currently classified, I looked at how they ‘should’ be classified by modelling their ‘woodland dependence’ as a function of bird traits and occurrence in different habitat types. This method also allowed me to investigate the influence of classifying ‘woodland’ habitats differently; something that was impossible in the chapter 4 analyses because sufficient detail was rarely included in articles. I found that the birds the model found to be woodland dependent are very similar regardless of whether ‘woodland’ is considered to

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be all woodland vegetation or only eucalypt woodlands. However, there were some major differences when considering all treed vegetation types (rather than restricting the definition to those with woodland structure), in which case a number of wet-forest specialist species were added to the list. I found even bigger differences when comparing the model coefficients and bird lists from models built on different regions of Australia. This suggests that it may be meaningful to have separate lists of woodland birds for different regions of Australia, rather than having a single list of Australian woodland birds. This might make studies less comparable between regions but would better represent the species that depend on woodland habitats in each region which may differ due habitat availability and climatic conditions (chapter 7). Without studying additional case studies of how ‘ecological units’ (Jax 2006) are classified, it is impossible to know how well these findings transfer to other, similar terms (invasive species, coral reef fish, migratory birds, etc.). However, my findings suggest that these terms may be used differently in articles that use different methods to distinguish when a species belongs to the unit. In these cases, it may be possible to work towards agreement between researchers about which species should be included in the ecological unit. However, there may be regional differences in how the terms are used that are ecologically important and care should be taken to retain these.

8.5. Resolving the inconsistent classification of woodland birds In the beginning of my study, I believed that resolving inconsistencies in woodland bird classification would be a simple matter of proving that consistency is required and presenting the results of a statistical model which predicts which species are woodland dependent. Over the course of developing my thesis I have interacted with the majority of Australia’s woodland bird experts and I have realised two important things. First, these people have a wealth of knowledge that I would be wasting by relying solely on a statistical model. Second, they are very unlikely to use any list of woodland birds I produce if they are not engaged with the process. Nevertheless, I created a model of woodland dependence which revealed some interesting insights into the value of woodland vegetation; suggesting that the majority of (woodland and non-woodland) species are able to persist in woodlands, which is not true of other habitat types. It also produced three regional lists of birds that, from a purely statistical perspective, should be considered woodland birds. The species included in these lists responded in the ways that, based on the literature and ecological principles, woodland birds are expected to respond.

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However, some of the findings from the model seem to be statistical artefacts. For example, the Musk Lorikeet is found to prefer degraded woodlands in south-eastern Australia, forests in northern Australia and open country in south west Australia but there is no easy ecological explanation of this. This is an instance where a blind modelling exercise is unable to fully capture the ecological story. The limitations of my model and the pervasive belief that it is necessary to change the selection of woodland birds depending on the aim of a study suggests that an approach based on expert opinion might be the best way to develop a list of woodland birds. This may allow the list to gain enough traction with woodland bird experts to reduce or resolve the inconsistency of woodland bird classification. To that end, chapter 6 describes a workshop I conducted for woodland bird experts that aimed to delineate the woodland bird community for the purposes of listing it as a Threatened Ecological Community under the Environment Protection and Biodiversity Conservation (EPBC) act. Working together we were able to come to consensus on woodland bird lists for 1) south-east Queensland, 2) Victoria, New South Wales and the Australian Capital Territory, and 3) South Australia. The species included in these lists had higher coefficients for woodland association according to the chapter 5 models than species excluded from the lists (chapter 7). This suggests that the lists produced in both chapter 5 and 6 capture some ‘true’ collection of woodland birds. Work towards a proposal to list the Woodland Bird Threatened Ecological Community is ongoing but, if the listing process is successful, the regional lists of woodland birds I propose will be readily available and applicable to conservation research contexts, increasing the likelihood that they will be used in future bird research to identify ‘woodland birds’. This could potentially resolve the ongoing inconsistent classification of woodland.

8.6. Future research directions There are three main limitations to the research in this thesis that could be addressed in future research: 1) using a single, specific, case study does not address the larger issues of inconsistent terminology in ecology, 2) the Australian bird lists devised in chapter 6 ignore Western Australia and Tasmania, and 3) even if my research resolves future inconsistency in woodland bird research it will not rectify the problem for existing research. 8.6.1. Representativeness of the single case study Despite widespread concern among ecologists, terminology is used inconsistently in ecology which may be partly because there is no definitive proof that it is problematic and partly

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because it seems too difficult to resolve. My research highlights the problem of inconsistency, shows its detrimental effects and demonstrates a way of resolving it. However, my woodland bird case study may not be sufficient to prove this point. Firstly, the fact that using the term woodland bird differently influences ecological inference does not necessarily mean that this will be true of any other term. There is also an issue of scale. While the term ‘woodland bird’ is commonly used, the number of researchers in the field is small, meaning that resolving the problem may be more tractable than for terms that are used inconsistently by more researchers. For example, it would not be remotely possible to get all researchers studying ‘habitat fragmentation’ together in a room to discuss how to use the term consistently. The issues raised in this thesis warrant further exploration across a range of other inconsistently used terms to overcome the limitations of my single case study. 8.6.2. Excluding Western Australia and Tasmania The vast majority of published Australian woodland bird research is concentrated in eastern mainland Australia with relatively little conducted in Western Australia or Tasmania. Despite this, both states contain woodland vegetation and its dependent bird species. It would be negligent to assess the conservation requirements of the woodland bird community in Australia (and potentially list it as a Threatened Ecological Community) without considering the communities in Western Australia and Tasmania. We originally limited our discussions to eastern mainland Australia due to the availability of research and expertise and the threat of Noisy Miners to woodland bird diversity (Mac Nally et al. 2012). However, woodland birds in Tasmania are subject to the same threat from Noisy Miners as birds on mainland Australia. Noisy miners are not present in Western Australia but development, drought and yellowthroated miners threaten the bird species there so it seems sensible to at least assess the conservation requirement of Western Australian woodland birds. I am currently in the process of organising a workshop targeted at woodland bird experts from Western Australia and Tasmania in the hope that I can include both regions in my assessment of whether they qualify for protection under the EPBC Act. 8.6.3. Resolving past inconsistency In the best-case scenario, the Woodland Bird Threatened Ecological Community will obtain protection under the EPBC Act and the attendant woodland bird lists would be used in future woodland bird research. However, even in this case, there is a vast body of existing woodland bird research which will not be directly applicable to the Woodland Bird Threatened Ecological Community. One way to address this would be to ask authors of woodland bird articles to rerun

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their analyses for inclusion in a book that would summarise what is currently known about the Woodland Bird Threatened Ecological Community, further rectifying the effect of inconsistent classification and providing a useful resource for managing the new Threatened Ecological Community.

8.7. In closing Throughout this thesis I have contributed to the understanding of inconsistent use of terminology in ecological research by providing evidence that, at least in one instance, using terms inconsistently is detrimental. I hope that my findings will encourage ecologists to think carefully about exactly what they mean when they use ambiguous terms like ‘habitat’ or ‘fragmentation’ and when they group species into ecological units like ‘woodland birds’ or ‘invasive species’. I have also thoroughly explored the classification of woodland birds and hope that the work in this thesis will be the first step towards resolving inconsistency in the classification of woodland birds.

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Chapter 10 Appendices

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Appendix 2.1. Article Selection Schematic Web of Science and Scopus searches return 383 articles

Targeted journal search returns 695 articles

This adds up to 937 unique articles

When articles which don’t mention woodland birds in the title or abstract or aren’t based in Australia are removed, 109 articles remain

7 superficially mention woodland birds

32 are about woodland birds but don’t specify which species

28 specify the woodland bird species studied only

27 specify both the woodland birds and nonwoodland birds

15 regard a subset of woodland birds (e.g. ‘declining woodland birds)

Figure A.2.1.1: The process of article selection used in chapter 2 to understand ecologists’ categorisation of woodland bird species and non-woodland bird species.

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Appendix 2.2. Articles evaluated for chapter 2 Information on the article, the method that the authors used to define and classiful woodland birds and whether it was possible to extract a list of the woodland birds and/or non-woodland birds used in the study. Table A.2.2.2: Artciles evaluated in chapter 2

Article

Method of classification

Amos, J. N., A. F. Bennett, et al. (2012). "Predicting Landscape-Genetic Consequences of Habitat Loss, Fragmentation and Mobility for Multiple Species of Woodland Birds." PLoS ONE 7(2): e30888.

Radford JQ, Bennett AF, Cheers GJ (2005) Landscape-level thresholds of habitat cover for woodlanddependent birds. Biological Conservation 124: 317–337. Radford JQ, Bennett AF, Cheers GJ (2005) Landscape-level thresholds of habitat cover for woodlanddependent birds. Biological Conservation 124: 317–337. birds at study sites

Amos, J. N., S. Balasubramaniam, et al. (2013). "Little evidence that condition, stress indicators, sex ratio, or homozygosity are related to landscape or habitat attributes in declining woodland birds." Journal of Avian Biology 44(1): 045-054.

Antos, M. J. and A. F. Bennett (2005). "How important are different types of temperate woodlands for ground-foraging birds?" Wildlife Research 32(6): 557-572.

Woodlan d and /or non woodland selection of woodland species only

woodland only: others not available

Antos, M. J., and Bennett, A. F. (2006). Foraging ecology of ground-feeding woodland birds in temperate woodlands of southern Australia. Emu 106, 29–40. doi:10.1071/MU05039

ns

Antos,M.J., Bennett, A. F., and White, J. G. (2008).Where exactly do ground foraging woodland birds forage? Foraging sites and microhabitat selection in temperate woodlands of southern Australia. Emu 108, 201–212. doi:10.1071/MU08005 Arnold, G. W., 1988. The effects of habitat structure and floristics on the densities of bird species in Wandoo woodland. Aust. Wildl. Res. 15: 499-510. Attwood, S. J., Park, S. E., Maron, M., Collard, S., Robinson, D., Reardon- Smith, K. M., and Cockfield, G. (2009). Declining birds in Australian agricultural landscapes may benefit from

no definition

woodland only: others not available woodland only: (non woodland werent recorded) no list

no definition

no list

no definition

no list

144

aspects of the European agrienvironment model. Biological Conservation 142, 1981–1991. doi:10.1016/j.biocon.2009.04.008 Baker, J., K. French, and R. J. Whelan. 2002. The edge effect and ecotonal species: bird communities across a natural edge in southeastern Australia. Ecology 83:3048–3059. Banks, P. B. and J. V. Bryant (2007). "Four-legged friend or foe? Dog walking displaces native birds from natural areas." Biology Letters 3(6): 611-613. Barrett G.W., Ford H.A. and Recher H.F. 1994. Conservation of woodland birds in a fragmented rural landscape. Pacific Conservation Biology 1: 245–256. Barrett G.W., Ford H.A. and Recher H.F. 1994. Conservation of woodland birds in a fragmented rural landscape. Pacific Conservation Biology 1: 245–256. Barrett G.W., Ford H.A. and Recher H.F. 1994. Conservation of woodland birds in a fragmented rural landscape. Pacific Conservation Biology 1: 245–256. Barrett, G. and Davidson I. 1999. Community Monitoring of Woodland Habitats - the Birds on Farms Survey. Pp 382-399 in Temperate Eucalypt Woodlands in Australia: Biology, Conservation, Management and Restoration, edited by R.J. Hobbs and C.J. Yates. Surrey Beatty & Sons, Chipping Norton, NSW Barrett, G. and Silcocks, A. 2002. Comparison of the first and second Atlas of Australian Birds to determine conservation status of woodland-dependent and other bird species in New South Wales over the last 20 years. Birds Australia report to NSW National Parks and Wildlife Service, Dubbo, NSW. Barrett, G. W., A. F. Silcocks, et al. (2007). "Comparison of atlas data to determine the conservation status of bird species in New South Wales, with an emphasis on woodlanddependent species." Australian Zoologist 34(1): 37-77. Barrett, G. W., Freudenberger, D., Drew, A., Stol, J., Nichols, A. O., and Cawsey, E. M. (2008). Colonisation of native tree and shrub plantings by woodland birds in an agricultural landscape. Wildlife Research 35, 19–32. doi:10.1071/WR07100 Barrett, G. W., Silcocks, A., Barry, S., Cunningham, R., and Poulter, R. (2003). ‘The New Atlas of Australian Birds.’ (RAOU: Melbourne.) Barrett, G., Silcocks, A. and Cunningham, R. 2002. Australian Bird Atlas 1998-2001. Supplementary Report No. 1 Comparison of Atlas 1 (1977-1981) and Atlas 2 1998-2001. Australian Government’s Natural Heritage Trust and Birds Australia, Hawthorn East, Victoria. Bennett, A. F. and D. M. Watson (2011). "Declining woodland birds - Is our science making a difference?" Emu 111(1): i-vi.

ordination (woodland/heathland /middling) no definition

both

expert opinion note that woodlands refers to any wooded area expert opinion note that woodlands refers to any wooded area expert opinion note that woodlands refers to any wooded area no definition

both

expert

both

expert opinion

both

Barrett et al. (1994), Frith (1976) and Ford et al. (1986).

both

no definition

no list

no definition

no list

na

no list

no list

both

both

no list

145

Bennett, A. F. and Radford, J. Q. 2009. Thresholds, incidence functions and species-specific cues: responses of woodland birds to landscape structure in south-eastern Australia. – In: Villard, M. A. and Jonsson, B. G. (eds), Setting conservation targets for managed forest landscapes. Cambridge Univ. Press, pp. 161–184. Bennett, A., Brown, G., Lumsden, L., Hespe, D., Krasna, S. and Silins, J. 1998. Fragments for the future - Wildlife in the Victorian Riverina the Northern Plains. Arthur Rylah Institute for Environmental Research, Dept Natural Resources and Environment, Melbourne. Bowen, M. E., C. A. McAlpine, et al. (2009). "The age and amount of regrowth forest in fragmented brigalow landscapes are both important for woodland dependent birds." Biological Conservation 142(12): 3051-3059.

Briggs, S. V., Seddon, J. A., and Doyle, S. J. (2007). Structures of bird communities in woodland remnants in central New South Wales, Australia. Australian Journal of Zoology 55(1), 29–40. doi:10.1071/ ZO06064 Cale, P. 1990. The value of road reserves to the avifauna of the central wheatbelt of Western Australia. Proceedings of the Ecological Society of Australia 16:359–367. Chan, K. 1995. Bird community patterns in fragmented vegetation zones around streambeds of the Northern Tablelands, New South Wales. Australian Bird Watcher 16:11– 20. Chazdon, R. L., A. Chao, et al. (2011). "A novel statistical method for classifying habitat generalists and specialists." Ecology 92(6): 1332-1343. (p<0.005)

no definition

no list

no definition

no list

Woodland dependency definitions followed similar groupings reported in Bennett and Ford (1997) and Reid (1999) and were primarily based on the information provided by the Handbook of Australian, New Zealand and Antarctic Birds (Marchant and Higgins, 1990–2006) no definition

both

no definition

no list

no definition

no list

Baker et al 2002 and We used a supermajority specialization threshold (K ¼ 2/3) and evaluated classifications at P ¼ 0.005 (appropriate for classification of individual species) and P ¼ 0.005 (suitable for assessing overall pattern). Thirty-three species

both

no list

146

Chazdon, R. L., A. Chao, et al. (2011). "A novel statistical method for classifying habitat generalists and specialists." Ecology 92(6): 1332-1343. (p<0.05)

Christidis, L., and W. E. Boles. 1994. The taxonomy and species of birds of Australia and its territories. Royal Australian Ornithological Union, Melbourne. Clarke MF, Griffioen P & Loyn RH (1999) Where do all the bush birds go? Wingspan Suppl 17: 1–16. Cooper RM & McAllan IAW (1995) The Birds of Western New South Wales: A Preliminary Atlas. New South Wales Bird Atlassers Inc., Albury, Australia. Cunningham, R. and P. Olsen (2009). "A statistical methodology for tracking long-term change in reporting rates of birds from volunteer-collected presence absence data." Biodiversity and Conservation 18(5).

(42.3%) were too rare to classify usingP¼0.05, whereas 36 species (46.2%) were too rare to classify using P ¼ 0.005, a difference of only three species (Appendix E) Baker et al 2002 and We used a supermajority specialization threshold (K ¼ 2/3) and evaluated classifications at P ¼ 0.05 (appropriate for classification of individual species) and P ¼ 0.005 (suitable for assessing overall pattern). Thirty-three species (42.3%) were too rare to classify usingP¼0.05, whereas 36 species (46.2%) were too rare to classify using P ¼ 0.005, a difference of only three species (Appendix E) no definition

both

no list

no definition

no list

no definition

no list

based on "Reid JRW (1999) Threatened and declining birds in the New South Wales sheep-wheat belt. 1. Diagnosis, characteristics and management. Unpublished report to NSW National Parks and Wildlife

woodland only: woodland species taken from bird atlas

147

Davidson, R. and Davidson, S. 1992. Bushland on farms – Do you have a choice? Australian Government Publishing Service, Canberra. Debus, S. J. S. 2008. The effect of noisy miners on small bush birds: an unofficial cull and its outcome. Pacific Conservation Biology 14:185–190. DEST 1995. Native Vegetation Clearance, Habitat Loss and Biodiversity Decline.Department of the Environment, Sport and Territories. Ekert, P. (2001). Saving the woodland birds of the Liverpool Plains. The Web (Newsletter of the Threatened Species Network, NSW) March 2001. Fischer J. and Lindenmayer D.B. 2002. The conservation value of paddock trees for birds in a variegated landscape in southern New South Wales. 2. Paddock trees as stepping stones. Biodiversity and Conservation 11: 833–849 (this issue). Fischer, J. and D. B. Lindenmayer (2002). "The conservation value of paddock trees for birds in a variegated landscape in southern New South Wales. 1. Species composition and site occupancy patterns." Biodiversity and Conservation 11(5). Fisher A. M. & Goldney D. C. (1997) Use by birds of riparian vegetation in an extensively fragmented landscape. Pac. Conserv. Biol. 3, 275–88. Ford H. A., Walters J. R., Cooper C. B., Debus S. J. S. & Doerr V. A. J. (2009) Extinction debt or habitat change? – Ongoing losses of woodland birds in north-eastern New SouthWales, Australia. Biol. Conserv. 142, 3182–90. Ford, H. & Howe, R. (1980) The future of birds in the Mount Lofty Ranges. South Australian Ornithologist, 28, 85–89. Ford, H. A. (2011). "The causes of decline of birds of eucalypt woodlands: Advances in our knowledge over the last 10 years." Emu 111(1): 1-9. Ford, H. A., Barrett, G. and Howe, R. W, 1995. Effect of habitat fragmentation and degradation on bird communities in Australian eucalypt woodland. Pp. 99-116 in Functioning and Dynamics of Natural and Perturbed Ecosystems ed by D. Bellan, G. Bonin and C. Emig. Intercept Limited, Andover, UK. Ford, H. A., Barrett, G. W., Saunders, D. A. and Recher, H. F. 2001. Why have birds in the woodlands of southern Australia declined? – Biol. Conserv. 97: 71–88. Ford, H., Barrett, G., and Recher, H. (1995). Birds in a degraded land- scape – safety nets for capturing regional biodiversity. In ‘Nature Conservation 4: The Role of Networks’. (Eds D. A. Saunders, J. I. Craig and E. M. Mattiske.) pp. 43–50. (Surrey Beatty: Sydney.) Ford, H.A. & Barrett, G.W. (1995) The role of birds and their conservation in agricultural systems. People and Nature

Service, CSIRO Wildlife and Ecology, Canberra" no definition

no list

no definition

no list

no definition

no list

no definition

no list

no definition

no list

na

both

no definition

no list

no definition

2 study species

no definition

no list

no definition

no list

no definition

no list

no definition

no list

occurrence at sites of different types

both

no definition

no list

148

Conservation (eds A. Bennett, S. Backhouse & T. Clark), pp. 123–128. Royal Zoological Society of New SouthWales,Mosman, Australia. Ford, H.A. (1986). Birds and Eucalypt dieback in northeastern NSW, pp.150–154 in H.A. Ford and D.C. Paton (Eds), The Dynamic Partnership: Birds and Plants in Southern Australia. Government Printer, South Australia. Freudenberger, D. (2001). Bush for the Birds: Biodiversity enhancement guidelines for the Saltshaker Project, Boorowa, NSW. Canberra, CSIRO Sustainable Ecosystems. Garnett, S. T., Crowley, G. M. and Barrett, G. 2002. Patterns and Trends in Australian Bird Distributions and Abundance: Preliminary Analysis of Data from Atlas of Australian Birds for the National Land & Water Resources Audit. NLWRA, Canberra. http://audit.ea.gov.au/anra/vegetation/vegetation_ frame.cfm?region_type=AUS®ion_code=AUS&info=bio_a sses&time_stamp=084357A Garrard, G. E., M. A. McCarthy, et al. (2012). "A predictive model of avian natal dispersal distance provides prior information for investigating response to landscape change." Journal of Animal Ecology 81(1): 14-23. Gibbons P. and Lindenmayer D.B. 1997. Conserving hollowdependent fauna in timber-production forests. Environmental Heritage Monograph Series No. 3, Forest Issues 2. NSW NPWS, Australia. Gibbons, P. and Lindenmayer, D. 2002. Tree hollows and wildlife conservation in Australia. CSIRO Publishing, Collingwood. Goldney, D. C. and Bowie, I. J. S. 1990. Some management implications for the conservation of vegetation remnants and associated fauna in the central western region of New SouthWales. Proceedings Ecological Society Australia 16: 427-440. Hardwood, W. and R. Mac Nally (2005). "Geometry of large woodland remnants and its influence on avifaunal distributions." Landscape Ecology 20(4).

no definition

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no definition

woodland only

no definition

no list

refer to radford 2007

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woodland only: modeled that way no list

no definition

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no definition

no list

The sets were restricted to taxa identified by the biodiversity management agency in Victoria (Department of Sustainability and Environment, DSE) as being ‘woodlanddependent’ birds, namely, species unable to persist in landscapes without the presence of extensive amounts of woodland vegetation

Both: List of woodland birds found in Victoria provided by ralph macnally, it was done considerin g all victorian birds so all victoran

149

(list available from R.M.).

Harrison, K. A., N. J. Pavlova, et al. (2012). "Fine-scale effects of habitat loss and fragmentation despite large-scale gene flow for some regionally declining woodland bird species." Landscape Ecology 27(6). Haslem, A. and A. F. Bennett (2008). "Birds in Agricultural Mosaics: The Influence of Landscape Pattern and Countryside Heterogeneity." Ecological Applications 18(1): 185-196.

Haslem, A. and A. F. Bennett (2011). "Countryside vegetation provides supplementary habitat at the landscape scale for woodland birds in farm mosaics." Biodiversity and Conservation 20(10): 2225-2242. Higgins PJ, Peter JM, Cowling SJ (2006) Handbook of Australian, New Zealand and Antarctic BIRDS, volume 7 (Dunnock to Starlings) part B. Oxford University Press, Melbourne Hobbs, R. J., and Yates, C. J. (2000). ‘Temperate Eucalypt Woodlands in Australia. Biology, Conservation, Management and Restoration.’ (Surrey Beatty & Sons: Sydney.) Holland-Clift, S., D. J. O'Dowd, et al. (2011). "Impacts of an invasive willow (Salix × rubens) on riparian bird assemblages in south-eastern Australia." Austral Ecology 36(5): 511-520. Howes, A. L., Maron, M., and McAlpine, C. A. (2010). Bayesian networks and adaptive management of wildlife habitat. Conservation Biology 24, 974–983. doi:10.1111/j.1523-1739.2010.01451.x Howes, A., and M. Maron. 2009. Interspecific competition and conservation management of continuous subtropical woodlands. Wildlife Research 36:617–626. Hsu, T., K. French, et al. (2010). "Avian assemblages in eucalypt forests, plantations and pastures in northern NSW, Australia." Forest Ecology and Management 260(6): 10361046.

na

birds that aren't included are assumed to be non woodland (all birds found in victoria across all studies covered in this review four woodland species

(‘‘woodland,’’ ‘‘opentolerant,’’ and ‘‘opencountry’’) as classified for the Gippsland region by Radford and Bennett (2005) Radford and Bennett 2005

both

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na

both

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no definition

no list

based on descriptions from the Handbooks for Australian, New Zealand and Antarctic

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woodland only (at present)

150

Huth, N. and H. P. Possingham (2011). "Basic ecological theory can inform habitat restoration for woodland birds." Journal of Applied Ecology 48(2): 293-300. Ikin, K., M. Beaty, et al. (2013). "Pocket parks in a compact city: how do birds respond to increasing residential density?" Landscape Ecology 28(1). James, P., D. Norman, et al. (2010). "Avian population dynamics and human induced change in an urban environment." Urban Ecosystems 13(4). Kath J, Maron M, Dunn PK (2009) Interspecific competition and small bird diversity in an urbanizing landscape. Landsc Urban Planning 92:72–79 Kavanagh R. P. & Turner R. J. (1994) Birds in eucalypt plantations: the likely role of retained habitat trees. Aust. Birds 28, 32–40. Kavanagh, R. & Recher, H.F. (1983) Effects of observer variability on the census of birds. Corella, 7, 93–100. Kavanagh, R. P., M. A. Stanton, et al. (2007). "Eucalypt plantings on farms benefit woodland birds in south-eastern Australia." Austral Ecology 32(6): 635-650.

Kavanagh, R. P., M. A. Stanton, et al. (2007). "Eucalypt plantings on farms benefit woodland birds in south-eastern Australia." Austral Ecology 32(6): 635-650.

Kavanagh, R. P., M. A. Stanton, et al. (2007). "Eucalypt plantings on farms benefit woodland birds in south-eastern Australia." Austral Ecology 32(6): 635-650.

Birds (AND EXPERT OPINION) Kitchener

from bird australia reference

no list ("see kitchener" ) both

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This classification was based on our own knowledge of species ecology and distributions, but also using guid- ance from recent classifications of the same bird species in NSW (Reid 2000) and Victoria (Bennett & Ford 1997). This classification was based on our own knowledge of species ecology and distributions, but also using guid- ance from recent classifications of the same bird species in NSW (Reid 2000) and Victoria (Bennett & Ford 1997). This classification was based on our own knowledge of species ecology and distributions, but also using guid- ance from recent classifications

both

both

both

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Keast A (1985) Bird community structure in southern forests and northern woodlands: A comparison. In: Keast A, Recher HF, Ford HA & Saunders D (eds) Birds of the eucalypt forests and woodlands: ecology, conservation and management. pp 97–116. Surrey- Beatty, Sydney Kennedy, S. J. (2003). A four-year study of a bird community in a woodland remnant near Moyston, western Victoria. Corella 27, 33–44. Kinross, C., and Nicol, H. (2008). Responses of birds to the characteristics of farm windbreaks in central NewSouth Wales, Australia. Emu 108, 139–152. doi:10.1071/MU06024

Kitchener, D. J., J. Dell, B. G. Muir, and M. Palmer. 1982. Birds in Western Australian wheat belt reserves— implications for conservation. Biological Conservation 22:127– 163. Kuhnert, P. M., T. G. Martin, K. Mengersen, and H. P. Possingham. 2004. Assessing the impacts of grazing levels on bird density in woodland habitat: a Bayesian approach using expert opinion. Environmetrics, in press. Laven, N. H. and Mac Nally, R., 1998. Association of birds with fallen timber in Box-Ironbark forests of central Victoria.. Corella 22: 56-60. Levin, N., C. McAlpine, et al. (2009). "Mapping forest patches and scattered trees from SPOT images and testing their ecological importance for woodland birds in a fragmented agricultural landscape." International Journal of Remote Sensing 30(12): 3147-3169. Lindenmayer D, Fischer J (2006) Habitat fragmentation and landscape change an ecological and conservation synthesis. Collingwood: CSIRO Publishing. Lindenmayer DB, Claridge A, Hazell D, Michael D, Crane M, MacGregor C, Cunningham R (2003) Wildlife on farms: how to conserve native animals. CSIRO Publishing, Collingwood

of the same bird species in NSW (Reid 2000) and Victoria (Bennett & Ford 1997). no definition

no list

no definition

no list

references

woodland only (two non woodland declining birds listed, a ful list of non woodland birds may be accessible ) both

resident in remnants vs non resident with residents interpreted as woodland birds no definition

no list

no definition

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Kavanagh et al. (2007)

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152

Lindenmayer DB, Cunningham RB (2011) Longitudinal patterns in bird reporting rates in a threatened ecosystem: is change regionally consistent? Biol Conserv 144:430–440 Lindenmayer DB, Knight EJ, Crane MJ, Montague-Drake R, Michael DR, MacGregor CI (2010) What makes an effective restoration planting for woodland birds? Biol Conserv 143:289–301 Lindenmayer, D. B., A. R. Northrop-Mackie, et al. (2012). "Not all kinds of revegetation are created equal: Revegetation type influences bird assemblages in threatened australian woodland ecosystems." PLoS ONE 7(4). Lindenmayer, D., A. F. Bennett, et al. (2010). "An overview of the ecology, management and conservation of Australia's temperate woodlands." Ecological Management and Restoration 11(3): 201-209. Loyn R. H. (1985) Birds in fragmented forests in Gippsland, Victoria. In: Birds of Eucalypt Forests andWoodlands: Ecology, Conservation, Management (eds A. Keast, H. F. Recher, H. Ford & D. Saunders) pp. 323–31. Royal Australasian Orni- thologists Union and Surrey Beatty and Sons, Sydney Loyn RH (1985a) Ecology distribution and density of birds in Victorian forests. In: Keast A, Recher HF, Ford HA & Saunders D (eds) Birds of the eucalypt forests and woodlands: ecology, conservation and management. pp 33–46. Surrey-Beatty, Sydney. Loyn, R. 1987. Effects of patch area and habitat on bird abundances, species numbers and tree health in fragmented Victorian forests. Pages 65–77 in D. A. Saunders, G. W. Arnold, A. A. Burbridge, and A. J. M. Hopkins, editors. Nature conservation: the role of remnants of native vegetation. Surrey Beatty and Sons, Chipping Norton, New South Wales, Australia. Loyn, R. H. (2002). "Patterns of ecological segregation among forest and woodland birds in south-eastern Australia." Ornithological Science 1. Loyn, R. H., Lumsden, L. F., and Ward, K. A. (2002). Vertebrate fauna of Barmah Forest, a large forest of river red gum Eucalyptus camal- dulensis on the floodplain of the Murray River. Victorian Naturalist 119, 114–132. Loyn, R.H., McNabb, E.G., Macak, P., Noble, P., 2007. Eucalypt plantations as habitat for birds on previously cleared farmland in south-eastern Australia. Biological Conservation 137, 533–548. Lynch, J. F. and Saunders, D. A, 1991. Responses of bird species to habitat fragmentation in the wheatbelt of Western Australia: interiors, edges and corridors. Pp. 143-58 in Nature Conservation 2: The Role of Corridors ed by D. A. Saunders and R. J. Hobbs. Surrey Beatty & Sons, Chipping Norton.

no definition

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all birds present (exept water birds) with some exemplar species

woodland only

no definition

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no definition

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no definition

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no definition

no list

no definition

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no definition

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forest or woodland bird, generalist, opencountry bird, water bird no definition

both

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153

Mac Nally R, Soderquist TR, Tzaros C (2000) The conservation value of mesic gullies in dry forest landscapes: avian assemblages in the box-ironbark ecosystem of southern Australia. Biol Conserv 93:293–302 Mac Nally R. 1994. Habitat-specific guild structure of forest birds in southeastern Australia: a regional scale perspective. Journal of Animal Ecology 63: 988–1001. Mac Nally R. and Horrocks G. 2002. Relative influences of site, landscape and historical factors on birds in a fragmented landscape. Journal of Biogeography 29: 395– 410. Mac Nally, R. (2007). "Consensus weightings of evidence for inferring breeding success in broad-scale bird studies." Austral Ecology 32(5): 479-484. Mac Nally, R. 1997. Population densities in a bird community of a wet sclerophyllous Victorian forest. Emu 97:253–258. Mac Nally, R. and G. Horrocks (2002). "Relative Influences of Patch, Landscape and Historical Factors on Birds in an Australian Fragmented Landscape." Journal of Biogeography 29(3): 395-410. Mac Nally, R. C. 1995b. A protocol for classifying regional dynamics, exemplified by using woodland birds in southeastern Australia. – Aust. J. Ecol. 20: 442–454. Mac Nally, R., A. F. Bennett, et al. (2009). "Collapse of an avifauna: Climate change appears to exacerbate habitat loss and degradation." Diversity and Distributions 15(4): 720-730. Mac Nally, R., G. Horrocks, et al. (2002). "Nestedness in fragmented landscapes: birds of the box-ironbark forests of south-eastern Australia." Ecography 25(6): 651-660. Mac Nally, R., M. Bowen, et al. (2011). "Despotic, highimpact species and the subcontinental scale control of avian assemblage structure." Ecology 93(3): 668-678. Mac Nally, R., Parkinson, A., Horrocks, G., Conole, L., Tzaros, C., 200 I. Relationships between terrestrial vertebrate diversity, abundance and availability of coarse woody debris on south-eastern Australian floodplains. Bioi. Cons. 99: 191205. MacNally R. & Bennett A. F. (1997) Species-specific predictions of the impact of habitat fragmentation: local extinction of birds in the Box-Ironbark forests of central Victoria, Australia. Biol. Conserv. 82, 147–55. Major R.E., Christie F.J. and Gowing G. 2001. Influence of remnant and landscape attributes on Australian woodland bird communities. Biological Conservation 102: 47–66.

no definition

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no definition

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no definition

3 target species

no definition

no list

no definition

no list

not relevant?

no list

no definition

not enough info in list no list

occurrence in woodlands, excluding introduced, nocturnal and exotic species no definition

bushbirds

no definition

woodland only

no definition

no list

no definition

no list

a synthesis of Lynch and Saunders, 1991; Saunders and de Rebeira 1991; Barrett et al., 1994; Robinson and Traill, 1996; Bennett and Ford, 1997; Fisher and

both

154

Maron, M. (2008). "Size isn't everything: The importance of small Remnants to the conservation of woodland birds in Australia." Australian Field Ornithology 25(2): 53-58. Maron, M., A. Main, et al. (2011). "Relative influence of habitat modification and interspecific competition on woodland bird assemblages in eastern Australia." Emu 111(1): 40-51. Martin TG, Possingham HP (2005) Predicting the impact of livestock grazing on birds using foraging height data. Journal of Applied Ecology 42: 400–408. Martin W. K., Eyears-Chaddock M., Wilson B. R. & Lemon J. (2004) The value of habitat reconstruction to birds at Gunnedah, New SouthWales. Emu 104, 177–89.

Goldney, 1997; Watson et al., 2000 no definition

no list

species judged by the authors to be dependent on woodland habitat no definition

no list

no definition

birds not clearly assigned as woodland /non woodland no list

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Martin, T. G. and S. McIntyre (2007). "Impacts of Livestock Grazing and Tree Clearing on Birds of Woodland and Riparian Habitats Martin, T. G., P. M. Kuhnert, et al. (2005). "The power of expert opinion in ecological models using Bayesian methods: Impact of grazing on birds." Ecological Applications 15(1): 266-280.

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McCollin, D. (1998). "Forest edges and habitat selection in birds: a functional approach." Ecography 21(3): 247-260. McGinness, H. M., A. D. Arthur, et al. (2010). "Woodland bird declines in the Murray-Darling Basin: Are there links with floodplain change?" Rangeland Journal 32(3): 315-327. Miller, J. R. and P. Cale (2000). "Behavioral mechanisms and habitat use by birds in a fragmented agricultural landscape." Ecological Applications 10(6): 1732-1748. Montague-Drake R, Lindenmayer DB, Cunningham R (2009) Factors effecting site occupancy by woodland bird species of conservation concern. Biol Conserv 142:2896–2903 Montague-Drake, R., D. B. Lindenmayer, et al. (2011). "A reverse keystone species affects the landscape distribution of woodland avifauna: a case study using the Noisy Miner (Manorina melanocephala) and other Australian birds." Landscape Ecology 26(10). Olsen P, Weston M, Tzaros C, Silcocks A (2005) The state of Australia’s birds 2005: woodlands and birds, Wingspan 15(4) supplement, p 32

no definition

31 woodland species only (may not have collected data on nonwoodland birds) no list

no definition

no list

presence

woodland only

no definition

na

13 example species no list

no definition

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expert opinion

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Olsen, P. (2008). The State of Australia’s Birds 2008. Supplement to Wingspan, Vol. 18, No. 4, Birds Australia: 21 Opdam, P., G. Rijsdijk, and F. Hustings. 1985. Bird communities in small woods in an agricultural landscape: effects of area and isolation. Biological Conservation 35: 333–352. Parkinson, A., Mac Nally, R., and Quinn, G. P. (2002). Differential macrohabitat use by birds on the unregulated Ovens River floodplain of southeastern Australia.River Research and Applications18(5), 495–506. doi:10.1002/rra.689 Paton, D. C., and O’Connor, J. (2009). The state of Australia’s birds 2009. Restoring woodland habitats for birds. Wingspan 20(Suppl. 1), 1–28. Paton, D. C., Carpenter, G., and Sinclair, R. G. (1994). A second bird atlas of the Adelaide region. Part 1: Changes in the distribution of birds: 1974–75 vs 1984–85. South Australian Ornithologist 31, 151–193. Polyakov, M., A. D. Rowles, et al. (2013). "Using habitat extent and composition to predict the occurrence of woodland birds in fragmented landscapes." Landscape Ecology 28(2): 329-341. Possingham, H. 2000. The extinction debt: The future of birds in the Mount Lofty Ranges. Environment South Australia, 8: 10. Radford, J. Q. and A. F. Bennett (2007). "The Relative Importance of Landscape Properties for Woodland Birds in Agricultural Environments." Journal of Applied Ecology 44(4): 737-747. Radford, J. Q., A. F. Bennett, et al. (2005). "Landscape-level thresholds of habitat cover for woodland-dependent birds." Biological Conservation 124(3): 317-337. Recher HF (1985) Synthesis: A model of forest and woodland bird communities. In: Keast A, Recher HF, Ford HA & Saunders D (eds) Birds of the eucalypt forests and woodlands: ecology, conservation and management. pp129– 35. Surrey-Beatty, Sydney Recher, H. F., and Davis, W. E. (2002). The foraging profile of a salmon gum woodland avifauna in Western Australia. Journal of the Royal Society of Western Australia 85, 103111

no definition

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no definition

no differentia ted list

no definition

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no definition

no list

not stated

29 woodland birds listed no list

Recher, H. F., and Holmes, R. T. (1985). Foraging ecology and seasonal patterns of abundance in a forest avifauna. In ‘Birds of Eucalypt Forests and Woodlands: Ecology, Conservation, Management’. (Eds A. Keast, H. F. Recher, H. Ford and D. Saunders.) pp. 79–96. (Surrey Beatty: Sydney.)

no definition

no definition

expert opinion

both (same as 2005)

expert opinion

both (same as 2007) no list

no definition

no definition

birds not differentia ted between woodland and nonwoodland no list

156

Recher, H. F., and W. E. Davis. 1998. The foraging profile of a wandoo woodland avifauna in early spring. Australian Journal of Ecology 23:514–527.

no definition

Recher, H. F., J. D. Majer, and S. Ganesh. 1996. Eucalypts, arthropods and birds: on the relation between foliar nutrients and species richness. Forest Ecology and Management 85:177–195. Recher, H. F., R. T. Holmes, et al. (1985). "Foraging patterns of breeding birds in eucalypt forest and woodland of southeastern Australia." Australian Journal of Ecology 10(4): 399-419.

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Reid J. W. R. (2000) Threatened and Declining Birds in the New South Wales Sheep-Wheat Belt: Landscape Relationships – Modelling Bird Atlas Data AgainstVegetation Cover. Consul- tancy report to NSW National Parks and Wildlife Service, Sydney. Reid J. W. R. (2000) Threatened and Declining Birds in the New South Wales Sheep-Wheat Belt: Landscape Relationships – Modelling Bird Atlas Data AgainstVegetation Cover. Consul- tancy report to NSW National Parks and Wildlife Service, Sydney. Reid, J. R. W. (1999). Threatened and declining birds in the New South Wales Sheep-Wheat Belt: I. Diagnosis, characteristics and management. Consultancy report to NSW National Parks and Wildlife Service. CSIRO Sustainable Ecosystems, Canberra. Ridpath, M. G. and R. E. Moreau (1966). "The birds of Tasmania: Ecology and evolution." Ibis 108(3): 348-393. Robinson D. 1991. Threatened birds in Victoria: their distributions, ecology and future. Vic. Nat. 3: 67–75. Robinson D. and Traill B.J. 1996. Conserving woodland birds in the wheat and sheep belts of southern Australia. Supplement to Wingspan RAOU. Robinson, D. (1994). Research plan for threatened woodland birds of southeastern Australia. Arthur Rylah Institute for Environmental Research Technical Report Series No. 133. SAC (2000). Final Recommendation on a nomination for listing: Victorian temperate-woodland bird community (nomination No. 512). Scientific Advisory Committee, Flora and Fauna Guarantee. Department of Natural Resources and Environment: Melbourne.

expert and expert adapted study

no definition

woodland only (no possiblity for non woodland ) no list

list of forest and woodland birds not differentia ted both (adapted bennet and ford)

expert and expert adapted study

both (author's definition)

no definition

no list

no definition

no list

no definition

no list

no definition

no list

no definition

partial list

nomination

woodland only: all other species are non woodland (fill in with all other victorian birds as 0)

157

Saunders D.A. and de Rebeira C.P. 1991. Values of corridors to avian populations in a fragmented landscape. In: Saunders D.A. and Hobbs R.J. (eds), Nature Conservation 2: the Role of Corridors. Surrey Beatty and Sons, Chipping Norton, Australia, pp. 221–240. Saunders D.A. and Ingram J. 1995. Birds of Southwestern Australia: an Atlas of Changes in Distribution and Abundance of the Wheatbelt Fauna. Surrey Beatty and Sons, Chipping Norton, Australia. Scotts D. J. (1991) Old-growth forests: their ecological characteristics and value to forest-dependent vertebrate fauna of south-east Australia. In: Conservation of Australia’s Forest Fauna (ed. D. Lunney) pp. 147–59. Royal Zoological Society of New SouthWales, Mosman, Sydney Seddon, J. A., Briggs, S. V. and Doyle, S. J. 2001. Birds in woodland remnants of the central wheat/sheep belt of New South Wales. Report to the Natural Heritage Trust. NSW NationalParks and Wildlife Service, Sydney. Seddon, J. A., S. V. Briggs, et al. (2003). "The relationship between bird species and characteristics of woodland remnants in central New South Wales." Pacific Conservation Biology 9(2). Selwood, K., R. Mac Nally, et al. (2009). "Native bird breeding in a chronosequence of revegetated sites." Oecologia 159(2): 435-446. Shanahan, D. F., H. P. Possingham, et al. (2011). "Foraging height and landscape context predict the relative abundance of bird species in urban vegetation patches." Austral Ecology 36(8): 944-953. Silcocks A, Tzaros C, Weston M, Olsen P (2005) An interim guild classification for woodland and grassland birds in Australia. Birds Australia Supplementary Report to State of the Environment Report 2006, Carlton

Stagoll, K., A. D. Manning, et al. (2010). "Using bird-habitat relationships to inform urban planning." Landscape and Urban Planning 98(1): 13-25. Sunnucks P (2011) Towards modelling persistence of woodland birds: the role of genetics. Emu 111(1):19–39 Szabo, J. K., R. A. Fuller, et al. (2012). "A comparison of estimates of relative abundance from a weakly structured mass-participation bird atlas survey and a robustly designed monitoring scheme." Ibis 154(3): 468-479.

no definition

no list

no definition

no list

no definition

no list

no definition

no list

process of elimination

both

no definition

no list

no definition

no list

expert + Garnett, S.T, Crowley, G.M., Fitzherbert, K. and S. Bennett 1992. Life history characteristics of Australian birds. Royal Australasian Ornithologists Union and Australian National Parks and Wildlife Service. silcocks et al 2005

both

no definition

no list

HANZAB

both

both

158

Szabo. S., A. V. Peter, et al. (2011). "Paying the extinction debt: woodland birds in the Mount Lofty Ranges, South Australia." Emu 111(1): 59-70.

expert opinion

Tassicker, A., Kutt, A., Vanderduys, E.M.,S., 2006. The effects of vegetation structure on the birds in a tropical savanna woodland in north-eastern Australia. Rangeland J. 28, 139– 152. Taws, N. 2000. The Greening Australia birdwatch project. Canberra Bird Notes 25: 89-94. Thompson, L., Jansen, A., Robertson, A., 2002. The Responses of Birds to Restoration of Riparian Habitat on Private Properties. Johnstone Centre Report No. 163. Charles Sturt University, Albury. Thomson, J. R., R. Mac Nally, et al. (2007). "Predicting bird species distributions in reconstructed landscapes." Conservation Biology 21(3): 752-766. Traill B, Duncan S (2000) Status of birds in the New South Wales temperate woodlands region. Australian Woodlands Conservancy, Chiltern Watson D.M., Mac Nally R. and Bennett A.F. 2000. The avifauna of severely fragmented, Buloke Allocasuarina luehmanni woodland, in western Victoria, Australia. Pacific Conservation Biology 6: 46–60. Watson, D. M. (2003). "Comparative evaluation of new approaches to survey birds." Wildlife Research 31(1): 1-11. Watson, D. M. 2011. A productivity-based explanation for woodland bird declines: poorer soils yield less food. – Emu 111: 10–18. Watson, D. M. and M. Herring (2012). "Mistletoe as a keystone resource: an experimental test." Proceedings of the Royal Society B: Biological Sciences 279(1743): 38533860. Watson, D. M., H. W. McGregor, et al. (2011). "Hemiparasitic shrubs increase resource availability and multi-trophic diversity of eucalypt forest birds." Functional Ecology 25(4): 889-899. Watson, D.M., 2002. Effects of mistletoe on diversity: a casestudy from southern New South Wales. Emu 102, 275–281. Watson, J. E. M., R. J. Whittaker, et al. (2005). "Bird Community Responses to Habitat Fragmentation: How Consistent Are They across Landscapes?" Journal of Biogeography 32(8): 1353-1370.

no definition

both (provided by authors) no list

no definition

no list

no definition

no list

expert?

both

no definition

no list

no definition

no list

no definition

both

no definition

no definition

declining woodland birds only no list

no definition

no list

expert?

both

no definition

Watson, J., Freudenberger, D., and Paull, D. (2001). An assessment of the focal-species approach for conserving birds in variegated landscapes in southeastern Australia. Conservation Biology 15(5), 1364–1373. doi:10.1046/j.15231739.2001.00166.x

Canberra Ornithologists group 1992, Robinson and Trail 1996

both: provided by James Watson (same as 2003+200 1) both (same as 2005+200 3)

159

Watson, J., Watson, A., Paull, D. and Freudenberger, D. 2003. Woodland fragmentation is causing the decline of species and functional groups of birds in southeastern Australia. Pacific Conservation Biology 8: 261-70. Westphal, M. I., S. A. Field, et al. (2003). "Effects of landscape pattern on bird species distribution in the Mt. Lofty Ranges, South Australia." Landscape Ecology 18(4).

Canberra Ornithologists group 1992, Robinson and Trail 1996 no definition

Woinarski, J. C. Z., J. C. McCosker, et al. (2006). "Monitoring change in the vertebrate fauna of central Queensland, Australia, over a period of broad-scale vegetation clearance, 1973-2002." Wildlife Research 33(4): 263-274. Yen, J. D. L., J. R. Thomson, et al. (2011). "To what are woodland birds responding? Inference on relative importance of in-site habitat variables using several ensemble habitat modelling techniques." Ecography 34(6): 946-954.

no definition

These groupings were based on habitat requirements (opencountry, opentolerant, woodlanddependent)

both (same as 2005+200 1) small subset given, only woodland birds no list

both

160

Appendix 2.3. Details of the Garrard et al. 2012 model as used in this thesis To determine whether inconsistency in use of the term ‘woodland bird’ has consequences for inference and/or conservation, I investigated how variation in woodland bird classification affected the findings and interpretation of a published ecological study. Garrard et al. (2012) developed a Bayesian model that investigated the relationship between dispersal ability and the vulnerability of woodland birds to landscape-scale changes in habitat fragmentation. They used information from published studies to build a Bayesian model of avian natal dispersal distance based on sex, body mass, wingspan and feeding guild. Once dispersal distances were estimated for each species, they used another Bayesian model to investigate the influence of dispersal ability on the probability of these species occurring in landscapes with varying levels of tree cover aggregation (Radford and Bennett 2007). Aggregation was measured such that high scores represent landscapes comprised of large patches of vegetation (Bennett et al. 2006). Using logistic regression, they modelled pij, the prevalence of species i in landscape j, as a function of landscape tree cover aggregation (aggj): logit (pij) = ĸi + γi ln(aggj) + ηj + φij Yij ~ binomial (pij, 40) ĸi and γi are the intercept and regression coefficient for species i, j and ij are random effects for landscape and the interaction between species and landscape and Yij is the observed number of presences of species i in landscape j. The parameters ηj, φij and ĸi were assumed to have been drawn from a Normal distribution where the mean of that distribution is drawn from another Normal distribution which has a mean of zero and a standard deviation of 1000. The standard deviation was drawn from a uniform distribution, ranging between 0 and 100. γi is an estimate of the species level response to habitat aggregation. Garrard et al. found that all species were more prevalent in landscapes that had more aggregated tree cover, however the magnitude of this response varied between species. They investigated whether variation in response to tree cover aggregation could be explained by dispersal ability using a second regression: γi = θ + δlnDi + ζi,

161

where Di is the predicted median natal dispersal distance of species i, θ is the intercept, δ is the regression coefficient for the relationship between median dispersal distance and increasing tree cover aggregation, and ζi is a random effect for residual variation at the species level. θ and δ were drawn from uninformative Normal prior distributions (mean = 0, sd = 1000) while ζi had a Normal prior distribution with a mean of zero and a standard deviation which was estimated from the data. Garrard et al. (2012) found a negative relationship between predicted dispersal ability and response to habitat aggregation, indicating that species with short median natal dispersal distances declined more steeply with fragmentation of tree cover than species with better dispersal ability. This has implications for understanding the impact of habitat fragmentation on woodland bird species and the potential ameliorating influence of habitat corridors and stepping stones. Garrard et al (2012) noted that, while they restricted analyses to woodland birds, some species may respond differently to the fragmentation of tree cover in the landscape. Species may be able to utilize and move through the treeless matrix differently. I expect that species which are more consistently classified as woodland birds are likely to depend more strongly on tree cover (have high values for γi), while those species less consistently classified as woodland birds might be better able to utilize and move through cleared land. To investigate this, I compared how the estimated effect of landscape aggregation (γ i) on prevalence (pij) of bird species depended on which species were included in the analysis. To do this, I repeated Garrard et al.’s (2012) analysis but used sets of species chosen to represent different levels of agreement about woodland-dependence. Whether a species was considered a ‘woodland bird’ was determined by the frequency with which it appeared on woodland birds lists, as described above. I re-fit the model 9 times on different combinations of species: 1) including all species, 2) species listed as woodland birds in 10% or more of studies, 3) listed as woodland birds in 20% or more of studies, 4) 30% or more, 5) 40% or more, 6) 50% or more, 7) 60% or more, 8) 70% or more, and 9) 80% or more. I examined how the estimate of mean γ i depended on the choice of species and compared it to the original estimate from Garrard et al (2012). Models were run in R using Jags and code is as follows:

162

#load data file bird <- read.csv('~/../Desktop/radford reanalysis file.csv') #install.packages('reshape2') #install.packages('R2jags') library('reshape2') library('R2jags') #format data data <- list( #form the data into list format Y = as.matrix(t(bird[-139,-c(1,26:27)])), #make the occurrence data into a matrix called Y predbird = scale(bird[-c(139),26])[,1]/2,# add in a vector for predbird (without lnAgg)(scaled and centered) wood = bird[-c(139),27]/100, # add in a vector for wood (without lnAgg) (scaled and centred using an arcsine transformation) lnAgg = as.vector(t(bird[139,-c(1,26,27)])) #Add in the vector lnAgg (without the site, predbird or wood variables being caught in) ) #input initial values inits <- function(){ #supply inits, can also supply as values from a distribution eg sdbin=runif(1),mbr=rnorm(1) list( sdbin = runif(1), sdland = runif(1), mbr = rnorm(1), sdbr = runif(1), ma = rnorm(1), br_dd =rnorm(1), sda = runif(1) ) } #input winbugs model from Garrard et al. 2012 below model <- function(x) { for (i in 1:138) # 138 bird species or change to however many species you want to include (listed in dataset from most to least consistently classified as woodland birds) { lnmed_dd[i] ~ dnorm(predbird[i], 0.352) # Include uncertainty in dispersal estimates. Common sd in ln med dispersal distances across all spp. a[i] ~ dnorm(ma, taua) # prior for a, with a mean ma and a precision taua br[i] <- mbr + br_dd*lnmed_dd[i] + rebr[i] # effect of predicted dispersal distance on response to landscape tree cover aggregation coefficient, br. (br_dd is the effect of dispersal distance on the occupancy regression coefficient br) rebr[i] ~ dnorm(0, taubr) # prior for rebr with a mean of 0 and a precision of taubr # incidence of species in the landscape for (j in 1:24) # 24 landscapes { extrabin[i, j] ~ dnorm(0, taubin) # extra variance around the occurrence of species

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logit(p[i, j]) <- a[i] + br[i] * lnAgg[j] + extraland[j] + extrabin[i, j] # equation for incidence in the land logit probability of incidence equals intercept plus effect of predicted dispersal difference plus fragmentation plus extra variance around fragmentation plus extra variance around the occurrence Y[j, i] ~ dbin(p[i, j], 40) # values for y depend on species and landscape and are drawn from a binomial distribution } } meanbr <- mean(br[]) for (j in 1:24) # 24 landscapes { extraland[j] ~ dnorm(0, tauland) # prior for the extra variance attributed to landscape with a mean of 0 and a precision of tauland } #specify uniformative priors ma ~ dnorm(0, 1.0E-4) mbr ~ dnorm(0, 1.0E-4) br_dd ~ dnorm(0, 1.0E-4) sda ~ dunif(0, 100) sdbr ~ dunif(0, 100) sdbin ~ dunif(0, 100) sdland ~ dunif(0, 100) #specify equations taua <- 1 / (sda * sda) taubr <- 1 / (sdbr * sdbr) taubin <- 1 / (sdbin * sdbin) tauland <- 1 / (sdland * sdland) } set.seed(123) #run model for 100,000 iterations model1 <-jags(data=data, inits=NULL, parameters.to.save=c('meanbr','br_dd','sdbr','sda','sdbin','sdland','br'), model.file=model, n.chains= 3, n.iter= 100000)

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Appendix 2.4. Moving Window Analysis We listed species in order of descending percentage woodland classification (the number of studies in which it is classified as a woodland bird divided by the total number of the studies it is recorded in). I divided this list into 5 sections to perform a moving window analysis (in which each section represents one window), investigating the effect that including different species has on the estimated effect of tree cover aggregation on species prevalence. Window 1 contained the fifth of the species which were most consistently regarded as woodland birds; window 2 contained the next fifth of species and so on. The number of species (n=126) did not divide evenly into 5 so one extra species was included in the first window; this was deemed least likely to bias the results towards finding a benefit of consistent classification if none existed. I fit the model (see Appendix 2.2. for details) once for each ‘window’ of species and compared the results with each other and with the results found by Garrard et al (2012). This analysis differs from that presented in chapter 2 (Figure 2.2) in that all of the points represent independent samples of species. This is beneficial as it allows for a direct comparison of the estimates and confidence intervals between different runs of the model (represented as points on the graph). However, it does not represent realistic lists of species. It is very unlikely that someone would classify the 25 species with the lowest percentage woodland classification as woodland birds and none of the species which are more commonly regarded as woodland birds.

165

Effect of tree cover aggregation

12 10 8 6 4 2 0 -2 -4 0

1

2

3

4

5

Window Figure A.2.2.1: Moving window analysis of estimated effect of tree cover aggregation on bird prevalence. The points to the left of the graph represent windows which include species that are most consistently regarded as woodland birds; those to the right are regarded as woodland birds infrequently. The mean estimate from the original Garrard et al. (2012) model is represented by the line and the 95% credible intervals by the grey shaded area.

Figure A2.2.1 shows that as species are less commonly regarded as woodland birds (moving to the right of the graph) their reliance on high tree cover aggregation decreases. In fact, species included in window 4 are not related to the effect of tree cover aggregation and those in window 5 show a negative effect. Importantly, the estimates for these last two windows differ significantly from the estimates from Garrard et al (2012), despite the fact that, depending on how woodland birds are classified, any of these species may be regarded as woodland birds.

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Appendix 2.5. Expert Survey Below is a plain text version of the survey woodland bird experts undertook as part of the data collection undertaken for chapter 2.

Introduction Dear Woodland Bird Researcher, This survey is part of a research project in the Quantitative and Applied Ecology (QAECO) group in the Botany Department, University of Melbourne, Melbourne, Australia. We are investigating why lists of woodland bird species differ among studies. We will ask questions about how you choose which species to call woodland birds and why you think that lists of woodland birds differ among studies. Regards, Hannah Pearson, QAECO Group, Botany Department, University of Melbourne, Victoria 3010, Australia

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Experience First, we want to know whether you have ever listed woodland birds for a study.

1.

Did you develop the list of woodland birds used in any of the studies listed in the invitation email? Yes, alone Yes, along with others No

If you answered ‘yes, along with others’ or no, please provide the names and affiliations of those who did.

2.

Have you developed a list of woodland bird species for any other studies or reports? Yes, alone Yes, along with others No

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Understanding Classification Now, we want to find out whether you think that different lists of woodland birds might be useful.

3.

It is possible that the aim of the project might dictate which species are listed as woodland birds. For example, the list of woodland birds might be different in a study that looks the richness of woodland birds in forests compared to a study of species richness on farmland. Please rate the following statement:

Researchers list different species as woodland birds depending on the aims of their study. Neither Agree nor Disagree Strongly Disagree

Disagree

Agree

Strongly Agree

4.

Studies list different species as woodland birds. Some of these lists might be more suited to answering certain questions than others. Please rate the following statement:

Using a standardised list of woodland birds would make it difficult to answer certain research questions. Neither Agree nor Disagree Strongly Disagree

Disagree

Agree

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169

How you choose woodland bird species Next, we want to find out how you choose which species are woodland birds Which characteristics influence whether you call a species a ‘woodland bird’? Please check all that apply.

5.

Present in woodlands

Prefers large areas of vegetation

Occurs more frequently in woodlands than in other habitats

Native/introduced

Nests in woodlands Classified as a woodland bird in a field guide/ bird handbook Forages in woodlands

Classified as a woodland bird by another author

Shelters in woodlands

Not wetland

Intolerant of fragmented sites

Not nocturnal

Intolerant of degraded sites (eg. grazed sites)

Not raptor

Other (please specify)

Which characteristics do you use to identify a site with ‘woodland’ vegetation? Please check all that apply.

6.

Presence of trees

Presence of shrubs

Tree species

Shrub species

Tree height

Shrub cover

Tree density

Soil properties

Canopy cover

Woodland EVC

Other (please specify)

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Difference among studies Now we want to investigate why lists of woodland birds differ between studies

7.

Researchers list different species as woodland birds. On a scale of 0 to 10, how much does each of the following options contribute to this? Please assign a score of 10 to the option/s that you believe contributes the most to the use of different lists of woodland birds among studies. Score the other options based on their contribution relative to that. If you don't believe an option contributes at all, assign it a 0. For example, if you believe that 'different aims in research' influences people to list different species the most you would assign it a 10. If you think that 'regional differences in the behaviour or habitat requirements of species' contributes around half as much, please assign it a value of 5. 0

1

2

3

4

5

6

7

8

9

10

Different aims of research Different ideas about what constitutes woodland vegetation

Different ideas about how to determine whether species rely on woodland vegetation

Uncertainty about the behaviour or habitat requirements of different species

Uncertainty about the distribution of the species in woodland and non woodland areas

Regional differences in the behaviour or habitat requirements of species

Regional differences in the distribution of species in woodland and non woodland areas

Other (please specify)

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Additional insights Lastly we want to see if you've got any other thoughts about the classification of woodland birds

8. Why do you think researchers use different lists of woodland birds?

9.

Do you think that including different species in lists of woodland birds is a problem for ecology research? Yes

No

10.

Do you have any suggestion about how to standardise woodland bird classification?

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Woodland Bird Classification

Conclusion Thank you for your time. Your response to this survey will help us understand the classification of woodland birds. We hope to use these data to improve woodland bird classification. If you do not wish to be contacted with further questions, please email me at [email protected]

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Appendix 2.6. Species list and check for biases Table A.2.6.1 includes a list of all species found in at least 10 species lists from the systematically reviewed articles used in chapter 2. These are the species included in subsequent analyses in chapter 2. Table A.2.6.2 the species that were found in 10 or fewer species lists and were therefore excluded from further analyses. Both tables present information about the number of species lists including the species and the percentage of those lists that refer to the species as a woodland bird. Table A.2.6.3 shows the number of species in different orders or birds that were present in 10 or fewer lists and therefore excluded from chapter 2 analyses. The intention of this table is to clarify any possible taxonomic biases in the chapter 2 analyses. Table A.2.6.4 shows the number of articles analysed in chapter 2 that cover the different states and territories of Australia. The intention of this table is to clarify any possible regional biases in the chapter 2 analyses.

Table A.2.6.1: Species present in 10 or more lists.

Order

Family

Species

Accipitriformes Accipitriformes Anseriformes Anseriformes Anseriformes Apodiformes Caprimulgiformes Caprimulgiformes Casuariiformes Charadriiformes Charadriiformes Ciconiiformes Ciconiiformes Columbiformes Columbiformes Columbiformes Columbiformes Columbiformes Coraciiformes Coraciiformes

Accipitridae Accipitridae Anatidae Anatidae Anatidae Apodidae Aegothelidae Podargidae Dromaiidae Burhinidae Charadriidae Threskiornithidae Threskiornithidae Columbidae Columbidae Columbidae Columbidae Columbidae Alcedinidae Coraciidae

Black-shouldered Kite Whistling Kite Australian Wood Duck Pacific Black Duck Australian Shelduck White-throated Needletail Australian Owlet-Nightjar Tawny Frogmouth Emu Bush Stone-curlew Masked Lapwing Australian White Ibis Straw-necked Ibis Common Bronzewing Crested Pigeon Peaceful Dove Diamond Dove Spotted Turtle-dove Azure Kingfisher Dollarbird

% articles listed as woodland bird 0.00 27.78 0.00 0.00 0.00 20.00 77.78 52.63 13.33 83.33 5.26 0.00 0.00 87.88 9.68 87.50 63.64 10.00 70.00 81.82

# articles 18 18 21 18 11 10 18 19 15 12 19 14 14 33 31 24 11 10 10 22 174

Order

Family

Species

Coraciiformes Coraciiformes Coraciiformes Cuculiformes Cuculiformes Cuculiformes Cuculiformes Cuculiformes Cuculiformes Falconiformes Falconiformes Falconiformes Falconiformes Falconiformes Falconiformes Falconiformes Falconiformes Falconiformes Falconiformes Galliformes Galliformes Gruiformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes

Halcyonidae Halcyonidae Meropidae Cuculidae Cuculidae Cuculidae Cuculidae Cuculidae Cuculidae Accipitridae Accipitridae Accipitridae Accipitridae Accipitridae Falconidae Falconidae Falconidae Falconidae Falconidae Phasianidae Phasianidae Rallidae Acanthizidae Acanthizidae Acanthizidae Acanthizidae Acanthizidae Acanthizidae Acanthizidae Acanthizidae Acanthizidae Acanthizidae Acanthizidae Alaudidae Artamidae Artamidae Artamidae Artamidae Artamidae Artamidae Artamidae Artamidae

Laughing Kookaburra Sacred Kingfisher Rainbow Bee-eater Horsfield’s Bronze-Cuckoo Pallid Cuckoo Fan-tailed Cuckoo Shining Bronze-cuckoo Brush Cuckoo Black-eared Cuckoo Little Eagle Brown Goshawk Wedge-tailed Eagle Collared Sparrowhawk Spotted Harrier Brown Falcon Nankeen Kestrel Australian Hobby Peregrine Falcon Black Falcon Stubble Quail Brown Quail Dusky Moorhen Weebill Yellow-rumped Thornbill Brown Thornbill Yellow Thornbill White-browed Scrubwren Western Gerygone White-throated Gerygone Speckled Warbler Southern Whiteface Chestnut-rumped Heathwren Chestnut-rumped Thornbill Skylark Australian Magpie Dusky Woodswallow Grey Butcherbird Pied Currawong Pied Butcherbird White-browed Woodswallow Grey Currawong Masked Woodswallow

% articles listed as woodland bird 51.35 87.50 23.08 83.33 37.93 81.48 83.33 62.50 66.67 16.67 34.78 9.09 33.33 0.00 8.00 0.00 13.64 15.00 9.09 9.09 25.00 0.00 94.29 35.29 96.88 93.55 93.33 88.00 87.50 100.00 90.00 60.00 84.62 0.00 22.86 84.85 54.84 65.52 26.92 40.00 40.00 33.33

# articles 37 32 26 30 29 27 24 16 12 24 23 22 21 12 25 25 22 20 11 22 16 10 35 34 32 31 30 25 24 23 20 15 13 10 35 33 31 29 26 25 20 18 175

Order

Family

Species

Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes

Campephagidae Campephagidae Campephagidae Cinclosomatidae Climacteridae Climacteridae Corcoracidae Corcoracidae Corvidae Corvidae Dicaeidae Estrildidae Estrildidae Estrildidae Estrildidae Fringillidae Hirundinidae Hirundinidae Hirundinidae Locustellidae Locustellidae Maluridae Maluridae Meliphagidae Meliphagidae Meliphagidae Meliphagidae Meliphagidae Meliphagidae Meliphagidae Meliphagidae Meliphagidae Meliphagidae Meliphagidae Meliphagidae Meliphagidae Meliphagidae Meliphagidae Meliphagidae Meliphagidae Meliphagidae Meliphagidae

Black-faced Cuckoo-shrike White-winged Triller White-bellied Cuckoo-shrike Spotted Quail-thrush White-throated Treecreeper Brown Treecreeper White-winged Chough Apostlebird Australian Raven Little Raven Mistletoebird Red-browed Finch Diamond Firetail Double-barred Finch Zebra Finch European Goldfinch Welcome Swallow Tree Martin Fairy Martin Rufous Songlark Brown Songlark Superb Fairy-wren Variegated Fairy-wren Brown-headed Honeyeater Noisy Miner Red Wattlebird Noisy Friarbird White-plumed Honeyeater Yellow-faced Honeyeater Eastern Spinebill White-eared Honeyeater White-naped Honeyeater Fuscous Honeyeater Black-chinned Honeyeater Blue-faced Honeyeater Little Friarbird New Holland Honeyeater White-fronted Chat Yellow-tufted Honeyeater Crescent Honeyeater Striped Honeyeater Little Wattlebird

% articles listed as woodland bird 45.71 82.14 83.33 70.00 94.29 100.00 85.29 92.86 19.35 13.64 87.50 69.23 95.65 56.25 7.69 5.88 6.06 50.00 5.56 25.93 14.29 65.71 50.00 91.43 45.71 82.86 90.63 81.25 83.87 89.66 86.21 88.89 90.00 89.47 89.47 88.89 38.89 0.00 87.50 71.43 71.43 46.15

# articles 35 28 18 10 35 27 34 14 31 22 32 26 23 16 13 17 33 28 18 27 14 35 12 35 35 35 32 32 31 29 29 27 20 19 19 18 18 16 16 14 14 13 176

Order

Family

Species

% articles listed as woodland bird

# articles

Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes

Meliphagidae Meliphagidae Meliphagidae Monarchidae Monarchidae Monarchidae Monarchidae Motacillidae Neosittidae Oreoicidae Oriolidae Pachycephalidae Pachycephalidae Pachycephalidae Pachycephalidae Pardalotidae Pardalotidae Pardalotidae Pardalotidae Pardalotidae Passeridae Petroicidae Petroicidae Petroicidae Petroicidae Petroicidae Petroicidae Petroicidae Pomatostomidae Pomatostomidae Ptilonorhynchidae Rhipiduridae Rhipiduridae Rhipiduridae Sturnidae Turdidae Zosteropidae

Painted Honeyeater Spiny-cheeked Honeyeater Singing Honeyeater Magpie-lark Restless Flycatcher Leaden Flycatcher Satin Flycatcher Australian Pipit Varied Sittella Crested Bellbird Olive-backed Oriole Rufous Whistler Grey Shrike-thrush Golden Whistler Crested Shrike-tit Spotted Pardalote Striated Pardalote Striated Thornbill Buff-rumped Thornbill Inland Thornbill House Sparrow Eastern Yellow Robin Jacky Winter Scarlet Robin Red-capped Robin Flame Robin Hooded Robin Rose Robin White-browed Babbler Grey-crowned Babbler Satin Bowerbird Grey Fantail Willie Wagtail Rufous Fantail Common Starling Common Blackbird Silvereye

92.31 75.00 80.00 11.43 53.33 84.21 50.00 5.88 96.77 76.92 88.46 97.30 94.44 91.43 90.00 94.44 52.94 93.75 93.55 75.00 0.00 96.77 96.67 90.00 100.00 31.82 90.48 60.00 95.65 100.00 63.64 92.11 22.22 72.73 9.09 6.25 42.42

13 12 10 35 30 19 10 17 31 13 26 37 36 35 30 36 34 32 31 12 14 31 30 30 24 22 21 10 23 18 11 38 36 11 22 16 33

Pelecaniformes Psittaciformes Psittaciformes Psittaciformes

Ardeidae Cacatuidae Cacatuidae Cacatuidae

White-faced Heron Galah Sulphur-crested Cockatoo Little Corella

0.00 11.43 11.76 9.09

17 35 34 22

177

Order

Family

Species

Psittaciformes Psittaciformes Psittaciformes Psittaciformes Psittaciformes Psittaciformes Psittaciformes Psittaciformes Psittaciformes Psittaciformes Psittaciformes Psittaciformes Psittaciformes Psittaciformes Psittaciformes Psittaciformes Psittaciformes Strigiformes Strigiformes Turniciformes

Cacatuidae Cacatuidae Cacatuidae Cacatuidae Psittaculidae Psittaculidae Psittaculidae Psittaculidae Psittaculidae Psittaculidae Psittaculidae Psittaculidae Psittaculidae Psittaculidae Psittaculidae Psittaculidae Psittaculidae Strigidae Tytonidae Turnicidae

Cockatiel Gang-gang Cockatoo Yellow-tailed Black-Cockatoo Long-billed Corella Eastern Rosella Crimson Rosella Red-rumped Parrot Australian King Parrot Musk Lorikeet Superb Parrot Australian Ringneck Little Lorikeet Rainbow Lorikeet Swift Parrot Turquoise Parrot Budgerigar Purple-crowned Lorikeet Southern Boobook Barn Owl Painted Button-quail

% articles listed as woodland bird 5.88 68.75 69.23 10.00 34.29 75.00 25.81 55.00 70.00 100.00 26.67 100.00 30.77 100.00 100.00 20.00 70.00 64.71 10.00 94.74

# articles 17 16 13 10 35 32 31 20 20 16 15 15 13 11 11 10 10 17 10 19

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Table A.2.6.2. Species present in fewer than 10 lists.

Order

Family

Species

Podicipediformes Passeriformes Charadriiformes Psittaciformes Passeriformes Suliformes Passeriformes Pelecaniformes Columbiformes Passeriformes Passeriformes Strigiformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Anseriformes Passeriformes Cuculiformes Passeriformes Columbiformes Passeriformes Psittaciformes Columbiformes Falconiformes Charadriiformes Columbiformes Cuculiformes Passeriformes Psittaciformes Anseriformes Turniciformes Psittaciformes Pelecaniformes Coraciiformes Passeriformes Pelecaniformes Passeriformes Caprimulgiformes

Podicipedidae Turdidae Charadriidae Psittaculidae Pachycephalidae Phalacrocoracidae Meliphagidae Ardeidae Columbidae Meliphagidae Alaudidae Strigidae Meliphagidae Artamidae Acrocephalidae Sturnidae Cinclosomatidae Anatidae Campephagidae Cuculidae Estrildidae Columbidae Artamidae Psittaculidae Columbidae Accipitridae Recurvirostridae Columbidae Cuculidae Campephagidae Cacatuidae Anatidae Turnicidae Cacatuidae Ardeidae Halcyonidae Climacteridae Threskiornithidae Acanthizidae Caprimulgidae

Australasian Grebe Bassian Thrush Black-fronted Dotterel Bluebonnet Gilbert's Whistler Little Pied Cormorant Regent Honeyeater White-necked Heron Wonga Pigeon Yellow-throated Miner Singing Bushlark Barking Owl Black Honeyeater Black-faced Woodswallow Clamorous Reed-Warbler Common Mynah Eastern Whipbird Grey Teal Ground Cuckoo-shrike Pacific Koel Plum-headed Finch Rock Dove White-breasted Woodswallow Yellow Rosella Bar-shouldered Dove Black Kite Black-winged Stilt Brush Bronzewing Channel-billed Cuckoo Cicadabird Glossy Black Cockatoo Hardhead Little Button-quail Major Mitchell's Cockatoo Nankeen Night Heron Red-backed Kingfisher Red-browed Treecreeper Royal Spoonbill Shy Heathwren Spotted Nightjar

% articles # listed as articles woodland bird 0.00 9 44.44 9 0.00 9 0.00 9 88.89 9 0.00 9 88.89 9 0.00 9 33.33 9 55.56 9 0.00 9 100.00 8 37.50 8 37.50 8 0.00 8 0.00 8 62.50 8 0.00 8 25.00 8 37.50 8 37.50 8 0.00 8 50.00 8 75.00 8 57.14 7 14.29 7 0.00 7 28.57 7 42.86 7 42.86 7 85.71 7 0.00 7 0.00 7 42.86 7 0.00 7 57.14 7 28.57 7 0.00 7 57.14 7 71.43 7 179

Order

Family

Species

Falconiformes Passeriformes Passeriformes Passeriformes Charadriiformes Anseriformes Gruiformes Suliformes Passeriformes Charadriiformes Psittaciformes Psittaciformes Falconiformes Passeriformes Passeriformes Falconiformes Gruiformes Passeriformes Passeriformes Passeriformes Passeriformes Psittaciformes Accipitriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Caprimulgiformes Passeriformes Pelecaniformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Falconiformes Falconiformes Passeriformes Podicipediformes

Accipitridae Meliphagidae Hirundinidae Meliphagidae Charadriidae Anatidae Rallidae Phalacrocoracidae Artamidae Charadriidae Psittaculidae Psittaculidae Accipitridae Menuridae Corvidae Accipitridae Rallidae Meliphagidae Passeridae Cisticolidae Corvidae Psittaculidae Accipitridae Meliphagidae Maluridae Petroicidae Maluridae Ptilonorhynchidae Meliphagidae Caprimulgidae Maluridae Threskiornithidae Pardalotidae Motacillidae Estrildidae Meliphagidae Corvidae Falconidae Accipitridae Meliphagidae Podicipedidae

Swamp Harrier Tawny-crowned Honeyeater White-backed Swallow Yellow-plumed Honeyeater Banded Lapwing Black Swan Eurasian Coot Great Cormorant Little Woodswallow Red-kneed Dotterel Red-winged Parrot Scaly-breasted Lorikeet Square-tailed Kite Superb Lyrebird Torresian Crow White-bellied Sea-Eagle Black-tailed Native-hen Brown Honeyeater Eurasian Tree Sparrow Golden-headed Cisticola Little Crow Mulga parrot Pacific Baza Scarlet Honeyeater Southern Emu-Wren Southern Scrub-robin Splendid Fairy-wren Spotted Bowerbird White-cheeked Honeyeater White-throated Nightjar White-winged Fairy-wren Yellow-billed Spoonbill Yellow-rumped Pardalote Australasian Pipit Chestnut-breasted Mannikin Crimson Chat Forest Raven Grey Falcon Grey Goshawk Grey-fronted Honeyeater Hoary-headed Grebe

% articles # listed as articles woodland bird 0.00 7 14.29 7 14.29 7 42.86 7 0.00 6 0.00 6 0.00 6 0.00 6 33.33 6 0.00 6 33.33 6 33.33 6 66.67 6 16.67 6 33.33 6 16.67 6 0.00 5 60.00 5 0.00 5 0.00 5 0.00 5 60.00 5 40.00 5 40.00 5 20.00 5 40.00 5 40.00 5 40.00 5 20.00 5 60.00 5 20.00 5 0.00 5 20.00 5 0.00 4 25.00 4 0.00 4 50.00 4 0.00 4 25.00 4 50.00 4 0.00 4 180

Order

Family

Species

Accipitriformes Passeriformes Galliformes Passeriformes Psittaciformes Suliformes Strigiformes Psittaciformes Passeriformes Anseriformes Passeriformes Passeriformes Anseriformes Ciconiiformes Passeriformes Suliformes Passeriformes Psittaciformes Charadriiformes Suliformes Anseriformes Charadriiformes Passeriformes Charadriiformes Passeriformes Galliformes Pelecaniformes Charadriiformes Passeriformes Gruiformes Falconiformes Passeriformes Charadriiformes Accipitriformes Passeriformes Falconiformes Gruiformes Passeriformes Gruiformes Charadriiformes Anseriformes

Accipitridae Locustellidae Megapodiidae Meliphagidae Psittaculidae Phalacrocoracidae Strigidae Cacatuidae Meliphagidae Anatidae Estrildidae Meliphagidae Anatidae Ardeidae Pomatostomidae Anhingidae Dasyornithidae Psittaculidae Scolopacidae Phalacrocoracidae Anatidae Scolopacidae Meliphagidae Charadriidae Dicruridae Megapodiidae Pelecanidae Glareolidae Acrocephalidae Rallidae Accipitridae Monarchidae Scolopacidae Accipitridae Maluridae Accipitridae Gruidae Acanthizidae Rallidae Sternidae Anatidae

Letter-winged Kite Little Grassbird Malleefowl Orange Chat Pale-headed Rosella Pied Cormorant Powerful Owl Red-tailed Black Cockatoo White-fronted Honeyeater Australasian Shoveler Beautiful Firetail Bell Miner Blue-billed Duck Cattle Egret Chestnut-crowned Babbler Darter Eastern Bristlebird Elegant Parrot Latham's Snipe Little Black Cormorant Mallard Marsh Sandpiper Purple-gaped Honeyeater Red-capped Plover Spangled Drongo Australian Brush-turkey Australian Pelican Australian Pratincole Australian Reed-Warbler Baillon’s Crake Black-breasted Buzzard Black-faced Monarch Black-tailed Godwit Black-winged Kite Blue-breasted fairy-wren Brahminy Kite Brolga Brown Gerygone Buff-banded Rail Caspian Tern Chestnut Teal

% articles # listed as articles woodland bird 0.00 4 0.00 4 50.00 4 0.00 4 50.00 4 0.00 4 50.00 4 100.00 4 50.00 4 0.00 3 33.33 3 0.00 3 0.00 3 0.00 3 33.33 3 0.00 3 0.00 3 66.67 3 0.00 3 0.00 3 0.00 3 0.00 3 33.33 3 0.00 3 66.67 3 50.00 2 0.00 2 0.00 2 0.00 2 0.00 2 50.00 2 50.00 2 0.00 2 0.00 2 100.00 2 0.00 2 0.00 2 50.00 2 0.00 2 0.00 2 0.00 2 181

Order

Family

Species

Passeriformes Charadriiformes Charadriiformes Passeriformes Coraciiformes Anseriformes Podicipediformes Pelecaniformes Charadriiformes Pelecaniformes Passeriformes Pelecaniformes Anseriformes Anseriformes Cuculiformes Falconiformes Apodiformes Cuculiformes Pelecaniformes Passeriformes Anseriformes Anseriformes Gruiformes Falconiformes Passeriformes Charadriiformes Passeriformes Psittaciformes Passeriformes Charadriiformes Passeriformes Pelecaniformes Anseriformes Passeriformes Psittaciformes Passeriformes Passeriformes Charadriiformes Pelecaniformes Suliformes Gruiformes

Cinclosomatidae Jacanidae Scolopacidae Oriolidae Halcyonidae Anatidae Podicipedidae Ardeidae Sternidae Ardeidae Meliphagidae Ardeidae Anseranatidae Anatidae Cuculidae Pandionidae Apodidae Cuculidae Ardeidae Petroicidae Anatidae Anatidae Rallidae Accipitridae Maluridae Recurvirostridae Pycnonotidae Psittaculidae Climacteridae Laridae Acanthizidae Ardeidae Anatidae Locustellidae Psittaculidae Meliphagidae Pardalotidae Stercorariidae Ardeidae Sulidae Otididae

Chestnut-backed Quail-thrush Comb-crested Jacana Common Greenshank Figbird Forest Kingfisher Freckled Duck Great Crested Grebe Great Egret Gull-billed Tern Intermediate Egret Lewin's Honeyeater Little Egret Magpie-Goose Musk Duck Oriental Cuckoo Osprey Pacific Swift Pheasant Coucal Pied Heron Pink Robin Pink-eared Duck Plumed Whistling Duck Purple Swamphen Red Goshawk Red-backed Fairy-wren Red-necked Avocet Red-whiskered Bulbul Regent Parrot Rufous Treecreeper Silver Gull Striated Fieldwren Striated Heron Sunda Teal Tawny Grassbird Western Rosella Western Spinebill Western Thornbill Arctic Jaeger Australasian Bittern Australasian Gannet Australian Bustard

% articles # listed as articles woodland bird 100.00 2 0.00 2 0.00 2 50.00 2 50.00 2 0.00 2 0.00 2 0.00 2 0.00 2 0.00 2 50.00 2 0.00 2 0.00 2 0.00 2 100.00 2 0.00 2 50.00 2 50.00 2 0.00 2 50.00 2 0.00 2 0.00 2 0.00 2 50.00 2 50.00 2 0.00 2 0.00 2 50.00 2 100.00 2 0.00 2 50.00 2 0.00 2 0.00 2 0.00 2 100.00 2 50.00 2 100.00 2 0.00 1 0.00 1 0.00 1 100.00 1 182

Order

Family

Species

Strigiformes Gruiformes Columbiformes Passeriformes Charadriiformes Passeriformes Passeriformes Charadriiformes Passeriformes Passeriformes Passeriformes Procellariiformes Passeriformes Ciconiiformes Passeriformes Passeriformes Coraciiformes Psittaciformes Columbiformes Passeriformes Turniciformes Gruiformes Anseriformes Psittaciformes Gruiformes Passeriformes Passeriformes Passeriformes Charadriiformes Charadriiformes Charadriiformes Passeriformes Charadriiformes Charadriiformes Passeriformes Passeriformes Charadriiformes Pelecaniformes Pelecaniformes Columbiformes Passeriformes

Tytonidae Rallidae Columbidae Meliphagidae Recurvirostridae Meliphagidae Hirundinidae Scolopacidae Corvidae Artamidae Motacillidae Diomedeidae Meliphagidae Ciconiidae Climacteridae Estrildidae Halcyonidae Psittaculidae Columbidae Meliphagidae Turnicidae Rallidae Anatidae Cacatuidae Turnicidae Acanthizidae Cinclosomatidae Motacillidae Scolopaci Sternidae Sternidae Estrildidae Scolopacidae Charadriidae Meliphagidae Petroicidae Scolopacidae Ardeidae Ardeidae Columbidae Fringillidae

Australian Masked Owl Australian Spotted Crake Banded Fruit-Dove Banded Honeyeater Banded Stilt Bar-breasted Honeyeater Barn Swallow Bar-tailed Godwit Black Currawong Black-backed Butcherbird Black-backed Wagtail Black-browed Albatross Black-headed Honeyeater Black-necked Stork Black-tailed Treecreeper Black-throated Finch Blue-winged Kookaburra Blue-winged Parrot Brown Cuckoo-dove Brown-backed Honeyeater Buff-breasted Button-quail Bush Hen Cape Barren Goose Carnaby's Black Cockatoo Chestnut-backed Button-quail Chestnut-rumped Hylacola Chirruping Wedgebill Citrine Wagtail Common Sandpiper Common Tern Crested Tern Crimson Finch Curlew Sandpiper Double-banded Plover Dusky Honeyeater Dusky Robin Eastern Curlew Eastern Great Egret Eastern Reef Egret Emerald Dove European Greenfinch

% articles # listed as articles woodland bird 100.00 1 0.00 1 100.00 1 100.00 1 0.00 1 100.00 1 100.00 1 0.00 1 100.00 1 100.00 1 0.00 1 0.00 1 100.00 1 0.00 1 100.00 1 100.00 1 100.00 1 100.00 1 0.00 1 100.00 1 100.00 1 100.00 1 0.00 1 0.00 1 100.00 1 0.00 1 100.00 1 0.00 1 0.00 1 0.00 1 0.00 1 100.00 1 0.00 1 0.00 1 100.00 1 100.00 1 0.00 1 0.00 1 0.00 1 0.00 1 0.00 1 183

Order

Family

Species

Passeriformes Passeriformes Columbiformes Pelecaniformes Psittaciformes Passeriformes Passeriformes Strigiformes Passeriformes Passeriformes Psittaciformes Passeriformes Passeriformes Passeriformes Passeriformes Charadriiformes Psittaciformes Passeriformes Passeriformes Psittaciformes Apodiformes Charadriiformes Galliformes Passeriformes Caprimulgiformes Columbiformes Passeriformes Charadriiformes Strigiformes Cuculiformes Charadriiformes Coraciiformes Sphenisciformes Charadriiformes Passeriformes Passeriformes Passeriformes Caprimulgiformes Passeriformes Passeriformes Passeriformes

Maluridae Ptilonorhynchidae Columbidae Threskiornithidae Psittaculidae Estrildidae Meliphagidae Tytonidae Ptilonorhynchidae Ptilonorhynchidae Psittaculidae Maluridae Meliphagidae Motacillidae Meliphagidae Scolopacidae Psittaculidae Pomatostomidae Meliphagidae Psittaculidae Apodidae Charadriidae Phasianidae Acanthizidae Caprimulgidae Columbidae Motacillidae Charadriidae Tytonidae Cuculidae Scolopacidae Alcedinidae Spheniscidae Sternidae Orthonychidae Estrildidae Acanthizidae Podargidae Estrildidae Sturnidae Pittidae

Eyrean Grasswren Fawn-breasted Bowerbird Flock Bronzewing Glossy Ibis Golden-shouldered Parrot Gouldian Finch Graceful Honeyeater Grass Owl Great Bowerbird Green Catbird Green Rosella Grey Grasswren Grey Honeyeater Grey Wagtail Grey-headed Honeyeater Grey-tailed Tattler Ground Parrot Hall's Babbler Helmeted Friarbird Hooded Parrot House Swift Inland Dotterel King Quail Large-billed Scrubwren Large-tailed Nightjar Laughing Dove Lemon-bellied Flycatcher Lesser Sand Plover Lesser Sooty Owl Little Bronze-cuckoo Little Curlew Little Kingfisher Little Penguin Little Tern Logrunner Long-tailed Finch Mangrove Gerygone Marbled Frogmouth Masked Finch Metallic Starling Noisy Pitta

% articles # listed as articles woodland bird 0.00 1 100.00 1 0.00 1 0.00 1 100.00 1 100.00 1 100.00 1 100.00 1 100.00 1 0.00 1 100.00 1 0.00 1 0.00 1 0.00 1 100.00 1 0.00 1 0.00 1 100.00 1 100.00 1 100.00 1 100.00 1 0.00 1 0.00 1 0.00 1 100.00 1 100.00 1 100.00 1 0.00 1 0.00 1 0.00 1 0.00 1 100.00 1 0.00 1 0.00 1 0.00 1 100.00 1 0.00 1 0.00 1 100.00 1 100.00 1 0.00 1 184

Order

Family

Species

Passeriformes Psittaciformes Passeriformes Psittaciformes Charadriiformes Charadriiformes Acrocephalidae Charadriiformes Passeriformes Passeriformes Psittaciformes Caprimulgiformes Passeriformes Columbiformes Passeriformes Passeriformes Charadriiformes Passeriformes Charadriformes Psittaciformes Passeriformes Galliformes Anseriformes Charadriformes Gruiformes Passeriformes Psittaciformes Gruiformes Passeriformes Charadriiformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Charadriiformes Passeriformes Passeriformes Passeriformes Passeriformes

Motacillidae Psittaculidae Pachycephalidae Psittaculidae Charadriidae Glareolidae Acrocephalus Charadriidae Petroicidae Petroicidae Cacatuidae Podargidae Paradisaeidae Columbidae Estrildidae Meliphagidae Haematopodidae Acanthizidae Pedionomidae Psittaculidae Maluridae Phasianidae Anatidae Scolopacidae Turnicidae Pardalotidae Psittaculidae Turnicidae Estrildidae Scolopacidae Hirundinidae Acanthizidae Motacillidae Maluridae Ptilonorhynchidae Acanthizidae Scolopacidae Acanthizidae Meliphagidae Meliphagidae Colluricinclidae

Northern Fantail Northern Rosella Olive Whistler Orange-bellied Parrot Oriental Plover Oriental Pratincole Oriental Reed-Warbler Pacific Golden Plover Pacific Robin Pale-yellow Robin Palm Cockatoo Papuan Frogmouth Paradise Riflebird Partridge Pigeon Pictorella Mannikin Pied Honeyeater Pied Oystercatcher Pilotbird Plains Wanderer Princess Parrot Purple-crowned Fairy-wren Quail Radjah Shelduck Red Knot Red-backed Buttonquail Red-browed Pardalote Red-capped Parrot Red-chested Buttonquail Red-eared Firetail Red-necked Stint Red-rumped Swallow Redthroat Red-throated Pipit Red-winged Fairy-wren Regent Bowerbird Rockwarbler Ruddy Turnstone Rufous Fieldwren Rufous-banded Honeyeater Rufous-throated Honeyeater Sandstone Shrike-thrush

% articles # listed as articles woodland bird 100.00 1 100.00 1 0.00 1 0.00 1 0.00 1 0.00 1 0.00 1 0.00 1 100.00 1 0.00 1 100.00 1 100.00 1 0.00 1 100.00 1 100.00 1 100.00 1 0.00 1 0.00 1 0.00 1 0.00 1 100.00 1 0.00 1 0.00 1 0.00 1 100.00 1 100.00 1 100.00 1 100.00 1 100.00 1 0.00 1 100.00 1 100.00 1 0.00 1 100.00 1 0.00 1 0.00 1 0.00 1 0.00 1 100.00 1 100.00 1 100.00 1 185

Order

Family

Species

Gruiformes Charadriiformes Procellariiformes Procellariiformes Passeriformes Strigiformes Charadriiformes Procellariiformes Gruiformes Columbiformes Charadriiformes Columbiformes Passeriformes Passeriformes Charadriiformes Gruiformes Passeriformes Passeriformes Columbiformes Psittaciformes Passeriformes Psittaciformes Passeriformes Charadriiformes Charadriiformes Passeriformes Passeriformes Passeriformes Charadriiformes Passeriformes Columbiformes Apodiformes Passeriformes Passeriformes Passeriformes Charadriiformes Columbiformes Passeriformes Passeriformes Passeriformes Passeriformes

Gruidae Scolopacidae Procellariidae Diomedeidae Meliphagidae Tytonidae Haematopodidae Procellariidae Rallidae Columbidae Charadriidae Columbidae Estrildidae Meliphagidae Scolopacidae Rallidae Acanthizidae Acanthizidae Columbidae Psittaculidae Campephagidae Cacatuidae Petroicidae Scolopacidae Sternidae Petroicidae Petroicidae Climacteridae Sternidae Meliphagidae Columbidae Apodidae Campephagidae Meliphagidae Meliphagidae Sternidae Columbidae Meliphagidae Meliphagidae Icteridae Motacillidae

Sarus Crane Sharp-tailed Sandpiper Short-tailed Shearwater Shy Albatross Silver-crowned Friarbird Sooty Owl Sooty Oystercatcher Sooty Shearwater Spotless Crake Spotted Dove Spur-winged Plover Squatter Pigeon Star Finch Strong-billed Honeyeater Swinhoe's Snipe Tasmanian Native-hen Tasmanian Scrubwren Tasmanian Thornbill Topknot Pigeon Varied Lorikeet Varied Triller Western Corella Western Yellow Robin Whimbrel Whiskered Tern White-breasted Robin White-browed Robin White-browed Treecreeper White-fronted Tern White-gaped Honeyeater White-headed Pigeon White-rumped Swiftlet White-shouldered Triller White-streaked Honeyeater White-throated Honeyeater White-winged Black Tern Wompoo Fruit-Dove Yellow Chat Yellow Honeyeater Yellow Oriole Yellow Wagtail

% articles # listed as articles woodland bird 0.00 1 0.00 1 0.00 1 0.00 1 100.00 1 0.00 1 0.00 1 0.00 1 0.00 1 0.00 1 0.00 1 100.00 1 0.00 1 100.00 1 0.00 1 0.00 1 100.00 1 100.00 1 0.00 1 100.00 1 100.00 1 100.00 1 100.00 1 0.00 1 0.00 1 100.00 1 100.00 1 100.00 1 0.00 1 100.00 1 0.00 1 100.00 1 0.00 1 100.00 1 100.00 1 0.00 1 0.00 1 0.00 1 100.00 1 100.00 1 0.00 1 186

Order

Family

Species

Passeriformes Passeriformes Passeriformes Procellariiformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes Passeriformes

Meliphagidae Zosteropidae Nectariniidae Diomedeidae Estrildidae Meliphagidae Meliphagidae Acanthizidae Meliphagidae Cisticolidae

Yellow Wattlebird Yellow White-eye Yellow-bellied Sunbird Yellow-nosed Albatross Yellow-rumped Mannikin Yellow-spotted Honeyeater Yellow-throated Honeyeater Yellow-throated Scrubwren Yellow-tinted Honeyeater Zitting Cisticola

% articles # listed as articles woodland bird 100.00 1 100.00 1 100.00 1 0.00 1 100.00 1 100.00 1 100.00 1 0.00 1 100.00 1 0.00 1

187

Table A.2.6.3. Analysis of whether certain orders of species are more or less likely to be excluded from my analyses

Possibly those species in water-associated or less species rich orders Order

Passeriformes Psittaciformes Falconiformes Cuculiformes Coraciiformes Columbiformes Anseriformes Ciconiiformes Accipitriformes Galliformes Caprimulgiformes Strigiformes Charadriiformes Turniciformes Apodiformes Pelecaniformes Gruiformes Acrocephalidae Podicipediformes Procellariiformes Sphenisciformes Suliformes Casuariiformes

No. included species 99 20 10 6 5 5 3 2 2 2 2 2 2 1 1 1 1 0 0 0 0 0 0

No. excluded species 148 25 10 5 4 15 15 2 3 4 5 6 45 2 3 14 15 1 3 5 1 6 1

Percentage of species in each order included 40.08 44.44 50.00 54.55 55.56 25.00 16.67 50.00 40.00 33.33 28.57 25.00 4.26 33.33 25.00 6.67 6.25 0.00 0.00 0.00 0.00 0.00 0.00

Water associated n n n n n n y y n n n n y/n n n y y n y y y y n

188

Table A.2.6.4 Geographic distribution of studies included in chapter 2 analyses

The results from this table suggest that if species’ ranges do not include NSW or Victoria they are less likely to have been considered in out analyses (because they are more likely to have been present in 10 or fewer lists of woodland birds) State NSW VIC ACT QLD SA WA

Number of Lists 12 10 7 7 7 6

189

Appendix 2.7. Comparison of the FFGA listed woodland bird community and my findings % refers to the percentage of lists included in chapter 2 that include the species as a woodland bird Flora and Fauna Guarantee Act nominated community Barking Owl Brown Treecreeper Grey-crowned Babbler Little Lorikeet Red-capped Robin Red-tailed Black Cockatoo Speckled Warbler Superb Parrot Swift Parrot Turquoise Parrot Jacky Winter Diamond Firetail Painted Button-quail Apostlebird Painted Honeyeater Brown-headed Honeyeater Hooded Robin Fuscous Honeyeater Black-chinned Honeyeater Regent Honeyeater * Western Gerygone Yellow-tufted Honeyeater Bush Stone-curlew Ground Cuckoo-shrike *represented in fewer than 10 lists

% 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 96.67 95.65 94.74 92.86 92.31 91.43 90.48 90.00 89.47 88.89 88.00 87.50 83.33 25.00

Victorian species in more than 83.33% of lists Brown Treecreeper Red-capped Robin Speckled Warbler Grey-crowned Babbler Superb Parrot Little Lorikeet Swift Parrot Turquoise Parrot Rufous Whistler Brown Thornbill Eastern Yellow Robin Varied Sittella Jacky Winter Diamond Firetail White-browed Babbler Painted Button-quail Grey Shrike-thrush Spotted Pardalote Weebill White-throated Treecreeper Striated Thornbill Buff-rumped Thornbill Yellow Thornbill White-browed Scrubwren Apostlebird Painted Honeyeater Grey Fantail Brown-headed Honeyeater Golden Whistler Noisy Friarbird Hooded Robin Crested Shrike-tit Scarlet Robin Fuscous Honeyeater Southern Whiteface Eastern Spinebill

% 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 97.30 96.88 96.77 96.77 96.67 95.65 95.65 94.74 94.44 94.44 94.29 94.29 93.75 93.55 93.55 93.33 92.86 92.31 92.11 91.43 91.43 90.63 90.48 90.00 90.00 90.00 90.00 89.66 190

Flora and Fauna Guarantee Act nominated community

%

Victorian species in more than 83.33% of lists Black-chinned Honeyeater Blue-faced Honeyeater Regent Honeyeater * White-naped Honeyeater Little Friarbird Olive-backed Oriole Western Gerygone Common Bronzewing Mistletoebird Sacred Kingfisher Peaceful Dove White-throated Gerygone Yellow-tufted Honeyeater White-eared Honeyeater White-winged Chough Dusky Woodswallow Chestnut-rumped Thornbill Leaden Flycatcher Yellow-faced Honeyeater Horsfield’s Bronze-Cuckoo Shining Bronze-cuckoo White-bellied Cuckoo-shrike Bush Stone-curlew

% 89.47 89.47 88.89 88.89 88.89 88.46 88.00 87.88 87.50 87.50 87.50 87.50 87.50 86.21 85.29 84.85 84.62 84.21 83.87 83.33 83.33 83.33 83.33

191

Appendix 3.1. International Article Selection Protocol International farmland, woodland and generalist bird search Web of Science, Scopus, Elsevier, JSTOR, Wiley online Library searches return 2593 articles

Articles focused on non-avian species or communities, single or pairs of species were removed. 439 articles remain

38 articles classify birds into any two of: ‘woodland’ ‘farmland’ or ‘generalist birds. 1 from Australia, 37 from Europe

Study adds to Australian dataset See Appendix 2.1 Europe (37 studies)

Woodland birds (34 studies)

Farmland birds (33 studies)

Generalist birds (15 studies)

Figure A.3.1.1: The process of article selection used to collect data on European birds and expand the Australian analyses in chapter 3 to understand ecologists’ categorisation of woodland, farmland and generalist bird species.

192

Appendix 3.2. Proportion of studies classifying species as woodland, farmland and generalist species. Table A.3.2.1. The proportion of studies used in chapter 3 analyses specifying each species as a farmland rather than woodland specialist and as a generalist as opposed to a specialist in either farmland or woodland habitats. Proportion Farmland (rather than woodland)

Proportion Woodland (rather than farmland)

Proportion Generalist (rather than specialist)

Proportion Specialist (rather than Generalist)

Region

Species

Europe

Accipiter nisus

0.00

1.00

0.67

0.33

Europe

Aegithalos caudatus

0.11

0.89

0.22

0.78

Europe

Alauda arvensis

1.00

0.00

0.00

1.00

Europe

Alectoris rufa

1.00

0.00

0.25

0.75

Europe

Anthus pratensis

1.00

0.00

0.00

1.00

Europe

Anthus trivialis

0.25

0.75

0.40

0.60

Europe

Buteo buteo

0.25

0.75

0.33

0.67

Europe

Carduelis cannabina

0.86

0.14

0.27

0.73

Europe

Carduelis carduelis

0.89

0.11

0.50

0.50

Europe

Carduelis chloris

0.79

0.21

0.60

0.40

Europe

Certhia brachydactyla

0.00

1.00

0.00

1.00

Europe

Certhia familiaris

0.08

0.92

0.00

1.00

Europe

Columba palumbus

0.67

0.33

0.92

0.08

Europe

Corvus corax

0.25

0.75

1.00

0.00

Europe

Corvus corone

0.75

0.25

0.89

0.11

Europe

Corvus frugilegus

1.00

0.00

0.00

1.00

Europe

Corvus monedula

1.00

0.00

0.83

0.17

Europe

Coturnix coturnix

1.00

0.00

0.00

1.00

Europe

Cuculus canorus

0.20

0.80

0.80

0.20

Europe

Cyanistes caeruleus

0.07

0.93

0.46

0.54

Europe

Dendrocopos major

0.00

1.00

0.11

0.89

Europe

Dendrocopos minor

0.00

1.00

0.00

1.00

Europe

Dryocopus martius

0.00

1.00

0.00

1.00

Europe

Emberiza calandra

1.00

0.00

0.00

1.00

Europe

Emberiza cirlus

1.00

0.00

0.57

0.43

Europe

Emberiza citrinella

0.92

0.08

0.00

1.00

Europe

Erithacus rubecula

0.08

0.92

0.43

0.57

Europe

Falco tinnunculus

1.00

0.00

0.29

0.71

Europe

Fringilla coelebs

0.13

0.88

0.67

0.33

Europe

Galerida cristata

1.00

0.00

0.67

0.33

Europe

Garrulus glandarius

0.00

1.00

0.50

0.50

Europe

Hirundo rustica

1.00

0.00

0.25

0.75

Europe

Lanius collurio

1.00

0.00

0.00

1.00

Europe

Lanius senator

0.75

0.25

0.67

0.33

Europe

Lullula arborea

0.67

0.33

0.20

0.80

Europe

Luscinia megarhynchos

0.14

0.86

0.80

0.20

193

Proportion Farmland (rather than woodland)

Proportion Woodland (rather than farmland)

Proportion Generalist (rather than specialist)

Proportion Specialist (rather than Generalist)

Region

Species

Europe

Miliaria calandra

1.00

0.00

0.40

0.60

Europe

Motacilla alba

1.00

0.00

0.25

0.75

Europe

Motacilla flava

1.00

0.00

0.29

0.71

Europe

Oriolus oriolus

0.00

1.00

0.67

0.33

Europe

Parus cristatus

0.00

1.00

0.00

1.00

Europe

Parus major

0.08

0.92

0.70

0.30

Europe

Parus palustris

0.00

1.00

0.00

1.00

Europe

Passer montanus

1.00

0.00

0.40

0.60

Europe

Perdix perdix

1.00

0.00

0.00

1.00

Europe

Periparus ater

0.00

1.00

0.00

1.00

Europe

Phoenicurus ochruros

1.00

0.00

0.40

0.60

Europe

Phylloscopus bonelli

0.00

1.00

0.00

1.00

Europe

Phylloscopus collybita

0.06

0.94

0.10

0.90

Europe

Phylloscopus trochilus

0.09

0.91

0.50

0.50

Europe

Pica pica

0.80

0.20

0.80

0.20

Europe

Picus viridis

0.13

0.88

0.80

0.20

Europe

Prunella modularis

0.09

0.91

0.90

0.10

Europe

Pyrrhula pyrrhula

0.20

0.80

0.14

0.86

Europe

Regulus ignicapillus

0.00

1.00

0.00

1.00

Europe

Regulus regulus

0.00

1.00

0.00

1.00

Europe

Saxicola rubetra

1.00

0.00

0.00

1.00

Europe

Saxicola rubicola

1.00

0.00

0.33

0.67

Europe

Serinus serinus

0.67

0.33

0.67

0.33

Europe

Sitta europaea

0.00

1.00

0.00

1.00

Europe

Streptopelia turtur

0.82

0.18

0.50

0.50

Europe

Sturnus unicolor

0.67

0.33

0.67

0.33

Europe

Sturnus vulgaris

0.79

0.21

0.17

0.83

Europe

Sylvia atricapilla

0.08

0.92

0.57

0.43

Europe

Sylvia communis

0.95

0.05

0.27

0.73

Europe

Troglodytes troglodytes

0.08

0.92

0.60

0.40

Europe

Turdus merula

0.17

0.83

1.00

0.00

Europe

Turdus philomelos

0.06

0.94

0.38

0.63

Europe

Turdus viscivorus

0.22

0.78

0.71

0.29

Europe

Upupa epops

1.00

0.00

0.57

0.43

Europe

Vanellus vanellus

1.00

0.00

0.00

1.00

Australia

Struthidea cinerea

0.00

1.00

0.00

1.00

Australia

Alisterus scapularis

0.00

1.00

0.00

1.00

Australia

Gymnorhina tibicen

0.67

0.33

0.25

0.75

Australia

Tyto novaehollandiae

0.33

0.67

0.00

1.00

Australia

Aegotheles cristatus

0.00

1.00

0.00

1.00

Australia

Anthus australis

0.86

0.14

0.00

1.00

194

Proportion Farmland (rather than woodland)

Proportion Woodland (rather than farmland)

Proportion Generalist (rather than specialist)

Proportion Specialist (rather than Generalist)

Region

Species

Australia

Corvus coronoides

0.63

0.38

0.14

0.86

Australia

Chenonetta jubata

0.67

0.33

0.00

1.00

Australia

Falco cenchroides

0.83

0.17

0.00

1.00

Australia

Alcedo azurea

0.00

1.00

0.00

1.00

Australia

Falco subniger

0.50

0.50

0.00

1.00

Australia

Melithreptus gularis

0.00

1.00

0.00

1.00

Australia

Chrysococcyx osculans

0.00

1.00

0.00

1.00

Australia

Coracina novaehollandiae

0.13

0.88

0.60

0.40

Australia

Entomyzon cyanotis

0.00

1.00

0.00

1.00

Australia

Falco berigora

0.80

0.20

0.00

1.00

Australia

Accipiter fasciatus

0.00

1.00

0.50

0.50

Australia

Acanthiza pusilla

0.00

1.00

0.00

1.00

Australia

Climacteris picumnus

0.00

1.00

0.00

1.00

Australia

Melithreptus brevirostris

0.00

1.00

0.00

1.00

Australia

Acanthiza reguloides

0.00

1.00

0.00

1.00

Australia

Nymphicus hollandicus

1.00

0.00

0.67

0.33

Australia

Phaps chalcoptera

0.00

1.00

0.00

1.00

Australia

Sturnus vulgaris

0.83

0.17

0.17

0.83

Australia

Ocyphaps lophotes

1.00

0.00

0.17

0.83

Australia

Falcunculus frontatus

0.10

0.90

0.00

1.00

Australia

Platycercus elegans

0.00

1.00

0.13

0.88

Australia

Stagonopleura guttata

0.00

1.00

0.00

1.00

Australia

Eurystomus orientalis

0.20

0.80

0.00

1.00

Australia

Artamus cyanopterus

0.00

1.00

0.00

1.00

Australia

Platycercus eximius

0.43

0.57

0.70

0.30

Australia

Acanthorhynchus tenuirostris

0.00

1.00

0.00

1.00

Australia

Eopsaltria australis

0.00

1.00

0.00

1.00

Australia

Carduelis carduelis

0.80

0.20

0.20

0.80

Australia

Petrochelidon ariel

1.00

0.00

0.25

0.75

Australia

Cuculus pyrrhophanus

0.14

0.86

0.00

1.00

Australia

Petroica phoenicea

0.33

0.67

0.83

0.17

Australia

Lichenostomus fuscus

0.00

1.00

0.00

1.00

Australia

Cacatua roseicapilla

1.00

0.00

0.33

0.67

Australia

Pachycephala pectoralis

0.00

1.00

0.00

1.00

Australia

Cracticus torquatus

0.00

1.00

0.57

0.43

Australia

Strepera versicolor

0.00

1.00

0.71

0.29

Australia

Rhipidura albiscapa

0.00

1.00

0.00

1.00

Australia

Colluricincla harmonica

0.00

1.00

0.00

1.00

Australia

Pomatostomus temporalis

0.00

1.00

0.00

1.00

Australia

Melanodryas cucullata

0.00

1.00

0.00

1.00

Australia

Chrysococcyx basalis

0.00

1.00

0.00

1.00

195

Proportion Farmland (rather than woodland)

Proportion Woodland (rather than farmland)

Proportion Generalist (rather than specialist)

Proportion Specialist (rather than Generalist)

Region

Species

Australia

Passer domesticus

1.00

0.00

0.00

1.00

Australia

Microeca fascinans

0.00

1.00

0.00

1.00

Australia

Dacelo novaeguineae

0.00

1.00

0.70

0.30

Australia

Myiagra rubecula

0.00

1.00

0.00

1.00

Australia

Cacatua sanguinea

1.00

0.00

0.50

0.50

Australia

Philemon citreogularis

0.00

1.00

0.00

1.00

Australia

Corvus mellori

1.00

0.00

0.33

0.67

Australia

Grallina cyanoleuca

0.78

0.22

0.29

0.71

Australia

Vanellus miles

1.00

0.00

0.00

1.00

Australia

Artamus personatus

0.25

0.75

1.00

0.00

Australia

Dicaeum hirundinaceum

0.00

1.00

0.14

0.86

Australia

Phylidonyris novaehollandiae

0.25

0.75

0.60

0.40

Australia

Philemon corniculatus

0.00

1.00

0.17

0.83

Australia

Manorina melanocephala

0.29

0.71

0.70

0.30

Australia

Oriolus sagittatus

0.00

1.00

0.00

1.00

Australia

Turnix varia

0.00

1.00

0.00

1.00

Australia

Cuculus pallidus

0.00

1.00

0.60

0.40

Australia

Geopelia striata

0.17

0.83

0.14

0.86

Australia

Cracticus nigrogularis

0.33

0.67

1.00

0.00

Australia

Strepera graculina

0.13

0.88

0.20

0.80

Australia

Merops ornatus

0.00

1.00

0.80

0.20

Australia

Anthochaera carunculata

0.00

1.00

0.25

0.75

Australia

Neochmia temporalis

0.00

1.00

0.17

0.83

Australia

Petroica goodenovi

0.00

1.00

0.00

1.00

Australia

Psephotus haematonotus

0.57

0.43

0.63

0.38

Australia

Myiagra inquieta

0.00

1.00

0.63

0.38

Australia

Cincloramphus mathewsi

0.00

1.00

0.67

0.33

Australia

Pachycephala rufiventris

0.00

1.00

0.11

0.89

Australia

Todiramphus sanctus

0.00

1.00

0.00

1.00

Australia

Myiagra cyanoleuca

0.00

1.00

0.00

1.00

Australia

Petroica boodang

0.00

1.00

0.00

1.00

Australia

Chrysococcyx lucidus

0.00

1.00

0.00

1.00

Australia

Zosterops lateralis

0.00

1.00

0.63

0.38

Australia

Alauda arvensis

1.00

0.00

0.00

1.00

Australia

Aphelocephala leucopsis

0.00

1.00

0.00

1.00

Australia

Chthonicola sagittatus

0.00

1.00

0.00

1.00

Australia

Pardalotus punctatus

0.00

1.00

0.00

1.00

Australia

Pardalotus striatus

0.00

1.00

0.56

0.44

Australia

Acanthiza lineata

0.11

0.89

0.13

0.88

Australia

Coturnix pectoralis

1.00

0.00

0.33

0.67

Australia

Cacatua galerita

1.00

0.00

1.00

0.00

196

Proportion Farmland (rather than woodland)

Proportion Woodland (rather than farmland)

Proportion Generalist (rather than specialist)

Proportion Specialist (rather than Generalist)

Region

Species

Australia

Malurus cyaneus

0.00

1.00

0.13

0.88

Australia

Podargus strigoides

0.00

1.00

0.00

1.00

Australia

Petrochelidon nigricans

0.20

0.80

0.33

0.67

Australia

Daphoenositta chrysoptera

0.00

1.00

0.00

1.00

Australia

Smicrornis brevirostris

0.00

1.00

0.00

1.00

Australia

Hirundo neoxena

1.00

0.00

0.29

0.71

Australia

Gerygone fusca

0.00

1.00

0.00

1.00

Australia

Coracina papuensis

0.00

1.00

0.00

1.00

Australia

Pomatostomus superciliosus

0.00

1.00

0.00

1.00

Australia

Sericornis frontalis

0.00

1.00

0.00

1.00

Australia

Artamus superciliosus

0.00

1.00

1.00

0.00

Australia

Lichenostomus leucotis

0.00

1.00

0.00

1.00

Australia

Epthianura albifrons

1.00

0.00

0.00

1.00

Australia

Melithreptus lunatus

0.00

1.00

0.00

1.00

Australia

Lichenostomus penicillatus

0.14

0.86

0.14

0.86

Australia

Gerygone olivacea

0.00

1.00

0.25

0.75

Australia

Cormobates leucophaeus

0.00

1.00

0.11

0.89

Australia

Corcorax melanorhamphos

0.11

0.89

0.14

0.86

Australia

Lalage tricolor

0.00

1.00

0.00

1.00

Australia

Rhipidura leucophrys

0.71

0.29

0.70

0.30

Australia

Acanthiza nana

0.00

1.00

0.00

1.00

Australia

Lichenostomus chrysops

0.14

0.86

0.00

1.00

Australia

Acanthiza chrysorrhoa

0.50

0.50

0.75

0.25

Australia

Lichenostomus melanops

0.00

1.00

0.00

1.00

197

Appendix 3.3. Targetted classifications used in chapter 3 analyses Table A.3.3.1: Species classed as farmland, generalist and woodland species in each of the Australian and European targeted classification used in chpter 3 analyses

Species

Farmland Generalist Woodland

Barrett GW, Ford HA, Recher HF, Barrett GW (1994) Conservation of woodland birds in a fragmented rural landscape. Pacific Conservation Biology, 1, 245–256. Australian King Parrot Australian Magpie 1 Australian Masked Owl Australian Owlet-Nightjar Australian Pipit 1 Australian Raven 1 Australian Wood Duck 1 Australian/ nankeen Kestrel 1 Black-faced Cuckoo-shrike 1 Brown Falcon 1 Brown Goshawk Brown Thornbill Brown-headed Honeyeater Brush Cuckoo Buff-rumped Thornbill Cicadabird Common Starling 1 Crested Pigeon 1 Crimson Rosella Dollarbird Dusky Woodswallow Eastern Rosella 1 Eastern Spinebill Eastern Yellow Robin European Goldfinch 1 Fairy Martin 1 Fan-tailed Cuckoo Forest Raven Fuscous Honeyeater Galah 1 Glossy Black Cockatoo Golden Whistler Grey Butcherbird 1 Grey Fantail Grey Shrike-thrush Horsfield's Bronze Cuckoo

1 1 1 1

1 1 1 1 1 1 1

1 1 1 1 1 1

1 1 1 1 1 1 1 1 1

198

Species House Sparrow Laughing Kookaburra Leaden Flycatcher Little Eagle Magpie-lark Masked Lapwing Mistletoebird Noisy Friarbird Noisy Miner Olive-backed Oriole Pallid Cuckoo Peaceful Dove Pied Currawong Powerful Owl Red Wattlebird Red-browed Finch Red-browed Treecreeper Red-rumped Parrot Restless Flycatcher Rufous Songlark Rufous Whistler Sacred Kingfisher Satin Bowerbird Satin Flycatcher Scarlet Robin Shining Bronze-cuckoo Silvereye Spotted Pardalote Spotted Quail-thrush Straw-necked Ibis Striated Pardalote Striated Thornbill Superb Fairy-wren Varied Sittella Weebill Welcome Swallow White-browed Scrubwren White-eared Honeyeater White-naped Honeyeater White-throated Gerygone White-throated Treecreeper White-winged Chough Willie Wagtail

Farmland Generalist Woodland 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

199

Species Wonga Pigeon Yellow-faced Honeyeater Yellow-rumped Thornbill Yellow-tailed Black-Cockatoo

Farmland Generalist Woodland 1 1 1 1 1

Calviño-Cancela M (2013) Effectiveness of eucalypt plantations as a surrogate habitat for birds. Forest Ecology and Management, 310, 692–699. Accipiter gentilis 1 Accipiter nisus 1 Aegithalos caudatus 1 Alauda arvensis 1 Anthus pratensis 1 Anthus trivialis 1 Apus apus 1 Buteo buteo 1 Carduelis cannabina 1 Carduelis carduelis 1 Carduelis chloris 1 Certhia brachydactyla 1 Columba palumbus 1 Corvus corone 1 Cuculus canorus 1 Cyanistes caeruleus 1 Delichon urbicum 1 Dendrocopos major 1 Emberiza cia 1 Emberiza cirlus 1 Erithacus rubecula 1 Falco peregrinus 1 Ficedula hypoleuca 1 Fringilla coelebs 1 Garrulus glandarius 1 Hirundo rustica 1 Lophophanes cristatus 1 Parus major 1 Periparus ater 1 Phoenicurus ochruros 1 Phylloscopus collybita 1 Picus viridis 1 Prunella modularis 1 Pyrrhula pyrrhula 1 Saxicola rubicola 1 Serinus serinus 1 Sitta europaea 1 Streptopelia turtur 1 200

Species Sylvia atricapilla Sylvia communis Sylvia undata Troglodytes troglodytes Turdus merula Turdus philomelos Turdus viscivorus

Farmland Generalist Woodland 1 1 1 1 1 1 1

EBCC (2014) European wild bird indicators, 2014 update. European Bird Census Council Accipiter nisus Alauda arvensis 1 Alectoris rufa 1 Anthus campestris 1 Anthus pratensis 1 Anthus trivialis Bombycilla garrulus Bonasa bonasia Burhinus oedicnemus 1 Calandrella brachydactyla 1 Carduelis cannabina 1 Carduelis spinus Certhia brachydactyla Certhia familiaris Ciconia ciconia 1 Coccothraustes coccothraustes Columba oenas Corvus frugilegus 1 Cyanopica cyanus Dendrocopos medius Dendrocopos minor Dryocopus martius Emberiza cirlus 1 Emberiza citrinella 1 Emberiza hortulana 1 Emberiza melanocephala 1 Emberiza rustica Falco tinnunculus 1 Ficedula albicollis Ficedula hypoleuca Galerida cristata 1 Galerida theklae 1 Garrulus glandarius Hirundo rustica 1 Lanius collurio 1

1

1 1 1

1 1 1 1 1 1 1 1 1

1 1 1

1

201

Species Lanius minor Lanius senator Limosa limosa Melanocorypha calandra Miliaria calandra Motacilla flava Nucifraga caryocatactes Oenanthe hispanica Parus ater Parus cristatus Parus montanus Parus palustris Passer montanus Perdix perdix Petronia petronia Phoenicurus phoenicurus Phylloscopus bonelli Phylloscopus collybita Phylloscopus sibilatrix Picus canus Pyrrhula pyrrhula Regulus ignicapilla Regulus regulus Saxicola rubetra Saxicola torquata Serinus serinus Sitta europaea Streptopelia turtur Sturnus unicolor Sturnus vulgaris Sylvia communis Tringa ochropus Turdus viscivorus Upupa epops Vanellus vanellus

Farmland Generalist Woodland 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Gregory RD, Vorisek P, Van Strien A et al. (2007) Population trends of widespread woodland birds in Europe. Ibis, 149, 78–97. Accipiter nisus 1 Aegithalos caudatus 1 Alauda arvensis 1 Anthus trivialis 1 Bonasa bonasia 1 Burhinus oedicnemus 1 Buteo buteo 1 202

Species Carduelis cannabina Carduelis carduelis Carduelis chloris Carduelis flammea Carduelis spinus Certhia brachydactyla Certhia familiaris Coccothraustes coccothraustes Columba palumbus Corvus cornix Corvus monedula Cuculus canorus Cyanistes caeruleus Dendrocopos major Dendrocopos minor Dryocopus martius Emberiza calandra Emberiza citrinella Erithacus rubecula Eurasian Wryneck Falco tinnunculus Ficedula albicollis Ficedula hypoleuca Fringilla coelebs Fringilla montifringilla Galerida cristata Garrulus glandarius Hippolais icterina Hirundo rustica Lanius collurio Lanius senator Limosa limosa Lullula arborea Luscinia megarhynchos Motacilla alba Motacilla flava Muscicapa striata Oriolus oriolus Parus major Passer montanus Periparus ater Phoenicurus phoenicurus Phylloscopus collybita

Farmland Generalist Woodland 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

203

Species Phylloscopus sibilatrix Phylloscopus trochilus Pica pica Picus canus Picus viridis Poecile montanus Poecile palustris Prunella modularis Pyrrhula pyrrhula Regulus regulus Saxicola rubetra Sitta europaea Streptopelia turtur Sturnus vulgaris Sylvia atricapilla Sylvia borin Sylvia communis Troglodytes troglodytes Turdus merula Turdus philomelos Turdus viscivorus Upupa epops Vanellus vanellus

Farmland Generalist Woodland 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Guilherme JL, Miguel Pereira H (2013) Adaptation of bird communities to farmland abandonment in a mountain landscape. PloS one, 8, e73619 Aegithalos caudatus Anthus trivialis 1 Carduelis chloris 1 Certhia brachydactyla Columba palumbus 1 Cuculus canorus Cyanistes caeruleus Dendrocopos major Emberiza citrinella 1 Erithacus rubecula 1 Fringilla coelebs Garrulus glandarius Hippolais polyglotta 1 Motacilla alba 1 Oriolus oriolus Parus cristatus Parus major 1 Passer domesticus 1 Periparus ater

1

1 1 1 1

1 1

1 1

1 204

Species Farmland Generalist Woodland Phoenicurus ochruros 1 Phylloscopus bonelli 1 Phylloscopus ibericus 1 Picus viridis 1 Prunella modularis 1 Pyrrhula pyrrhula 1 Regulus ignicapillus 1 Serinus serinus 1 Sitta europaea 1 Streptopelia turtur 1 Sturnus unicolor 1 Sylvia atricapilla 1 Sylvia communis 1 Troglodytes troglodytes 1 Turdus merula 1 Turdus philomelos 1 Turdus viscivorus 1 Haslem A, Bennett AF (2008) Countryside elements and the conservation of birds in agricultural environments. Agriculture, Ecosystems & Environment, 125, 191–203. Australian King-Parrot 1 Australian Magpie 1 Bassian Thrush 1 Black-faced Cuckoo-shrike 1 Brown Thornbill 1 Brown-headed Honeyeater 1 Brush Cuckoo 1 Buff-rumped Thornbill 1 Common Blackbird 1 Common Bronzewing 1 Common Myna 1 Common Starling 1 Crescent Honeyeater 1 Crested Shrike-tit 1 Crimson Rosella 1 Diamond Firetail 1 Dusky Woodswallow 1 Eastern Rosella 1 Eastern Spinebill 1 Eastern Whipbird 1 Eastern Yellow Robin 1 Emu 1 European Goldfinch 1 Fan-tailed Cuckoo 1 Flame Robin 1 205

Species Flycatcher Sp Galah Gang-gang Cockatoo Golden Whistler Grey Butcherbird Grey Currawong Grey Fantail Grey Shrike-thrush Horsfield's Bronze-Cuckoo House Sparrow Jacky Winter Laughing Kookaburra Little Corella Little Wattlebird Magpie-lark Masked Lapwing Mistletoebird Musk Lorikeet New Holland Honeyeater Noisy Friarbird Noisy Miner Olive-backed Oriole Pallid Cuckoo Pied Currawong Rainbow Lorikeet Raven Sp Red Wattlebird Red-browed Finch Restless Flycatcher Richard's Pipit Rose Robin Rufous Fantail Rufous Whistler Sacred Kingfisher Satin Bowerbird Scarlet Robin Shining Bronze-cuckoo Silvereye Skylark Spotted Pardalote Spotted Turtle-Dove Striated Pardalote Striated Thornbill

Farmland Generalist Woodland 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

206

Species Stubble Quail Sulphur-crested Cockatoo Superb Fairy-wren Tree Martin Varied Sittella Weebill Welcome Swallow White-browed Scrubwren White-eared Honeyeater White-fronted Chat White-naped Honeyeater White-throated Gerygone White-throated Needletail White-throated Treecreeper White-winged Chough White-winged Triller Willie Wagtail Wonga Pigeon Yellow Thornbill Yellow-faced Honeyeater Yellow-rumped Thornbill Yellow-tailed Black-Cockatoo

Farmland Generalist Woodland 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Mimet A, Maurel N, Pellissier V, Simon L, Julliard R (2014) Towards a unique landscape description for multi-species studies: A model comparison with common birds in a humandominated French region. Ecological Indicators, 36, 19–32 Alauda arvensis 1 Anthus pratensis 1 Buteo buteo 1 Carduelis cannabina 1 Certhia brachydactyla 1 Columba palumbus 1 Corvus corone 1 Corvus frugilegus 1 Cuculus canorus 1 Cyanistes caeruleus 1 Dendrocopos major 1 Emberiza citrinella 1 Erithacus rubecula 1 Falco tinnunculus 1 Fringilla coelebs 1 Garrulus glandarius 1 Hippolais polyglotta 1 Luscinia megarhynchos 1 207

Species Miliaria calandra Motacilla flava Oriolus oriolus Parus major Parus palustris Perdix perdix Phasianus colchicus Phylloscopus collybita Phylloscopus trochilus Picus viridis Prunella modularis Saxicola rubicola Sitta europaea Sylvia atricapilla Sylvia communis Troglodytes troglodytes Turdus merula Turdus philomelos

Farmland Generalist Woodland 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Radford JQ, Bennett AF, Cheers GJ (2005) Landscape-level thresholds of habitat cover for woodland-dependent birds. Biological Conservation, 124, 317–337 Apostlebird Australian Hobby 1 Australian Magpie 1 Australian Owlet-Nightjar Australian Raven 1 Australian/ nankeen Kestrel 1 Azure Kingfisher Barn Owl 1 Black Falcon 1 Black Kite 1 Black-chinned Honeyeater Black-eared Cuckoo Black-faced Cuckoo-shrike 1 Blue-faced Honeyeater Brown Falcon 1 Brown Goshawk 1 Brown Quail Brown Thornbill Brown Treecreeper Brown-headed Honeyeater Buff-rumped Thornbill Bush Stone Curlew Chestnut-rumped Heathwren

1

1

1

1 1 1

1 1 1 1 1 1 1

208

Species Chestnut-rumped Thornbill Clamorous Reed-Warbler Cockatiel Collared Sparrowhawk Common Blackbird Common Bronzewing Common Mynah Common Starling Crested Bellbird Crested Pigeon Crested Shrike-tit Crimson Rosella Diamond Firetail Dollarbird Dusky Woodswallow Eastern Rosella Eastern Spinebill Eastern Yellow Robin Emu European Goldfinch Fairy Martin Fan-tailed Cuckoo Flame Robin Fuscous Honeyeater Galah Gilbert's Whistler Golden Whistler Grey Butcherbird Grey Currawong Grey Fantail Grey Shrike-thrush Grey-crowned Babbler Hooded Robin Horsfield's Bronze Cuckoo House Sparrow Jacky Winter Laughing Kookaburra Leaden Flycatcher Little Corella Little Eagle Little Friarbird Little Grassbird Little Lorikeet

Farmland Generalist Woodland 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

209

Species Little Raven Long-billed Corella Magpie-lark Masked Lapwing Masked Woodswallow Mistletoebird Musk Lorikeet New Holland Honeyeater Noisy Friarbird Noisy Miner Olive-backed Oriole Painted Button-quail Pallid Cuckoo Peaceful Dove Peregrine Falcon Pied Butcherbird Pied Currawong Purple-crowned Lorikeet Rainbow Bee-eater Red Wattlebird Red-browed Finch Red-capped Robin Red-rumped Parrot Restless Flycatcher Rufous Songlark Rufous Whistler Sacred Kingfisher Scarlet Robin Shining Bronze-cuckoo Silvereye Southern Boobook Southern Whiteface Speckled Warbler Spotted Pardalote Spotted Quail-thrush Striated Pardalote Striated Thornbill Sulphur-crested Cockatoo Superb Fairy-wren Swamp Harrier Swift Parrot Tawny Frogmouth Tree Martin

Farmland Generalist Woodland 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

210

Species Varied Sittella Wedge-tailed Eagle Weebill Welcome Swallow Western Gerygone Whistling Kite White-backed Swallow White-bellied Cuckoo-shrike White-breasted Woodswallow White-browed Babbler White-browed Scrubwren White-browed Woodswallow White-eared Honeyeater White-fronted Chat White-naped Honeyeater White-plumed Honeyeater White-throated Treecreeper White-winged Chough White-winged Triller Willie Wagtail Yellow Rosella Yellow Thornbill Yellow-faced Honeyeater Yellow-rumped Thornbill Yellow-tufted Honeyeater Zebra Finch

Farmland Generalist Woodland 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Silcocks A, Tzaros C, Weston M, Olsen P (2005) An interim guild classification for woodland and grassland birds in Australia. Melbourne, 1-11 pp Apostlebird 1 Australian Brush-turkey 1 Australian Bustard 1 Australian Hobby 1 Australian Masked Owl 1 Australian Owlet-Nightjar 1 Australian Pipit 1 Australian Pratincole 1 Australian Raven 1 Australian Ringneck 1 Australian Shelduck 1 Australian White Ibis 1 Australian Wood Duck 1 Australian/ nankeen Kestrel 1 Azure Kingfisher 1

211

Species Banded Fruit-Dove Banded Honeyeater Banded Lapwing Bar-breasted Honeyeater Barking Owl Barn Owl Barn Swallow Bar-shouldered Dove Black Currawong Black Falcon Black Honeyeater Black Kite Black-backed Butcherbird Black-backed Wagtail Black-breasted Buzzard Black-chinned Honeyeater Black-faced Cuckoo-shrike Black-faced Woodswallow Black-headed Honeyeater Black-shouldered Kite Black-tailed Native-hen Black-tailed Treecreeper Black-throated Finch Bluebonnet Blue-breasted fairy wren Blue-faced Honeyeater Blue-winged Kookaburra Blue-winged Parrot Brolga Brown Goshawk Brown Honeyeater Brown Quail Brown Songlark Brown Thornbill Brown Treecreeper Brown-backed Honeyeater Brown-headed Honeyeater Brush Cuckoo Budgerigar Buff-banded Rail Buff-breasted Button-quail Buff-rumped Thornbill Bush Hen

Farmland Generalist Woodland 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

212

Species Bush Stone Curlew Cape Barren Goose Caspian Tern Cattle Egret Channel-billed Cuckoo Chestnut-backed Button-quail Chestnut-breasted Mannikin Chestnut-rumped Heathwren Cicadabird Citrine Wagtail Clamorous Reed-Warbler Cockatiel Collared Sparrowhawk Common Bronzewing Common Koel Crescent Honeyeater Crested Bellbird Crested Pigeon Crested Shrike-tit Crimson Finch Crimson Rosella Diamond Dove Diamond Firetail Dollarbird Double-barred Finch Dusky Honeyeater Dusky Robin Dusky Woodswallow Eastern Bristlebird Eastern Rosella Eastern Spinebill Eastern Yellow Robin Elegant Parrot Emu Eyrean Grasswren Fairy Martin Fan-tailed Cuckoo Fawn-breasted Bowerbird Figbird Flame Robin Flock Bronzewing Forest Kingfisher Forest Raven

Farmland Generalist Woodland 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

213

Species Fork-tailed or Pacific Swift Fuscous Honeyeater Galah Gilbert's Whistler Glossy Black Cockatoo Golden Whistler Golden-headed Cisticola Golden-shouldered Parrot Gouldian Finch Graceful Honeyeater Grass Owl Great Bowerbird Green Rosella Grey Butcherbird Grey Currawong Grey Falcon Grey Fantail Grey Grasswren Grey Shrike-thrush Grey Wagtail Grey-crowned Babbler Grey-fronted Honeyeater Grey-headed Honeyeater Ground Cuckoo-shrike Gull-billed Tern Hall's Babbler Helmeted Friarbird Hooded Parrot Hooded Robin Horsfield's Bronze Cuckoo House Swift Inland Dotterel Inland Thornbill Jacky Winter King Quail Large-tailed Nightjar Latham's Snipe Laughing Kookaburra Leaden Flycatcher Lemon-bellied Flycatcher Letter-winged Kite Little Button-quail Little Corella

Farmland Generalist Woodland 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

214

Species Little Curlew Little Eagle Little Friarbird Little Grassbird Little Kingfisher Little Raven Little Wattlebird Little Woodswallow Long-billed Corella Long-tailed Finch Magpie-Goose Magpie-lark Malleefowl Masked Finch Masked Lapwing Masked Woodswallow Metallic Starling Mistletoebird Musk Lorikeet New Holland Honeyeater Noisy Friarbird Noisy Miner Northern Fantail Northern Rosella Olive-backed Oriole Orange Chat Orange-bellied Parrot Oriental Cuckoo Oriental Plover Oriental Pratincole Pacific Baza Pacific Black Duck Painted Button-quail Painted Honeyeater Pale-headed Rosella Pallid Cuckoo Palm Cockatoo Papuan Frogmouth Partridge Pigeon Peaceful Dove Peregrine Falcon Pheasant Coucal Pictorella Mannikin

Farmland Generalist Woodland 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

215

Species Pied Butcherbird Pied Heron Plains Wanderer Plumed Whistling Duck Plum-headed Finch Purple-crowned Fairy-wren Radjah Shelduck Rainbow Bee-eater Rainbow Lorikeet Red Goshawk Red Wattlebird Red-backed Buttonquail Red-backed Fairy-wren Red-backed Kingfisher Red-browed Pardalote Red-capped Parrot Red-capped Robin Red-chested Buttonquail Red-eared Firetail Red-rumped Parrot Red-rumped Swallow Red-tailed Black Cockatoo Red-throated Pipit Red-winged Fairy-wren Red-winged Parrot Regent Honeyeater Regent Parrot Restless Flycatcher Rufous Songlark Rufous Treecreeper Rufous Whistler Rufous-banded Honeyeater Rufous-throated Honeyeater Sacred Kingfisher Sandstone Shrike-thrush Sarus Crane Satin Flycatcher Scaly-breasted Lorikeet Scarlet Honeyeater Scarlet Robin Shining Bronze-cuckoo Shy Heathwren Silver-crowned Friarbird

Farmland Generalist Woodland 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

216

Species Silvereye Singing Bushlark Singing Honeyeater Skylark Southern Boobook Southern Whiteface Spangled Drongo Speckled Warbler Spiny-cheeked Honeyeater Spotted Bowerbird Spotted Harrier Spotted Nightjar Spotted Pardalote Spotted Quail-thrush Square-tailed Kite Squatter Pigeon Star Finch Straw-necked Ibis Striated Pardalote Striated Thornbill Striped Honeyeater Strong-billed Honeyeater Stubble Quail Sulphur-crested Cockatoo Superb Fairy-wren Superb Parrot Swamp Harrier Swift Parrot Swinhoe's Snipe Tasmanian Native-hen Tasmanian Scrubwren Tasmanian Thornbill Tawny Frogmouth Tawny Grassbird Torresian Crow Tree Martin Turquoise Parrot Varied Lorikeet Varied Sittella Wedge-tailed Eagle Weebill Welcome Swallow Western Corella

Farmland Generalist Woodland 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

217

Species Western Gerygone Western Rosella Western Spinebill Western Thornbill Western Yellow Robin Whistling Kite White-backed Swallow White-bellied Cuckoo-shrike White-breasted Robin White-breasted Woodswallow White-browed Babbler White-browed Scrubwren White-browed Woodswallow White-cheeked Honeyeater White-eared Honeyeater White-faced Heron White-fronted Chat White-fronted Honeyeater White-gaped Honeyeater White-naped Honeyeater White-plumed Honeyeater White-rumped Swiftlet White-streaked Honeyeater White-throated Gerygone White-throated Honeyeater White-throated Needletail White-throated Nightjar White-throated Treecreeper White-winged Black Tern White-winged Chough White-winged Triller Willie Wagtail Yellow Chat Yellow Honeyeater Yellow Oriole Yellow Thornbill Yellow Wagtail Yellow Wattlebird Yellow White-eye Yellow-bellied Sunbird Yellow-faced Honeyeater Yellow-rumped Mannikin Yellow-rumped Thornbill

Farmland Generalist Woodland 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

218

Species Yellow-spotted Honeyeater Yellow-tailed Black-Cockatoo Yellow-throated Honeyeater Yellow-throated Miner Yellow-tinted Honeyeater Yellow-tufted Honeyeater Zebra Finch Zitting Cisticola

Farmland Generalist Woodland 1 1 1 1 1 1 1 1

219

Appendix 4.1. mvabund model code Below is the code used to undertake the mvabund analyses in chapter 4. #load data mvbirds<-read.csv("C:/Users/hpearson/Dropbox/PhD/Chapter 3/Analysis May 16/Australia mvabund binary.csv") #install packages library(mvabund) library(glm2)

# construct some predictors Spp <-mvbirds[, c(18:559)] # subset species in columns, here they are in columns 2 to 231 predictor<-data.frame(mvbirds$Region..Nth..SE..SA..WA..combo., mvbirds$Case.Study, mvbirds$Vegetation.Components, mvbirds$Fragmentation, mvbirds$Population.trends, mvbirds$Community.assemblage,mvbirds$Restoration, mvbirds$Expert.opinion, mvbirds$occurrence, mvbirds$Existing.classification, mvbirds$traits, mvbirds$exclusion.criteria, mvbirds$Everything.seen.in.woodland)

# to make it more manageable in comp time I will reduce to only the species with more than 10 non NA's (the rest do not tell us much anyway) nValues = apply(is.na(Spp)==FALSE,2,sum) Spp = Spp[,nValues>10] #need to include a dummy factor for all species and factor variables so that all species have at least one non-na value in each category. The factor variables will need to be manually edited

SppNew = rbind(Spp,0) SppNew <- mvabund(SppNew) predNew=data.frame(SppNew[,0]) #create an empty data frame with the same number of rows as birdNew predNew$Region<-as.factor(c(mvbirds$Region..Nth..SE..SA..WA..combo.,"0")) predNew$Case.Study<-as.factor(c(mvbirds$Case.Study,"2")) predNew$Vegetation.Components<-as.factor(c(mvbirds$Vegetation.Components,"2")) predNew$Fragmentation<-as.factor(c(mvbirds$Fragmentation,"2")) predNew$Population.trends<-as.factor(c(mvbirds$Population.trends,"2")) 220

predNew$Community.assemblage<-as.factor(c(mvbirds$Community.assemblage,"2")) predNew$Restoration<-as.factor(c(mvbirds$Restoration,"2")) predNew$Expert.opinion<-as.factor(c(mvbirds$Expert.opinion,"2")) predNew$occurrence<-as.factor(c(mvbirds$occurrence,"2")) predNew$Existing.classification<-as.factor(c(mvbirds$Existing.classification,"2")) predNew$traits<-as.factor(c(mvbirds$traits,"2")) predNew$exclusion.criteria<-as.factor(c(mvbirds$exclusion.criteria,"2")) predNew$Everything.seen.in.woodland
CTRL = glm.control(epsilon=1.e-12,maxit=100) predNew$offset = 0

# define the models to be compared #### these are the lines to change if you want to change what is being tested ####

altModel1 = manyany("glm",SppNew, data=predNew, SppNew~ Region+Case.Study+Vegetation.Components+Fragmentation+Population.trends+Community .assemblage+Restoration+Expert.opinion+occurrence+Existing.classification+traits+exclusio n.criteria+Everything.seen.in.woodland, family="binomial",control=CTRL) # full model with all variables

altModel2 = manyany("glm",SppNew, data=predNew, SppNew~ Case.Study+Vegetation.Components+Fragmentation+Population.trends+Community.assembl age+Restoration, family="binomial",control=CTRL) #objectives model

altModel3 = manyany("glm",SppNew, data=predNew, SppNew~ Expert.opinion+occurrence+Existing.classification+traits+exclusion.criteria+Everything.seen .in.woodland, family="binomial",control=CTRL) #classification strategy model

altModel4 = manyany("glm",SppNew, data=predNew, SppNew~ Region, family="binomial",control=CTRL) # spatial context model

221

nullModel1 = manyany("glm",SppNew, data=predNew, SppNew~ offset(offset), family="binomial",control=CTRL) nullModel2 = manyany("glm",SppNew, data=predNew, SppNew~ offset(offset), family="binomial",control=CTRL) nullModel3 = manyany("glm",SppNew, data=predNew, SppNew~ offset(offset), family="binomial",control=CTRL) nullModel4 = manyany("glm",SppNew, data=predNew, SppNew~ offset(offset), family="binomial",control=CTRL)

nBoot = 83 #number of bootstrap resamples

nRows = dim(Spp)[1] bootIDs = replicate(nBoot,sample(1:nRows,replace=TRUE)) bootIDs = rbind(bootIDs, nRows+1)

# now store matrix of offsets to use in re-fitting the null model (to recentre parameters around alternative) offsetMat1 = altModel1$linear.predictor offsetMat2 = altModel2$linear.predictor offsetMat3 = altModel3$linear.predictor offsetMat4 = altModel4$linear.predictor

# store the test stats for the observed data statObs.j1 = pmax( logLik(altModel1)-logLik(nullModel1), 0 ) # test stat for each column statObs.j2 = pmax( logLik(altModel2)-logLik(nullModel2), 0 ) statObs.j3 = pmax( logLik(altModel3)-logLik(nullModel3), 0 ) statObs.j4 = pmax( logLik(altModel4)-logLik(nullModel4), 0 )

statObs1 = sum(statObs.j1) #overall test stat statObs2 = sum(statObs.j2) statObs3 = sum(statObs.j3) statObs4 = sum(statObs.j4)

222

# save original versions of objects (they will be overwritten in resampling) predNewOrig = predNew SppNewOrig = SppNew offsetMatOrig = offsetMat1

# make some big matrices to store the resampled stats in, add observed data as the first sample statij1 = matrix(NA,length(statObs.j1),nBoot+1) statij2 = matrix(NA,length(statObs.j2),nBoot+1) statij3 = matrix(NA,length(statObs.j3),nBoot+1) statij4 = matrix(NA,length(statObs.j4),nBoot+1)

statij1[,1] = statObs.j1 statij2[,1] = statObs.j2 statij3[,1] = statObs.j3 statij4[,1] = statObs.j4

nullLikei1 = rep(NA, length(statObs.j1)) nullLikei2 = rep(NA, length(statObs.j2)) nullLikei3 = rep(NA, length(statObs.j3)) nullLikei4 = rep(NA, length(statObs.j4))

# now do the resampling for(iBoot in 1:nBoot) { bootRef = bootIDs[,iBoot] #the indices for the current resample predNew1 = predNewOrig[bootRef,] predNew2 = predNewOrig[bootRef,] predNew3 = predNewOrig[bootRef,] predNew4 = predNewOrig[bootRef,]

#resampled predictors SppNew = SppNewOrig[bootRef,] # resampled responses offsetMat1 = offsetMatOrig[bootRef,] # resampled offsets 223

offsetMat2 = offsetMatOrig[bootRef,] offsetMat3 = offsetMatOrig[bootRef,] offsetMat4 = offsetMatOrig[bootRef,]

altModeli1 = eval(altModel1$call) # refitting the null model altModeli2 = eval(altModel2$call) altModeli3 = eval(altModel3$call) altModeli4 = eval(altModel4$call)

SppNewAll = SppNew # to be overwritten in resampling null for(jSpp in 1:dim(SppNew)[2]) { predNew1$offset = offsetMat1[,jSpp] predNew2$offset = offsetMat2[,jSpp] predNew3$offset = offsetMat3[,jSpp] predNew4$offset = offsetMat4[,jSpp]

SppNew = SppNewAll[,jSpp] nullModelij1 = eval(nullModel1$call) nullModelij2 = eval(nullModel2$call) nullModelij3 = eval(nullModel3$call) nullModelij4 = eval(nullModel4$call)

nullLikei1[jSpp] = logLik(nullModelij1) nullLikei2[jSpp] = logLik(nullModelij2) nullLikei3[jSpp] = logLik(nullModelij3) nullLikei4[jSpp] = logLik(nullModelij4)

predNew1$offset = 0 predNew2$offset = 0 predNew3$offset = 0 predNew4$offset = 0

224

nullModelij21 = eval(nullModel1$call) nullModelij22 = eval(nullModel2$call) nullModelij23 = eval(nullModel3$call) nullModelij24 = eval(nullModel4$call

nullLikei1[jSpp] = max( logLik(nullModelij1), logLik(nullModelij21) ) nullLikei2[jSpp] = max( logLik(nullModelij2), logLik(nullModelij22) ) nullLikei3[jSpp] = max( logLik(nullModelij3), logLik(nullModelij23) ) nullLikei4[jSpp] = max( logLik(nullModelij4), logLik(nullModelij24) ) } statij1[,iBoot+1]=pmax( logLik(altModeli1)-nullLikei1, 0) statij2[,iBoot+1]=pmax( logLik(altModeli2)-nullLikei2, 0) statij3[,iBoot+1]=pmax( logLik(altModeli3)-nullLikei3, 0) statij4[,iBoot+1]=pmax( logLik(altModeli4)-nullLikei4, 0)

} stati1 = apply(statij1,2,sum) stati2 = apply(statij2,2,sum) stati3 = apply(statij3,2,sum) stati4 = apply(statij4,2,sum)

P1 = mean(stati1>statObs1-1.e-4) P2 = mean(stati2>statObs2-1.e-4) P3 = mean(stati3>statObs3-1.e-4) P4 = mean(stati4>statObs4-1.e-4)

Pj1 = apply(statij1>statObs.j1-1.e-4,1,mean) Pj2 = apply(statij2>statObs.j2-1.e-4,1,mean) Pj3 = apply(statij3>statObs.j3-1.e-4,1,mean) Pj4 = apply(statij4>statObs.j4-1.e-4,1,mean)

225

names(Pj1) = dimnames(SppNewOrig)[[2]] names(Pj2) = dimnames(SppNewOrig)[[2]] names(Pj3) = dimnames(SppNewOrig)[[2]] names(Pj4) = dimnames(SppNewOrig)[[2]]

#significance full model print(paste0("the sum-of-deviance test stat is ", round(statObs1,3), ", summing over ", dim(SppNewOrig)[2], " species") ) print(paste0("the P-value is ", mean(stati1>statObs1-1.e-4), ", estimated from ", nBoot, " resamples" ))

#significance objectives model print(paste0("the sum-of-deviance test stat is ", round(statObs2,3), ", summing over ", dim(SppNewOrig)[2], " species") ) print(paste0("the P-value is ", mean(stati2>statObs2-1.e-4), ", estimated from ", nBoot, " resamples" ))

#significance classification strategy model print(paste0("the sum-of-deviance test stat is ", round(statObs3,3), ", summing over ", dim(SppNewOrig)[2], " species") ) print(paste0("the P-value is ", mean(stati3>statObs3-1.e-4), ", estimated from ", nBoot, " resamples" ))

#significance region model print(paste0("the sum-of-deviance test stat is ", round(statObs4,3), ", summing over ", dim(SppNewOrig)[2], " species") ) print(paste0("the P-value is ", mean(stati4>statObs4-1.e-4), ", estimated from ", nBoot, " resamples" ))

226

Appendix 4.2. glm2 model code Below is the code used to undertake the glm2 analyses in chapter 4. #' --#' title: "Fitting GLM's for individual spp." #' date: "20 and 21 September 2016" #' output: html_document #' --#' ## Load libraries and prepare data:

library(magrittr) # For using the return-pipe operator '%<>%' library(dplyr) # For manipulating data library(tidyr) # For 'nesting' dataframes - storing DF's within DF's library(purrr) # For applying functions to nested DF's library(broom) # For tidying fitted model output into tidy DF's library(ggplot2) library(gridExtra) # for facetting multiple plots into a single plot

mvbirds<-read.csv("C:/Users/hpearson/Dropbox/PhD/Chapter 3/Analysis May 16/Australia mvabund binary.csv") %>% # tbl_df() #tbl_df prevents your console from being flooded

# construct some predictors nValues = apply(is.na(mvbirds)==FALSE,2,sum) mvbirds = mvbirds[,nValues>10]

# Remove non-species variables Spp <-mvbirds[c(-1,-2,-10,-17,-278)]

# Make long / gather everything except fragmentation and everything seen in woodland, call new column value, call variable name 'species birdslong

<-

tidyr::gather(Spp,

key

=

"species",

value

=

"value",

-

Region..Nth..SE..SA..WA..combo.,-Case.Study,-Vegetation.Components,-Fragmentation,-

227

Population.trends,-Community.assemblage,-Restoration,-Expert.opinion,-occurrence,Existing.classification,-traits,-exclusion.criteria,-Everything.seen.in.woodland) %>% group_by(species) %>% tidyr::nest()

#' ## Write a function to fit the binomial GLM model

binom_mod1 <- function(data){ mod

<-

glm(value~Region..Nth..SE..SA..WA..combo.+Case.Study+Vegetation.Components+Fragme ntation+Population.trends+Community.assemblage+Restoration+

Expert.opinion+occurrence+Existing.classification+traits+exclusion.criteria+Everything.seen .in.woodland, family="binomial", data = data, control = glm.control(trace = FALSE,maxit = 200)) return(mod) }

#' ## Fit the Models

fitted_models1 <- birdslong %>% dplyr::mutate( model = purrr::map(data,binom_mod1), estimate = purrr::map(model, broom::tidy), fit_stats = purrr::map(model, broom::glance) )

model_coefs <- fitted_models %>% tidyr::unnest(estimate) %>% dplyr::select(species,term,estimate,std.error) %>% dplyr::arrange(estimate,std.error) %>% dplyr::mutate(species = factor(species, levels = as.vector(species))) # order spp by estimate for plotting# order spp by estimate for plotting 228

# order spp by estimate for plotting

#' ## Find and export model coeficients and deviance model_coefs <- fitted_models1 %>% tidyr::unnest(estimate) %>% dplyr::select(species,term,estimate,std.error) %>% #

dplyr::arrange(estimate,std.error) %>% # order spp by estimate for plotting

dplyr::mutate(species = factor(species, levels = as.vector(species))) # order spp by estimate for plotting#' ## Plotting Model Coefficient Estimates and their SE write.csv(model_coefs,file="C:/Users/hpearson/Dropbox/PhD/Chapter

3/Analysis

May

16/au_coefs.csv")

model_devs1 <- fitted_models1 %>% tidyr::unnest(fit_stats) %>% dplyr::select(species,null.deviance,deviance) %>% # dplyr::arrange(deviance,null.deviance) %>% # order spp by estimate for plotting dplyr::mutate(species = factor(species, levels = as.vector(species))) # order spp by estimate for plotting model_devs["percent explained"]<-NA model_devs$`percent

explained`<-(model_devs1$null.deviance-

model_devs1$deviance)/model_devs1$null.deviance*100 (model_devs1$null.deviance-model_devs1$deviance)/model_devs1$null.deviance*100 write.csv(model_devs1,file="C:/Users/hpearson/Dropbox/PhD/Chapter

3/Analysis

May

16/au_deviance.csv")

# Draw plots #without error bars frag_plot <- model_coefs %>% filter(term =="Fragmentation") %>% dplyr::mutate(species = factor(species, levels = as.vector(species))) frag_plot
geom_point() + coord_flip() + geom_hline(yintercept=0, color="grey60",linetype="dashed") + theme(axis.text.y = element_text(size = 10)) + scale_y_continuous(breaks = round(seq(-55, 20, by = 5),1)) + facet_grid(~term)

everything_plot <- model_coefs %>% filter(term =="Everything.seen.in.woodland") %>% dplyr::mutate(species = factor(species, levels = as.vector(species)))

everything_plot
=

round(seq(min(everything_plot$estimate),

max(everything_plot$estimate), by = 5),1)) + facet_grid(~term) library(gridExtra) AU_coef_estimates_no_bars <- gridExtra::grid.arrange(frag_plot, everything_plot, ncol = 2) ggsave(filename = "au_coef_plots_no_bars", plot = AU_coef_estimates_no_bars, path = "./figures/", device = "tiff",width = 594 ,height = 841,units = "mm")

#Add error bars au_coefs_std_bars % ggplot(aes(x = species, y = estimate)) + geom_point() + geom_errorbar(aes(ymax = estimate + std.error, ymin = estimate - std.error)) + coord_flip() + theme(axis.text.y = element_text(size = 10)) + facet_grid(.~term) 230

plot(au_coefs_std_bars)

231

Appendix 4.3. Australian glm2 full model coefficients and deviance Table A.4.3.1: Null, residual and % deviance explained by the full glm2 model including all objectives, spatial contexts and classification strategies.

Species

null.deviance deviance

Proportion explained deviance

Apostlebird

23.27

0.00

1.00

Australasian.Grebe

12.06

0.00

1.00

Australasian.Pipit

15.78

2.77

0.82

Australian.Brush.turkey

16.75

0.00

1.00

Australian.Bustard

15.28

0.00

1.00

Australian.Hobby

15.56

0.00

1.00

Australian.King.Parrot

15.33

0.00

1.00

Australian.Magpie

10.07

0.00

1.00

Australian.Owlet.Nightjar

14.83

0.00

1.00

9.84

0.00

1.00

Australian.Ringneck

13.94

0.00

1.00

Australian.Shelduck

12.56

0.00

1.00

Australian.White.Ibis

20.86

0.00

1.00

9.02

0.00

1.00

Azure.Kingfisher

21.25

0.00

1.00

Banded.Lapwing

14.05

0.00

1.00

Bar.shouldered.Dove

15.84

0.00

1.00

Barking.Owl

16.57

0.00

1.00

Barn.Owl

20.02

0.00

1.00

Bassian.Thrush

17.99

0.00

1.00

Black.chinned.Honeyeater

20.45

0.00

1.00

Black.eared.Cuckoo

16.22

0.00

1.00

Black.faced.Cuckoo.shrike

14.55

0.00

1.00

Black.faced.Monarch

12.89

0.00

1.00

Black.faced.Woodswallow.1

15.56

0.00

1.00

Black.Falcon

17.53

0.00

1.00

Black.fronted.Dotterel

10.14

0.00

1.00

Black.Honeyeater

13.50

0.00

1.00

Black.Kite

16.22

0.00

1.00

Black.shouldered.Kite

14.55

0.00

1.00

Black.Swan

19.29

0.00

1.00

Black.tailed.Native.hen

14.05

0.00

1.00

Black.winged.Stilt

13.50

0.00

1.00

Blue.faced.Honeyeater

19.07

0.00

1.00

Bluebonnet

24.63

0.00

1.00

Australian.Raven

Australian.Wood.Duck

232

Species

null.deviance deviance

Proportion explained deviance

Brown.Falcon

21.46

0.00

1.00

Brown.Goshawk

15.56

0.00

1.00

Brown.headed.Honeyeater

15.84

0.00

1.00

Brown.Honeyeater

20.29

2.77

0.86

Brown.Quail

19.07

2.77

0.85

Brown.Songlark

23.40

2.77

0.88

Brown.Thornbill

17.40

0.00

1.00

Brown.Treecreeper

10.14

0.00

1.00

Brush.Bronzewing

15.01

0.00

1.00

Brush.Cuckoo

22.97

0.00

1.00

Budgerigar

20.45

0.00

1.00

Buff.rumped.Thornbill

17.47

0.00

1.00

Bush.Stone.curlew

18.35

0.00

1.00

Channel.billed.Cuckoo

17.99

0.00

1.00

Chestnut.breasted.Mannikin

14.05

0.00

1.00

Chestnut.rumped.Heathwren

20.86

0.00

1.00

Chestnut.rumped.Thornbill

14.70

0.00

1.00

Cicadabird

19.07

0.00

1.00

Clamorous.Reed.warbler

18.55

0.00

1.00

Cockatiel

23.27

0.00

1.00

Collared.Sparrowhawk

14.96

0.00

1.00

Common.Blackbird

21.25

2.77

0.87

9.96

0.00

1.00

Common.Koel

17.40

0.00

1.00

Common.Myna

17.99

2.77

0.85

Common.Starling

20.11

2.77

0.86

Crescent.Honeyeater

17.81

0.00

1.00

Crested.Bellbird

18.84

0.00

1.00

Crested.Pigeon

0.00

0.00

Crested.Shrike.tit

9.84

0.00

1.00

Crimson.Chat

15.28

0.00

1.00

Crimson.Rosella

23.17

2.77

0.88

Diamond.Dove

13.59

0.00

1.00

Diamond.Firetail

16.27

0.00

1.00

Dollarbird

21.46

2.77

0.87

Double.barred.Finch

24.11

2.77

0.89

Dusky.Moorhen

15.44

2.77

0.82

Dusky.Woodswallow

10.14

0.00

1.00

Eastern.Rosella

17.18

0.00

1.00

Eastern.Spinebill

26.42

2.77

0.90

Common.Bronzewing

NA

233

Species

null.deviance deviance

Proportion explained deviance

Eastern.Whipbird

16.75

0.00

1.00

Eastern.Yellow.Robin

23.82

2.77

0.88

Emu

22.32

0.00

1.00

Eurasian.Coot

12.89

0.00

1.00

Eurasian.Skylark

19.07

2.77

0.85

European.Goldfinch

18.60

2.77

0.85

Fairy.Martin

9.02

2.77

0.69

Fan.tailed.Cuckoo

9.64

0.00

1.00

Fawn.breasted.Bowerbird

15.28

0.00

1.00

Figbird

14.42

0.00

1.00

Flame.Robin

25.12

0.00

1.00

Forest.Kingfisher

15.44

0.00

1.00

Forest.Raven

14.42

0.00

1.00

Fuscous.Honeyeater

21.76

0.00

1.00

9.96

0.00

1.00

Gang.gang.Cockatoo

18.35

0.00

1.00

Gilbert.s.Whistler

12.79

0.00

1.00

Glossy.Black.cockatoo

16.75

0.00

1.00

Golden.headed.Cisticola

10.07

0.00

1.00

Golden.shouldered.Parrot

19.56

2.77

0.86

Golden.Whistler

15.28

0.00

1.00

Great.Cormorant

13.50

0.00

1.00

Grey.Butcherbird

16.54

0.00

1.00

Grey.crowned.Babbler

20.65

0.00

1.00

Grey.Currawong

14.42

0.00

1.00

Grey.Falcon

10.33

0.00

1.00

Grey.Fantail

0.00

0.00

Grey.fronted.Honeyeater

15.44

2.77

0.82

Grey.Shrike.thrush

25.12

0.00

1.00

Grey.Teal

13.50

0.00

1.00

Ground.Cuckoo.shrike

15.84

0.00

1.00

Hardhead

13.50

0.00

1.00

Hooded.Robin

15.88

0.00

1.00

Horsfield.s.Bronze.cuckoo

16.71

0.00

1.00

House.Sparrow

17.81

2.77

0.84

Inland.Thornbill

17.22

0.00

1.00

9.96

0.00

1.00

Laughing.Kookaburra

10.14

0.00

1.00

Leaden.Flycatcher

21.31

2.77

0.87

Letter.winged.Kite

14.42

0.00

1.00

Galah

Jacky.Winter

NA

234

Species

null.deviance deviance

Proportion explained deviance

Lewin.s.Honeyeater

15.28

0.00

1.00

Little.Button.quail

15.01

0.00

1.00

Little.Corella

25.12

0.00

1.00

Little.Crow

14.05

0.00

1.00

Little.Eagle

15.56

0.00

1.00

Little.Friarbird

15.33

0.00

1.00

Little.Grassbird

14.55

0.00

1.00

Little.Lorikeet

19.50

0.00

1.00

Little.Pied.Cormorant

16.57

2.77

0.83

Little.Raven

20.29

0.00

1.00

Little.Wattlebird

20.86

0.00

1.00

Little.Woodswallow

17.40

0.00

1.00

Long.billed.Corella

18.08

0.00

1.00

Magpie.lark

10.03

0.00

1.00

Major.Mitchell.s.Cockatoo

17.40

0.00

1.00

Masked.Finch

14.42

0.00

1.00

Masked.Lapwing

22.97

0.00

1.00

9.08

0.00

1.00

Mistletoebird

10.03

0.00

1.00

Musk.Lorikeet

24.11

0.00

1.00

9.40

0.00

1.00

Nankeen.Night.heron

14.05

2.77

0.80

New.Holland.Honeyeater

14.83

2.77

0.81

Noisy.Friarbird

10.03

0.00

1.00

Noisy.Miner

17.40

0.00

1.00

Olive.backed.Oriole

16.54

0.00

1.00

Pacific.Baza

15.28

0.00

1.00

Pacific.Black.Duck

8.84

0.00

1.00

Painted.Button.quail

9.14

0.00

1.00

Painted.Honeyeater

18.08

0.00

1.00

Pale.headed.Rosella

17.40

0.00

1.00

Pallid.Cuckoo

16.36

0.00

1.00

Peaceful.Dove

15.78

0.00

1.00

Peregrine.Falcon

15.09

0.00

1.00

Pheasant.Coucal

14.42

0.00

1.00

Pied.Butcherbird

30.66

0.00

1.00

Pied.Cormorant

14.55

0.00

1.00

Pied.Currawong

16.95

0.00

1.00

Pink.eared.Duck

12.89

0.00

1.00

Plum.headed.Finch

18.55

0.00

1.00

Masked.Woodswallow

Nankeen.Kestrel

235

Species

null.deviance deviance

Proportion explained deviance

Powerful.Owl

14.42

0.00

1.00

Purple.crowned.Lorikeet

13.40

0.00

1.00

9.59

0.00

1.00

Rainbow.Lorikeet

21.25

0.00

1.00

Red.backed.Fairy.wren

17.11

2.77

0.84

Red.backed.Kingfisher

16.75

0.00

1.00

Red.browed.Finch

18.55

0.00

1.00

Red.browed.Treecreeper

22.44

2.77

0.88

Red.capped.Robin

16.05

0.00

1.00

Red.kneed.Dotterel

9.64

0.00

1.00

Red.rumped.Parrot

14.42

0.00

1.00

Red.tailed.Black.cockatoo

16.79

0.00

1.00

Red.Wattlebird

14.05

0.00

1.00

Red.winged.Parrot

18.55

0.00

1.00

Regent.Honeyeater

17.22

0.00

1.00

Restless.Flycatcher

9.92

0.00

1.00

Rock.Dove

16.05

0.00

1.00

Rose.Robin

17.99

0.00

1.00

Royal.Spoonbill

14.42

0.00

1.00

Rufous.Fantail

20.86

0.00

1.00

0.00

0.00

Rufous.Treecreeper

10.43

0.00

1.00

Rufous.Whistler

10.33

0.00

1.00

Sacred.Kingfisher

10.03

0.00

1.00

Satin.Bowerbird

18.55

0.00

1.00

Satin.Flycatcher

19.56

0.00

1.00

Scaly.breasted.Lorikeet

17.40

0.00

1.00

Scarlet.Honeyeater

15.01

0.00

1.00

Scarlet.Robin

16.88

0.00

1.00

Shining.Bronze.cuckoo

15.98

0.00

1.00

Shy.Heathwren

14.05

0.00

1.00

9.92

0.00

1.00

Singing.Bushlark

17.99

0.00

1.00

Singing.Honeyeater

17.81

0.00

1.00

Southern.Boobook

14.83

0.00

1.00

Southern.Emu.Wren

14.42

0.00

1.00

Southern.Whiteface

21.15

0.00

1.00

Spangled.Drongo

14.55

0.00

1.00

Speckled.Warbler

27.36

2.77

0.90

Spiny.cheeked.Honeyeater

17.81

0.00

1.00

Rainbow.Bee.eater

Rufous.Songlark

Silvereye

NA

236

Species

null.deviance deviance

Proportion explained deviance

Splended.Fairy.wren

13.50

0.00

1.00

Spotted.Bowerbird

16.75

0.00

1.00

Spotted.Harrier

17.53

0.00

1.00

Spotted.Nightjar

15.84

0.00

1.00

Spotted.Pardalote

10.39

0.00

1.00

Spotted.Quail.thrush

19.56

0.00

1.00

Spotted.Turtle.dove

17.40

0.00

1.00

Square.tailed.Kite

14.55

0.00

1.00

Straw.necked.Ibis

20.86

2.77

0.87

Striated.Pardalote

10.17

0.00

1.00

Striated.Thornbill

17.18

0.00

1.00

Striped.Honeyeater

21.98

0.00

1.00

Stubble.Quail

15.56

2.77

0.82

Sulphur.crested.Cockatoo

17.26

0.00

1.00

Superb.Fairy.wren

17.54

0.00

1.00

Superb.Lyrebird

15.28

0.00

1.00

Superb.Parrot

22.97

2.77

0.88

Swamp.Harrier

17.40

0.00

1.00

Swift.Parrot

21.63

0.00

1.00

Tawny.crowned.Honeyeater

14.70

0.00

1.00

Tawny.Frogmouth

14.05

0.00

1.00

Torresian.Crow

17.99

0.00

1.00

9.76

0.00

1.00

Turquoise.Parrot

15.84

0.00

1.00

Varied.Sittella

10.14

0.00

1.00

Variegated.Fairy.wren

21.25

0.00

1.00

Wedge.tailed.Eagle

15.21

0.00

1.00

Weebill

10.36

0.00

1.00

Welcome.Swallow

9.92

2.77

0.72

Western.Gerygone

9.55

0.00

1.00

Whistling.Kite

14.70

0.00

1.00

White.backed.Swallow

14.55

0.00

1.00

White.bellied.Cuckoo.shrike

25.35

0.00

1.00

White.bellied.Sea.eagle

14.05

0.00

1.00

White.breasted.Woodswallow

19.07

0.00

1.00

White.browed.Babbler

16.18

0.00

1.00

9.88

0.00

1.00

White.browed.Woodswallow

22.04

0.00

1.00

White.cheeked.Honeyeater

13.50

0.00

1.00

White.eared.Honeyeater

22.57

0.00

1.00

Tree.Martin

White.browed.Scrubwren

237

Species

null.deviance deviance

Proportion explained deviance

White.faced.Heron

14.41

0.00

1.00

White.fronted.Chat

19.29

2.77

0.86

White.fronted.Honeyeater

13.50

0.00

1.00

White.naped.Honeyeater

9.72

0.00

1.00

White.necked.Heron

12.06

0.00

1.00

White.plumed.Honeyeater

10.10

0.00

1.00

White.throated.Gerygone

21.76

2.77

0.87

White.throated.Honeyeater

16.75

0.00

1.00

White.throated.Needletail

19.07

0.00

1.00

White.throated.Nightjar

16.75

0.00

1.00

White.throated.Treecreeper

17.47

0.00

1.00

White.winged.Chough

17.60

0.00

1.00

White.winged.Fairy.wren

13.50

0.00

1.00

9.80

0.00

1.00

Willie.Wagtail

10.07

0.00

1.00

Wonga.Pigeon

17.99

0.00

1.00

Yellow.faced.Honeyeater

14.05

0.00

1.00

Yellow.plumed.Honeyeater

23.06

0.00

1.00

Yellow.Rosella

23.29

2.77

0.88

Yellow.rumped.Thornbill

11.16

0.00

1.00

Yellow.tailed.Black.cockatoo

10.03

0.00

1.00

Yellow.Thornbill

21.98

0.00

1.00

Yellow.throated.Miner

16.22

0.00

1.00

Yellow.tufted.Honeyeater

20.65

0.00

1.00

Zebra.Finch

22.32

0.00

1.00

White.winged.Triller

238

Table A.4.3.2 Estimates and standard errors for intercept and objective classes in the full Australian model based on glm2 analyses (Intercept) Species

Est

SEM

Community assemblage

Case Study Est

SEM

Est

SEM

Population trends

Fragmentation Est

SEM

Est

SEM

Vegetation Components

Restoration Est

SEM

Est

SEM

Apostlebird

56.00

525309

-14.76

436996

-28.86

226811 48.07

142222

4.48 148349 -1.78 349047

-19.99

182813

Australasian Grebe

-23.57

399581

0.55

626799

-23.02

442823 26.18

429904

22.77 276991 -22.37 297098

-2.81

464234

Australasian Pipit

22.68

46621

0.01

187864

-0.46

29802 22.25

27704

53808

-21.72

40933

Australian Brush turkey

529090 -102.26

864000 51.13

432000 -25.57 305470 76.70 591541

-51.13

305470

-49.13

185277

0.05 207435

-0.28

140234

10.99

42881 10.82

127.83

1035900

-25.57

Australian Bustard

24.57

292949

73.70

628305

0.00

320909 49.13

262021 -24.57 185277

Australian Hobby

26.38

257128

-0.02

336131

-0.15

191243 -0.29

175001

Australian King Parrot

50.66

1595538

1.92

3473271

-23.89

1569297 49.15

193432

-1.86 606739 -21.19 1846546

-25.06

1562563

Australian Magpie

12.04

500018

7.10

426894

13.94

335533 19.37

300334

10.51 449231 14.43 368606

-18.35

378198

Australian Owlet Nightjar

75.28

261730

-48.59

494881

-48.35

202773 48.46

149798

0.13 186416 -1.33 502720

-0.01

161517

Australian Raven

16.46

628394

4.38

862734

9.16

581371 16.84

915495

14.18 472721 16.53 1144735

-10.61

483763

Australian Ringneck

25.57

178974

0.00

273246

0.00

162294

0.00

143688

0.00 172238

0.00 209380

0.00

106152

Australian Shelduck

25.57

353468

0.00

251327

0.00

279792

0.00

245183

0.00 144432

0.00 193312

0.00

174659

Australian White Ibis

26.66

310623

26.53

764910

-1.19

233184 49.73

217504

24.89 298449 23.31 348327

-0.29

267748

Australian Wood Duck

0.07 173509

9.51

1237997

9.02

2461900

14.88

1255281 15.98

1262548

1.99 2984271 21.77 345544

-23.06

2979554

Azure Kingfisher

26.32

311969

-2.60

427823

-2.00

290031 31.93

314640

15.73 158098 48.60 389617

-15.07

212625

Banded Lapwing

26.11

266574

0.00

305470

-1.65

235821 47.63

127723 -25.05 322583 71.04 410642

-49.36

198668

Bar shouldered Dove

62.16

333052

-45.22

1113609

-38.00

312373 36.36

340782

12.11 200446 17.54 503924

-11.75

265115

Barking Owl

51.13

237436

0.00

478369

-25.57

217920

0.00

303647

0.00 213413 51.13 541541

-25.57

190737

Barn Owl

49.20

267596

1.21

580780

-23.70

267594 24.14

569778

-0.48 259867 22.80 441567

-26.30

218977

Bassian Thrush

25.57

374123

-51.13

347501

0.00

245720

0.00 347501

0.00

165665

Black chinned Honeyeater

19.88

925692

-19.42

534673

9.69

495111 17.78

368141

13.56 727801

-16.46

428627

Black eared Cuckoo

70.45

496909

-22.78

1454059

-45.36

509238 12.31

785639

20.03 594278

-5.11

459349

Black faced Cuckoo shrike

17.70

629044

5.92

394094

7.92

607001 12.14

371542

11.73 304891 19.81 498034

-13.22

281267

Black faced Monarch

73.70

292949

-49.13

763917

-49.13

370554

0.00

262022

0.00

185277

Black faced Woodswallow 1

25.24

736753

71.81

1843497

-0.73

747676

1.55

282554 -23.88 599808 71.33 1336206

-48.66

653630

Black Falcon

42.93

655890

21.44

1131848

-16.96

559378 48.20

146950

-32.62

548903

Black fronted Dotterel

9.50 495980

0.00 262021

-5.55 494875 37.24 790609

-25.57

571483

51.13

683052

0.00

305470 51.13

305470

0.00 216000

0.00 216000

0.00

305470

Black Honeyeater

61.98

410476

-48.47

447269

-36.34

305987 11.53

272514

24.62 312095

2.34 475571

-12.97

255667

Black Kite

76.70

374123

-51.13

989837

-51.13

432000 51.13

305470

0.00 305470 -51.13 748247

0.00

305470

239

Black shouldered Kite

49.33

302174

Black Swan

-24.57

226917

Black tailed Native hen

-10.32

665080

-23.34

221396 48.07

141149

12.28 259169 12.95 282165

-25.30

214177

0.00

185277 49.13

185277

0.00 262021 49.13 320910

0.00

185277

259966

0.00

173311

0.00 173311

0.00 226917

0.00

157076

0.00 262021

0.00 262021

0.00

185277

10.20 301484 34.61 414982

-30.58

485355

73.70

291112

0.00

185277

-49.13

Black winged Stilt

-24.57

226917

0.00

185277

0.00

185277 49.13

185277

Blue faced Honeyeater

43.09

610886

-11.64

509102

-16.69

482686 46.65

99443

Bluebonnet

12.88 47454026

Brown Falcon

50.43

Brown Goshawk Brown headed Honeyeater Brown Honeyeater

4745491 4745658 11.74 47453835 61.97 47453642 -36.89 8 35.95 4

-50.23

398579

2533695

15.09

726287

-15.51

27.17

257491

0.07

425598

-1.19

16.40

782213

6.35

600813

91.75

703285

-75.20

Brown Quail

35.68

68587

-9.59

Brown Songlark

50.92

113224

Brown Thornbill

21.93

Brown Treecreeper Brush Bronzewing

-61.97 47454625

732318 48.94

148545

4.02 1063123 31.19 465513

-33.55

744023

217145

1.84

249049

-0.92 175115 45.38 262794

-0.61

166184

9.26

788580 12.16

463984

11.05 359489 21.21 543147

-13.62

362260

754933

-66.91

629946 40.41

266129

15.90 249995 17.68 376966

-9.60

214260

144006

-12.65

12.04 47453787

36681

-2.82

40.19

5285953

-16.86

25.57

550695

58930

9.77

64217

-4.38

116237 18.31

114679

77112

-0.48

24531 21.59

19454

3763780

6.25

2963224

4.91

6.01

94655

-8.35

56125

-8.20 120505 31.38

53577

-18.75

120870

31639

-28.18

68829

23.97 872997 43.85 551837

-18.14

2224014

0.00

953831

0.00

264545 -51.13 1132715 51.13 953831

1504506

4.52

54481

83263 20.40

1867132 -7.70 6

-51.13

1090748

1867236 9.23 7

-16.89

7470342

Brush Cuckoo

56.57

7472272

-7.91

3782430

-31.63

7470697 17.22

Budgerigar

25.52

195030

71.41

435172

-1.22

177429 49.11

170843 -24.37 208412 22.58 241980

-48.14

190587

Buff rumped Thornbill

20.60

763458

2.73

458969

5.70

690526

8.82

607406

13.83 416981 21.20 389037

-11.27

397669

Bush Stone curlew

84.63

489185

-74.02

1571284

-53.79

825759

2.32

643440

17.38 274403 -21.52 839415

3.13

795427

16.37 229501

-8.37

169275

0.00 185277

0.00

185277

-48.68

369448

Channel billed Cuckoo

102.69 47455063

-86.78 47455480

-78.60 47454727 39.68 23727770

226917

-49.13

600367

-49.13

262021

0.00

262021

26.39

350083

-1.30

616777

-1.06

293050

2.72

518599 -21.50 905964 49.85 461101

26.38

262443

-0.21

273214

-0.33

182574

0.89

199792

-0.74 135983 48.33 299806

0.05

130762

71.00

400507

-67.18

1088486

-46.98

387824

1.90

359582

22.41 427415 93.76 829527

-2.21

321640

76.70

1142965

-36.30

206194

-1.36 175352 47.25 260552

-0.51

152769

-2.70

84778

-2.57

50242

14.13 400674 16.27 715452

-10.78

408902

Chestnut breasted Mannikin

73.70

Chestnut rumped Heathwren Chestnut rumped Thornbill Cicadabird Clamorous Reed warbler

7470823

4.20

-76.70

1212298

153.40

1700787

102.26

1231390 51.13

127.8 305470 102.26 1153125 3 989837

Cockatiel

38.74

362715

43.88

594674

-12.39

197091 47.85

134795 -11.10 197602 56.79 489144

Collared Sparrowhawk

25.67

266433

2.13

449770

-0.37

230187

1.18

253881

0.21

89711

-18.56

51837 20.39

16178

16.59

548951

9.45

463249 17.32

629457

Common Blackbird Common Bronzewing Common Koel

50.30 54794725

Common Myna

0.00

41341

Common Starling

-32.26

158781

5.19

645176

-3.23 164383488

38.74

194653

-26.14 54794760 48.54 0.00

26146 21.57

-4.14

134515 22.02

160338

45031

5.18

5479485 0.90 9

-23.81 54794334

24458 -21.57

60618 43.13

21098

97752

12.29

85226

-21.57

39219

5.10 212996

-17.43

137674

240

Crescent Honeyeater

75.30

423681

-0.26

496702

-48.78

356909

1.35

313909

-1.92 317914

2.95 438344

-1.43

278945

Crested Bellbird

81.50

1654655

4.18

611466

-29.97

644884 26.09

263358

-1.10 2362658 20.04 694750

-19.13

647539

Crested Pigeon

26.57

251706

0.00

197250

0.00

136170

0.00

118648

0.00 146215

0.00

129412

Crested Shrike tit

39.51

990572

-14.25

582054

1.86

546601

2.95

469178

30.67 348938 43.58 675357

-14.06

409077

Crimson Chat

24.57

226917

-49.13

668026

-49.13

262021 49.13

185277

0.00 262021 -49.13 414292

0.00

185277

Crimson Rosella

21.92

38306

-2.00

64939

-0.26

23777 21.66

19677

29497

-29.46

46180

Diamond Dove

68.47

309584

-47.86

325091

-44.21

322351 21.63

800368

-0.54 392801 -10.11 1192032

-4.63

287058

Diamond Firetail

17.85

1280587

-12.76

1290896

10.34

1074852 10.52

1011484

33.90 477807 44.90 521793

-11.04

398716

Dollarbird

23.51

52503

-11.50

277071

-1.25

32982 21.08

22566

12.34

55683

-20.16

42343

Double barred Finch

22.80

37463

23.53

111146

-1.38

29342 21.17

18763

-5.41 105352

4.80 111408

-19.50

35002

Dusky Moorhen

21.57

50632

64.70

85226

0.00

35802 21.57

29232

21.57

0.00

54689

0.00

35802

Dusky Woodswallow

17.29

1257607

-14.57

920104

21.03

Eastern Rosella

16.92

955644

1.57

520583

Eastern Spinebill

47.26

66010

-17.40

Eastern Whipbird

527230

0.00 128320

3.07

54158 20.65

49944 10.63

50632

1.43

361996

8.33 444912 21.38 544003

5.82

396535

9.28

871742 11.34

891440

13.29 1001628 22.21 405379

-11.86

1018451

66043

-2.23

37507 21.03

20467

-8.96

-19.07

45531

432000

0.00

264545

-4.62

22420

-47.83

379194

0.00

185277

-43.13

41341

85459 18.97

43709

-25.57

476915

0.00

404099

0.00

305470 51.13

Eastern Yellow Robin

61.30

57743

-3.52

40138

-15.55

22162 20.91

Emu

28.15

355509

69.68 47459072

-2.28

324134 49.84

222455

-1.18 436910

-24.57

226917

0.00

185277 49.13

185277

49.13 262021

0.00

41341

21.57

50632 43.13

53371 -32.35

38671

-27.80

92401

-8.29

33411 20.81

19972

13.87

40982 -1.41

52597

-12.51

34273

Fairy Martin

22.38

37848

15.88

80037

-0.74

23042 20.63

16861

0.32

28071 20.10

27937

-3.44

64030

Fan tailed Cuckoo

47.67

419895

-5.36

316299

-8.06

210619

236441 -12.29 183108 15.06 468906

9.43

214308

Fawn breasted Bowerbird

0.00

292949

98.26

585898

24.57

320909 73.70

292949 -24.57 185277

-49.13

185277

Figbird

98.26

320909

0.00

320909

-73.70

226917 -24.57

207146 -24.57 185277

-49.13

185277

Flame Robin

34.91

943383 -144.55

740978

-4.03

390986 48.59

164832

-44.86

426652

Forest Kingfisher

76.70

377347

348082

-51.13

274794

200041

0.00

174500

Forest Raven

24.57

226917

Fuscous Honeyeater

26.53

239133

-46.76

Galah

20.27

7272424

-23.55 25.57

Eurasian Coot Eurasian Skylark European Goldfinch

Gang gang Cockatoo Gilbert s Whistler

53.92

-51.13

88102

3.62

0.00

0.00 506565

13887 -14.91

22456 10.06

39836

0.00 185277

-1.33 285425 46.50 453034 0.00 157302

0.00 369099

0.00

185277 12.28

196516 -12.28 217257

-36.85

217257

519446

-0.65

156430 49.48

175875

2.25 281742 49.44 241846

-48.86

212788

5.83

1763411

5.35

7893375

10.84 2407844 19.73 1110653

-14.06

2406475

496652

-46.53

464535

-0.38

188261 49.39

186613 -46.04 385433 45.48 475104

-48.16

242009

212085

0.00

268588

0.00

151335

217515

0.00 154792

0.00

165324

160338

5479485 0.90 9

Glossy Black cockatoo

50.30 54794725

Golden headed Cisticola

29.54

53120

7247590

9.92

0.00

-26.14 54794760 48.54

35.07

177120

-8.02

44789 15.43

62336 -16.64

0.00 289509

82965 30.22

-23.81 54794334

83733

-27.00

83810

241

Golden shouldered Parrot

24.57

131011

73.70

393032

Golden Whistler

20.37

765250

-9.77

1466263

5.41

Great Cormorant

49.13

185277 -24.57 185277

-49.13

185277

661149 18.07

519589

4.84 600395 11.30 911747

-19.78

591347

-116.97

1073009

142.54

908161

43.89

813638 48.72

167095

46.31 420802 46.31 519987

47.51

297552

Grey Butcherbird

28.11

319899

0.45

467946

-2.00

202936 48.43

156288

2.81 336511 46.46 260818

-47.44

294819

Grey crowned Babbler

91.43

877208

-14.07

689596

-32.17

433569 48.68

166624

-3.09 1163991 29.74 991961

-16.91

419612

Grey Currawong

32.35

795690

89.77

1582647

-4.96

625393 48.68

166474

-1.24 344301 45.95 677754

-44.03

649102

Grey Falcon

24.57

226917

73.70

600366

0.00

262021 24.57

185277 -24.57 185277 24.57 346622

-49.13

185277

Grey Fantail

21.00

901970

1.97

1238195

4.58

855873 10.72

471737

-11.96

432999

Grey fronted Honeyeater

73.70

226917

-49.13

600367

-49.13

262021

0.00

185277

0.00 185277

0.00

185277

Grey Shrike thrush

26.57

222454

0.00

148747

0.00

105048

0.00

108342

0.00 114857

0.00

109858

-18.63

91868

61.76

70863

-0.98

34281 20.59

17907

-1.96

75951

77.26

303537

-84.18

518111

-53.07

296988 49.31

207567

27.90 219296

3.01

150908

-24.57

346622

0.00

320909 49.13

262021

0.00 262021

0.00 262021

0.00

185277

Hooded Robin

27.15

539732

-5.85

1605707

4.57

831434 47.91

288564

-4.45 646423

5.87 881454

-2.78

530107

Horsfield s Bronze cuckoo

26.46

202372

-0.17

195870

-0.57

128140

0.96

126982

-0.64 111547 46.86 212878

-0.06

107871

House Sparrow

-6.92 47453180

29662 20.33

15782

16.87

53905

-8.14

32908

Inland Thornbill

85.62

542131

-47.36

414469

-62.01

542126

1.44

394994

37.45 723491 38.92 762115

13.22

473077

Jacky Winter

35.52

2249961

-10.12

887072

3.28

888744

5.59

829043

27.78 768430 38.90 1962058

-13.37

670936

Laughing Kookaburra

15.65

613012

4.17

642955

10.03

530053 14.18

293200

9.71 279870 20.26 661440

-15.19

274814

Leaden Flycatcher

24.96

33219

-13.83

52493

-3.36

23669 18.25

25827

78887

-18.55

26939

Letter winged Kite

24.57

226917

73.70

600366

0.00

262021 24.57

185277 -24.57 185277 24.57 346622

-49.13

185277

Lewin s Honeyeater

49.13

245099

-49.13

320909

-98.26

320909 -24.57

207146 -73.70 424523

24.57

160455

Little Button quail

25.57

374123

76.70

989837

0.00

432000 25.57

610941 -25.57 305470 -25.57 432000

-51.13

305470

Little Corella

28.89

375081

0.29 47453910

-2.35

188829 47.76

108977

-44.91

220114

Little Crow

24.57

226917

600367

0.00

262021 24.57

370554 -24.57 185277

-49.13

185277

Little Eagle

18.16 27662093

-10.91 18465232

16.96

9225900 47.91

298873 -16.18 9226792 17.94 9227603

-14.50

9231449

Little Friarbird

22.44

359953

-17.70

307457

3.27

219570

358658

13.31 228674 18.53 465405

-11.55

227002

Little Grassbird

25.57

482991

76.70

890591

0.00

432000 76.70

571483 -25.57 305470 76.70 571483

-51.13

305470

-36.09 47455482

-28.64

330705

9.41

292241

-19.97

313375

17907

26352

21.57

62011

163262 -10.65 190422 16.73 242925

-5.49

185448

-2.89

429557

-63.92

517950

0.00 196585

0.00

132246

11.40 326477 19.58 479774

-13.53

305117

Grey Teal Ground Cuckoo shrike Hardhead

Little Lorikeet

-13.13

73.70

8.33

4.07

807177

Little Pied Cormorant

-30.39

68949

49.99

76311

-0.98

27349 20.59

Little Raven

28.14

445761

-20.10

833130

15.07

224534 42.46

Little Wattlebird

70.77

1097659

-1.65

590094

-45.25

Little Woodswallow

12.78

540000

115.05

1387286

12.78

Long billed Corella

25.57

269057

0.00

282410

Magpie lark

18.65

662112

6.20

376739

1040120

3.84

1017229

12.71 432240 18.03 656208

-1.96

13.92

0.00 134445

48481 21.57

40556 -9.10

34828 13.40

50632

-1.19 235214 48.09 217941

13.02 306814 9.80

26352

-1.59 852231

540000 63.92

470761 -38.35 470761

0.00

182022

0.00

164183

6.96

637663 11.69

471310

0.00 151699

9.86 379307 9.80

4.62 1038111

242

Major Mitchell s Cockatoo

24.66

453231

Masked Finch

24.57

131011

73.70

393032

Masked Lapwing

27.05

298767

44.57

567422

-1.65

Masked Woodswallow

54.15

693701

-0.71

597006

-13.70

Mistletoebird

13.64

373913

5.02

687443

11.69

284502 12.24

411505

Musk Lorikeet

35.86

344314

-27.63

828076

-8.68

Nankeen Kestrel

0.00

432000 50.60 49.13

272119 -25.57 460634 24.66 746706

-50.60

409098

185277 -24.57 185277

-49.13

185277

223277 48.75

139479

0.82 241823 47.17 274211

-2.49

378959

466229

296881 -15.44 235067 13.26 468042

4.57

456532

10.92 475129 24.13 388014

-13.51

486753

207680 72.46

146823 -15.19 197785 55.45 380870

-39.80

204819

218645

1.33

297121

20.00 401462 21.10 272054

-4.23

419058

41341 21.57

29232

21.57

50632

0.00

58465

43.13

58465

-5.76

23419

2.32

52764

4.51

22625

0.00 138222

0.00

120120

2.58

24.83

249151

-19.05

959691

-0.40

Nankeen Night heron

-64.70

77342

64.70

87697

0.00

New Holland Honeyeater

48.23

53993

8.01

94748

-25.24

29084

1.74

33149

Noisy Friarbird

26.57

228944

0.00

182043

0.00

111062

0.00

113760

Noisy Miner

12.26

517814

4.05

553289

14.06

373866 17.34

265541

14.43 431659 14.50 400178

-16.59

313613

Olive backed Oriole

19.81

296176

-7.49

671435

5.17

229293

3.38

245676

15.07 398301 -0.30 190569

-11.61

203592

Pacific Baza

49.13

292949

-49.13

320909

-24.57

262022 49.13

185277

24.57 226917 -24.57 262022

0.00

185277

Pacific Black Duck

4.74

534211

29.11 47460351

20.07

548378 21.11

532728

3.94 2526625 21.84 313881

-21.51

2527564

Painted Button quail

71.34

404184

-1.00

316597

-1.48

247217

200881

-2.28 245371 -0.02 238914

1.83

186558

Painted Honeyeater

64.19

403888

-10.45

290870

-38.01

277176 48.62

163830 -14.98 598209 11.00 310957

-11.46

236656

Pale headed Rosella

125.08

1335319

-96.62

852214

-87.43

895478 72.62

578373

-7.74

201070

Pallid Cuckoo

26.57

262520

0.00

281366

0.00

146372

0.00

148247

0.00 173067

0.00

135464

Peaceful Dove

24.74

232717

-14.58

439680

-0.39

201864

6.53

558186

10.61 338638 16.62 559906

-13.91

335719

Peregrine Falcon

26.31

235957

1.32

463305

-0.37

222158

2.29

296816

-1.31 185670 46.23 270722

-0.93

174860

Pheasant Coucal

98.26

320909

0.00

490197

-73.70

Pied Butcherbird

171.33

837524

Pied Cormorant

3.02

479269

Pied Currawong

25.18

360511

Pink eared Duck

73.70

346622

Plum headed Finch

65.64

308547

Powerful Owl

-1.68

0.77

-49.74

-24.57

540171

Purple crowned Lorikeet

24.27

1242380

-2.92

Rainbow Bee eater

12.88

256676

4.87

Rainbow Lorikeet Red backed Fairy wren

69.74 204.53

Red backed Kingfisher

126.32

Red browed Finch

21.64

355384

479279

391705

652608 -228.81 -2.92

17.72 255464 -29.36 933835 0.00 154476

292949 49.13

185277 -24.57 185277

-49.13

185277

-99.45

282467 48.97

1329707 1329332 179797 -18.09 0 23.71 7

50.40

214455

-1.21

246204 48.72

167095

46.31 420802

1.21 392338

23.76

411197

0.40

312418

1.46

371837

21.51 477283 23.33 316494

-3.19

479070

-49.13

320909

0.00

262021 -49.13 262021

0.00

185277

-41.55

290771 39.68 23727770

16.37 229501

-8.37

169275

0.00 414292

0.00

453834

0.00 185277

0.00

997748 49.13

668026

713028

-3.69

693301 49.23

191653

-0.74 347707 47.35 669354

3.02

709908

368748

11.94

188699 12.79

248829

10.65 177670 22.07 302170

-12.87

228432

340993

2739995 32.99 8 91.77 797389

-2.39

306517

1212298 51.13

432000 -25.57 305470 76.70 591541

-51.13

305470

638046 176.38

1442963

304.4 76.09 727810 4 2675839

51.06

609574

27821 21.54

23927

-22.94

52421

-76.49 27413811

1374617 -102.26

37465

538084

1.50

0.00 121646

-45.88

716391 -178.96

2042854 -101.66 75778

-0.24

343318

2.76

9.89

51871 12.78

56444

243

Red browed Treecreeper

24.57

217257

Red capped Robin

21.60

1645334

Red kneed Dotterel

0.00 -5.19

891739

146474

0.00

146474 -49.13 358788

20.76

887502 27.46

633501

0.00

185277 49.13

185277

-49.13

245099

-6.56

339201

0.00 262021

0.00

185277

12.21 536790 14.51 494639

-14.32

558580

-49.13

185277

31630

-29.00

49842

16.76 227396 29.00 406589

-9.12

167501

-4.52 688088

-2.08

464539

-11.52

629276

-49.13

262021

-1.68

196369

0.00 185277

0.00

185277

-3.28 1045795 -36.40 493024

-10.08

427829

0.00

142824

21.37 726797 21.10 873908

-24.57

226917

Red rumped Parrot

11.48

538707

8.49

1106290

14.07

464680 22.15

706052

Red tailed Black cockatoo

73.70

226917

24.57

414292

-49.13

185277 -49.13

370554 -24.57 185277

Red Wattlebird

22.16

36446

-2.72

66212

-0.80

25134 21.73

17469

Red winged Parrot

66.41

427702

-50.25

481126

-42.12

412195 34.98

246331

Regent Honeyeater

93.57

1084575

-47.17

502222

8.00

1831889

Restless Flycatcher

44.55

1191387

-13.27

792527

1.98

573776

9.28

924154

Rock Dove

24.57

226917

98.26

763917

0.00

320909 49.13

185277 -98.26 453834

Rose Robin

23.33

383909

265020

1.86

223825

-3.54 575718

-24.57

226917

49.13

320910

0.00

185277 49.13

185277

0.00 262021

Rufous Fantail

22.13

850924

-47.25

312708

-38.03

443188 11.08

424835

Rufous Songlark

26.57

262521

0.00

251062

0.00

148473

0.00

122374

0.00 143323

320909 -49.13

262021

49.13 262021

49.13

226917

Royal Spoonbill

Rufous Treecreeper

-47.29

0.00 262021

2.96

52173 20.12

25.31 583990 29.50 963639

0.00 150128

122.83

346622

0.00

185277

-98.26

Rufous Whistler

26.46

781341

-3.74

758750

6.23

765270

4.64

551645

33.78 1616688 44.40 380541

-8.65

1218879

Sacred Kingfisher

24.39

402373

22.54

860287

1.53

347959

3.79

433557

19.78 493217 21.91 375364

-4.93

496243

4745753 -2.26 535761 74.46 6

-0.81

156688

285741 -11.41 376859 46.95 686789

-36.45

311769

716391 -51.13 683052

-76.70

716391

Satin Bowerbird

-0.15 47453977

23.31 47457307

-24.61 47453227 25.08 47453443

Satin Flycatcher

38.81

315756

33.71

961592

-14.15

0.00

571483

153.40

2049157

25.57

Scaly breasted Lorikeet

344232

0.00

683052 76.70

Scarlet Honeyeater

44.85 54795446

Scarlet Robin

26.09

378165

-49.05

480343

2.07

316770 47.99

194216

-2.03 255741

3.21 369921

-1.08

217016

Shining Bronze cuckoo

26.57

270256

0.00

269037

0.00

175036

0.00

152160

0.00 151531

0.00 220156

0.00

137233

Shy Heathwren

24.57

226917

98.26

668026

0.00

262021

0.00

185277 -24.57 185277

-49.13

185277

Silvereye

16.64

790102

7.30

496531

9.53

704349 13.56

354049

10.62 325189 19.91 632895

-14.29

329237

Singing Bushlark

50.40

285466

-24.58

285459 48.92

181727

1.75 422009 54.23 354837

-24.21

285458

Singing Honeyeater

60.34

679858

-28.03

2097985

-34.19

738664 48.80

172073

10.17 641321

-15.13

714328

Southern Boobook

26.76

282213

-0.44

353896

-0.79

219703

0.69

184747

-0.89 137950 47.58 296480

-0.12

144650

0.00

207146

Southern Emu Wren

11.14 144973343

5479489 -20.58 54795641 29.47 54795359 -16.34 8

-15.38 54794362

-24.57

346622

0.00

307247

0.00

160455 49.13

185277

Southern Whiteface

26.00

233775

-3.07

496348

0.07

177473

2.87

272684

-1.82 223254 45.77 281674

-1.37

229162

Spangled Drongo

178.96

1979674 51.13

822504

0.00 305470 -51.13 1557600

0.00

305470

Speckled Warbler

42.88

131374

-7.42

61819

-12.17

38922 22.93

31663

-8.54

38129

Spiny cheeked Honeyeater

68.29

377323

-51.54

809334

-42.27

372845 48.65

164544

18.39 314569

-6.96

322077

Splended Fairy wren

24.57

226917

98.26

997748

0.00

262021 49.13

185277 -49.13 262021

-49.13

262021

1895394 -153.40

2518973 -153.40

0.00 160455

2.32

63429 28.36

57207

244

Spotted Bowerbird

68.18

387732

-51.13

432000

-42.61

Spotted Harrier

25.57

238701

0.00

473381

0.00

Spotted Nightjar

121.75

846968 -216.15 47545103

-96.19

321994 17.04

203647

17.04 203647

210364

160831

0.00 121436

925918 48.35

154322

4746249 72.14 8

480092

8.84

363218

0.00

-8.52

160997

0.00

182515

47.83

934084

14.42 261621 19.93 407321

-10.08

245431

0.00 204087

Spotted Pardalote

20.58

503739

-7.95

1650118

4.80

Spotted Quail thrush

46.97

296657

-23.91

335962

-22.69

274057 47.67

128895

8.46 712430 69.86 509218

-25.07

274061

Spotted Turtle dove

25.57

482991

-34.09

288000 17.04

337710

0.00 305470

0.00 305470

-17.04

227684

4745421 4745530 24.62 2 -24.09 8

0.00

305470

45241

-40.56

70394

Square tailed Kite

49.66 47454816

Straw necked Ibis

21.54

Striated Pardalote Striated Thornbill Striped Honeyeater

-48.72

555718

63916

14.02

122714

0.12

24.63

324865

21.52

495457

0.94

258372

2.04

300552

21.55 381588 22.89 290192

-2.96

379968

25.46

458144

-0.88

343083

0.75

351238

2.47

348236

22.15 373073 22.81 297907

-3.25

371307

63.96

854285

-40.55

2437977

-38.15

888427 48.37

153856

13.24 675131 -48.36 1294912

-11.21

868992

Stubble Quail

21.89

55127

13.04

158380

0.52

36854 22.89

35647

10.39

65504

-22.90

47508

Sulphur crested Cockatoo

13.25

355265

11.50

532087

12.49

264085 23.03

309338

12.21 344226 12.90 306593

-13.48

328325

Superb Fairy wren

18.65

750249

-23.87

881771

7.86

653262 10.30

678995

8.30 761379 21.74 376457

-16.59

758659

Superb Lyrebird

24.57

226917

262022

185277

0.00 414292

0.00

185277

Superb Parrot

24.56

59523

9.39

70462

0.64

31783 22.17

28060

-22.87

49444

Swamp Harrier

25.57

318499

73.44

774904

-0.91

346751 49.69

373496 -24.66 301678 23.75 395550

-49.69

214912

Swift Parrot

55.74

2739200

-63.77

1612965

-25.09

204530 40.35

722487

-24.48

204527

Tawny crowned Honeyeater

-24.57

393032

49.13

370554

-49.13

262021 49.13

444279

0.00 262021

49.13

370554

Tawny Frogmouth

25.57

175363

0.00

218166

0.00

134210

129349

0.00 132150

0.00

91966

Torresian Crow

72.72 109589355

Tree Martin

13.33

262692

-24.09 47455308 50.19

-49.13

-25.65 189813405 -7.35

566904

56032 22.61

0.00

0.00

254676

49240 -30.57

26781 -13.04

-48.56 109589269 35.92 27397891 11.26

67473 30.98

47905 12.04

51488 30.68

-7.38 742000 32.68 723234

0.00 190825

5479485 5479541 0.90 9 25.90 1

-23.81 54794334

191117 14.46

372030

6.76 304815 20.03 367986

-17.33

315926

246819 48.29

4745507 152236 -24.15 443562 60.36 7

-22.97

246818

2.60

940307

26.84 6467359 46.36 328832

-13.65

4809846

448225 48.91

175140

-3.86 685761

-15.26

366921

124450

0.00

120160

0.00 106319

0.00 124198

0.00

92966

10.78

384901 11.87

459047

11.39 501102 23.79 338566

-13.15

521082

237089

-1.34

27497 21.44

15571

34270

-7.01

233287

5.75

393683

10.05

342764 14.33

441018

12.74 351218 18.60 579211

-10.91

364329

208415

0.00

246933

0.00

151060

0.00

133591

0.00 143223

0.00

102159

25.57

374123

76.70

778800

0.00

305470 -25.57

529090

76.70 648000 76.70 648000

51.13

610941

51.70

503914

-6.16

1861922

-26.13

459499 73.61

178025

0.67 438252 25.35 611009

-24.16

444025

73.70

226917

-49.13

262021

185277 -24.57 185277 24.57 453834

0.00

185277

Turquoise Parrot

49.58

246819

Varied Sittella

26.90

1344640

-8.15

3012599

7.73

Variegated Fairy wren

70.43 21228972

-15.43

357635

-35.07

Wedge tailed Eagle

25.57

188699

0.00

187077

0.00

Weebill

14.78

488002

1.52

1921615

Welcome Swallow

24.96

64180

9.62

Western Gerygone

14.93

384128

Whistling Kite

25.57

White backed Swallow White bellied Cuckoo shrike White bellied Sea eagle

-25.27

2354039

0.00

0.62

35412 19.94

0.00 115238

245

White breasted Woodswallow

42.83

250672

11.93

608189

-18.43

245248 29.53

173814

1.59 170020

White browed Babbler

26.07

412521

-1.13

682502

2.43

514719 48.20

220713

-3.64 430112

White browed Scrubwren

15.84

481391

8.57

702594

10.21

350466 12.95

White browed Woodswallow

338533

-1.64

217956

1.96

-31.95

298812

3.23 513909

-2.18

363452

491763

13.17 357859 21.08 619564

-11.40

373045

166714

-1.02 135970 44.24 281950

-0.32

127789

28.08

303955

-1.02

White cheeked Honeyeater

147.40

680752

-49.13

262021 -122.83

585898 -24.57

453835 -24.57 185277

-49.13

185277

White eared Honeyeater

50.14

517025

-33.49

477822

-12.75

287995 48.56

134363 -10.44 288019 36.91 349229

-35.41

294845

White faced Heron

25.48

453693

24.58 47457710

0.70

477471

2.24

446778

-0.69

458420

White fronted Chat

27.31

32494

16.56

66436

-6.38

27060 21.51

25447

-15.78

28864

White fronted Honeyeater

-24.57

346622

0.00

1014805

0.00

524043 49.13

185277

0.00

320909

White naped Honeyeater

18.15

217184

-17.22

346301

6.55

156655

2.47

475781

10.38 295107 -0.58 287233

-11.88

225468

White necked Heron

11.33

442927

-23.62

331660

-23.77

294181

0.33

199757 -12.53 165638 -10.92 322433

24.22

251680

White plumed Honeyeater

26.57

223371

0.00

197131

0.00

118306

0.00

109971

0.00

117915

White throated Gerygone

25.82

44203

-15.38

135452

-4.06

36806 18.97

39237

49913

-17.49

34492

White throated Honeyeater

25.57

216000

51.13

529090

0.00 529090

-38.35

341526

White throated Needletail

24.37

182532

White throated Nightjar

38.35

-1.54 470991 46.84 308889 -6.10

29490 24.46

36459

0.00 262021

0.00 123919

14.86

38376

550695 -12.78 305470

0.00 138698

8.04

-0.14

145035 49.27

154219

22.94 385458

-1.68

288177

629743 76.70

903593

25.57 374123

0.00

305470

540945

3.42

544619

20.90 580785 23.44 305480

-4.52

573762

15.31 259566 15.36 266349

-15.91

259108

0.00

346622

0.00

591541

0.00

482991

25.57

White throated Treecreeper

23.86

576335

-1.45

419553

2.13

White winged Chough

11.31

356019

2.34

425349

14.86

237523 16.69

200902

White winged Fairy wren

24.57

226917

-49.13

729437

-49.13

393032 49.13

185277

White winged Triller

18.05

454823

-7.52

1046374

7.63

404759

9.69

320943

12.21 196910 20.88 247231

-11.97

181113

Willie Wagtail

20.43

632579

6.88

357325

5.62

574685 11.76

463730

11.42 317932 18.08 454893

-13.62

301972

Wonga Pigeon

25.57

550695

404099

0.00

264545

0.00

264545

Yellow faced Honeyeater

22.41

31630

21224 21.30

16268

-21.59

36759

Yellow plumed Honeyeater

73.70

Yellow Rosella

-51.13 -19.70

226917 -393.06

78019

-0.95

1728150 -196.53

0.00 131011

0.00 793635 10.65

0.00 732492

42865 20.06

27311

786064

0.00

185277

49.13 414292

147.40

668026

641819

0.00

185277 -245.66 926386 98.26 370554 -147.40

585898

-24.57

226917

-49.13

262022

147.40

Yellow rumped Thornbill

17.55

605617

5.99

382892

8.15

580898 12.26

371449

11.81 307542 20.00 482945

-12.98

289834

Yellow tailed Black cockatoo

25.99

433230

-22.66

513658

-1.09

404675 48.72

459305

20.05 618792 26.01 322400

-3.85

599557

Yellow Thornbill

26.78

197201

0.56

198599

-0.89

145057

123918

-0.48 129217 45.35 199401

-0.28

114748

1.66

246

Yellow throated Miner

64.57 54795992

Yellow tufted Honeyeater

74.18

658242

-16.11

Zebra Finch

72.12

429062

-59.55

4.58 94909908

-38.78 54795918 50.43

234666

-1.39 265374

685616

-33.07

494747 50.70

248559 -16.14 428310 32.78 875441

1102079

-47.39

400123 16.97

470474

2.49 488829 -0.55 635248

-26.19

307543

-15.89

492448

-3.45

510729

247

Table A.4.3.3: Estimates and standard errors for classification strategy classes in the full Australian glm2 model. traits Species

Est

SEM

occurrence Est

SEM

everything seen in woodland Est

SEM

exclusion criteria Est

SEM

Existing classification Est

SEM

Apostlebird

-9

540855

25

615425

-32

517288

7

281164

-6

512224

Australasian Grebe

47

356733

2

409717

24

302127

24

361887

0

431280

-22

53171

0

31382

-11

56507

77

458205

26

216000

25

131011

Australasian Pipit

Expert opinion Est

SEM

-54

788450

1

209110

9

246477

21

21418

-77

629743

51

374123

Australian Bustard

0

185277

-25

262021

Australian Hobby

0

185897

0

302556

-52

260006

0

145862

0

168153

0

322346

Australian King Parrot

1

700450

49

196604

29

1592162

1

223249

1

738248

50

1091826

Australian Magpie

5

307307

0

321188

-32

467045

-5

275899

-11

539457

-12

506322

Australian Owlet Nightjar

0

213253

1

235325

-2

222811

1

194302

-1

195739

2

496720

-14

527966

-12

608597

0

219819

Australian Brush turkey

Australian Raven

1

634699

-1

268707

-40

540460

-8

109189 3

Australian Ringneck

0

149475

0

279191

-51

229091

0

138262

0

169525

Australian Shelduck

0

374123

0

427459

-51

265982

0

257062

0

235095

Australian White Ibis

24

842269

2

721806

-51

399746

1

284120

-23

578701

-22

558277

-1

297994 3

-10

2461136

-31

489946

Australian Wood Duck

20

909044

-9

2890553

-26

2982014

-17

122517 8

Azure Kingfisher

-2

583363

32

668425

-36

345005

16

303435

73

454233

Banded Lapwing Bar shouldered Dove

-3

403572

27

297671

-11

312652

11

271458

-37

302610

12

321670

-10

303257

-33

574492

0

303647

0

381474

-26

288161

26

288161

0

228496

0

398563

26

418838

1

488602

-26

366348

-1

507951

0

224761

2

426610

Bassian Thrush

0

412501

0

462676

51

384970

0

412502

Black chinned Honeyeater

0

333830

1

294828

-37

715310

-5

525930

-19

813378

-23

480588

Black eared Cuckoo

21

671182

-37

779616

-46

467306

37

801872

-18

338250

15

683634

Black faced Cuckoo shrike

5

524555

-1

261395

-37

296515

-4

406942

-12

267482

-10

527578

-49

320909

49

414292

49

453835

71

652555

1

953454

47

319287

25

586136

-25

1087108

55

128406 7

-16

1066091

-21

804180

-13

738752

9

118382 2

4

394956

0

432000

51

482991

0

432000

0

305470

0

432000

14

381245

-25

439871

-61

452937

-12

324664

-24

211586

36

432365

0

648001

0

432000

0

216000

0

529090

0

321484

2

181890

12

278688

-11

328380

49

262022

0

185277

0

262022

-33

455366

-98

94907911

-29

530754

Barking Owl Barn Owl

Black faced Monarch Black faced Woodswallow Black Falcon Black fronted Dotterel Black Honeyeater Black Kite Black shouldered Kite

14

275779

Black Swan

-2

-28

596493

357053

Black tailed Native hen

0

173311

0

364718

0

126851

Black winged Stilt

0

262021

49

262021

0

262022

-17

426796

38

731981

-10

346773

12

47455506

-13

Blue faced Honeyeater

-54

829816

3

273736

Bluebonnet

25

414337

74

9490736 9

474533 43 206213 6

Brown Falcon

33

485563

-3

574911

-53

2901499

6

Brown Goshawk

-1

231333

1

320401

-50

258206

0

238733

0

240510

-2

376461

Brown headed Honeyeater

4

676204

-1

267592

-36

356196

-3

527536

-11

331394

-9

530999

Brown Honeyeater

-9

219685

-23

404466

-41

315555

9

219685

-15

335850

18

473952

Brown Quail

4

116253

21

22536

-39

99422

12

70967

-4

65357

6

141040

38 -12

474553 72 211040 6

248

Brown Songlark

6

222380

22

33555

-50

106688

-26

91767

-17

107117

-1

162417

Brown Thornbill

18

74543

21

17560

-14

65670

1

28620

-3

60115

0

50276

Brown Treecreeper

-5

153042 3

-3

1503302

-52

704939

-3

558684

-4

697916

0

1618633

Brush Bronzewing

51

808199

51

1366105

0

591541

0

506565

0

1286970

-24

112077 26

49

199409

16

749392 6

22

11208067

22

254124

49

4

500562

1

21

894894

-28

-9

474534 80

-37

452368

0

185277

0

507402

22

926007

48

882987

-3

570929

47

0

197367

1

274633

-51

224375

-44

444426

23

607383

-47

Brush Cuckoo Budgerigar Buff rumped Thornbill Bush Stone curlew Channel billed Cuckoo Chestnut breasted Mannikin Chestnut rumped Heathwren Chestnut rumped Thornbill Cicadabird

-34

7474877

32

747154 3

201157

-1

234115

-1

165601

26

223588

-23

217042

294782

-40

435571

-2

338505

-15

439377

-16

572240

481615

-60

491820

-6

512943

-9

416685

69

1258690

9

237274 47

-14

325807

0

131011

49

763917

553044

26

925802

-3

679468

0

199047

0

178062

-1

331318

373095

47

403729

-20

438950

-47

600674

1163196

-26

482991

-102

128696 9

-128

1035900

11

395670

-55

567346

-41

240384

Clamorous Reed warbler

102

101313 0

15 3

1331514

12 8

Cockatiel

-11

138543 0

58

1405129

-17

475127

2

236788

Collared Sparrowhawk

-3

640298

5

731733

-49

250186

-1

284532

1

235631

-4

580462

Common Blackbird

46

122962

5

146266

-2

73640

2

31488

20

63866

-3

95361

0

588781

0

275915

-40

504234

-8

627587

-15

495599

-13

673669

-1

547945 59

23

9490703 6

1

547944 67

-46

94907053

22

29232

59

186753

22

-46

766758

52

Common Bronzewing Common Koel Common Myna Common Starling Crescent Honeyeater

-25

54794467 0

26146

22

48034

-22

50632

29528

30

137467

0

32399

37

173024

0

74980

416665

1

386641

48

380412

2

341265

-50

645241

-8

163739 9

-11

1283202

Crested Bellbird

17

919267

4

2159075

-58

1657913

15

154482 3

Crested Pigeon

0

165848

0

156772

0

220809

0

129344

0

138548

0

187501

Crested Shrike tit

-2

576242

-27

1128097

-51

808373

-2

672672

-3

803804

23

1420856

0

453834

0

490197

21

18019

-13

42098

1

26384

-2

36595

0

52516

16

896785

6

217798

27

804951

11

363435

-19

803348

-1

592607

-41

730697

-2

552207

7

737984

8

752297

Crimson Chat Crimson Rosella Diamond Dove

19 -10

56540 100757 8 114694 3

Diamond Firetail

0

Dollarbird

5

112991

21

21061

-24

65073

3

90460

-13

64267

-10

67468

Double barred Finch

4

112754

21

24209

-25

55135

0

23502

13

147867

-5

110888

-43

71605

43

54689

0

41341

43

68556

0

80056

11

497535

-42

661606

-23

550494

-20

607140

20

926424

-14

965298

-16

63420

Dusky Moorhen Dusky Woodswallow

-68

622480

Eastern Rosella

1

524814

0

265469

-39

1057491

-1

382388

-14

104986 0

Eastern Spinebill

24

94408

7

71266

-49

80629

1

28526

-14

72236

Eastern Whipbird

0

425197

51

425197

0

425197

0

305470

0

366246

Eastern Yellow Robin

9

40855

21

20206

-20

53744

4

40311

-8

41564

-9

47408

Emu

0

253882

49

438471

-4

449934

0

222646

47

428679

-72

47456417

-49

320909

49

262022

49

320909

Eurasian Skylark

75

75005

22

29232

European Goldfinch

31

68981

21

19277

26

-19

43699

21

18472

19

Eurasian Coot

Fairy Martin

-22

57234

32

25316

-32

52700

77730

1

27677

31

63229

2

48913

74961

2

37125

1

40564

-19

43672

249

Fan tailed Cuckoo Fawn breasted Bowerbird

11

465664

19

333369

0

185277

-25

262022

-24

384391

Figbird Flame Robin

5

360362

4

546666

25

131011

74

277916

25

131011

46

648885

49

192228

-50

717591

1

236660

-3

577328

0

195587

0

237444

0

282325

0

262934

0

270330

25

262021

37

314153

-12

196516

12

146474

0

230012

49

193088

-3

292191

8

952554 5

0

276260

-36

49

370520

47

381177

0

198934

0

304137

Glossy Black cockatoo

-1

547945 59

23

9490703 6

-25

54794467

Golden headed Cisticola

8

102310

22

39513

-16

87590

Golden shouldered Parrot

0

262021

-25

262021

13

893538

Forest Kingfisher Forest Raven Fuscous Honeyeater Galah Gang gang Cockatoo Gilbert s Whistler

Golden Whistler Great Cormorant Grey Butcherbird Grey crowned Babbler Grey Currawong

-1

259764

48

458007

463303

197868

-1

236506

-49

371153

2388299

-4

109446 9

-11

238254 4

-10

2399117

46

543968

0

205847

-2

342902

-46

518526

-51

242851

0

216680

0

167901

0

325476

1

547944 67

-46

94907053

-30

649419

4

70537

17

100681

0

185277

25

131011

-12

896070

-5

664143

-3

946774

-1

392337

505771

48

160285

-4

396112

1

180160

-3

358283

0

394913

-12

755362

33

763243

-70

1012468

3

457654

-17

798000

-28

1058695

2

897892

50

291396

-44

1013911

3

491079

2

498924

-93

1102644

25

131011

-13

445281

-8

1165792

0

131011

49

555832

0

170632

16

272916

7

597301

-1

240576

0

393032

0

133253

0

145230

65

96953

-42

79388

Ground Cuckoo shrike

7

238813

-39

339816

Hardhead

0

262021

49

414292

Grey Teal

0

46

Grey fronted Honeyeater Grey Shrike thrush

364568

1

Grey Falcon Grey Fantail

-6

Hooded Robin

4

Horsfield s Bronze cuckoo

-38

0

-53

457905

229757

208979

-6

577682

0

106741

0

118721

-22

50632

22

50632

-1

213256

-26

207465

0

262021

0

262021

-2

755440

-39

1964546

0

142235

-1

163467

12

52641

898404

-54

919336

-10

188351 6

0

142419

-50

176412

0

129573

-17

4745317 8

6

47453149

2

30829

8

474531 40

-55

741589

-62

484293

47

414464

-33

476830

33

476787

-1

182862 5

3

1316665

840790

3

0

149860

House Sparrow

18

4745320 0

Inland Thornbill

3

1112623

-5

683852

-49

1833612

-7

193480 8

528837

-1

250265

-35

310044

-4

577018

-10

284914

-8

450172

45214

21

20511

-24

42033

3

34141

-14

40861

-10

79631

25

131011

Jacky Winter

1

814334

Laughing Kookaburra

5

Leaden Flycatcher

4

Letter winged Kite Lewin s Honeyeater

74

277916

-49

320909

-98

453834

Little Button quail

26

571483

51

748246

26

216000

-51

1183081

Little Corella

-2

281182

49

189486

2

297068

-48

262747

Little Crow

25

346622

49

453834

10

1845595 8

23

3687391 1

-45

6

311986

-14

468782

-38

77

571483

Little Eagle Little Friarbird Little Grassbird

-44

546644

2

218362

25

131011

-49

717575

27658247

-30

925692 5

6

276586 20

-20

9269069

306936

-3

395180

-15

308982

-2

623024

-26

432000

26

216000

-205

1832822

Little Lorikeet

25

341939

-14

223738

16

467174

39

320649

-2

268868

30

590243

Little Pied Cormorant

29

102579

-19

108893

-12

49025

-10

39357

-2

73084

-9

46801

-19

525043

36

584348

-62

365292

-35

359389

-9

377328

-46

857067

Little Raven

250

-48

523468

46

106069 3

2

917091

4

728474

947697

13

540000

-13

324000

38

494918

-115

1148056

201430

-51

235753

0

194594

0

174674

0

370827

0

272407

-37

322528

-5

386177

-11

297840

-11

504716

398450

75

752130

1

462113

0

305470

27

480600

-100

857260

0

185277

-25

262021

25

131011

2

462315

45

593039

-48

421634

1

199827

0

401501

-46

371731

-17

315556

20

490107

-20

371131

3

282836

9

722912

-3

590472

Mistletoebird

3

505620

-2

345433

-36

481706

-1

298235

-10

436197

-7

685786

Musk Lorikeet

7

224427

50

466847

-12

320043

-11

336597

12

325219

-86

446620

17

1148818

-37

1359031

-44

449665

-2

256438

-20

425823

18

1059305

0

71605

0

41341

0

82682

Little Wattlebird

-44

964826

52

960228

Little Woodswallow

13

324000

89

Long billed Corella

0

184163

0

Magpie lark

6

600299

Major Mitchell s Cockatoo

1

Masked Finch Masked Lapwing Masked Woodswallow

Nankeen Kestrel Nankeen Night heron New Holland Honeyeater

-18

52378

26

55696

-6

53954

19

37115

5

55346

-12

100437

Noisy Friarbird

0

146565

0

156235

0

238559

0

116506

0

141756

0

193298

Noisy Miner

2

345653

0

263473

-34

386596

-3

402682

-16

409478

-16

478455

Olive backed Oriole

7

168131

-2

220092

-37

218796

0

172959

-8

346447

-5

329468

-49

262022

-25

262022

Pacific Baza Pacific Black Duck

22

444104

-7

2531541

-28

2531697

-21

457974

-3

252043 6

-30

47459554

Painted Button quail

-1

217132

2

321630

-47

361947

0

184952

-1

217113

0

302060

Painted Honeyeater

-44

562406

70

950196

14

1007113

7

501292

15

685237

-72

880024

Pale headed Rosella

-54

721432

-57

332415

-22

469719

-30

399494

Pallid Cuckoo

0

188712

0

215303

-53

318029

0

144547

0

174035

0

244358

Peaceful Dove

-6

701391

-6

771992

-34

353945

-7

552101

-10

354288

-4

776871

Peregrine Falcon

0

262342

2

367855

-49

254354

0

274888

0

228046

-3

443890

0

262021

25

131011 28

25736956

-22

429794

-23

47455317

Pheasant Coucal Pied Butcherbird

-4

2395927 5

50

228382

Pied Cormorant

25

248617

-23

538810

Pied Currawong

2

403818

Pink eared Duck

0

318916

49

320909

14 8

-47

Plum headed Finch

-9

4745348 0

0

4745457 8

Powerful Owl

0

262021

49

962728

Purple crowned Lorikeet

0

467602

49

620703

2

Rainbow Bee eater

3

189892

-2

262586

-36

Rainbow Lorikeet

-34

2740927 2

3

949626

Red backed Fairy wren

-77

629743

-26

432000

Red backed Kingfisher

10 1

840139

-26

Red browed Finch

24

349895

0

284255

-21

476000

-49

185277

9

237274 47

-14

325807

0

555831

0

741109

1151391

2

454659

-1

472134

250182

-3

208195

-11

190333

-7

351379

-37

27406199

240384

377433

45

371048

-10

274026 28

77

458205

26

216000

129788 5

-74

656858

1

472243

31621

-8

65514

11

98203

0

320909

0

364718

-12

913935

-19

712080

2

236183

49

207146

Red capped Robin

-8

408396

-10

744115

Red kneed Dotterel

0

262022

49

262022

262021

392337

54889

49

25

-1

-20

16824

Red tailed Black cockatoo

132972 06

646349

20

1

-27

594767

99769

379045

201569

12 6

2

1

-47

517397

1

10 1

Red browed Treecreeper

Red rumped Parrot

-41

698679

351812

-60

-36

931299

619772

-9

892199

0

262022

-8

107042 8

-12

611715

-13

555410

74

277916

25

131011

25

358788

251

Red Wattlebird Red winged Parrot

18

52329

21

18133

-13

46607

1

27588

-2

43460

2

58059

2

334449

-11

393261

-41

276290

14

229975

-14

348727

-14

551603

-17

116098 1

15

1443477

Regent Honeyeater

20

1064428

-11

1663928

-19

1059708

42

185650 3

Restless Flycatcher

-2

1001563

-10

970375

-59

970173

-16

900512

-11

952745

-2

949958

14 7

717575

0

185277

-49

262021

-98

524043

47

295191

-1

213036

-1

382091

0

262022

Rock Dove Rose Robin

2

282011

Royal Spoonbill

4

582960

49

262022

0

422143

Rufous Fantail

4

875628

13

394359

1

839086

37

431535

9

778266

-18

1285878

Rufous Songlark

0

156826

0

177228

0

256974

0

138416

0

148375

0

197210

Rufous Treecreeper

0

185277

-49

262022

-49

262022

Rufous Whistler

-2

356949

-3

501518

-48

347738

-2

367705

0

320916

2

517284

Sacred Kingfisher

2

484535

2

571279

-45

540845

-1

348476

-21

509171

-23

584827

24

474534 86

0

442972

-49

589153

Satin Bowerbird

2

349731

26

4745371 8

Satin Flycatcher

23

415519

25

480601

-13

303484

14

420495

13

254381

-13

382567

26

432000

10 2

1142965

26

610941

-26

610941

51

529090

-128

1296001

-18

5479463 3

63

5479651 8

18

54794977

19

547952 54

18

547945 32

44

692667

1

446736

-52

455354

-46

335090

-1

398865

-3

526700

0

187158

0

262232

-53

325809

0

162383

0

173659

0

272862

25

262022

49

320909

25

346622

25

131011

-49

555831

3

521165

-1

253364

-36

380061

-3

548866

-11

362091

-11

506328

Singing Bushlark

-15

437051

37

639471

-29

585871

-8

283607

-2

374661

-54

412905

Singing Honeyeater

-10

467263

15

1043095

-38

570767

12

549641

-11

506698

-5

1438483

0

240471

0

256308

-51

213261

0

283335

0

175237

-1

236669

49

185277

0

490197

-1

393010

Scaly breasted Lorikeet Scarlet Honeyeater Scarlet Robin Shining Bronze cuckoo Shy Heathwren Silvereye

Southern Boobook Southern Emu Wren Southern Whiteface

1

226240

2

283761

Spangled Drongo

-51

610941

-51

1557600

Speckled Warbler

-5

89689

21

26355

-43

-17

366635

6

692932

-46

0

262021

98

717575

-9

425958

-43

321994

-9

333851

-17

203144

0

247463

-51

235380

0

156275

-72

4745624 8

12 0

4748140 2

-98

866698

-24

Spotted Pardalote

5

409154

-1

254936

-40

274496

Spotted Quail thrush

1

383929

16

1076999

-24

-51

665757

Spiny cheeked Honeyeater Splended Fairy wren Spotted Bowerbird Spotted Harrier Spotted Nightjar

0

Spotted Turtle dove

-49

248445

-2

255467

1

220321

0

458206

0

216000

139220

-1

37441

-1

129895

-29

60776

382101

2

223488

-19

281572

10

624856

-98

849047

321994

26

683053

0

189834

0

374141

474564 25

-49

836189

121

47499040

-4

345370

-14

275485

3

1674018

341981

-22

290129

12

398904

-27

880479

0

432000

34

391060

17

227684

34

455368

Square tailed Kite

1

388137

-50

397708

-1

388138

Straw necked Ibis

11

70848

42

107712

-2

73571

-2

54321

30

83835

-31

82427

Striated Pardalote

1

358659

1

384370

-47

441654

-1

247462

-22

409258

-22

384919

Striated Thornbill

0

287214

-20

625862

-48

448978

1

256270

-22

438846

-4

651027

-12

377488

11

1364598

-41

813550

12

449556

-12

681082

-44

1442906

Striped Honeyeater Stubble Quail

11

82348

21

25072

-22

63042

1

41341

-10

67990

-13

96927

Sulphur crested Cockatoo

1

325348

0

259655

-37

358360

-11

464019

-13

333050

-13

404810

Superb Fairy wren

6

533159

0

276649

-34

688544

-1

382208

-10

674318

-10

676385

252

Superb Lyrebird Superb Parrot

0

262021

0

320909

31

44992

21

23995

74

543590

Swamp Harrier Swift Parrot

34

1420021

Tawny crowned Honeyeater Tawny Frogmouth

26

2087364

0

262021

-23

-31

2742551

0

320909

0

414292

-1

35966

8

42578

-1

55222

-24

395550

26

268727

-75

838924

-17

2789349

-15

740274

18

274106 6

0

481364

-49

320909

-98

735296

0

116579

0

131996

0

212813

1

547944 67

145220

0

240989

Torresian Crow

-1

5479455 9

1

9490691 7

-25

54794467

13

273976 22

Tree Martin

6

262933

0

186972

-31

316878

-3

340306

-7

297271

4

790478

-14

4745374 1

62

4745632 2

25

405471

0

164843

26

285845

-59

47454434

1

845406

-6

1821233

-45

1376710

0

386318

3

8

3012542

-12

2122355 8

31

2122263 6

-46

21228305

2

267939

-20

-18

613865

Wedge tailed Eagle

0

159222

0

167495

-51

208673

0

122367

0

123469

0

153750

Weebill

2

425850

-1

274584

-37

602293

-1

277597

-11

608333

-3

2001328

Welcome Swallow

19

63296

21

20586

13

244415

1

31862

-2

45717

1

56833

Western Gerygone

1

378646

-1

203975

-38

399147

-5

538000

-13

385205

-11

488417

Whistling Kite

0

279236

0

302582

-51

235978

0

133711

0

161910

0

248586

748247

77

648000

-77

808199

496488

1

528111

-1

489089

-47

687020

-25

346622

-25

226917

98

641819

Varied Sittella Variegated Fairy wren

White backed Swallow

10 2

916411

77

716391

10 2

White bellied Cuckoo shrike

25

405622

0

1017484

-26

White bellied Sea eagle

214174

262022

0

Turquoise Parrot

-51

42797

49

137630 2 212239 86

25

185277

0

320909

White breasted Woodswallow

6

276596

23

408476

-19

332683

19

169024

15

438895

-56

657288

White browed Babbler

2

492087

3

544278

-51

434218

-4

814917

0

411629

-46

839179

0

333788

-1

269108

-39

415582

-3

628615

-14

389448

-12

469280

0

165290

1

160320

-50

198893

-1

168859

-1

179469

0

189729

White cheeked Honeyeater

12 3

571063

74

185277

25

131011

White eared Honeyeater

11

367312

49

184462

-30

503479

2

205138

-1

298607

37

417125

White faced Heron

1

370454

2

544851

-50

428006

-2

450533

0

415694

-26

47456887

White fronted Chat

-2

51989

23

43928

-26

36742

0

35832

6

36536

-23

48635

49

585898

0

262021

0

717575

-5

408663

White browed Scrubwren White browed Woodswallow

White fronted Honeyeater White naped Honeyeater

8

274827

-5

429779

-37

243206

1

243287

-9

262556

White necked Heron

-1

394388

-11

518391

-37

321815

0

277214

-24

395473

0

133031

0

170485

0

225847

0

118110

0

127799

0

195082

4

62829

21

21646

-25

45621

3

42018

-15

45571

2

64001

13

482991

-13

374123

-13

305470

-13

374123

13

341526

1

239616

25

498801

-48

351716

0

134219

-21

401073

White plumed Honeyeater White throated Gerygone White throated Honeyeater White throated Needletail White throated Nightjar

-51

610940

26

458205

-51

432000

-26

699920

-26

432000

White throated Treecreeper

0

410038

0

264410

-46

612757

0

296940

-20

626624

-21

717694

White winged Chough

1

235923

0

237395

-35

332552

-2

292171

-16

245134

-17

345557

White winged Fairy wren

0

262021

0

490197

0

370554

White winged Triller

2

314474

0

179353

-37

217436

-1

229080

-14

218233

-15

306382

Willie Wagtail

7

552397

0

269871

-37

330048

-6

342291

-12

309292

-12

496622

Wonga Pigeon

0

432000

0

648001

0

591541

51

374123

0

404099

0

732492

253

Yellow faced Honeyeater

2

47234

Yellow plumed Honeyeater

-49

262021

Yellow Rosella

-98

490197

5

501889

-1

281941

-37

306303

-4

-19

1163813

51

275543

-46

626844

0

146351

0

204941

-50

Yellow rumped Thornbill Yellow tailed Black cockatoo Yellow Thornbill Yellow throated Miner Yellow tufted Honeyeater Zebra Finch

21 14 7 19 7

17193

-21

46058

1

27348

-10

47148

10

56529

49

262021

-98

414292

246

1096114

-49

320910

-98

453835

387466

-12

273933

-10

530937

2

404457

-20

611830

175334

0

121731

-1

150601

0

185756

-25

328944

-1

194819

1

293120

-44

54796965

828585 693244

-23

461243

35

5479610 6

17

339846

50

256418

1

623799

1

588793

2

581571

-51

762822

4

785483

-4

982649

-47

583176

31

478151

0

488750

45

1166227

254

Table A.4.3.4: Estimates and standard errors for spatial context in the full Australian glm2 model. Northern Australia Species Apostlebird

Est

SEM

-7.56

South Australia Est

SEM

713216

Australasian.Grebe Australasian.Pipit Australian.Brush.turkey

Western Australia Est

SEM

Combined Est

SEM

-25.01

181589

-32.79

477600

24.37

356427

0.88

489375

11.55

91562

10.98

27940

12.84

90944

-76.70

629743

-25.57

216000

-102.26

432000

Australian.Bustard

0.00

185277

-24.57

131011

-24.57

185277

Australian.Hobby

0.34

296241

0.26

212345

50.83

346148

Australian.King.Parrot

0.16

731197

-27.24

1441899

-82.13

1573090

Australian.Magpie

20.14

1214628

0.21

851087.14

29.78

319445

28.20

738903

Australian.Owlet.Nightjar

-2.08

349315

-49.38

430514.11

-50.27

195463

0.58

438790

Australian.Raven

10.35

969112

-3.70

1326999.81

25.21

195892

35.35

1263022

0.00

178418

0.00

187700

51.13

327093

Australian.Ringneck

0.29

466021.72

Australian.Shelduck Australian.White.Ibis Australian.Wood.Duck Azure.Kingfisher

30.95 -15.42

3524068.19

383506

Banded.Lapwing Bar.shouldered.Dove

0.00

202382

51.13

412505

-27.16

483792

-26.42

591094

25.63

224972

42.44

1277331

-29.94

316645

-46.16

512709

-26.22

297671

26.16

367785

-3.44

655727

-26.51

335913

-36.69

534296

-25.57

384014

-25.57

215356

0.00

438382

2.61

832636

-24.03

234243

27.79

528100

32.29

978898

-36.70

876270

8.61

447519

Black.faced.Monarch

-49.13

Black.faced.Woodswallow.1

Barking.Owl Barn.Owl Bassian.Thrush Black.chinned.Honeyeater Black.eared.Cuckoo Black.faced.Cuckoo.shrike

Black.Falcon

0.00

412501.51

0.00

305470

-51.13

654323

-8.33

958242.55

30.55

392106

-28.12

790562

-26.86

436145

-9.95

991312

25.22

198106

30.24

559540

370554

-49.13

185277

-98.26

555831

-45.57

405974

-24.45

189115

-20.11

438800

13.34

779256

-26.06

685613

-13.62

1287957

0.00

529090

51.13

610941

-6.05

1401942.38

Black.fronted.Dotterel Black.Honeyeater Black.Kite Black.shouldered.Kite

11.42

283638

-13.44

289655

48.30

453575

0.00

432000

-51.13

216000

-51.13

571483

-0.94

481758

-36.19

350123

14.85

556032

0.00

320909

49.13

414292

-49.13

126851

-49.13

185277

0.00

320909

49.13

370554

-49.56

534869.65

Black.Swan Black.tailed.Native.hen Black.winged.Stilt Blue.faced.Honeyeater

10.60

934878

-9.13

624680

-12.26

889081

Bluebonnet

-62.25

47457986

-25.21

201330

-12.54

47454097

Brown.Falcon

-14.29

900352

-56.93

2307537.37

-13.31

1764786

35.73

2765146

Brown.Goshawk

0.34

386852

-0.18

425812.84

-0.36

129535

51.08

448922

Brown.headed.Honeyeater

9.68

481494

-3.09

1721839.80

25.08

182222

30.10

810092

-6.96

341006

-25.49

223093

16.37

402885

-11.80

110197

-11.01

81076

23.27

149216

12.74

47453214

-10.86

29058

60.83

124091

Brown.Honeyeater Brown.Quail Brown.Songlark

-47.40

126181.28

255

Brown.Thornbill

-32.66

170707

-38.89

77984.88

9.37

36192

-36.19

92012

1.38

1619458

-15.58

5279961.31

6.26

1449851

-42.63

1669102

-51.13

864000.81

0.00

529090

51.13

529090

-29.91

7481599

-47.77

704416

-41.69

18676933

Budgerigar

2.31

239329

-25.71

249669

-23.28

369343

Buff.rumped.Thornbill

5.31

782874

24.98

179399

-25.98

697955

Bush.Stone.curlew

2.05

1387662

-49.23

189745

44.47

1310538

-7.34

23727269

-26.19

268418

-34.16

23730289

0.00

185277

-49.13

131011

-49.13

185277

-26.81

836051

-73.50

1070791

Brown.Treecreeper Brush.Bronzewing Brush.Cuckoo

Channel.billed.Cuckoo Chestnut.breasted.Mannikin Chestnut.rumped.Heathwren Chestnut.rumped.Thornbill Cicadabird Clamorous.Reed.warbler Cockatiel Collared.Sparrowhawk

-3.71

-48.37

670954.41

458643.94

0.43

358376

-0.15

173457

52.02

449854

-45.12

468670

-26.31

281480

-71.88

549174

-153.40

1864370

-25.57

216000

0.00

550694

-3.22

424949

-24.41

289199

-27.05

517904

1.36

354306

Common.Blackbird Common.Bronzewing

9.23

387707

Common.Koel

1.41

240933

-51.13

432000.13

0.98

468324.40

-0.11

169632

52.61

513756

-25.50

237905.08

0.56

60750

-1.64

80970

-2.89

1111473.60

25.20

188444

34.71

887679

-26.16

263078

-25.24

340391

Common.Myna

0.00

56230

0.00

56230

Common.Starling

-37.41

188212.30

16.77

127461

-30.04

151282

Crescent.Honeyeater

-53.94

550571.21

-51.42

229839

-100.52

588823

-48.56

162785

19.08

3297975

Crested.Bellbird Crested.Pigeon Crested.Shrike.tit Crimson.Chat

-41.49

792268

0.00

255277

0.00

448749.00

0.00

197539

0.00

329923

-11.70

598492

11.13

1719158.49

4.04

412672

9.31

1151863

49.13

262021

0.00

185277

0.00

262021

9.65

29044

-35.82

89048

-54.92

377172

-31.58

861632

3.95

496498

-46.23

942444

Crimson.Rosella Diamond.Dove Diamond.Firetail Dollarbird Double.barred.Finch

-39.82

64271.37

-23.13

2575440.84

-17.16

605932

1.50

1097556

14.71

267224

10.93

26805

-37.56

123543

0.34

41874

-14.61

133352

-14.08

139853

-64.70

74528

-64.70

94724

Dusky.Moorhen Dusky.Woodswallow

33.16

980675

33.14

1105601.32

46.59

356533

72.14

837387

Eastern.Rosella

14.40

1211560

-1.35

687451.98

25.05

195409

-26.72

725058

-46.81

93521.02

-11.73

74305

-11.43

112324

Eastern.Spinebill Eastern.Whipbird

0.00

529090

0.00

529090

-33.17

33020

-38.99

94470

-49.84

225281

-47.60

828305

Eurasian.Coot

-49.13

370554

0.00

370554

Eurasian.Skylark

-10.78

25316

10.78

75005

Eastern.Yellow.Robin Emu

-20.51

76235

-0.31

331930

European.Goldfinch Fairy.Martin Fan.tailed.Cuckoo Fawn.breasted.Bowerbird Figbird

-49.16

437316.83

-15.45

105500.70

18.11

65715

-27.00

86331

-2.16

51660

-2.04

69174.17

-19.32

60162

-20.20

83707

-18.03

618563

-40.12

623868.72

-27.16

370034

18.83

510349

0.00

185277

-24.57

131011

-24.57

185277

-73.70

381959

-24.57

131011

-98.26

262021

256

Flame.Robin Forest.Kingfisher

0.00

262934

91.68

943445

8.46

1646774

Forest.Raven Fuscous.Honeyeater Galah

-11.51

14478372.49

Gang.gang.Cockatoo Gilbert.s.Whistler Glossy.Black.cockatoo Golden.headed.Cisticola Golden.shouldered.Parrot Golden.Whistler

Grey.crowned.Babbler

-51.13

215903

-51.13

431016

-24.57

131011

-24.57

185277

-0.23

211985

-54.18

659415

25.12

187397

31.59

1257256

49.56

359821

-2.40

550045

0.00

172228

51.13

366271

263078

-25.24

340391

-2.44

53126

-11.57

29184

-15.92

92534

0.00

262022

-24.57

131011

-24.57

262021

-5.55

1366022

25.18

181813

39.70

1100630

1.21

246204

53.55

655050

-10.61

1150825.04

40.94

842901

0.65

183425

1.72

645161

-34.62

764655

-49.64

231310

-17.18

1577815

-9.36

873361

42.63

1347285

-24.57

131011

-24.57

185277

24.86

178863

33.09

849495

-49.13

131011

-49.13

185277

-57.09 0.00

185277

Grey.Fantail

8.83

485374

Grey.fronted.Honeyeater

0.00

185277

Grey.Shrike.thrush

0.00

229763

Grey.Teal 2.26

-4.52

1594527.88

848696.21

0.00

430292.37

0.00

183758

0.00

437770

19.61

91280.69

0.00

58465

64.70

65366

-25.34

217592

26.92

373471

0.00

320909

49.13

555831

322386

Hardhead Hooded.Robin 0.52

230845

-2.40

988925.60

1.22

277762

64.05

2158642

0.31

422039.27

0.19

120873

51.69

398230

20.44

61859

-26.74

83075

-27.55

550613

-21.60

707616

House.Sparrow Inland.Thornbill

936772

-26.16

Grey.Falcon

Horsfield.s.Bronze.cuckoo

-8.16

54795470

Grey.Currawong

Ground.Cuckoo.shrike

629576

-73.78

Great.Cormorant Grey.Butcherbird

-6.89

16.38

1029233

Jacky.Winter

-3.62

541676

-31.30

1613217.57

7.45

650224

17.00

2589092

Laughing.Kookaburra

16.50

660447

-1.77

958766.66

24.99

188146

31.07

850126

Leaden.Flycatcher

12.25

70056

10.52

26534

-37.99

90777

Letter.winged.Kite

0.00

185277

-24.57

131011

-24.57

185277

73.70

277916

98.26

585898

98.26

641819

0.00

305470

-51.13

432000.37

-25.57

216000

25.57

305470

40.67

47455849

-48.09

497768.73

-7.21

455001

-9.32

705779

Little.Crow

0.00

185277

-24.57

131011

24.57

185277

Little.Eagle

80.00

18547473

1.44

269336

83.98

9282402

Little.Friarbird

22.50

262799

24.73

146760

-24.43

558705

Little.Grassbird

25.57

432000

-25.57

216000

51.13

610941

Little.Lorikeet

25.79

47463606

22.26

849475

-69.56

991406

Lewin.s.Honeyeater Little.Button.quail Little.Corella

Little.Pied.Cormorant

51.95

74889.55

11.76

49025

64.70

65366

Little.Raven

-8.14

661706.95

5.10

154857

37.33

575048

-51.61

555578.19

-49.04

788882

-48.10

1739805

-25.57

216000

-12.78

418282

Little.Wattlebird Little.Woodswallow

12.78

445296

Long.billed.Corella

0.00

246928

0.00

313489.68

0.00

164559

0.00

356727

Magpie.lark

8.79

549235

-8.36

1458069.85

25.09

188262

31.72

707700

257

Major.Mitchell.s.Cockatoo Masked.Finch

0.00

185277

-17.55

327504

13.77

432201

Masked.Lapwing Masked.Woodswallow Mistletoebird Musk.Lorikeet Nankeen.Kestrel

6.22

901456

Noisy.Friarbird

268727

-25.57

460633

-24.57

131011

-24.57

185277

-46.80

352748

-5.36

567906

-29.58

449761

17.81

389275

2.00

802464.37

25.30

224439

28.98

697753

-52.20

564440.93

-24.07

117856

-15.16

520731

1.24

377739.93

25.14

150840

28.17

466818

21.57

29232

64.70

65366

-32.56

27958

-8.17

48627

0.00

202815

-53.13

455509

Nankeen.Night.heron New.Holland.Honeyeater

-26.49

-29.26

75735.69

0.00

248793

Noisy.Miner

15.19

763740

31.79

289980

-31.32

839020

Olive.backed.Oriole

10.88

315599

21.20

400179

-29.71

562758

0.00

185277

-24.57

131011

-24.57

185277

48.85

47523685

38.16

2677095.35

25.81

235260

46.60

645159

Painted.Button.quail

-43.51

546953

-44.12

603313.65

-44.90

420687

5.97

552156

Painted.Honeyeater

-7.71

393152

-54.04

780690

-58.81

1009466

Pale.headed.Rosella

9.15

340303

-24.65

272946

-1.59

353441

Pallid.Cuckoo

0.00

295381

Peaceful.Dove

Pacific.Baza Pacific.Black.Duck

-1.46

971000.60

0.00

371025.60

0.00

233470

53.13

491458

25.95

303159

2.54

450559.22

24.92

166719

-20.44

697437

Peregrine.Falcon

1.22

514446

-0.39

450738.38

-0.40

146650

51.61

475763

Pheasant.Coucal

0.00

185277

-24.57

131011

-24.57

185277

Pied.Butcherbird

-46.50

759933

-119.68

13296545

65.32

13333783

Pied.Cormorant

21.34

630546

-73.68

369306

-72.48

474262

Pied.Currawong

49.68

693989

25.18

182222

-27.70

669804

Pink.eared.Duck

-98.26

320909

0.00

185277

0.00

262021

-7.34

23727269

-26.19

268418

-34.16

23730289

0.00

869027

0.00

490197

-48.37

746347

0.23

1466792.98

-47.19

967395

3.04

1035635

13.61

267551

1.97

518283.13

25.21

164590

29.51

450381

Rainbow.Lorikeet

-43.55

439048

-47.47

237338.53

-35.70

27399865

-80.80

27401927

Red.backed.Fairy.wren

-76.70

629743

-25.57

216000

-102.26

432000

Red.backed.Kingfisher

126.69

1378039

-26.49

268727

100.22

1254207

15.42

138649

9.43

31894

-36.87

92884

0.00

453834

0.00

490197

21.19

634172

32.37

1390943

0.00

320909

0.00

370554

27.21

693776

-21.19

1826512

-24.57

131011

-49.13

262021

10.08

22549

12.12

87318

Plum.headed.Finch Powerful.Owl Purple.crowned.Lorikeet Rainbow.Bee.eater

Red.browed.Finch

-25.85

116122.04

Red.browed.Treecreeper Red.capped.Robin

3.86

1025627

Red.kneed.Dotterel Red.rumped.Parrot Red.tailed.Black.cockatoo

4.34

1483708

-73.70

381959

Red.Wattlebird

2.03

-40.44

716178.20

96501.62

Red.winged.Parrot

-12.72

221990

-25.64

266376

-40.02

430598

Regent.Honeyeater

-35.15

1498058

-52.33

499730

-89.20

1937355

Restless.Flycatcher

-0.87

1062090

31.25

1715629

Rock.Dove Rose.Robin

-26.55

1113548.09

11.89

978101

-49.13

320909.46

49.13

262021

49.13

453834

1.86

378695

-45.10

657939

258

Royal.Spoonbill Rufous.Fantail Rufous.Songlark

26.87

1073585

0.00

292113

0.00

454863.85

Rufous.Treecreeper

0.00

320909

0.00

370554

-7.11

1560848

-43.97

1155683

0.00

216283

0.00

472297

-49.13

131011

0.00

185277

Rufous.Whistler

1.26

402343

-27.44

1951086.23

5.88

463179

10.64

950007

Sacred.Kingfisher

26.48

1162103

-20.40

949604.40

25.09

163948

28.37

700237

1.45

624303

-22.35

47459007

-26.49

268727

-39.72

556223

Satin.Bowerbird Satin.Flycatcher Scaly.breasted.Lorikeet Scarlet.Honeyeater

-13.24

452413

25.57

529090

-25.57

216000

0.00

683052

-17.99

54795485

-51.84

290857

-70.30

54796778

0.00

294432

Scarlet.Robin Shining.Bronze.cuckoo

-49.69

373495.93

-43.22

891196.91

1.02

164451

99.06

701600

0.00

352721.12

0.00

227943

53.13

503525

-24.57

131011

0.00

370554

25.42

193133

35.99

834764

-23.49

288303

-16.25

504611

-24.50

296903

15.42

696928

Shy.Heathwren Silvereye

9.54

480565

-2.16

1499728.07

Singing.Bushlark Singing.Honeyeater Southern.Boobook

-12.90

467424

0.32

470578

Southern.Emu.Wren Southern.Whiteface

-0.21

477929.26

0.06

167698

51.73

497665

0.00

381958.59

0.00

185277

0.00

262021

-0.41

430413.38

-0.34

127405

-0.15

456896

Spangled.Drongo

0.00

629743

-51.13

216000

-51.13

432000

Speckled.Warbler

-6.51

155244

-20.80

19558

-12.64

117668

Spiny.cheeked.Honeyeater

-2.35

385527

-24.80

288672

26.51

513986

-147.40

980394

0.00

185277

49.13

262021

Spotted.Bowerbird

8.52

221919

-25.57

216000

-17.04

461025

Spotted.Harrier

0.00

291517

0.00

191598

0.00

332941

Spotted.Nightjar

24.04

47459375

-48.09

143134

49.95

608903

Spotted.Pardalote

14.18

1587249

Splended.Fairy.wren

-1.98

646126.37

Spotted.Quail.thrush Spotted.Turtle.dove Square.tailed.Kite

51.13 0.00

591540.57

305470

Straw.necked.Ibis

25.02

181171

31.95

522360

-34.82

507382

-12.28

418385

-17.04

380988

-51.13

591541

-48.72

169959

25.57

47453720

-11.47

29166

-10.02

78258

Striated.Pardalote

28.09

632400

-20.88

830666.33

25.23

168335

27.99

655379

Striated.Thornbill

23.07

806307

18.25

829151.19

24.95

193587

-28.95

664130

-11.36

377537

-26.51

432580

-36.75

661408

Striped.Honeyeater Stubble.Quail

0.61

94107

-40.79

102925.06

10.94

33620

11.79

97152

Sulphur.crested.Cockatoo

14.87

383832

-0.24

623160.49

26.51

290984

-18.08

920269

Superb.Fairy.wren

24.97

699385

-2.45

708765.78

25.13

168001

-26.53

720442

0.00

453834

-49.13

614495

-10.58

19775

-10.62

64189

-26.49

268727

-1.82

647889

-48.52

160339

-4.11

602527

49.13

370554

98.26

548057

0.00

174657

51.13

309694

-26.16

263078

-37.86

27399843

Superb.Lyrebird Superb.Parrot Swamp.Harrier

24.66

429327

Swift.Parrot Tawny.crowned.Honeyeater Tawny.Frogmouth Torresian.Crow

0.00

174339

-11.21

27397691

0.00

284070.89

259

Tree.Martin

5.48

473822

1.15

465895.74

Turquoise.Parrot Varied.Sittella

0.95

1016041

-11.53

21224102

0.00

262597

0.00

Weebill

14.36

521973

Welcome.Swallow

-2.27

80514

Western.Gerygone

8.98

270838

Whistling.Kite

0.00

256814

Variegated.Fairy.wren Wedge.tailed.Eagle

-21.48

3.01

28.99

508908

-51.54

287575

-50.63

395637

7.91

1772557

10.99

1863743

42445189

-14.27

1014093

314331.39

0.00

170816

51.13

324940

0.38

922022.72

24.72

193167

37.33

580101

-41.44

103569.78

-14.47

237988

-15.81

251189

24.78

130272

31.76

662230

0.00

180845

51.13

336368

-25.57

216000

-51.13

748246

-25.07

149318

-28.42

801333

-24.57

262021

0.00

262021

-32.29

272972

-52.10

386156

388778.73

White.backed.Swallow White.bellied.Cuckoo.shrike

125771

-24.86

0.00

6539350.56

24.28

1437426

White.bellied.Sea.eagle White.breasted.Woodswallow

-18.30

222156

White.browed.Babbler

-48.05

725648

-0.31

812301.45

1.03

193696

57.08

915980

White.browed.Scrubwren

3.93

714634

-0.70

718126.27

25.66

196720

37.03

570593

White.browed.Woodswallow

0.70

246990

-0.24

110658

-1.17

404626

White.cheeked.Honeyeater

0.00

185277

-24.57

131011

24.57

185277

53.12

1010295

-24.56

405314

28.56

742399

White.eared.Honeyeater White.faced.Heron

-0.20

505840.42

White.fronted.Chat White.fronted.Honeyeater

49.13

320909

White.naped.Honeyeater

25.08

630049

14.30

622325.38

White.necked.Heron 0.00

435955.51

0.18

142296

53.47

596502

-11.39

24365

33.35

65494

0.00

185277

49.13

262021

25.58

283345

27.28

458313

37.35

321815

87.72

486659

0.00

196335

-53.13

449824

White.plumed.Honeyeater

0.00

265979

White.throated.Gerygone

11.48

85205

11.12

28059

-37.83

92700

White.throated.Honeyeater

12.78

374123

-25.57

216000

-12.78

529090

-25.99

255991

-25.01

360837

White.throated.Nightjar

25.57

699920

-25.57

216000

0.00

748246

White.throated.Treecreeper

22.39

795608

-0.73

580350.75

24.53

223891

-28.89

683396

White.winged.Chough

15.18

464268

0.12

481418.77

32.33

228925

-32.88

744803

White.winged.Fairy.wren

49.13

370554

0.00

414292

49.13

414292

White.winged.Triller

16.69

971050

-6.03

912413.14

24.67

121667

26.99

436302

8.07

425486

-11.10

1324455.95

25.10

181517

32.60

687699

0.00

778800

-51.13

864001

White.throated.Needletail

Willie.Wagtail Wonga.Pigeon Yellow.faced.Honeyeater

10.02

25979

-35.41

88027

Yellow.plumed.Honeyeater

49.13

320909

49.13

262022

Yellow.Rosella

98.26

414292

98.26

453834

Yellow.rumped.Thornbill

29.27

146566

-31.32

56416.95

8.74

484162

-6.04

1366656.15

25.30

195371

31.61

708123

Yellow.tailed.Black.cockatoo

12.88

1837005

-52.77

683001.52

-26.27

259767

-27.52

600578

Yellow.Thornbill

-0.07

206491

0.00

418136.28

-0.11

107432

-2.30

394347

Yellow.throated.Miner

-0.62

222110

-24.81

185057

27.02

348558

-100.40

1028061

-51.53

280387

-54.78

877805

-16.47

622668

-44.95

703224

-38.23

758379

Yellow.tufted.Honeyeater Zebra.Finch

260

Appendix 4.4. European glm2 classification model coefficients and deviance Table A.4.4.1: Null, residual and % deviance explained by the classification glm2 model including all classification strategy classes. Deviance Species

Null

Residual

% explained

Barn swallow

0.00

0.00

Black redstart

10.81

0.00

0.00

0.00

Carrion crow

36.55

26.53

27.42

Cirl bunting

15.44

0.00

100.00

9.92

5.74

42.13

Common blackbird

67.27

50.36

25.14

Common Buzzard

24.73

21.38

13.54

Common chaffinch

60.05

54.64

9.01

Common chiffchaff

28.82

20.08

30.34

Common cuckoo

41.46

35.74

13.79

Common firecrest

0.00

0.00

Common kestrel

8.40

0.00

100.00

Common linnet

26.01

15.41

40.73

Common nightingale

34.16

12.37

63.80

Common quail

0.00

0.00

Common raven

28.68

23.15

19.27

Common Redpoll

14.05

6.59

53.07

Common redstart

23.27

10.55

54.66

Common reed bunting

12.56

0.00

100.00

Common starling

37.10

24.17

34.84

Common whitethroat

37.10

13.88

62.58

Common wood pigeon

62.98

54.10

14.11

Corn bunting

15.56

0.00

100.00

Dunnock

55.62

50.79

8.69

Eurasian blackcap

58.19

45.48

21.85

Eurasian blue tit

53.70

49.76

7.33

Eurasian bullfinch

30.66

26.40

13.91

Eurasian collared dove

6.88

0.00

100.00

Eurasian golden oriole

31.49

23.68

24.79

Eurasian jay

34.42

27.66

19.64

Eurasian Magpie

17.81

7.64

57.12

0.00

0.00

13.00

8.18

37.10

9.14

0.00

100.00

Eurasian Sparrowhawk

27.55

13.29

51.77

Eurasian tree sparrow

19.71

8.96

54.53

Black woodpecker

Coal tit

Eurasian nuthatch Eurasian siskin Eurasian Skylark

NA 100.00 NA

NA

NA

NA

261

Eurasian treecreeper

0.00

Eurasian Woodcock

11.16

3.82

65.79

Eurasian wren

55.11

49.90

9.45

Eurasian wryneck

24.95

20.57

17.56

European crested tit

0.00

0.00

European goldfinch

31.16

10.72

65.58

European green woodpecker

50.92

45.77

10.12

European greenfinch

50.45

35.32

29.99

European pied flycatcher

13.59

9.50

30.08

European robin

56.76

51.58

9.12

European serin

24.43

10.74

56.05

European stonechat

7.72

0.00

100.00

European turtle dove

41.88

24.56

41.35

Fieldfare

23.03

11.46

50.26

Garden warbler

25.35

18.49

27.05

9.64

5.41

43.89

Great spotted woodpecker

23.40

16.90

27.79

Great tit

61.21

49.03

19.90

Grey Partidge

13.21

0.00

100.00

Hawfinch

0.00

0.00

Hazel grouse

6.70

3.82

43.02

Hoopoe

7.20

0.00

100.00

House sparrow

13.40

0.00

100.00

Icterine warbler

12.89

3.82

70.37

8.40

6.03

28.21

Lesser whitethroat

31.76

26.21

17.46

Long tailed Tit

40.19

35.94

10.58

Marsh tit

16.08

7.75

51.84

Marsh Warbler

6.88

2.77

59.72

Meadow Pipit

0.00

0.00

Melodious warbler

17.32

8.29

52.12

Mistle thrush

53.41

47.84

10.44

Northern Goshawk

11.16

2.77

75.16

Northern lapwing

0.00

0.00

NA

Northern wheatear

0.00

0.00

NA

Red backed shrike

13.77

5.74

58.30

Red Crossbill

12.06

3.82

68.32

Red Legged Partridge

6.88

0.00

100.00

Ring necked Pheasant

11.78

0.00

100.00

Rock bunting

15.28

0.00

100.00

Rook

19.07

2.77

85.46

Sardinian warbler

6.88

0.00

Short toed treecreeper

0.00

0.00

41.93

37.27

Goldcrest

Lesser spotted woodpecker

Song thrush

0.00

NA

NA

NA

NA

100.00 NA 11.10

262

Spotted flycatcher

36.31

29.29

Stock dove

34.30

21.24

38.06

Subalpine warbler

10.43

0.00

100.00

Tawny owl

16.30

10.04

38.38

Tree Pipit

46.40

24.86

46.43

0.00

0.00

21.98

5.41

75.41

Western yellow wagtail

8.31

0.00

100.00

Whinchat

0.00

0.00

13.40

0.00

100.00

8.63

3.82

55.74

Willow warbler

25.35

14.55

42.58

Wood warbler

8.70

4.50

48.29

Woodchat shrike

10.81

3.82

64.68

Woodlark

26.92

21.62

19.68

Yellowhammer

35.05

15.72

55.15

Western Bonelli s warbler Western Jackdaw

White wagtail Willow tit

19.33

NA

NA

263

Table A.4.4.2: Coefficient estimates and standard error for the European classification glm2 model including all classification strategy classes. Everything seen in a woodland

Intercept Species

Est

SEM

Est

SEM

Barn swallow

-26

13942 7

Black redstart

-25

14767 9

51

26

12037 0

0

Black woodpecker Carrion crow Cirl bunting Coal tit Common blackbird

Exclusion criteria

19

Est

SEM

Existing Classification Est

Expert opinion

SEM

Est

0

156879

0

139009

261658

0

171706

0

152126

154338

0

147148

0

134728

2212

-1

2

18

2212

Metric Es t

SEM 0

0

-19

2212

-26

16566 5

20

51

225328

0

177655

0

177655

0

23

18189

-21

18189

0

19935

0

18268

0

1

1

1

2

18

1976

0

1

-36

1903 58

1372 93 2212 2160 00 2480 8

Occurrence Es t

SEM

0

2457 25

4 9 0 1 8 5 1 0

Traits Es t

SEM

SEM

0

1664 98

0

1537 50

1607 81

4 9

2148 59

0

1521 26

1600 05

0

1417 13

0

1226 77

2212

-1

1

-1

2371 95 2088 6

0 0

2722 15 1983 9

0 0

2 1283 23 1851 4

3696

0

1

-1

1

0

1

2790

0

2

0

2

Common Buzzard

0

1

1

2

-1

2

0

1

0

2

1 8

Common chaffinch

2

1

0

1

17

2269

-1

1

-1

1

0

1

0

1

0

1

Common chiffchaff

2

2

-1

2

19

5322

1

2

17

7441

-3

2

2

2

17

4428

1

0

2

-1

1

-1

1

-16

2400

1

1

-1

1

-1

0

167855

0

126847

0

105678

0

51

244495

0

107706

0

109811

0

Common cuckoo

1

Common firecrest

26

Common kestrel

-26

11235 4 11455 1

2375 32 1588 83

0 0

Common linnet

-21

6581

42

29964

1

2

19

6581

20

6581

1 7

Common nightingale

-20

10271

41

19757

39

13577

20

10271

1

1125 0

8 0

Common quail

-25

10981 2

0

126568

0

103626

0

1036 26

Common raven

-2

2

1

2

2

2

18

2400

Common Redpoll Common redstart Common reed bunting

22

29232

3 -22

2 29232

-40

17333

-21

29232

1

0

1

1

2

0

18390

21

8316

-21

-26

12654 8

51

250341

0

147699

0

124032

51

1785 01 1906 24 1016 7 2244 1

0 0 1 8 4 0

1

1

2 0

4573

2

0

1423 7 2452 52

0 0

1 1074 17 1191 62

7476

0

2

1457 3

40

1111 1

1286 38

0

9287 6

2

2

1

3 9 2 0

3625 5 1318 7 1319 15

21

3522

1

1

4808

2

2

0

3155 2 1247 3 2662 08

2153 66 1161 69

0

0

Common starling

-2

2

22

7604

0

1

1

2

2

2

1 6

Common whitethroat

-3

2

24

8865

-18

7387

1

2

-18

7903

3

0

1

1

1

-1

1

-1

1

0

1

1

1

-1

1

0

1

-71

31271 5

99

665231

48

132697

-1

265591

47

2717 78

4 5

5388 38

-2

2115 75

-2

2162 35

Dunnock

0

1

1

2

2

1

1

1

0

1

0

1

0

1

0

1

Eurasian blackcap

3

1

-1

2

18

2200

-1

1

-3

2

0

1

-2

1

0

1

Eurasian blue tit

2

1

0

2

1

1

-1

1

-2

2

0

1

0

1

1

1

Eurasian bullfinch

1

1

0

2

18

5034

1

1

18

5711

1 8

4064

0

2

0

1

-25

19284 3

49

233135

0

92639

0

163399

0

2139 40

0

1952 99

0

1069 70

3

2

16

4612

-1

2

-2

2

16

3743

-1

2

0

2

1

1

4

2

-2

2

18

5776

-2

2

15

7559

-2

1

-1

2

17

4175

1065 5

20

1072 6

Common wood pigeon Corn bunting

Eurasian collared dove Eurasian golden oriole Eurasian jay Eurasian Magpie Eurasian nuthatch Eurasian siskin Eurasian Skylark Eurasian Sparrowhawk Eurasian tree sparrow

1 7 1 9

-1

2 3155 2 1247 3 1684 40

-19

9456

41

30724

-19

9748

19

9456

17

2386 9

-2

3072 4

1 9

27

13953 6

0

201514

0

164368

0

142020

0

1895 21

0

1649 47

0

1416 68

0

1414 45

1661 7

1 7

7712

17

2304 4

19

7712

-19

7712

-16

20816

-17

7712

-16

2734 4

1 6

-26

90083

51

234032

0

129067

0

91177

0

1320 06

0

1566 42

0

9767 8

0

1076 74

1

1

-1

2

-21

4913

19

4913

-40

9757

1 9

7508

0

2

39

9757

1702 2

1 8

9668

19

8975

-20

8141

41

30345

1

2

17

8141

19

8141

-1

264

Eurasian treecreeper

27

15557 2

0

212932

0

179842

0

170690

Eurasian Woodcock

22

29232

-21

29232

-41

21081

0

33755

2

1

16

1769

0

1

0

1

Eurasian wren Eurasian wryneck European crested tit European goldfinch European green woodpecker

1

1

26

10856 6

-1 0

2 145320

0 0

119822

0

1 103604

0

2145 07

0 -2

1

-1

-1

1

3956

0

2310 95

0

1184 17

6293

44

18013

2

2

18

6293

20

6293

2

1

15

2400

-2

1

0

1

-1

1

0

-2

1

21

3766

1

1

2

1

1

2

European pied flycatcher

19

7548

-18

7548

-17

14331

-17

7548

-17

1652 8

European robin

3

1

-1

2

1

1

-1

1

-2

1

19

8838

-18

5220

-20

1

0

2

18

1882

-1

1

-3

51

195229

0

184842

0

119815

0

0

211635

0

175982

0

153205

0

-20

12537

0

15355

0

51

265523

0

177827

0

159379

0

99

435676

47

109779

0

210964

48

33170

-62

37612

-41

21715

-41

33170

0

Lesser spotted woodpecker

21

15108

1

22652

18

11108

-19

15108

1

Lesser whitethroat

-2

1

2

2

-17

2774

2

1

1

Marsh tit Marsh Warbler Meadow Pipit Melodious warbler Mistle thrush

20

8636

-20 -26

1

2

20

10032

21

14355

-21

18860

-2

26243

-21

16537 9

0

209701

0

166992

0

23187

0

2

20

14942

1

1

16

1769

0

1

0

1

-26

Northern wheatear

-26

60364

15585

20

11228

-25

Ring necked Pheasant

-26 -25

-20

77244

10964 9 12607 1

-22

Red Legged Partridge

-2

0

41

Northern lapwing

Rock bunting

-1

14942

43

Red Crossbill

25599

1

-21

Northern Goshawk

Red backed shrike

2

1

10262 7 23212 1 10048 1

27321

-43

60364

-40

0

161803

0

106326

0

0

173924

0

126071

0

1074 3

20

6388

0

1478 56

0

1263 38

5099

0

1

7714

20

7141

5585

2349 42 1653 39 2171 5 2023 70 2386 71 1773 0 2239 0

0 0

-1

2

0

0

2018 3

7461

1 7

6754

0

2

1

0

1

1

2075 26 2111 85

4 5

2720 14

0

2

2

2

1

2

2

1 6

2410

0

1

0

1

1495 3

2 0

8636

19

8636

1932 1 1820 4 3089 62

2 5943 3 1715 77 1951 11

2 1 -3 0

4357 7 1916 64

1

0

1

0

1

2396 9

2 2

5796 9

20

5796 9

2 1 0 0

1928 54 2719 51

0

2653 97

0

109967

159011

0

169990

49

154851

0

100481

0

1435 5 2097 01

-1

1429 8 1478 00 3170 75 1188 90

109967

20

9924

1 9

0

0

2895 4 1669 92

2 1

3326 7

0

1

-1

2 0

-17

277864

21

0

4832

1309 1

166422

2496 56 1773 0 2662 9

1366 10 1824 35 3071 0 2009 1

-1

2 2

51

0

2662 9

1729 5

49

0

1 1848 42 1319 75

1

1

11228

0 2 0

15585

-19

0

1315 24 1918 09 2171 5

0

20

28912

0

0

19303

-1

1 8 2 0 2 0

1

2126 2

1 31315

165107

-1

-41

-41

49

9925

-1

5220

2

1

2

-19

-1

-1

5220

19396

5220

1

1

4 0

1 6

0

42

2

0

0

22153

2

Icterine warbler

Long tailed Tit

1

0

19

-72

7682

2

2

House sparrow

18

2604 1

0

-26

1025 7

-2

1

Hoopoe

1 8

2772 9

2

15442 3 25097 6

1787 1

9125

-20

12537

1

19

7141

21

1

1

20

Hazel grouse

1

1

7141

26

-1

6162

-19

Hawfinch

3709

2061 2

1

-26

1

1 8

1

Grey Partidge

0

1773 0

5377

12159 9 14649 7

1

-21

22

1

-1

7141

1

Great tit

1

20

-2

21

2

9360

European turtle dove

0

1

2

0

17906

7601

2

170963

-21

1186 6

5340

0

17906

0

1 7

-18

247494

23

0

1

51

Great spotted woodpecker

1 1030 91

1

-26

Goldcrest

-1

0

-2

4 0

2 1344 59

121382

2

40

1610 74 3926 8

-1

1869 70

European stonechat

Garden warbler

-1

-2

0

-1

1884 6

12081 8

Fieldfare

18846

1 6 1 7

1939 58 3249 5 1

-2

6388

-1

1 9

-22

-19

0

-1

1 6

40

2062 84 4134 1

1

European greenfinch

European serin

0

0 0 4 9

0 0 2 0 2 0 0 0

1494 2

2

2

1258 51 1843 00 1786 3 2703 0 1664 22 1931 96 1421 01

0 0 0 19 0 0 0

1674 26 1518 94 1894 9 1122 8 8832 7 2096 35 9921 7

265

Rook

-1

2723 7

0

1555 17

1476 40

0

1429 84

-1

2

-1

15

2269

-1

-21

12832

43

36416

-20

15348

21

12832

Sardinian warbler

25

20334 5

0

241895

0

108179

0

167590

Short toed treecreeper

26

10105 7

0

238472

0

122138

0

94927

0

Song thrush

3

1

-1

2

17

2272

0

1

Spotted flycatcher

2

2

-1

2

-1

2

0

1

Stock dove

62

2 1 4 9

1352 9

83

4056 9

2145 47

0

9675 8

0

1283 39

0

9038 7

1

0

1

-1

1

1

-2

2

0

2

1 9

4089

2 0

4089

0

1

4 9

1642 55

9 9

4067 16

14 6

3168 83

1308 0

1 9

7445

1

2

1

2

1

1

18

10754

-2

1

-1

1

Subalpine warbler

12 1

29799 6

148

464355

48

152411

97

232289

Tawny owl

19

7445

0

13080

-20

7445

-20

7445

-1

Tree Pipit

1

1

0

2

-20

3542

37

5158

-1

2

1

2

-1

2

-2

2

26

12036 9

0

2440 28

0

2345 85

0

2913 27

0

1187 01

2089 7

4 0

1768 9

4 0

2111 7

77

2458 8

Western Bonelli s warbler Western Jackdaw

-20

Western yellow wagtail

-26

Whinchat

-26

White wagtail

-26

Willow tit

22

9484 11255 6 12074 8 12470 8 13632

0

247275

0

143074

0

122014

0

3198 5

43

49120

-19

10563

18

9484

59

51

243567

0

164060

0

111826

0

0

268339

0

124708

0

51

197180

0

127894

0

128841

0

-21

13632

0

16398

0

15174

0

Willow warbler

1

1

0

2

57

15047

20

7159

-39

Wood warbler

22

16877

-20

16877

0

22553

0

19488

0

-41

25933

62

39078

-1

34508

20

18338

-1

-1

2

1

2

-1

2

-1

2

-2

Woodchat shrike Woodlark Yellowhammer

-19

4359

40

11126

1

2

17

4359

18

2

1840 73 1405 38 1513 87 1712 1 1173 2 2255 3 2763 2

0 0 0 0 -1 0 -1

2

2

4359

1 9

2106 62 2564 86 1716 50 2356 5 2 2429 1 3450 8 2 4359

0 0 0 0 0 0 -1 3 -1

1341 58 1634 34 1472 68 1757 3 2 2429 1 3450 8

0 0 0 0 18 0 20

1608 07 1505 56 1315 82 1439 7 6989 2429 1 1833 8

2

2

2

2

18

4740

266

Appendix 5.1. Bayesian hierarchical model code Below is the R code used to analyse chapter 5 Analysis<-read.csv("Z:/Hannah Analysis/refinedanalysisnew1.csv") #load bird occurrence data traits<-read.csv("Z:/Hannah Analysis/traitdatanew.csv") #load bird trait data library('R2jags') Y <- as.matrix(Analysis[,c(4:357)],ncols=354) #create a matrix of columns with species data #create vectors for each of the species traits Hollows <- as.vector(traits$Hollow.nest) Treenest<-as.vector(traits$nest.in.tree.or.shrub.tree) DD <- as.vector(traits$pred.ln.DD.) FG<-as.vector(traits$forage.ground) FS<-as.vector(traits$forage.shrub) FB<-as.vector(traits$forage.bark) FA<-as.vector(traits$forage.air) FC<-as.vector(traits$forage.canopy) #create vectors for tree cover, and each of three interpretations of 'woodland' treecover <- as.vector(Analysis$treecover) NVISA <- as.vector(Analysis$NVISA) #all woodlands NVISE <- as.vector(Analysis$NVISE) #eucalypt woodlands NVIST <- as.vector(Analysis$NVIST) #all treed vegetation data <- list("Y", "Hollows", "DD","Treenest", "NVISE","FG","FS","FB","FA","FC") model <- function() { # influence of species traits for (i in 1:354) #no. species { a[i] ~ dnorm(ma, taua) # prior for a, with a mean ma and a precision taua b[i] <- mb + h_nb*Hollows[i]+t_nb*Treenest[i]+ f_gb*FG[i] + f_sb*FS[i]+f_bb*FB[i]+f_ab*FA[i]+f_cb*FC[i]+d_db*DD[i]+eb[i] c[i] <- mc + h_nc*Hollows[i]+t_nc*Treenest[i]+ f_gc*FG[i] + f_sc*FS[i]+f_bc*FB[i]+f_ac*FA[i]+f_cc*FC[i]+d_dc*DD[i]+ec[i] eb[i] ~ dnorm(0, taueb) # prior for eb, with a mean of 0 and a precision taueb ec[i] ~ dnorm(0, tauec) # prior for ec, with a mean of 0 and a precision tauec # incidence of species in the landscape for (j in 1:5891) { Y[j,i] ~ dbern(p[j,i]) logit(p[j, i]) <- a[i] + b[i]*log(NVISE[j]+1) + c[i]*log(treecover[j]+1) } }

267

#assigning prior probabilities sdeb ~ dunif(0, 100) #uniform distribution taueb <- 1 / (sdeb * sdeb) sdec ~ dunif(0, 100) # uniform distribution tauec <- 1 / (sdec * sdec) ma ~ dnorm(0, 1.0E-4) #normal distribution sda ~ dunif(0, 100) #uniform distribution taua <- 1 / (sda * sda) mb ~ dnorm(0, 1.0E-4) #normal distribution mc ~ dnorm(0, 1.0E-4) #normal distribution d_d~ dnorm(0, 1.0E-4) #normal distribution h_n~ dnorm(0, 1.0E-4) #normal distribution t_n~ dnorm(0, 1.0E-4) #normal distribution f_g~ dnorm(0, 1.0E-4) #normal distribution f_s~ dnorm(0, 1.0E-4) #normal distribution f_b~ dnorm(0, 1.0E-4) #normal distribution f_a~ dnorm(0, 1.0E-4) #normal distribution f_c~ dnorm(0, 1.0E-4) #normal distribution } #run model set.seed(123) W <-jags(data=data, inits=NULL, parameters.to.save=c('a','b','c','ec','eb','h_nb','t_nb','d_db','f_gb','f_sb','f_bb','f_ab','f_cb','h_nc','t_n c','d_dc','f_gc','f_sc','f_bc','f_ac','f_cc'), model.file=model, n.chains= 3, n.iter= 100000) #save model output W<-W$BUGSoutput$summary

268

Appendix 5.2. Chapter 5 lists of species by group Species classified as I (Intact Woodland), D (Degraded Woodland), F (Forest), O (Open Country) and U (uncertain) specialists according the the Bayesian Model in chapter 5. Here I present results from all six model-fits: the three representing different classifications of woodland (all treed habitats, all woodlands, eucalypt woodlands) across the dataset from the whole of Australia; and the three representing the dependence of birds on eucalypt woodlands in different regions of Australia (ecoregions 7, 12 and 4). Species are marked NA when they were not present in a particular regional dataset. Table A.5.2.1: Species classified into 5 bird groups (Intact Woodland, Degraded Woodland, Forest, Open Habitat and Uncertain) based on the 6 model fits described in chapter 5: a: Australia all treed habitats, b: Australia all woodland, c: Australia eucalypt woodland, d: Ecoregion 7 eucalypt woodland, e: Ecoregion 12 eucalypt woodland, f: Ecoregion 4 eucalypt woodland Common Name

Scientific Name

a

b

c

d

e

f

Spiny-cheeked honeyeater Inland thornbill

Acanthagenys rufogularis Acanthiza apicalis Acanthiza chrysorrhoa Acanthiza ewingii Acanthiza inornata Acanthiza iredalei Acanthiza katherina Acanthiza lineata Acanthiza nana

D D U F D U F I I

D D D F U U F I F

D D D F D U F I I

U F U NA NA NA NA F F

D I U NA F U NA F U

U U D F NA NA NA I D

Acanthiza pusilla Acanthiza reguloides Acanthiza robustirostris Acanthiza uropygialis Acanthorhynchus superciliosus Acanthorhynchus tenuirostris

F D U D U I

F I U D U I

F I U D U I

F F NA U NA F

F U NA D F F

I D NA U NA I

Accipiter cirrocephalus Accipiter fasciatus

U U

U D

U U

U U

F D

O U

Accipiter novaehollandiae Acrocephalus australis Aegotheles cristatus Ailuroedus crassirostris Ailuroedus melanotis Alauda arvensis Alectura lathami Alisterus scapularis

F O D F I O F F

F O D F F O F F

F O D F F O F F

U U U NA U NA F F

NA U I NA NA U NA NA

F O D F NA D F F

Amytornis dorotheae Amytornis goyderi

U U

U U

U U

U NA

NA NA

NA NA

Yellow-rumped thornbill Striated thornbill Western thornbill Slender-billed thornbill Mountain thornbill Striated thornbill Yellow thornbill Brown thornbill Buff-rumped thornbill Slaty-backed thornbill Chestnut-rumped thornbill Western spinebill Eastern spinebill Collared sparrowhawk Brown goshawk Grey goshawk Australian reed warbler Australian owlet-nightjar Green catbird Black-eared catbird Eurasian skylark Australian brushturkey Australian king parrot Carpentarian grasswren Eyrean grasswren

269

Short-tailed grasswren Dusky grasswren Striated grasswren Western grasswren Magpie goose Red wattlebird Little wattlebird Western wattlebird Yellow wattlebird Southern whiteface Banded whiteface Metallic starling Red-winged parrot Pacific swift Wedge-tailed eagle Cattle Eegret Intermediate egret Australian bustard Pied monarch Frilled monarch Black-faced woodswallow Dusky woodswallow White-breasted woodswallow Little woodswallow Masked woodswallow White-browed woodswallow Gibberbird Rufous scrubbird Pacific baza Australian ringneck Bush stone-curlew Sulphur-crested cockatoo Muir's corella Little corella Long-billed corella Chestnut-breasted cuckoo Fan-tailed cuckoo Pallid cuckoo Brush cuckoo Rufous fieldwren Shy heathwren Striated fieldwren Gang-gang cockatoo Red-tailed black-cockatoo Baudin's black cockatoo Yellow-tailed black-cockatoo

Amytornis merrotsyi Amytornis purnelli Amytornis striatus Amytornis textilis Anseranas semipalmata Anthochaera carunculata Anthochaera chrysoptera Anthochaera lunulata Anthochaera paradoxa Aphelocephala leucopsis Aphelocephala nigricincta Aplonis metallica Aprosmictus erythropterus Apus pacificus Aquila audax Ardea ibis Ardea intermedia

U U U U U U F U F D U F D U D F U

U U D U U O F U F D U F D U D F U

U U U U U D F U F D U F D U D F U

NA NA NA NA U F NA NA NA NA NA U D NA U U U

NA NA U NA U F F NA NA D NA NA NA U I U U

NA NA NA NA O D NA NA F U NA NA D NA D O O

Ardeotis australis Arses kaupi Arses telescopthalmus Artamus cinereus

U U U U

D F U D

D F U U

U NA U U

NA NA NA U

NA NA NA NA

Artamus cyanopterus Artamus leucorynchus Artamus minor Artamus personatus Artamus superciliosus Ashbyia lovensis Atrichornis rufescens Aviceda subcristata

D U D D D U I F

D U D D D U F F

D U D D D U F F

U U U U U NA NA U

I U U D D NA NA NA

D O NA U D NA F F

Barnardius zonarius Burhinus grallarius

D F

D F

D F

U U

D NA

D O

Cacatua galerita Cacatua pastinator Cacatua sanguinea Cacatua tenuirostris

I D U U

I U U U

I D O U

F NA U NA

U U O U

D NA O U

Cacomantis castaneiventris Cacomantis flabelliformis

U I

U I

U I

U F

NA F

NA I

Cacomantis pallidus Cacomantis variolosus Calamanthus campestris Calamanthus cautus Calamanthus fuliginosus Callocephalon fimbriatum Calyptorhynchus banksii Calyptorhynchus baudinii Calyptorhynchus funereus

D F U D U I D D F

D F U D U F D U F

D F U D U I D U F

U U NA NA NA NA U NA NA

D NA U I U F F F F

D F NA D U I U NA F

270

Glossy black-cockatoo Carnaby's black cockatoo Large-tailed nightjar European goldfinch White-eared monarch Southern cassowary Pheasant coucal Cape Barren goose Pied honeyeater Azure kingfisher Little kingfisher Horsfield's bronze-cuckoo Shining bronze-cuckoo Little bronze cuckoo Black-eared cuckoo Common emerald dove Inland dotterel White-backed swallow European greenfinch Speckled warbler Brown songlark Rufous songlark Chestnut-breasted quail-thrush Chestnut quail-thrush Cinnamon quail-thrush Spotted quail-thrush Swamp harrier Spotted harrier Banded honeyeater Golden-headed cisticola Zitting cisticola White-browed Treecreeper Red-browed treecreeper Black-tailed treecreeper Brown Treecreeper Rufous Treecreeper Bower's shrikethrush Grey shrike-thrush Little shrikethrush Sandstone shrikethrush White-headed pigeon Rock dove Rufous-banded honeyeater Rufous-throated honeyeater Barred cuckooshrike Ground cuckooshrike

Calyptorhynchus lathami Calyptorhynchus latirostris Caprimulgus macrurus Carduelis carduelis Carternornis leucotis Casuarius casuarius Centropus phasianinus Cereopsis novaehollandiae Certhionyx variegatus Ceyx azureus Ceyx pusilla Chalcites basalis Chalcites lucidus Chalcites minutillus Chalcites osculans Chalcophaps indica Charadrius australis

F U U U U U F U U F U D F F U F U

F U D O U U F U D F U D F I D F U

F U D O U U F U U F U D F I U F U

U NA U NA NA NA F NA NA U NA U U U U NA NA

NA F F NA NA NA NA U D F NA U F NA U NA NA

F NA U O U NA F U NA F NA D F U U F NA

Cheramoeca leucosterna Chloris chloris Chthonicola sagittata Cincloramphus cruralis

U U I U

U O I U

U O I O

U NA F U

U U NA U

U O D U

Cincloramphus mathewsi Cinclosoma castaneothorax Cinclosoma castanotum Cinclosoma cinnamomeum Cinclosoma punctatum Circus approximans Circus assimilis Cissomela pectoralis

D U D U F F U U

D U D U F U U D

D O D O F U U D

U NA NA NA U U U U

U NA D NA NA F U NA

D NA NA NA U O U NA

Cisticola exilis Cisticola juncidus

F U

U U

U U

U U

U NA

O NA

Climacteris affinis Climacteris erythrops Climacteris melanura Climacteris picumnus

D F D D

D F D D

D F D D

NA F D D

D NA NA D

U F NA D

Climacteris rufa Colluricincla boweri

D F

D F

D F

NA NA

D NA

NA NA

Colluricincla harmonica Colluricincla megarhyncha Colluricincla woodwardi Columba leucomela Columba livia Conopophila albogularis Conopophila rufogularis Coracina lineata Coracina maxima

I F U F O U U F U

I F U F O D D F U

I F U F O D D F U

U F U NA U U U U U

D NA NA NA O NA NA NA U

I F NA F O NA NA U U

271

Black-faced cuckoo-shrike White-bellied cuckoo-shrike Common cicadabird White-winged chough White-throated treecreeper Little crow Australian raven Little raven Torresian crow Forest raven Stubble quail Brown quail Black-backed butcherbird Pied butcherbird Black butcherbird Australian magpie Grey butcherbird Oriental cuckoo Double-eyed fig parrot Blue-winged kookaburra Laughing kookaburra Varied sittella Eastern bristlebird Rufous bristlebird Mistletoebird Spangled drongo Emu Southern scrub-robin Pied imperial pigeon Eclectus parrot White-faced heron Pied heron Black-shouldered kite Letter-winged kite Painted finch Blue-faced honeyeater Galah Eastern yellow robin White-breasted robin Western yellow robin White-fronted chat Orange chat Crimson chat Spinifexbird Gouldian finch Blue-faced parrotfinch

Coracina novaehollandiae Coracina papuensis Coracina tenuirostris Corcorax melanorhamphos Cormobates leucophaea Corvus bennetti Corvus coronoides Corvus mellori Corvus orru Corvus tasmanicus Coturnix pectoralis Coturnix ypsilophora Cracticus mentalis Cracticus nigrogularis Cracticus quoyi Cracticus tibicen Cracticus torquatus

U D F D I U D O F F U U D U F U I

U I F D I D D O F F U U D U F O I

U I F D I U D U F F O U D U F O I

U D F F I U U U U NA NA U U U U U I

U NA NA D I D U U U I U O NA D NA U D

D D F D I NA D D F F U U NA O NA O D

Cuculus optatus Cyclopsitta diophthalma Dacelo leachii Dacelo novaeguineae

U F D I

U F D F

U F D F

NA NA D F

NA NA NA F

NA NA O I

Daphoenositta chrysoptera Dasyornis brachypterus Dasyornis broadbenti Dicaeum hirundinaceum Dicrurus bracteatus Dromaius novaehollandiae Drymodes brunneopygia Ducula bicolor

D U U D F D D F

I U U D F D D F

I U U D F D D F

F NA NA U F U NA F

I NA F U NA D I NA

D U U U U U NA NA

Eclectus roratus Egretta novaehollandiae

F O

F O

F O

F O

NA O

NA O

Egretta picata Elanus axillaris Elanus scriptus Emblema pictum

U O U U

D O U U

D O U U

U U NA U

NA O NA NA

NA O NA NA

Entomyzon cyanotis Eolophus roseicapillus

F D

F D

F U

F O

U U

O U

Eopsaltria australis Eopsaltria georgiana Eopsaltria griseogularis Epthianura albifrons Epthianura aurifrons Epthianura tricolor Eremiornis carteri Erythrura gouldiae Erythrura trichroa

I F U O O O U U U

I F U U O U U D U

F F U U O O U U U

F NA NA NA NA U U U NA

F F U O NA U NA NA NA

I NA NA U NA O NA NA NA

272

Pacific koel Spotted nightjar White-throated nightjar Oriental dollarbird Brown falcon Nankeen kestrel Grey falcon Australian hobby Peregrine falcon Black falcon Crested shrike-tit Latham's snipe Buff-banded rail Diamond dove Bar-shouldered dove Peaceful dove Spinifex pigeon Squatter pigeon White-throated gerygone Green-backed gerygone Western gerygone Mangrove gerygone Large-billed gerygone Brown gerygone Fairy gerygone Musk lorikeet Purple-crowned lorikeet Little lorikeet Green-backed honeyeater Tawny-crowned honeyeater Magpie-lark Painted honeyeater Sarus crane Brolga Brahminy kite Whistling kite Black-breasted buzzard Pictorella mannikin Grey-headed robin Little eagle White-throated needletail Welcome swallow Barn swallow Varied triller White-winged Triller Swift parrot

Eudynamys orientalis Eurostopodus argus Eurostopodus mystacalis Eurystomus orientalis Falco berigora Falco cenchroides Falco hypoleucos Falco longipennis Falco peregrinus Falco subniger Falcunculus frontatus Gallinago hardwickii Gallirallus philippensis Geopelia cuneata Geopelia humeralis Geopelia striata Geophaps plumifera

F D U F U O U U U U I F U D F D D

F D F F U O U U U U F U U D F D D

F D U F U O U U U U I U U U F D D

F U U U U O U U U U F U U U D U U

NA U NA NA U O NA U U U F U U U NA U NA

NA D U U U O NA O U U F O O U F U NA

Geophaps scripta Gerygone albogularis Gerygone chloronota Gerygone fusca

D F U D

D F U D

D F U D

U U U U

NA NA NA U

U F NA D

Gerygone levigaster Gerygone magnirostris Gerygone mouki Gerygone palpebrosa Glossopsitta concinna Glossopsitta porphyrocephala Glossopsitta pusilla Glycichaera fallax

U F I D U D F U

U F F I U U F U

U F F I U U F U

U F U F F NA F U

NA NA NA NA U F U NA

O NA F U D D U NA

Glyciphila melanops Grallina cyanoleuca

U O

U O

U O

NA O

F O

O O

Grantiella picta Grus antigone Grus rubicunda Haliastur indus

U D D F

U D D F

U D D F

NA U U U

U NA NA NA

NA NA U O

Haliastur sphenurus Hamirostra melanosternon

D U

D U

D U

U U

U U

O NA

Heteromunia pectoralis Heteromyias cinereifrons Hieraaetus morphnoides Hirundapus caudacutus Hirundo neoxena Hirundo rustica Lalage leucomela Lalage sueurii Lathamus discolor

U I U F F U F D F

U F U F F U F D F

U F U F F U F D F

U U U U U U F U NA

NA NA U U U NA NA D U

NA NA U F O NA U D U

273

Malleefowl Wonga pigeon Lewin's rail Yellow-faced honeyeater Purple-gaped honeyeater Mangrove honeyeater Yellow-tinted honeyeater Yellow-throated honeyeater Yellow honeyeater Bridled honeyeater Fuscous honeyeater Grey-headed honeyeater Yellow-tufted honeyeater Yellow-plumed honeyeater White-plumed honeyeater Grey-fronted honeyeater White-gaped honeyeater Varied honeyeater Singing honeyeater Brown honeyeater Chestnut-breasted mannikin Scaly-breasted munia Major Mitchell's cockatoo Square-tailed kite Topknot pigeon Yellow-breasted boatbill Amboyna cuckoo-dove Lovely fairywren Purple-crowned fairywren Superb Fairy-wren Red-winged fairywren Variegated fairy-wren White-winged fairywren Red-backed fairywren Blue-breasted fairywren Splendid fairy-wren Yellow-throated miner Noisy miner Bell miner Little grassbird Tawny grassbird Orange-footed scrubfowl Hooded robin Dusky robin White-lined honeyeater Graceful honeyeater

Leipoa ocellata Leucosarcia picata Lewinia pectoralis Lichenostomus chrysops Lichenostomus cratitius Lichenostomus fasciogularis Lichenostomus flavescens Lichenostomus flavicollis Lichenostomus flavus Lichenostomus frenatus Lichenostomus fuscus Lichenostomus keartlandi Lichenostomus melanops Lichenostomus ornatus Lichenostomus penicillatus Lichenostomus plumulus Lichenostomus unicolor

D F F I U U U F D F D O D D D D D

D F F I U O D F I F I U I D D D D

D F F I U O D F I F I U I D D D D

NA NA NA F NA NA U NA D NA U U F NA D D U

D NA U F U NA NA NA NA NA NA NA U D U NA NA

NA F U I U O NA NA F NA D NA D U D NA NA

Lichenostomus versicolor Lichenostomus virescens Lichmera indistincta Lonchura castaneothorax

U U O F

U U U U

U U O U

NA U U U

NA O U NA

NA U U O

Lonchura punctulata Lophochroa leadbeateri Lophoictinia isura Lopholaimus antarcticus Machaerirhynchus flaviventer Macropygia amboinensis Malurus amabilis Malurus coronatus

U D F F I F U D

U D F F F F U D

U D F F F F U D

U NA U NA U U U U

NA D U NA NA NA NA NA

O NA U F NA F NA NA

Malurus cyaneus Malurus elegans

F F

F F

F F

F NA

F F

D NA

Malurus lamberti Malurus leucopterus Malurus melanocephalus Malurus pulcherrimus

D O F D

D O F U

D O F U

U U U NA

D U NA F

I O U NA

Malurus splendens Manorina flavigula

D D

D D

D D

NA U

I U

U U

Manorina melanocephala Manorina melanophrys Megalurus gramineus Megalurus timoriensis Megapodius reinwardt Melanodryas cucullata Melanodryas vittata Meliphaga albilineata Meliphaga gracilis

F I U F F D F U F

F F O F F D F U F

F F O F F D F U F

F NA U U F U NA U F

U NA U NA NA D NA NA NA

O I O O NA U F NA NA

274

Lewin's honeyeater Yellow-spotted honeyeater Black-headed honeyeater White-throated honeyeater Brown-headed honeyeater Black-chinned honeyeater White-naped honeyeater Strong-billed honeyeater Budgerigar Albert's lyrebird Superb lyrebird Rainbow bee-eater Jacky winter Lemon-bellied flyrobin Black kite Horsfield's bush lark Black-winged monarch Black-faced monarch Shining flycatcher Satin flycatcher Restless flycatcher Leaden flycatcher Broad-billed flycatcher Red-headed myzomela Dusky myzomela Scarlet honeyeater Olive-backed sunbird Plum-headed finch Crimson finch Star finch Red-browed finch Blue-winged parrot Elegant parrot Turquoise parrot Scarlet-chested parrot Bourke's parrot Barking owl Southern boobook Rufous owl Powerful owl Blue bonnet Nankeen night heron Cockatiel Crested pigeon Crested bellbird Fernwren

Meliphaga lewinii Meliphaga notata Melithreptus affinis Melithreptus albogularis Melithreptus brevirostris Melithreptus gularis Melithreptus lunatus Melithreptus validirostris Melopsittacus undulatus Menura alberti Menura novaehollandiae Merops ornatus Microeca fascinans Microeca flavigaster Milvus migrans Mirafra javanica Monarcha frater

F F F I D D I U D I I D D D D U U

F F F I D D I U D F F D D I D O U

F I F I D D I U D F F D D I D O U

F F NA F U U U NA U NA NA D U U U U U

NA NA NA NA D I F NA D NA NA D D NA U U NA

F U F F D D D U U F I O D NA O O NA

Monarcha melanopsis Myiagra alecto Myiagra cyanoleuca Myiagra inquieta

F I F D

F I F D

F F F D

NA F U U

NA NA U I

NA F F D

Myiagra rubecula Myiagra ruficollis Myzomela erythrocephala Myzomela obscura Myzomela sanguinolenta Nectarinia jugularis Neochmia modesta Neochmia phaeton

F U U I F F U U

I U U I F F U D

I U U I F F U D

I U U F F F U U

NA NA NA NA NA NA NA NA

F NA NA U F NA U NA

Neochmia ruficauda Neochmia temporalis

U F

O F

U F

U F

NA F

NA F

Neophema chrysostoma Neophema elegans Neophema pulchella Neophema splendida

D U I D

D U I D

D U I D

NA NA F NA

I U NA NA

U NA D NA

Neopsephotus bourkii Ninox connivens

U D

U D

U U

NA U

NA D

NA U

Ninox novaeseelandiae Ninox rufa Ninox strenua Northiella haematogaster Nycticorax caledonicus Nymphicus hollandicus Ocyphaps lophotes Oreoica gutturalis Oreoscopus gutturalis

D U F D U U O D F

I U F D D D O D F

I U F U D U O D F

U U NA U D U O U NA

I NA U D U U O D NA

U NA F U O O O D NA

275

Rockwarbler Green oriole Olive-backed oriole Chowchilla Australian logrunner Gilbert's whistler Mangrove golden whistler Olive whistler Golden whistler Rufous whistler Red-lored whistler Grey whistler Spotted pardalote Forty-spotted pardalote Red-browed pardalote Striated pardalote House sparrow Eurasian tree sparrow Indian peafowl Fairy martin Tree martin Scarlet robin Red-capped robin Flame robin Rose robin White-quilled rock pigeon Chestnut-quilled rock pigeon Eastern ground parrot Common bronzewing Brush bronzewing Flock bronzewing Silver-crowned friarbird Helmeted friarbird Little friarbird White-cheeked honeyeater New Holland honeyeater Crescent honeyeater Noisy pitta Pale-headed rosella Green rosella Crimson rosella Eastern Rosella Western rosella Northern rosella Striped Honeyeater Glossy ibis

Origma solitaria Oriolus flavocinctus Oriolus sagittatus Orthonyx spaldingii Orthonyx temminckii Pachycephala inornata Pachycephala melanura Pachycephala olivacea Pachycephala pectoralis Pachycephala rufiventris Pachycephala rufogularis Pachycephala simplex Pardalotus punctatus Pardalotus quadragintus Pardalotus rubricatus Pardalotus striatus Passer domesticus

F I F F I D U F I D U F I U U D O

I I F F F D U F F D U F I U D D O

I I F F F D U F I D U F I U D D O

NA F D NA NA NA U NA F U NA F F NA U F U

NA NA U NA NA D NA F F D U NA F NA NA D O

I U NA NA F U NA F I D NA NA I U NA U O

Passer montanus Pavo cristatus Petrochelidon ariel Petrochelidon nigricans

U U O D

U U O D

U U O D

NA U U U

NA NA O U

O NA O U

Petroica boodang Petroica goodenovii Petroica phoenicea Petroica rosea Petrophassa albipennis Petrophassa rufipennis Pezoporus wallicus Phaps chalcoptera

D D F I U U U D

I D F F U U U D

I D F F U U U D

NA U NA U U U NA U

F D F U NA NA NA I

D D D F NA NA U D

Phaps elegans Phaps histrionica

F U

I U

I U

NA NA

F NA

D NA

Philemon argenticeps Philemon buceroides Philemon citreogularis Phylidonyris niger

D F U F

D I D F

D F D F

D F D NA

NA NA U U

NA NA O F

Phylidonyris novaehollandiae Phylidonyris pyrrhopterus

U I

U I

D I

NA NA

F F

D F

Pitta versicolor Platycercus adscitus Platycercus caledonicus Platycercus elegans Platycercus eximius Platycercus icterotis Platycercus venustus Plectorhyncha lanceolata Plegadis falcinellus

F F F I F F D D O

F I F F F F D D O

F F F F F F D D O

U F NA F F NA U F U

NA NA NA F D F NA D U

F U F I D NA NA O O

276

Papuan frogmouth Tawny Frogmouth Buff-sided robin White-browed robin Long-tailed finch Black-throated finch Masked finch Regent parrot Superb parrot Hall's babbler Chestnut-crowned babbler White-browed babbler Grey-crowned babbler Palm cockatoo Hooded parrot Red-rumped parrot Mulga parrot Varied lorikeet Chirruping wedgebill Western whipbird Chiming wedgebill Eastern whipbird Wompoo fruit dove Rose-crowned fruit dove Superb fruit dove Western bowerbird Spotted bowebird Great bowerbird Satin bowerbird Magnificent riflebird Paradise riflebird Victoria's riflebird White-fronted honeyeater Red-capped parrot Red-whiskered bulbul Pilotbird Redthroat Red-necked crake Bar-breasted honeyeater Brown-backed honeyeater Grey fantail Arafura fantail Willie Wagtail Mangrove fantail Rufous fantail Northern fantail

Podargus papuensis Podargus strigoides Poecilodryas cerviniventris Poecilodryas superciliosa Poephila acuticauda Poephila cincta Poephila personata Polytelis anthopeplus Polytelis swainsonii Pomatostomus halli Pomatostomus ruficeps Pomatostomus superciliosus Pomatostomus temporalis Probosciger aterrimus Psephotus dissimilis Psephotus haematonotus Psephotus varius

U F U F U D D D U U D D U U U U D

D F U I D D D D U U D D D U U U D

D F D I D D D D U U D D D D U D D

U U U U U U U NA NA NA NA U U U U U NA

NA F NA NA NA NA NA U NA NA D D U NA NA U D

NA F NA NA NA NA NA NA O NA NA D U NA NA D U

Psitteuteles versicolor Psophodes cristatus Psophodes nigrogularis Psophodes occidentalis

D U U U

D U U U

D O U U

D NA NA NA

NA NA U U

NA NA NA NA

Psophodes olivaceus Ptilinopus magnificus Ptilinopus regina Ptilinopus superbus Ptilonorhynchus guttatus Ptilonorhynchus maculatus Ptilonorhynchus nuchalis Ptilonorhynchus violaceus

F F F F U D D F

F F F F D D D F

F F F F O D D F

U U F F U U D NA

NA NA NA NA NA U NA NA

F F F U NA NA NA F

Ptiloris magnificus Ptiloris paradiseus

D F

D F

D F

F NA

NA NA

NA F

Ptiloris victoriae Purnella albifrons Purpureicephalus spurius Pycnonotus jocosus

I D U F

F D U F

F D U F

NA NA NA NA

NA D F NA

NA U NA NA

Pycnoptilus floccosus Pyrrholaemus brunneus

F D

I D

I U

NA NA

NA U

NA NA

Rallina tricolor Ramsayornis fasciatus Ramsayornis modestus Rhipidura albiscapa Rhipidura dryas Rhipidura leucophrys Rhipidura phasiana Rhipidura rufifrons Rhipidura rufiventris

U D F I U O U I D

U D I F U O U F D

U D I I U O U F D

NA U F F U U U U U

NA NA NA F NA O NA NA NA

NA D I I NA O NA F NA

277

Tooth-billed bowerbird Channel-billed cuckoo Tropical scrubwren Yellow-throated scrubwren White-browed scrubwren Tasmanian scrubwren Atherton scrubwren Large-billed scrubwren Regent bowerbird Weebill Australasian figbird Diamond firetail Pied currawong Red-eared firetail Australian pratincole Southern emu-wren Mallee emu-wren Rufous-crowned emu-wren Black currawong Pied currawong Grey currawong Spotted Dove Laughing Dove Apostlebird Common myna Common starling Black Honeyeater Yellow-billed kingfisher Spectacled monarch Double-barred finch Zebra finch Buff-breasted paradise kingfisher Australian white ibis Straw-necked ibis Collared kingfisher Forest kingfisher Red-backed kingfisher Sacred kingfisher Pale-yellow robin White-faced robin Tasmanian nativehen Black-tailed nativehen White-streaked honeyeater Scaly-breasted lorikeet Rainbow lorikeet Common blackbird

Scenopoeetes dentirostris Scythrops novaehollandiae Sericornis beccarii Sericornis citreogularis Sericornis frontalis Sericornis humilis Sericornis keri Sericornis magnirostra Sericulus chrysocephalus Smicrornis brevirostris Sphecotheres vieilloti Stagonopleura bella Stagonopleura guttata Stagonopleura oculata Stiltia isabella Stipiturus malachurus Stipiturus mallee

F F U I F F F F F D F U D F U F U

F F U F F F F F F D F U D F U F U

F F U F F F F F F D F U D F U F U

NA U U NA F NA NA NA NA D F NA U NA U NA NA

NA NA NA NA F NA NA NA NA D NA F D F NA U U

NA F NA F F F NA F F D U U U NA NA U F

Stipiturus ruficeps Strepera fuliginosa Strepera graculina Strepera versicolor

U F F D

U F F I

U F F I

NA NA F U

NA NA F I

NA NA F D

Sphecotheres vieilloti Streptopelia senegalensis Struthidea cinerea Sturnus tristis Sturnus vulgaris Sugomel niger Syma torotoro Symposiarchus trivirgatus

F O D F O D U F

O O D U O D U F

O O D U O D U F

U NA U U U NA U U

O O U NA U D NA NA

O NA U O O NA NA F

Taeniopygia bichenovii Taeniopygia guttata

D U

D D

D U

U U

NA U

O O

Tanysiptera sylvia Threskiornis molucca Threskiornis spinicollis Todiramphus chloris

U U O U

U O U O

U O U O

U U U U

NA O U NA

NA O O O

Todiramphus macleayii Todiramphus pyrrhopygius

F D

I D

I D

U U

NA D

U U

Todiramphus sanctus Tregellasia capito Tregellasia leucops Tribonyx mortierii Tribonyx ventralis Trichodere cockerelli Trichoglossus chlorolepidotus Trichoglossus haematodus Turdus merula

D I U F O U F F O

D F U F U U F F O

U F U F U U F F O

U NA U NA U U F F U

U NA NA NA O NA NA U U

U F NA U U NA U O U

278

Song thrush Turdus philomelos Red-chested buttonquail Painted Button-quail Little Button-quail Eastern barn owl Eastern grass owl Masked owl Sooty owl Masked lapwing Banded lapwing Tawny-breasted honeyeater Macleay's honeyeater Russet-tailed thrush Bassian thrush Silvereye Australian yellow white-eye

Turdus philomelos Turnix maculosus Turnix pyrrhothorax Turnix varius Turnix velox Tyto javanica Tyto longimembris Tyto novaehollandiae Tyto tenebricosa Vanellus miles Vanellus tricolor Xanthotis flaviventer Xanthotis macleayanus Zoothera heinei Zoothera lunulata Zosterops lateralis Zosterops luteus

U U U D D U U U U F O U F F F F U

U U U D D U U U U U O U F F F F U

U U U D U U U U U U O U I F F F U

NA NA U NA U U NA NA NA U U F U NA NA F U

NA NA NA I U U NA NA NA U U NA NA NA U F NA

U NA O D U U NA U U O U NA NA F F F NA

279

Appendix 6.1. Woodland bird workshop spreadsheet Table A.6.1.1: Spreadsheet filled in by woodland bird experts at the workshop aimed at defining the woodland bird community for eastern mainland Australia What is the community that we are trying to identify?

We are seeking to identify those birds that characterise an intact woodland avifauna but are not seen together in other habitat types. While the constituent species of the community will vary geographically, an intact community will always comprise an association of the same characteristic guilds of woodland birds. We are also interested in understanding which birds are more or less likely to occur in a degraded community

Where is the community that we are trying to identify?

Temperate and sub-tropical regions typified by open canopied trees (0-40% cover), in Eastern Australia between South Australia and Central Queensland. In light of discussions on Friday these will later be subdivided into smaller regions

Definition of 'intact' woodland bird community

A community in which most of the important woodland bird families and/or functional groups are well represented. While the constituent species of the community will vary geographically, an intact community will always comprise the same characteristic families or functional groups of woodland birds.

Definition of 'degraded' woodland bird community

A community in which certain woodland bird families or functional groups are not present, or are underrepresented in terms of number of species, because of threats associated with anthropogenic habitat modification and land use.

Family

Common Name

Scientific Name

Diet

Foraging Location

Hollow nest (Y/N)

Body mass (g)

Acanthizidae

Yellow-rumped Thornbill

Acanthiza chrysorrhoa

Insectivore

Ground

N

9.16

Acanthizidae

Gibberbird

Ashbyia lovensis

Insectivore

Ground

N

17.52

Acanthizidae

Speckled Warbler

Chthonicola sagittata

Insectivore

Ground

N

13.3

Acanthizidae

Rockwarbler

Origma solitaria

Insectivore

Ground

N

14.42

Acanthizidae

Pilotbird

Pycnoptilus floccosus

Insectivore

Ground

N

31.22

Acanthizidae

Slender-billed Thornbill

Acanthiza iredalei

Insectivore

Ground and Shrub

N

5.88

Redthroat

Pyrrholaemus brunneus

Granivore + Insectivore

Ground and Shrub

N

12.3

Acanthizidae

Inland Thornbill

Acanthiza apicalis

Insectivore

Tree

N

7.41

Acanthizidae

Tasmanian Thornbill

Acanthiza ewingii

Insectivore

Tree

Y

7.17

Acanthizidae

Striated Thornbill

Acanthiza lineata

Insectivore

Tree

N

7.5

Brown Thornbill

Acanthiza pusilla

Insectivore

Tree

N

6.84

Scrubtit

Acanthornis magnus

Insectivore

Tree

N

10.08

Weebill

Smicrornis brevirostris

Insectivore

Tree

N

6.13

Acanthizidae

Acanthizidae Acanthizidae Acanthizidae

Yellow Thornbill

Acanthiza nana

Insectivore

Shrub and Tree

N

6.35

Acanthiza reguloides

Insectivore

Shrub and Tree

N

7.75

Acanthizidae

Buff-rumped Thornbill Chestnutrumped Thornbill

Acanthiza uropygialis

Insectivore

Shrub and Tree

Y

6.37

Accipitridae

White-bellied Sea-Eagle

Haliaeetus leucogaster

Carnivore

Other

N

2907.2 7

Accipitridae

Little Eagle

Hieraaetus morphnoides

Carnivore

Other

N

895.56

Accipitridae

Collared Sparrowhawk

Accipiter cirrocephalus

Carnivore

Air

N

180.26

Accipitridae

Letter-winged Kite

Elanus scriptus

Carnivore

Air

N

315.25

Accipitridae

Red Goshawk

Erythrotriorchis radiatus

Carnivore

Air

N

793.33

Accipitridae

Black-breasted Buzzard

Hamirostra melanosternon

Carnivore

Air

N

1200.6 7

Acanthizidae Acanthizidae

Would you expect to see this species in most woodland bird communities (degraded or intact)? (1=yes, 0=no, U=unsure)

Would you expect to only (or mostly) see this species in intact communities (1=yes, 0=no, U=unsure)

Would you expect to see this species more often in degraded than intact communities (1=yes, 0=no, U=unsure)

280

Square-tailed Kite

Lophoictinia isura

Carnivore + Insectivore

Air

N

627.75

Pacific Baza

Aviceda subcristata

Generalist

Air

N

321

Whistling Kite

Haliastur sphenurus

Generalist

Air

N

757.45

Accipitridae

Brown Goshawk

Accipiter fasciatus

Carnivore

Ground and Air

N

514.33

Accipitridae

Blackshouldered Kite

Carnivore

Ground and Air

N

280.79

N

569

Accipitridae Accipitridae Accipitridae

Elanus notatus

Accipitridae

Black Kite

Milvus migrans

Carnivore

Ground and Air

Accipitridae

Wedge-tailed Eagle

Aquila audax

Carnivore

Ground

N

3462.4 4

Accipitridae

Spotted Harrier

Circus assimilis

Carnivore

Ground

N

558.64

Swamp Harrier

Circus approximans

Carnivore + Insectivore

Ground

N

766.5

Carnivore + Insectivore

Ground

N

558.63

Carnivore

Ground and Tree

N

606.25

Y

44

Accipitridae Accipitridae

Brahminy Kite

Accipitridae

Grey Goshawk

Haliastur indus Accipiter novaehollandia e

Aegothelidae

Australian Owlet-nightjar

Aegotheles cristatus

Insectivore

Ground and Air

Alaudidae

Horsfield’s Bushlark

Mirafra javanica

Insectivore

Ground

N

21.85

Alauda arvensis

Granivore + Insectivore

Ground

N

38.72

Other

Y

34.37

Alaudidae

Eurasian Skylark

Alcedinidae

Azure Kingfisher

Ceyx azurea

Carnivore + Insectivore

Apodidae

White-throated Needletail

Hirundapus caudacutus

Insectivore

Air

N

96.42

Ardeidae

Little Egret

Egretta garzetta

Carnivore

Other

N

311.8

Ardeidae

White-necked Heron

Ardea pacifica

Carnivore + Insectivore

Other

N

881

Carnivore + Insectivore

Other

N

259

Carnivore + Insectivore

Ardeidae

Pied Heron

Ardeidae

White-faced Heron

Ardea picata Egretta novaehollandia e

Other

N

570.64

Ardeidae

Nankeen NightHeron

Nycticorax caledonicus

Carnivore + Insectivore

Other

N

760

Ardeidae

Cattle Egret

Bubulcus ibis

Insectivore

Ground

N

361.69

Ardeidae

Australasian Bittern

Botaurus poiciloptilus

Carnivore

Ground

N

1159

Ardeidae

Intermediate Egret

Ardea intermedia

Carnivore + Insectivore

Ground

N

453

Artamidae

White-browed Woodswallow

Artamus superciliosus

Insectivore

Other

N

35.91

Artamidae

Dusky Woodswallow

Artamus cyanopterus

Insectivore

Air

N

34.56

Artamidae

White-breasted Woodswallow

Artamus leucorynchus

Insectivore

Air

N

42.79

Artamidae

Little Woodswallow

Artamus minor

Insectivore

Air

Y

16.11

Artamidae

Masked Woodswallow

Artamus personatus

Nectarivore

Air

N

34.59

Artamidae

Black-faced Woodswallow

Artamus cinereus

Insectivore

Tree

N

35.42

Atrichornithid ae

Rufous Scrubbird

Atrichornis rufescens

Insectivore

Ground

N

24

Burhinidae

Bush Stonecurlew

Burhinus grallarius

Carnivore + Insectivore

Ground and Air

N

688.79

Cacatuoidea

Major Mitchell's Cockatoo

Cacatua leadbeateri

Generalist

Other

Y

376.38

Cacatuoidea

Sulphur-crested Cockatoo

Cacatua galerita

Granivore

Ground

Y

789.75

Galah

Cacatua roseicapilla

Granivore

Ground

Y

335.69

Cacatuoidea

Little Corella

Cacatua sanguinea

Granivore

Ground

Y

382.4

Cacatuoidea

Long-billed Corella

Cacatua tenuirostris

Granivore

Ground

Y

564.44

Cockatiel

Nymphicus hollandicus

Granivore

Ground

Y

86.82

Palm Cockatoo

Probosciger aterrimus

Frugivore

Tree

Y

791.75

Cacatuoidea

Cacatuoidea Cacatuoidea

281

Cacatuoidea

Glossy BlackCockatoo

Calyptorhynchu s lathami

Granivore

Tree

Y

437.59

Cacatuoidea

Yellow-tailed Black-Cockatoo

Calyptorhynchu s funereus

Granivore + Insectivore

Tree

Y

750.17

Cacatuoidea

Red-tailed BlackCockatoo

Calyptorhynchu s banksii

Granivore

Ground and Tree

Y

612.4

Cacatuoidea

Gang-gang Cockatoo

Callocephalon fimbriatum

Granivore

Shrub and Tree

Y

256.26

Campephagid ae

Ground Cuckooshrike

Coracina maxima

Insectivore

Ground

N

133.71

Campephagid ae

White-winged Triller

Lalage sueurii

Generalist

Ground

N

25.32

Campephagid ae

White-bellied Cuckoo-shrike

Coracina papuensis

Insectivore

Tree

N

67.49

Campephagid ae

Cicadabird

Coracina tenuirostris

Insectivore

Tree

N

68.26

Campephagid ae

Barred Cuckooshrike

Frugivore

Tree

N

100

Campephagid ae

Black-faced Cuckoo-shrike

Coracina lineata Coracina novaehollandia e

Frugivore

Tree

N

117.1

Varied Triller

Lalage leucomela

Frugivore

Tree

N

33.07

Caprimulgidae

Spotted Nightjar

Eurostopodus argus

Insectivore

Air

N

93.41

Caprimulgidae

White-throated Nightjar

Eurostopodus mystacalis

Insectivore

Air

N

127.64

Caprimulgidae

Large-tailed Nightjar

Caprimulgus macrurus

Insectivore

Air

N

66.18

Cerylidae

Laughing Kookaburra

Dacelo novaeguineae

Carnivore + Insectivore

Ground

Y

351.47

Cerylidae

Blue-winged Kookaburra

Dacelo leachii

Carnivore + Insectivore

Ground and Tree

Y

290.86

Charadriidae

Masked Lapwing

Vanellus miles

Insectivore

Ground

N

368.99

Banded Lapwing

Vanellus tricolor

Granivore + Insectivore

Ground

N

184.64

Charadriidae

Inland Dotterel

Peltohyas australis

Generalist

Ground

N

83.51

Cinclosomatid ae

Chirruping Wedgebill

Psophodes cristatus

Insectivore

Ground

N

40.92

Cinclosomatid ae

Western Whipbird

Psophodes nigrogularis

Insectivore

Ground

N

44.49

Cinclosomatid ae

Chiming Wedgebill

Psophodes occidentalis

Insectivore

Ground

N

40.25

Cinclosomatid ae

Chestnut Quailthrush

Cinclosoma castanotum

Insectivore

Ground

N

76.71

Cinclosomatid ae

Cinclosoma cinnamomeum

Insectivore

Ground

N

56.24

Cinclosomatid ae

Cinnamon Quailthrush Chestnutbreasted Quailthrush

Cinclosoma castaneothorax

Granivore + Insectivore

Ground

N

60.66

Cinclosomatid ae

Spotted Quailthrush

Cinclosoma punctatum

Granivore + Insectivore

Ground

N

114.8

Cinclosomatid ae

Eastern Whipbird

Psophodes olivaceus

Insectivore

Shrub and Tree

N

63.48

Cisticolidae

Zitting Cisticola

Cisticola juncidis

Insectivore

Ground

N

6.8

Cisticolidae

Golden-headed Cisticola

Cisticola exilis

Insectivore

Ground and Shrub

N

7.41

Climacteridae

White-browed Treecreeper

Climacteris affinis

Insectivore

Other

Y

20

Climacteridae

Red-browed Treecreeper

Climacteris erythrops

Insectivore

Tree

Y

23.54

Climacteridae

Rufous Treecreeper

Climacteris rufus

Insectivore

Ground and Tree

Y

32.95

Climacteridae

Brown Treecreeper

Climacteris picumnus

Insectivore

Shrub and Tree

Y

29.08

Climacteridae

White-throated Treecreeper

Corombates leucophaeus

Insectivore

Shrub and Tree

Y

22.42

Columbidae

Banded FruitDove

Ptilinopus cinctus

Frugivore

Ground

N

510

Columbidae

Rock Dove

Columba livia

Granivore

Ground

N

350.43

Columbidae

Diamond Dove

Geopelia cuneata

Granivore

Ground

N

32.1

Columbidae

Bar-shouldered Dove

Geopelia humeralis

Granivore

Ground

N

129.14

Columbidae

Peaceful Dove

Geopelia striata

Granivore

Ground

N

49.15

Campephagid ae

Charadriidae

282

Squatter Pigeon

Geophaps scripta

Granivore

Ground

N

220.41

Wonga Pigeon

Leucosarcia melanoleuca

Granivore

Ground

N

429

Columbidae

Crested Pigeon

Ocyphaps lophotes

Granivore

Ground

N

204.45

Columbidae

Common Bronzewing

Phaps chalcoptera

Granivore

Ground

N

332.11

Columbidae

Brush Bronzewing

Phaps elegans

Granivore

Ground

N

211.47

Columbidae

Flock Bronzewing

Phaps histrionica

Granivore

Ground

N

290.37

Columbidae

Spotted Dove

Streptopelia chinensis

Granivore

Ground

N

160.52

Columbidae

White-headed Pigeon

Columba leucomela

Frugivore

Tree

N

419.8

Columbidae

Topknot Pigeon

Lopholaimus antarcticus

Frugivore

Tree

N

537.5

Columbidae

Brown CuckooDove

Macropygia amboinensis

Frugivore

Tree

N

237.12

Columbidae

Wompoo FruitDove

Ptilinopus magnificus

Frugivore

Tree

N

399.67

Columbidae

Rose-crowned Fruit-Dove

Ptilinopus regina

Frugivore

Tree

N

98.96

Columbidae

Superb FruitDove

Ptilinopus superbus

Frugivore

Tree

N

156.25

Dollarbird

Eurystomus orientalis

Insectivore

Air

Y

128.79

Corcoracidae

Apostlebird

Struthidea cinerea

Generalist

Ground and Shrub

N

131.75

Corvidae

Little Raven

Insectivore

Ground

N

534.96

Corvidae

White-winged Chough

Corvus mellori Corcorax melanorhamph os

Granivore + Insectivore

Ground

N

364.59

Australian Raven

Corvus coronoides

Carnivore

Ground

N

644.8

Corvidae

Pied Butcherbird

Cracticus nigrogularis

Carnivore + Insectivore

Ground

N

127.98

Corvidae

Australian Magpie

Gymnorhina tibicen

Carnivore + Insectivore

Ground

N

278.66

Grey Currawong

Strepera versicolor

Carnivore + Insectivore

Ground

N

384.58

Corvidae

Little Crow

Corvus bennetti

Generalist

Ground

N

397.65

Corvidae

Torresian Crow

Corvus orru

Generalist

Ground

N

574.5

Corvidae

Forest Raven

Corvus tasmanicus

Generalist

Ground

N

671.63

Corvidae

Black Butcherbird

Cracticus quoyi

Generalist

Ground

N

174.21

Corvidae

Black Currawong

Strepera fuliginosa

Generalist

Ground

N

388.99

Corvidae

Grey Butcherbird

Cracticus torquatus

Carnivore + Insectivore

Shrub and Tree

N

88.08

Corvidae

Pied Currawong

Strepera graculina

Generalist

Shrub and Tree

N

331.63

Cuculidae

Horsfield's Bronze-Cuckoo

Chrysococcyx basalis

Insectivore

Other

N

18.85

Cuculidae

Pheasant Coucal

Centropus phasianinus

Carnivore + Insectivore

Tree

N

382.83

Cuculidae

Fan-tailed Cuckoo

Cacomantis flabelliformis

Insectivore

Ground

N

49.82

Cuculidae

Black-eared Cuckoo

Chrysococcyx osculans

Insectivore

Ground

N

30.4

Cuculidae

Shining BronzeCuckoo

Chrysococcyx lucidus

Insectivore

Shrub

N

23.83

Cuculidae

Pallid Cuckoo

Cuculus pallidus

Insectivore

Ground and Shrub

N

87.75

Cuculidae

Chestnutbreasted Cuckoo

Cacomantis castaneiventris

Insectivore

Tree

N

30.38

Insectivore

Tree

N

103.8

Frugivore

Tree

N

684.1

Insectivore

Ground and Tree

N

41.8

Insectivore

Shrub and Tree

N

16.66

Columbidae Columbidae

Coraciidae

Corvidae

Corvidae

Cuculidae

Oriental Cuckoo

Cuculidae

Channel-billed Cuckoo

Cuculus optatus Scythrops novaehollandia e

Cuculidae

Brush Cuckoo

Cacomantis variolosus

Cuculidae

Little BronzeCuckoo

Chrysococcyx minutillus

283

Dasyornithida e

Eastern Bristlebird

Dasyornis brachypterus

Granivore + Insectivore

Ground

N

42.19

Dasyornithida e

Rufous Bristlebird

Dasyornis broadbenti

Generalist

Ground

N

71.84

Dicruridae

Spangled Drongo

Dicrurus bracteatus

Insectivore

Air

N

85.19

Emblema pictum

Granivore

Ground

N

10.96

Lonchura castaneothorax

Estrildidae

Estrildidae

Painted Finch Chestnutbreasted Mannikin

Granivore

Ground

N

13.67

Estrildidae

Plum-headed Finch

Neochmia modesta

Granivore

Ground

N

12.1

Estrildidae

Black-throated Finch

Poephila cincta

Granivore

Ground

N

14.93

Beautiful Firetail

Stagonopleura bella

Granivore

Ground

N

13.93

Estrildidae

Diamond Firetail

Stagonopleura guttata

Granivore

Ground

N

17.63

Estrildidae

Double-barred Finch

Taeniopygia bichenovii

Granivore

Ground

N

8.89

Zebra Finch

Taeniopygia guttata

Granivore

Ground

N

12.24

Estrildidae

Crimson Finch

Neochmia phaeton

Granivore + Insectivore

Ground

N

9.6

Estrildidae

Red-browed Finch

Neochmia temporalis

Granivore

Ground and Shrub

N

10.72

Falconidae

Black Falcon

Falco subniger

Carnivore

Other

N

738.08

Brown Falcon

Falco berigora

Carnivore + Insectivore

Other

N

588.82

Grey Falcon

Falco hypoleucos

Carnivore

Air

N

490

Peregrine Falcon

Falco peregrinus

Carnivore

Air

N

790.87

Falconidae

Nankeen Kestrel

Falco cenchroides

Carnivore + Insectivore

Air

N

170.19

Falconidae

Australian Hobby

Falco longipennis

Carnivore

Tree

N

248.5

Falcunculidae

Crested Shriketit

Falcunculus frontatus

Insectivore

Tree

N

28.48

Fringillidae

Common Greenfinch

Carduelis chloris

Granivore

Tree

N

26.15

Fringillidae

European Goldfinch

Carduelis carduelis

Granivore

Shrub and Tree

N

15.95

Glareolidae

Oriental Pratincole

Glareola maldivarum

Insectivore

Air

N

75.6

Glareolidae

Australian Pratincole

Stiltia isabella

Insectivore

Ground

N

65.48

Halcyonidae

Sacred Kingfisher

Todiramphus sanctus

Insectivore

Ground

Y

43.29

Halcyonidae

Forest Kingfisher

Todiramphus macleayii

Carnivore + Insectivore

Ground

Y

37.82

Halcyonidae

Collared Kingfisher

Todiramphus chloris

Carnivore + Insectivore

Ground

Y

66.66

Halcyonidae

Red-backed Kingfisher

Todiramphus pyrrhopygia

Carnivore + Insectivore

Ground

Y

51.53

Hirundinidae

White-backed Swallow

Cheramoeca leucosternum

Insectivore

Air

Y

14.1

Hirundinidae

Fairy Martin

Hirundo ariel

Insectivore

Air

N

10.84

Hirundinidae

Welcome Swallow

Hirundo neoxena

Insectivore

Air

N

14.78

Hirundinidae

Tree Martin

Hirundo nigricans

Insectivore

Air

Y

16.01

Hirundinidae

Barn Swallow

Hirundo rustica

Insectivore

Air

N

16.87

Tawny Grassbird

Megalurus timoriensis

Insectivore

Ground

N

18.54

Locustellidae

Little Grassbird

Megalurus gramineus

Insectivore

Ground and Shrub

N

13.42

Maluridae

Short-tailed Grasswren

Amytornis merrotsyi

Granivore + Insectivore

Ground and Air

N

22.43

Maluridae

Superb Fairywren

Malurus cyaneus

Insectivore

Ground

N

9.63

Maluridae

Blue-breasted Fairy-wren

Malurus pulcherrimus

Insectivore

Ground

N

9.26

Maluridae

Splendid Fairywren

Malurus splendens

Insectivore

Ground

N

8.96

Maluridae

Southern Emuwren

Stipiturus malachurus

Insectivore

Ground

N

7.51

Estrildidae

Estrildidae

Falconidae Falconidae Falconidae

Locustellidae

284

Maluridae

Thick-billed Grasswren

Amytornis textilis

Maluridae

Variegated Fairy-wren

Maluridae

Red-backed Fairy-wren

Malurus lamberti Malurus melanocephalu s

Maluridae

Mallee Emuwren

Stipiturus mallee

Brown Songlark

Cincloramphus cruralis

Megaluridae

Rufous Songlark

Cincloramphus mathewsi

Meliphagidae

Yellow-throated Honeyeater

Lichenostomus flavicollis

Meliphagidae

Red Wattlebird

Anthochaera carunculata

Meliphagidae

Yellow-throated Miner

Manorina flavigula

Meliphagidae

Australasian Figbird

Sphecotheres vieilloti

Meliphagidae

Black-chinned Honeyeater

Melithreptus gularis

Meliphagidae

White-fronted Chat

Ephthianura albifrons

Meliphagidae

Yellow Chat

Epthianura crocea

Meliphagidae

Singing Honeyeater

Lichenostomus virescens

Meliphagidae

Tawny-crowned Honeyeater

Meliphagidae

New Holland Honeyeater

Phylidonyris melanops Phylidonyris novaehollandia e

Meliphagidae

Crimson Chat

Ephthianura tricolor

Meliphagidae

Yellow Wattlebird

Anthochaera paradoxa

Meliphagidae

White-eared Honeyeater

Lichenostomus leucotis

Meliphagidae

Bell Miner

Manorina melanophrys

Meliphagidae

Strong-billed Honeyeater

Melithreptus validirostris

Meliphagidae

Striped Honeyeater

Plectorhyncha lanceolata

Meliphagidae

Blue-faced Honeyeater

Entomyzon cyanotis

Meliphagidae

Mangrove Honeyeater

Lichenostomus fasciogularis

Meliphagidae

Fuscous Honeyeater

Lichenostomus fuscus

Meliphagidae

Yellow-tufted Honeyeater

Lichenostomus melanops

Meliphagidae

Brown-headed Honeyeater

Melithreptus brevirostris

Meliphagidae

White-naped Honeyeater

Melithreptus lunatus

Meliphagidae

Dusky Honeyeater

Myzomela obscura

Meliphagidae

Scarlet Honeyeater

Myzomela sanguinolenta

Meliphagidae

Little Friarbird

Philemon citreogularis

Meliphagidae

White-cheeked Honeyeater

Phylidonyris nigra

Meliphagidae

Regent Honeyeater

Xanthomyza phrygia

Meliphagidae

Spiny-cheeked Honeyeater

Acanthagenys rufogularis

Meliphagidae

Painted Honeyeater

Meliphagidae

Yellow-faced Honeyeater

Generalist

Ground

N

22.19

Insectivore

Shrub

N

8

Insectivore

Shrub

N

7.48

Insectivore

Ground and Shrub

N

5.5

Insectivore

Ground

N

38.37

Insectivore

Ground

N

28.39

Insectivore

Other

N

35.18

Nectarivore

Other

N

113.33

Nectarivore

Other

N

61.62

Frugivore

Tree

N

132.18

Generalist

Tree

N

21.04

Insectivore

Ground

N

13.73

Insectivore

Ground

N

9.32

Insectivore

Shrub

N

27.16

Nectarivore

Shrub

N

18.61

Nectarivore

Shrub

N

20.56

Insectivore

Ground and Shrub

N

10.71

Nectarivore

Ground and Shrub

N

169.44

Insectivore

Tree

N

24.66

Insectivore

Tree

N

31.47

Insectivore

Tree

N

25.86

Insectivore

Tree

N

40.14

Insectivore

Tree

N

104.98

Nectarivore

Tree

N

28.23

Nectarivore

Tree

N

25.7

Nectarivore

Tree

N

26.07

Nectarivore

Tree

N

12.73

Nectarivore

Tree

N

14.48

Nectarivore

Tree

N

12.63

Nectarivore

Tree

N

8.41

Nectarivore

Tree

N

64.49

Nectarivore

Tree

N

18.23

Nectarivore

Tree

N

42.3

Frugivore

Tree

N

46.91

Grantiella picta

Frugivore

Tree

N

21.56

Lichenostomus chrysops

Frugivore

Tree

N

17.31

Meliphagidae

White-plumed Honeyeater

Lichenostomus penicillatus

Frugivore

Tree

N

19.21

Meliphagidae

Yellow-plumed Honeyeater

Lichenostomus ornatus

Generalist

Tree

N

17.53

Meliphagidae

Black-eared Miner

Manorina melanotis

Generalist

Tree

N

58.2

Megaluridae

285

Black-headed Honeyeater

Melithreptus affinis

Generalist

Tree

N

15.02

Noisy Friarbird

Philemon corniculatus

Generalist

Tree

N

103.6

Meliphagidae

Noisy Miner

Manorina melanocephala

Generalist

Ground and Tree

N

61.18

Meliphagidae

Rufous-throated Honeyeater

Conopophila rufogularis

Insectivore

Shrub and Tree

N

10.84

Meliphagidae

Grey-headed Honeyeater

Lichenostomus keartlandi

Insectivore

Shrub and Tree

N

14.89

Meliphagidae

White-throated Honeyeater

Melithreptus albogularis

Insectivore

Shrub and Tree

N

11.76

Eastern Spinebill

Acanthorhynch us tenuirostris

Nectarivore

Shrub and Tree

N

11.76

Meliphagidae

Little Wattlebird

Anthochaera chrysoptera

Nectarivore

Shrub and Tree

N

70.95

Meliphagidae

Black Honeyeater

Certhionyx niger

Nectarivore

Shrub and Tree

N

9.9

Meliphagidae

Pied Honeyeater

Certhionyx variegatus

Nectarivore

Shrub and Tree

N

26.57

Meliphagidae

Purple-gaped Honeyeater

Lichenostomus cratitius

Nectarivore

Shrub and Tree

N

20.35

Meliphagidae

Brown Honeyeater

Lichmera indistincta

Nectarivore

Shrub and Tree

N

11

Meliphagidae

Lewin's Honeyeater

Meliphaga lewinii

Nectarivore

Shrub and Tree

N

35.87

Meliphagidae

White-fronted Honeyeater

Phylidonyris albifrons

Nectarivore

Shrub and Tree

N

17.44

Meliphagidae

Bar-breasted Honeyeater

Ramsayornis fasciatus

Nectarivore

Shrub and Tree

N

12.84

Meliphagidae

Lewin's Honeyeater

Meliphaga lewinii

Nectarivore

Shrub and Tree

N

35.87

Meliphagidae

Crescent Honeyeater

Phylidonyris pyrrhoptera

Frugivore

Shrub and Tree

N

16.6

Meliphagidae

Grey-fronted Honeyeater

Lichenostomus plumulus

Generalist

Shrub and Tree

N

16.67

Menuridae

Albert's Lyrebird

Insectivore

Ground

N

927.8

Menuridae

Superb Lyrebird

Menura alberti Menura novaehollandia e

Insectivore

Ground

N

986.76

Meropidae

Rainbow Beeeater

Merops ornatus

Insectivore

Air

Y

28.03

Monarchidae

Black-faced Monarch

Monarcha melanopsis

Insectivore

Other

N

22.69

Monarchidae

Shining Flycatcher

Myiagra alecto

Insectivore

Air

N

19.93

Monarchidae

Restless Flycatcher

Myiagra inquieta

Insectivore

Ground and Air

N

20.87

Monarchidae

Magpie-lark

Grallina cyanoleuca

Insectivore

Ground

N

87.33

Monarchidae

Pied Monarch

Arses kaupi

Insectivore

Tree

N

13

Monarchidae

White-eared Monarch

Monarcha leucotis

Insectivore

Tree

N

12.14

Monarchidae

Spectacled Monarch

Monarcha trivirgatus

Insectivore

Tree

N

12.91

Motacillidae

Yellow Wagtail

Insectivore

Ground

N

21.68

Motacillidae

Australasian Pipit

Motacilla flava Anthus novaeseelandia e

Granivore + Insectivore

Ground

N

25.78

Muscicapidae

Russet-tailed Thrush

Zoothera heinei

Insectivore

Ground

N

82.82

Bassian Thrush

Zoothera lunulata

Insectivore

Ground

N

115.34

Mistletoebird

Dicaeum hirundinaceum

Frugivore

Tree

N

9.14

Varied Sittella

Daphoenositta chrysoptera

Insectivore

Tree

N

11.76

Oreoicidae

Crested Bellbird

Oreoica gutturalis

Insectivore

Ground

N

65.29

Oriolidae

Olive-backed Oriole

Oriolus sagittatus

Frugivore

Tree

N

95.53

Orthonychida e

Australian Logrunner

Orthonyx temminckii

Insectivore

Ground

N

59.33

Otididae

Australian Bustard

Ardeotis australis

Generalist

Ground

N

4814.7 4

Pachycephalid ae

Red-lored Whistler

Pachycephala rufogularis

Insectivore

Other

N

36.66

Meliphagidae Meliphagidae

Meliphagidae

Muscicapidae Nectariniidae Neosittidae

286

Pachycephalid ae Pachycephalid ae Pachycephalid ae

Little Shrikethrush

Colluricincla megarhyncha

Insectivore

Shrub

N

37.24

Olive Whistler

Pachycephala olivacea

Insectivore

Shrub

N

40.87

Golden Whistler

Pachycephala pectoralis

Insectivore

Tree

N

26.23

Pachycephalid ae

Rufous Whistler

Pachycephala rufiventris

Insectivore

Tree

N

24.88

Pachycephalid ae

Grey Shrikethrush

Colluricincla harmonica

Carnivore + Insectivore

Tree

N

66.3

Pachycephalid ae

Gilbert's Whistler

Pachycephala inornata

Insectivore

Shrub and Tree

N

31.01

Pardalotidae

Southern Whiteface

Aphelocephala leucopsis

Insectivore

Ground

N

13.3

Pardalotidae

Rufous Fieldwren

Calamanthus campestris

Insectivore

Ground

N

14.85

Pardalotidae

Shy Heathwren

Hylacola cauta

Insectivore

Ground

N

14.36

Pardalotidae

Yellow-throated Scrubwren

Sericornis citreogularis

Insectivore

Ground

N

16.93

Pardalotidae

White-browed Scrubwren

Sericornis frontalis

Insectivore

Ground

N

14.15

Pardalotidae

Banded Whiteface

Aphelocephala nigricincta

Granivore + Insectivore

Ground

N

10.45

Calamanthus fuliginosus

Insectivore

Ground and Shrub

N

19.9

Pardalotidae

Striated Fieldwren Chestnutrumped Heathwren

Hylacola pyrrhopygia

Insectivore

Ground and Shrub

N

16.78

Pardalotidae

Tasmanian Scrubwren

Sericornis humilis

Insectivore

Ground and Shrub

N

17.89

Pardalotidae

Western Gerygone

Gerygone fusca

Insectivore

Tree

N

6.64

Pardalotidae

Mangrove Gerygone

Gerygone levigaster

Insectivore

Tree

N

6.24

Pardalotidae

Brown Gerygone

Gerygone mouki

Insectivore

Tree

N

5.23

Pardalotidae

White-throated Gerygone

Gerygone olivacea

Insectivore

Tree

N

7.27

Pardalotidae

Spotted Pardalote

Pardalotus punctatus

Insectivore

Tree

Y

8.39

Pardalotidae

Forty-spotted Pardalote

Pardalotus quadragintus

Insectivore

Tree

Y

10.73

Pardalotidae

Red-browed Pardalote

Pardalotus rubricatus

Insectivore

Tree

Y

10.63

Pardalotidae

Large-billed Scrubwren

Sericornis magnirostris

Insectivore

Tree

N

9.75

Pardalotidae

Striated Pardalote

Pardalotus striatus

Nectarivore

Tree

Y

12.11

Passeridae

Eurasian Tree Sparrow

Passer montanus

Granivore + Insectivore

Ground

Y

21.05

Passeridae

House Sparrow

Passer domesticus

Generalist

Tree

N

27.3

Petroicidae

Red-capped Robin

Petroica goodenovii

Insectivore

Other

N

8.77

Petroicidae

Jacky Winter

Microeca leucophaea

Insectivore

Ground and Air

N

15.68

Petroicidae

Southern Scrubrobin

Drymodes brunneopygia

Insectivore

Ground

N

34.14

Hooded Robin

Melanodryas cucullata

Insectivore

Ground

N

23.97

Petroicidae

Flame Robin

Petroica phoenicea

Insectivore

Ground

N

13.25

Petroicidae

Western Yellow Robin

Eopsaltria griseogularis

Insectivore

Shrub

N

18.89

Petroicidae

Eastern Yellow Robin

Eopsaltria australis

Insectivore

Ground and Shrub

N

18.69

Dusky Robin

Melanodryas vittata

Insectivore

Ground and Shrub

Y

26.87

Petroicidae

Pink Robin

Petroica rodinogaster

Insectivore

Ground and Shrub

N

9.97

Petroicidae

Lemon-bellied Flycatcher

Microeca flavigaster

Insectivore

Tree

N

12.36

Petroicidae

Pale-yellow Robin

Tregellasia capito

Insectivore

Tree

N

14.1

Scarlet Robin

Petroica multicolor

Insectivore

Shrub and Tree

N

12.93

N

8.2

N

38.55

Pardalotidae

Petroicidae

Petroicidae

Petroicidae Petroicidae Phasianidae

Rose Robin

Petroica rosea

Insectivore

Shrub and Tree

King Quail

Coturnix chinensis

Granivore

Ground

287

Stubble Quail

Coturnix pectoralis

Granivore + Insectivore

Ground

N

100.51

Phasianidae

Brown Quail

Coturnix ypsilophora

Generalist

Ground

N

94.57

Podargidae

Tawny Frogmouth

Podargus strigoides

Insectivore

Ground

N

330.35

Podargidae

Marbled Frogmouth

Podargus ocellatus

Insectivore

Tree

N

220.85

Pomatostomus halli

Insectivore

Ground

N

40.69

Pomatostomus ruficeps

Insectivore

Ground

N

57.52

Phasianidae

Pomatostomi dae Pomatostomi dae

Hall's Babbler Chestnutcrowned Babbler

Pomatostomi dae

Grey-crowned Babbler

Pomatostomus temporalis

Insectivore

Ground

N

75.55

Pomatostomi dae

White-browed Babbler

Pomatostomus superciliosus

Insectivore

Ground and Tree

N

39.63

Psittaculidae

Superb Parrot

Polytelis swainsonii

Granivore

Other

Y

153.48

Psittaculidae

Australian KingParrot

Alisterus scapularis

Generalist

Other

Y

233.09

Psittaculidae

Australian Ringneck

Barnardius zonarius

Granivore

Ground

Y

143.82

Budgerigar

Melopsittacus undulatus

Granivore

Ground

Y

28.75

Elegant Parrot

Neophema elegans

Granivore

Ground

Y

44.12

Turquoise Parrot

Neophema pulchella

Granivore

Ground

Y

41.14

Bourke's Parrot

Neopsephotus bourkii

Granivore

Ground

Y

42.34

Blue Bonnet

Northiella haematogaster

Granivore

Ground

Y

88.99

Eastern Rosella

Platycercus eximius

Granivore

Ground

Y

104.75

Princess Parrot

Polytelis alexandrae

Granivore

Ground

Y

104.17

Regent Parrot

Polytelis anthopeplus

Granivore

Ground

Y

177.89

Psittaculidae

Hooded Parrot

Psephotus dissimilis

Granivore

Ground

Y

45.78

Psittaculidae

Red-rumped Parrot

Psephotus haematonotus

Granivore

Ground

Y

62.79

Psittaculidae

Mulga Parrot

Psephotus varius

Granivore

Ground

N

61.48

Psittaculidae

Blue-winged Parrot

Neophema chrysostoma

Granivore

Ground and Shrub

Y

46.08

Musk Lorikeet

Glossopsitta concinna

Nectarivore

Tree

Y

76.07

Psittaculidae

Swift Parrot

Lathamus discolor

Nectarivore

Tree

Y

65

Psittaculidae

Rainbow Lorikeet

Trichoglossus haematodus

Nectarivore

Tree

Y

131.35

Little Lorikeet

Glossopsitta pusilla

Frugivore

Tree

Y

39.41

Green Rosella

Platycercus caledonicus

Granivore

Tree

Y

136.21

Platycercus elegans

Granivore

Tree

Y

133.24

Psephotus chrysopterygius

Granivore

Ground and Tree

Y

41.27

Granivore

Ground and Tree

Y

116.28

Nectarivore

Shrub and Tree

Y

44.74

Nectarivore

Shrub and Tree

Y

87.12

Psittaculidae Psittaculidae Psittaculidae Psittaculidae Psittaculidae Psittaculidae Psittaculidae Psittaculidae

Psittaculidae

Psittaculidae Psittaculidae Psittaculidae

Psittaculidae

Crimson Rosella Goldenshouldered Parrot

Psittaculidae

Red-capped Parrot

Psittaculidae

Purple-crowned Lorikeet

Purpureicephal us spurius Glossopsitta porphyrocephal a

Psittaculidae

Scaly-breasted Lorikeet

Trichoglossus chlorolepidotus

Psittaculidae

Pale-headed Rosella

Platycercus adscitus

Frugivore

Shrub and Tree

Y

107.47

Psittaculidae

Red-winged Parrot

Aprosmictus erythropterus

Granivore

Shrub and Tree

Y

140.23

Ptilonorhynchi dae

Green Catbird

Ailuroedus crassirostris

Frugivore

Tree

N

47.31

Ptilonorhynchi dae

Satin Bowerbird

Ptilonorhynchu s violaceus

Frugivore

Tree

N

218.75

Ptilonorhynchi dae

Regent Bowerbird

Sericulus chrysocephalus

Frugivore

Tree

N

101.5

Ptilonorhynchi dae

Spotted Bowerbird

Chlamydera maculata

Frugivore

Shrub and Tree

N

141.36

288

Ptilonorhynchi dae

Great Bowerbird

Chlamydera nuchalis

Frugivore

Shrub and Tree

Pycnonotidae

Red-whiskered Bulbul

N

197.52

Pycnonotus jocosus

Frugivore

Shrub and Tree

N

31.7

Rallidae

Tasmanian Native-hen

Gallinula mortierii

Granivore

Ground

N

1185.6 2

Rallidae

Black-tailed Native-hen

Generalist

Ground

N

392.31

Ratite

Emu

Gallinula ventralis Dromaius novaehollandia e

Generalist

Ground

N

34200

Willie Wagtail

Rhipidura leucophrys

Insectivore

Ground and Air

N

19.93

Grey Fantail

Rhipidura fuliginosa

Insectivore

Tree

N

8.31

Rhipiduridae

Satin Flycatcher

Myiagra cyanoleuca

Insectivore

Tree

N

17.49

Rhipiduridae

Leaden Flycatcher

Myiagra rubecula

Insectivore

Shrub and Tree

N

12.59

Insectivore

Shrub and Tree

N

11.21

Rhipiduridae Rhipiduridae

Rhipiduridae

Rufous Fantail

Strigidae

Masked Owl

Rhipidura rufifrons Tyto novaehollandia e

Carnivore

Air

Y

608.64

Sooty Owl

Tyto tenebricosa

Carnivore

Air

Y

802.75

Barking Owl

Ninox connivens

Carnivore + Insectivore

Air

Y

563.68

Strigidae Strigidae Strigidae

Rufous Owl

Ninox rufa

Carnivore + Insectivore

Air

Y

952.88

Strigidae

Eastern Grass Owl

Tyto longimembris

Carnivore + Insectivore

Air

N

332

Strigidae

Southern Boobook

Ninox boobook

Carnivore + Insectivore

Tree

Y

248.37

Strigidae

Eastern Barn Owl

Tyto alba delicatula

Carnivore

Ground

Y

355.44

Strigidae

Powerful Owl

Ninox strenua

Carnivore

Tree

Y

1367.5 5

Sturnidae

Common Myna

Acridotheres tristis

Generalist

Ground

Y

131.5

Sturnidae

Common Starling

Sturnus vulgaris

Generalist

Ground

N

84.86

Sylviidae

Australian ReedWarbler

Acrocephalus australis

Insectivore

Ground

N

17.96

Threskiornithi dae

Glossy Ibis

Plegadis falcinellus

Insectivore

Ground

N

533.33

Threskiornithi dae

Australian White Ibis

Threskiornis molucca

Carnivore + Insectivore

Ground

N

1792.6 6

Threskiornithi dae

Straw-necked Ibis

Threskiornis spinicollis

Carnivore + Insectivore

Ground

N

1335

Turdidae

Common Blackbird

Turdus merula

Insectivore

Ground

N

110.74

Turdidae

Song Thrush

Turdus philomelos

Insectivore

Ground

N

68.69

Turnicidae

Red-backed Button-quail

Turnix maculosus

Granivore + Insectivore

Ground

N

38.52

Turnicidae

Red-chested Button-quail

Turnix pyrrhothorax

Granivore + Insectivore

Ground

N

46.32

Turnicidae

Little Buttonquail

Turnix velox

Granivore + Insectivore

Ground

N

43.91

Turnicidae

Black-breasted Button-quail

Turnix melanogaster

Granivore + Insectivore

Ground

N

82.9

Turnicidae

Painted Buttonquail

Turnix varius

Generalist

Ground

N

91.85

Pale White-eye

Zosterops citrinellus

Frugivore

Ground

N

9

Silvereye

Zosterops lateralis

Insectivore

Tree

N

11.65

Zosteropidae Zosteropidae

289

Appendix 6.2. Online bird community condition survey Below is a plain text version of the survey undertaken by woodland bird experts to convey their understanding of the condition of a range of woodland bird communities. Woodland Bird Community Condition Survey (Page 1) Thank you for taking part in this survey. If you continue to the next page you are signifying your consent for the information you give to be used to quantify the condition of the woodland bird community. This research will be used to assist in an application for protection of the 'woodland bird ecological community' under the EPBC Act as well as forming a basis for future research into woodland birds.

290

Regional Expertise (Page 2) 1. Please nominate the region that you are most familiar with (in terms of woodland birds) and answer the rest of the survey with that region in mind • Temperate south eastern Australia (ranging from Victoria to northern New South Wales) • Sub-tropical Queensland • South Australia • Western Australia • Tasmania

291

Assessing community condition (Page 3) Our aim is to list a woodland bird community as a Threatened Ecological Community under the EPBC Act. In order to do this, we need a robust measure of the condition of the woodland bird community. In this survey you will be asked to evaluate the condition of various woodland bird communities (as sampled using 2 ha 20-minute occurrence only bird surveys). 2. If you were provided with species lists, what criteria would you use to assess the condition of the woodland bird community?

292

Calibrating differences between experts (Page 4) This section is designed to identify differences expert assessments of community condition for five sites with different bird assemblages. All of the species lists describing woodland bird communities are from 2ha, 20-minute bird occurrence surveys conducted in woodland habitats. If the woodland bird community was listing under the EPBC Act, what would the condition of the following sites be? 3. Please assign the following woodland bird communities a score for condition on a scale of 0 to 100 where 0 is maximally degraded and 100 is perfectly intact Species List Australian Raven Crested Pigeon Grey Butcherbird Species List Grey Butcherbird Noisy Miner Pier Currawong Striated Pardalote Species List Brown-headed Honeyeater Brown Thornbill Golden Whistler Grey Shrike-thrush Little Friarbird Little Lorikeet Noisy Friarbird Noisy Miner Rufous Whistler Yellow-faced Honeyeater Species List Brown Thornbill Common Myna Eastern Yellow Robin Fan-tailed Cuckoo Golden Whistler Grey Fantail Grey Shrike-thrush Little Raven New Holland Honeyeater Red-browed Finch Red Wattlebird Rufous Whistler Spotted Dove Superb Fairy-wren White-browed Scrubwren White-naped Honyeater Yellow-faced Honeyeater

Species List Brown-headed Honeyeater Brown Thornbill Crested Shrike-tit Crimson Rosella Eastern Spinebill Eastern Yellow Robin Fan-tailed Cuckoo Golden Whistler Grey Currawong Grey Fantail Grey Shrike-thrush Laughing Kookaburra Red Wattlebird Rufous Whistler Sacred Kingfisher Satin Flycatcher Silvereye Spotted Pardalote Striated Pardalote Striated Thornbill Superb Fairy-wren White-eared Honeyeater White-naped Honeyeater White-throated Honeyeater Yellow-faced Honeyeater

293

Assigning relative weights to communities in different conditions (Page 5) In this section you will assign relative weights to different woodland bird communities. This will allow us to create a continuous scale of woodland bird community condition. All of the species lists describing woodland bird communities are from 2ha, 20-minute bird occurrence surveys conducted in woodland habitats. In this exercise you will be asked to compare bird communities in different conditions. You will assign a value to each community based on how much you would prefer to have that community in that condition to having a low condition, baseline community. 4. Consider a situation where you have a community with 3 species: Australian Raven, Crested Pigeon and Grey Butcherbird. Imagine you could convert that community into each of the communities below. Please assign the community that you would most prefer converting to (that is in the highest condition) a value of 100. Assign the other communities a value based on how much you would like to convert to each of them relative to the one you assigned a value of 100. Please check only one box per column For example, I would assign the community that I deem to be in the highest condition 100, if I think that the condition of the next best community is half as good, I would assign it a value of 50. Species List

Species List

Species List

Species List

Brown Thornbill

Brown Thornbill

Flame Robin

Black-Faced Cuckoo-Shrike

Golden Whistler

Golden Whistler

Willie Wagtail

Black-Faced Woodswallow

Grey Fantail

Brown Treecreeper

Scarlet Robin

Cockatiel

Spotted Pardalote

Jacky Winter

Striated Thornbill

Singing Honeyeater

Superb Fairy-Wren

Spiny-Cheeked Honeyeater

White-Eared Honeyeater

Striated Pardalote

White-Naped Honeyeater

Variegated Fairy-Wren

White-Throated Treecreeper

Weebill Willie Wagtail Yellow-Rumped Thornbill

294

5. Consider a situation where you have a community with 2 species: Noisy Miner and Spotted Quailthrush. Imagine you could convert that community into each of the communities below. Please assign the community that you would most prefer converting to (that is in the highest condition) a value of 100. Assign the other communities a value based on how much you would like to convert to each of them relative to the one you assigned a value of 100. Please check only one box per column For example, I would assign the community that I deem to be in the highest condition 100, if I think that the condition of the next best community is half as good, I would assign it a value of 50. Species List

Species List

Species List

Species List

Brown Thornbill

Brown-Headed Honeyeater

Apostlebird

Apostlebird

Common Myna

Brown Thornbill

Australian Magpie

Australian Magpie

Eastern Yellow Robin

Golden Whistler

Bar-Shouldered Dove

Black-Faced Woodswallow

Fan-Tailed Cuckoo

Grey Shrike-Thrush

Black-Faced Cuckoo-Shrike

Blue-Faced Honeyeater

Golden Whistler

Little Friarbird

Common Bronzewing

Brown Goshawk

Grey Fantail

Little Lorikeet

Crested Bellbird

Crested Pigeon

Grey Shrike-Thrush

Noisy Friarbird

Crested Pigeon

Grey-Crowned Babbler

Little Raven

Noisy Miner

Double-Barred Finch

Grey Butcherbird

New Holland Honeyeater

Rufous Whistler

Grey Butcherbird

Grey Fantail

Red-Browed Finch

Yellow-Faced Honeyeater

Grey Fantail

Laughing Kookaburra

Red Wattlebird

Grey Shrike-Thrush

Magpie-Lark

Rufous Whistler

Jacky Winter

Mistletoebird

Spotted Dove

Little Friarbird

Pale-Headed Rosella

Superb Fairy-Wren

Noisy Friarbird

Peaceful Dove

White-Browed Scrubwren

Pale-Headed Rosella

Pied Butcherbird

White-Naped Honeyeater

Pied Butcherbird

Rainbow Lorikeet

Yellow-Faced Honeyeater

Red-Winged Parrot

Rufous Songlark

Rufous Whistler

Rufous Whistler

Singing Honeyeater

Singing Honeyeater

Spiny-Cheeked Honeyeater

Southern Boobook

Spotted Bowerbird

Squatter Pigeon

Striated Pardalote

Striated Pardalote

Striped Honeyeater

Striped Honeyeater

Variegated Fairy-Wren

Sulphur-Crested Cockatoo

Weebill

Variegated Fairy-Wren

Western Gerygone

Weebill

White-Throated Gerygone

Willie Wagtail

Willie Wagtail

Yellow-Rumped Thornbill

Yellow-Rumped Thornbill

295

6. Consider a situation where you have a community with 4 species: Grey Butcherbird, Noisy Miner, Pied Currawong, and Striated Pardalote. Imagine you could convert that community into each of the communities below. Please assign the community that you would most prefer converting to (that is in the highest condition) a value of 100. Assign the other communities a value based on how much you would like to convert to each of them relative to the one you assigned a value of 100. Please check only one box per column For example, I would assign the community that I deem to be in the highest condition 100, if I think that the condition of the next best community is half as good, I would assign it a value of 50. Species List

Species List

Species List

Species List

Australian Magpie

Australian Magpie

Crested Pigeon

Australian Magpie

Common Blackbird

Black-Faced Cuckoo-Shrike

Grey Fantail

Black-Faced Cuckoo-Shrike

Common Bronzewing

Common Bronzewing

Magpie-Lark

Common Myna

Common Myna

Crimson Rosella

Common Starling

Common Starling

Dusky Woodswallow

Eurasian Skylark

Eastern Rosella

Galah

European Goldfinch

Galah

Grey Butcherbird

House Sparrow

Grey Shrike-Thrush

Grey Fantail

Little Raven

Little Pied Cormorant

Magpie-Lark

Long-Billed Corella

Little Raven

Noisy Miner

Magpie-Lark

Magpie-Lark

Rainbow Lorikeet

Noisy Miner

New Holland Honeyeater

Red Wattlebird

Red-Rumped Parrot

Noisy Miner

Striated Pardalote

Red Wattlebird

Red-Browed Finch

Sulphur-Crested Cockatoo

White-Plumed Honeyeater

Red Wattlebird

Superb Fairy-Wren

Willie Wagtail

Sacred Kingfisher

White-Naped Honeyeater

Spotted Pardalote

Yellow-Faced Honeyeater

Superb Fairy-Wren Welcome Swallow White-Eared Honeyeater Yellow-Faced Honeyeater

296

7. Consider a situation where you have a community with 3 species: Crested Pigeon, Grey Fantail, and Magpie-lark. Imagine you could convert that community into each of the communities below. Please assign the community that you would most prefer converting to (that is in the highest condition) a value of 100. Assign the other communities a value based on how much you would like to convert to each of them relative to the one you assigned a value of 100. Please check only one box per column For example, I would assign the community that I deem to be in the highest condition 100, if I think that the condition of the next best community is half as good, I would assign it a value of 50. Species List

Species List

Species List

Species List

Australian Magpie

Grey Butcherbird

Australian Owlet-Nightjar

Australian Magpie

Australian Wood Duck

Noisy Miner

Black-Faced Cuckoo-Shrike

Brown Thornbill

Common Starling

Pied Currawong

Cicadabird

Common Bronzewing

Eastern Rosella

Striated Pardalote

Grey-Crowned Babbler

Common Myna

Galah

Grey Butcherbird

Galah

Grey Teal

Little Friarbird

Grey Butcherbird

Little Raven

Noisy Friarbird

Laughing Kookaburra

Long-Billed Corella

Olive-Backed Oriole

Magpie-Lark

Pacific Black Duck

Pacific Baza

Musk Lorikeet

Red-Rumped Parrot

Pale-Headed Rosella

Noisy Miner

Striated Pardalote

Peaceful Dove

Rainbow Lorikeet

Welcome Swallow

Pied Butcherbird

Spotted Pardalote

White-Plumed Honeyeater

Red-Winged Parrot

Superb Fairy-Wren

Yellow-Rumped Thornbill

Rufous Whistler

Bell Miner

Tawny Frogmouth Weebill

297

8. Consider a situation where you have a community with 2 species: Flame Robin and Willie Wagtail. Imagine you could convert that community into each of the communities below. Please assign the community that you would most prefer converting to (that is in the highest condition) a value of 100. Assign the other communities a value based on how much you would like to convert to each of them relative to the one you assigned a value of 100. Please check only one box per column For example, I would assign the community that I deem to be in the highest condition 100, if I think that the condition of the next best community is half as good, I would assign it a value of 50. Species List

Species List

Species List

Species List

Australian Magpie

Australian Magpie

Australian Raven

Noisy Miner

Australian Shelduck

Common Blackbird

Crested Pigeon

Spotted Quail-Thrush

Australian White Ibis

Common Myna

Grey Butcherbird

Australian Wood Duck

Galah

Common Starling

Grey Butcherbird

Eastern Rosella

Little Raven

Great Egret

Magpie-Lark

Grey Teal

Musk Lorikeet

Laughing Kookaburra

Noisy Miner

Little Pied Cormorant

Rainbow Lorikeet

Magpie-Lark

Spotted Dove

Red-Rumped Parrot

Sulphur-Crested Cockatoo

Red Wattlebird

Welcome Swallow

Striated Pardalote Welcome Swallow Willie Wagtail Yellow-Billed Spoonbill Yellow-Rumped Thornbill

298

9. Consider a situation where you have a community with 2 species: Brown Thornbill and Golden Whistler. Imagine you could convert that community into each of the communities below. Please assign the community that you would most prefer converting to (that is in the highest condition) a value of 100. Assign the other communities a value based on how much you would like to convert to each of them relative to the one you assigned a value of 100. Please check only one box per column For example, I would assign the community that I deem to be in the highest condition 100, if I think that the condition of the next best community is half as good, I would assign it a value of 50. Species List

Species List

Species List

Species List

Noisy Miner

Eastern Rosella

Australian White Ibis

Brown-Headed Honeyeater

Spotted Quail-Thrush

Grey Fantail

Australian Wood Duck

Brown Thornbill

Grey Shrike-Thrush

Brown Goshawk

Crested Shrike-Tit

Magpie-Lark

Eastern Rosella

Crimson Rosella Eastern Spinebill Eastern Yellow Robin Fan-Tailed Cuckoo Golden Whistler Grey Currawong Grey Fantail Grey Shrike-Thrush Laughing Kookaburra Red Wattlebird Rufous Whistler Sacred Kingfisher Satin Flycatcher Silvereye Spotted Pardalote Striated Pardalote Striated Thornbill Superb Fairy-Wren White-Eared Honeyeater White-Naped Honeyeater White-Throated Treecreeper Yellow-Faced Honeyeater

299

Comments (Page 6) 10. Do you have any comments or concerns about woodland bird condition or this process?

300

Survey Complete (Page 7) Thank you very much for taking the time to complete this survey. Your contribution will be extremely useful. Please let Hannah ([email protected]) know if you would not like to be contacted with regards to any questions that arise from this survey. Also fee free to contact Hannah if you have any further comments or questions

301

Appendix 6.3. Regional lists for the Woodland Bird Threatened Ecological Community The table below includes a full list of the species considered to be part of the woodland bird community in sub-tropical Queensland, the temperate south east and South Australia. Table A.6.3.1: List of species in the woodland bird community by region based on expert opinion

Woodland bird community These species were considered part of the woodland bird community by 70% of experts. Species marked 'I' are associated with intact woodland bird communities, blank white cells represent species which are common constituents of the woodland bird community but not particularly associated with intact or degraded communities, species marked 'D' are part of the woodland bird community but are more common in degraded communities. Light grey cells show species which were not included in the woodland bird community in a particular region. Dark grey cells show species which are absent from regions Family

Common Name

Scientific Name

Petroicidae

Brown Quail

Coturnix ypsilophora

Turnicidae

Painted Button-quail

Turnix varius

Turnicidae

Little Button-quail

Turnix velox

Columbidae

Peaceful Dove

Geopelia striata

Columbidae

Common Bronzewing

Columbidae

QLD

SA

SE (VIC, ACT, NSW) I

I

I

I

Phaps chalcoptera

I

I

I

Crested Pigeon

Ocyphaps lophotes

D

D

D

Burhinidae

Bush Stone-curlew

Burhinus grallarius

I

I

I

Accipitridae

Brown Goshawk

Accipiter fasciatus

Accipitridae

Collared Sparrowhawk

Accipiter cirrocephalus

Accipitridae

Square-tailed Kite

Lophoictinia isura

I

I

I

Strigidae

Southern Boobook

Ninox boobook

Strigidae

Barking Owl

Ninox connivens

I

I

I

Psittaculidae

Musk Lorikeet

Psittaculidae

Purple-crowned Lorikeet

Glossopsitta concinna Glossopsitta porphyrocephala

I

I

Psittaculidae

Little Lorikeet

Glossopsitta pusilla

I

I

Cacatuoidea

Red-tailed Black-Cockatoo

Calyptorhynchus banksii

Cacatuoidea

Glossy Black-Cockatoo

Calyptorhynchus lathami

Cacatuoidea

Yellow-tailed Black-Cockatoo

Calyptorhynchus funereus

Cacatuoidea

Sulphur-crested Cockatoo

Cacatuoidea

I

I I

I

I

Cacatua galerita

D

D

D

Galah

Cacatua roseicapilla

D

D

D

Psittaculidae

Superb Parrot

Polytelis swainsonii

Psittaculidae

Red-winged Parrot

Aprosmictus erythropterus

Psittaculidae

Pale-headed Rosella

Platycercus adscitus

Psittaculidae

Eastern Rosella

Platycercus eximius

D

D

D

Psittaculidae

Australian Ringneck

Barnardius zonarius

I

302

QLD

SA

SE (VIC, ACT, NSW)

I

I

Family

Common Name

Scientific Name

Psittaculidae

Red-rumped Parrot

Psephotus haematonotus

Psittaculidae

Mulga Parrot

Psephotus varius

Psittaculidae

Blue Bonnet

Northiella haematogaster

Psittaculidae

Turquoise Parrot

Neophema pulchella

Psittaculidae

Elegant Parrot

Neophema elegans

Psittaculidae

Swift Parrot

Lathamus discolor

Psittaculidae

Budgerigar

Melopsittacus undulatus

Podargidae

Tawny Frogmouth

Podargus strigoides

Aegothelidae

Australian Owlet-nightjar

Aegotheles cristatus

Cerylidae

Laughing Kookaburra

Dacelo novaeguineae

Cerylidae

Blue-winged Kookaburra

Dacelo leachii

Halcyonidae

Sacred Kingfisher

Todiramphus sanctus

Meropidae

Rainbow Bee-eater

Merops ornatus

Caprimulgidae

White-throated Nightjar

Eurostopodus mystacalis

Caprimulgidae

Spotted Nightjar

Eurostopodus argus

Cuculidae

Pallid Cuckoo

Cuculus pallidus

Cuculidae

Fan-tailed Cuckoo

Cacomantis flabelliformis

I

I

I

Cuculidae

Black-eared Cuckoo

Chrysococcyx osculans

I

I

I

Cuculidae

Horsfield's Bronze-Cuckoo

Chrysococcyx basalis

I

I

I

Cuculidae

Shining Bronze-Cuckoo

Chrysococcyx lucidus

I

I

I

Hirundinidae

Tree Martin

Hirundo nigricans

Rhipiduridae

Grey Fantail

Rhipidura fuliginosa

I

I

I

Rhipiduridae

Willie Wagtail

Rhipidura leucophrys

Rhipiduridae

Leaden Flycatcher

Myiagra rubecula

I

I

Rhipiduridae

Satin Flycatcher

Myiagra cyanoleuca

I

I

Petroicidae

Jacky Winter

Microeca leucophaea

Petroicidae

Scarlet Robin

Petroica multicolor

Petroicidae

Red-capped Robin

Petroicidae

I

I I

I

I

I

I

I

I

I

I

I

I

I I

I

I

I

I

I

Petroica goodenovii

I

I

I

Hooded Robin

Melanodryas cucullata

I

I

I

Petroicidae

Eastern Yellow Robin

Eopsaltria australis

I

I

I

Pachycephalidae

Golden Whistler

Pachycephala pectoralis

I

I

I

Pachycephalidae

Rufous Whistler

Pachycephala rufiventris

I

I

I

Pachycephalidae

Gilbert's Whistler

Pachycephala inornata

I

I

I

Pachycephalidae

Grey Shrike-thrush

Colluricincla harmonica

I

I

I

Monarchidae

Magpie-lark

Grallina cyanoleuca

D

D

D

Monarchidae

Restless Flycatcher

Myiagra inquieta

I

I

I

Falcunculidae

Crested Shrike-tit

Falcunculus frontatus

I

I

I

Oreoicidae

Crested Bellbird

Oreoica gutturalis

I

I

I

Campephagidae

Black-faced Cuckoo-shrike

Coracina novaehollandiae

Campephagidae

White-bellied Cuckoo-shrike

Coracina papuensis

I

I

I

Campephagidae

White-winged Triller

Lalage sueurii

Cinclosomatidae

Spotted Quail-thrush

Cinclosoma punctatum

Petroicidae

Southern Scrub-robin

Drymodes brunneopygia

I I

I I

I

303

SA

SE (VIC, ACT, NSW)

Family

Common Name

Scientific Name

QLD

Pomatostomidae

Grey-crowned Babbler

Pomatostomus temporalis

I

Pomatostomidae

White-browed Babbler

Pomatostomus superciliosus

I

Pardalotidae

White-throated Gerygone

Gerygone olivacea

I

Pardalotidae

Western Gerygone

Gerygone fusca

I

Acanthizidae

Weebill

Smicrornis brevirostris

I

Pardalotidae

Southern Whiteface

Aphelocephala leucopsis

I

I

I

Acanthizidae

Striated Thornbill

Acanthiza lineata

I

I

I

Acanthizidae

Yellow Thornbill

Acanthiza nana

I

I

I

Acanthizidae

Brown Thornbill

Acanthiza pusilla

I

I

I

Acanthizidae

Inland Thornbill

Acanthiza apicalis

I

I

I

Acanthizidae

Chestnut-rumped Thornbill

Acanthiza uropygialis

I

I

I

Acanthizidae

Buff-rumped Thornbill

Acanthiza reguloides

I

I

I

Acanthizidae

Yellow-rumped Thornbill

Acanthiza chrysorrhoa

D

D

Pardalotidae

White-browed Scrubwren

Sericornis frontalis

I

I

I

Pardalotidae

Chestnut-rumped Heathwren

Hylacola pyrrhopygia

I

I

I

Acanthizidae

Speckled Warbler

Chthonicola sagittata

I

Megaluridae

Rufous Songlark

Cincloramphus mathewsi

Maluridae

Superb Fairy-wren

Malurus cyaneus

Maluridae

Splendid Fairy-wren

Malurus splendens

Maluridae

Variegated Fairy-wren

Malurus lamberti

Artamidae

Masked Woodswallow

Artamus personatus

Artamidae

White-browed Woodswallow

Artamus superciliosus

Artamidae

Black-faced Woodswallow

Artamus cinereus

I

Artamidae

Dusky Woodswallow

Artamus cyanopterus

I

I

I

Neosittidae

Varied Sittella

Daphoenositta chrysoptera

I

I

I

Climacteridae

Brown Treecreeper

Climacteris picumnus

I

I

I

Climacteridae

White-throated Treecreeper

Corombates leucophaeus

I

I

I

Climacteridae

White-browed Treecreeper

Climacteris affinis

I

I

I

Nectariniidae

Mistletoebird

Dicaeum hirundinaceum

I

I

I

Pardalotidae

Spotted Pardalote

Pardalotus punctatus

I

I

I

Pardalotidae

Red-browed Pardalote

Pardalotus rubricatus

Pardalotidae

Striated Pardalote

Pardalotus striatus

Zosteropidae

Silvereye

Zosterops lateralis

Meliphagidae

White-naped Honeyeater

Melithreptus lunatus

I

I

I

Meliphagidae

White-throated Honeyeater

Melithreptus albogularis

Meliphagidae

Black-chinned Honeyeater

Melithreptus gularis

I

I

I

Meliphagidae

Brown-headed Honeyeater

Melithreptus brevirostris

I

I

I

Meliphagidae

Striped Honeyeater

Plectorhyncha lanceolata

I

I

I

Meliphagidae

Scarlet Honeyeater

Myzomela sanguinolenta

I

Meliphagidae

Eastern Spinebill

Acanthorhynchus tenuirostris

I

I

I

Meliphagidae

Tawny-crowned Honeyeater

Phylidonyris melanops

I

I

Meliphagidae

Brown Honeyeater

Lichmera indistincta

Meliphagidae

Painted Honeyeater

Grantiella picta

I I

I I

I

I I

I

I

I

I

304

Common Name

Scientific Name

QLD

Meliphagidae

Regent Honeyeater

Xanthomyza phrygia

I

Meliphagidae

Fuscous Honeyeater

Lichenostomus fuscus

I

I

I

Meliphagidae

Yellow-faced Honeyeater

Lichenostomus chrysops

I

I

I

Meliphagidae

White-eared Honeyeater

Lichenostomus leucotis

I

I

I

Meliphagidae

Yellow-tufted Honeyeater

Lichenostomus melanops

I

Meliphagidae

Yellow-plumed Honeyeater

Lichenostomus ornatus

Meliphagidae

White-plumed Honeyeater

Lichenostomus penicillatus

Meliphagidae

Crescent Honeyeater

Phylidonyris pyrrhoptera

Meliphagidae

Noisy Miner

Manorina melanocephala

Meliphagidae

Red Wattlebird

Anthochaera carunculata

Meliphagidae

Spiny-cheeked Honeyeater

Acanthagenys rufogularis

Meliphagidae

Noisy Friarbird

Philemon corniculatus

I

Meliphagidae

Little Friarbird

Philemon citreogularis

I

Estrildidae

Diamond Firetail

Stagonopleura guttata

I

Estrildidae

Double-barred Finch

Taeniopygia bichenovii

I

Estrildidae

Red-browed Finch

Neochmia temporalis

I

Estrildidae

Black-throated Finch

Poephila cincta

I

Oriolidae

Olive-backed Oriole

Oriolus sagittatus

Corcoracidae

Apostlebird Chestnut-breasted Quailthrush

Struthidea cinerea Cinclosoma castaneothorax

I

Spotted Bowerbird

Chlamydera maculata

I

Corvidae

Australian Raven

Corvus coronoides

D

Corvidae

Grey Currawong

Strepera versicolor

Corvidae

Pied Butcherbird

Cracticus nigrogularis

D

Corvidae

Grey Butcherbird

Cracticus torquatus

D

D

D

Corvidae

Australian Magpie

Gymnorhina tibicen

D

D

D

Cinclosomatidae Ptilonorhynchidae

SA

SE (VIC, ACT, NSW)

Family

I

I I

D

I

I

D

D

I

I

I

I I I

I D

D D

305

Table A.6.3.2: List of species that are not in the woodland bird community but are associated with degraded woodland bird communities based on expert opinion

Non-woodland species that are prevalent in degraded woodland bird communities These species are not considered woodland birds but are common to degraded woodlands- they might provide good indicators of the condition of the woodland bird community. Light grey cells show species which were not included in the woodland bird community in a particular region. Dark grey cells show species which are absent from regions Family

Common Name

Scientific Name

QLD

SA

SE (VIC, ACT, NSW)

Columbidae

Crested Pigeon

Ocyphaps lophotes

D

D

D

Falconidae

Brown Falcon

Falco berigora

D

D

D

Cacatuoidea

Little Corella

Cacatua sanguinea

D

D

D

Cacatuoidea

Long-billed Corella

Cacatua tenuirostris

D

D

Hirundinidae

Welcome Swallow

Hirundo neoxena

D

D

D

Megaluridae

Brown Songlark

Cincloramphus cruralis

D

D

D

Motacillidae

Australasian Pipit

Anthus novaeseelandiae

D

D

D

Corvidae

Torresian Crow

Corvus orru

D

Corvidae

Pied Currawong

Strepera graculina

D

D

D

Corvidae

Little Raven

Corvus mellori

D

D

D

Columbidae

Rock Dove

Columba livia

D

D

D

Columbidae

Spotted Dove

Streptopelia chinensis

D

D

D

Passeridae

House Sparrow

Passer domesticus

D

D

D

Sturnidae

Common Myna

Acridotheres tristis

D

D

D

Sturnidae

Common Starling

Sturnus vulgaris

D

D

D

306

Appendix 6.4. Sensitivity analysis of community condition models

Species richness

Table A.6.4.1 Sensitivity analysis using the Australia-wide model showing changes in predicted community condition according to different values of species richness and proportion small species

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

0.0 13 14 15 16 18 19 21 22 24 26 28 30 32 34 36 38 41 43 46 48 50 53 55 58 60 62 65 67 69 71 73 75 77 78 80 82 83 84 86 87

0.1 14 16 17 18 20 22 23 25 27 29 31 33 35 38 40 42 45 47 49 52 54 57 59 61 64 66 68 70 72 74 76 78 79 81 82 84 85 86 87 88

0.2 16 18 19 21 23 24 26 28 30 32 34 37 39 41 44 46 48 51 53 56 58 60 63 65 67 69 71 73 75 77 79 80 82 83 84 86 87 88 89 90

0.3 19 20 22 24 25 27 29 31 33 36 38 40 43 45 47 50 52 55 57 59 62 64 66 68 70 72 74 76 78 80 81 83 84 85 86 87 89 89 90 91

Proportion of small species 0.4 0.5 0.6 21 24 27 23 26 29 25 27 31 26 29 33 28 32 35 30 34 37 32 36 40 35 38 42 37 41 44 39 43 47 42 45 49 44 48 52 46 50 54 49 53 56 51 55 59 54 57 61 56 60 63 58 62 66 61 64 68 63 67 70 65 69 72 68 71 74 70 73 76 72 75 77 74 76 79 75 78 81 77 80 82 79 81 84 80 83 85 82 84 86 83 85 87 85 87 88 86 88 89 87 89 90 88 90 91 89 90 92 90 91 92 91 92 93 92 93 94 92 93 94

0.7 30 32 34 36 39 41 43 46 48 51 53 55 58 60 62 65 67 69 71 73 75 77 78 80 82 83 84 86 87 88 89 90 91 91 92 93 93 94 95 95

0.8 33 35 38 40 42 45 47 50 52 54 57 59 61 64 66 68 70 72 74 76 78 79 81 82 84 85 86 87 88 89 90 91 92 93 93 94 94 95 95 96

0.9 37 39 41 44 46 48 51 53 56 58 61 63 65 67 69 71 73 75 77 79 80 82 83 85 86 87 88 89 90 91 92 92 93 94 94 95 95 96 96 96

1.0 40 43 45 47 50 52 55 57 60 62 64 66 68 71 73 74 76 78 80 81 83 84 85 86 88 89 89 90 91 92 93 93 94 94 95 95 96 96 96 97

307

Table A.6.4.2 Sensitivity analysis using the South Australian model excluding small birds showing changes in predicted community condition according to different values of species richness and proportion small species

Species richness

Proportion small species 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1

17

17

17

17

17

17

17

17

17

17

17

2

19

19

19

19

19

19

19

19

19

19

19

3

21

21

21

21

21

21

21

21

21

21

21

4

24

24

24

24

24

24

24

24

24

24

24

5

26

26

26

26

26

26

26

26

26

26

26

6

29

29

29

29

29

29

29

29

29

29

29

7

31

31

31

31

31

31

31

31

31

31

31

8

34

34

34

34

34

34

34

34

34

34

34

9

37

37

37

37

37

37

37

37

37

37

37

10

40

40

40

40

40

40

40

40

40

40

40

11

43

43

43

43

43

43

43

43

43

43

43

12

46

46

46

46

46

46

46

46

46

46

46

13

49

49

49

49

49

49

49

49

49

49

49

14

53

53

53

53

53

53

53

53

53

53

53

15

56

56

56

56

56

56

56

56

56

56

56

16

59

59

59

59

59

59

59

59

59

59

59

17

62

62

62

62

62

62

62

62

62

62

62

18

65

65

65

65

65

65

65

65

65

65

65

19

68

68

68

68

68

68

68

68

68

68

68

20

70

70

70

70

70

70

70

70

70

70

70

21

73

73

73

73

73

73

73

73

73

73

73

22

75

75

75

75

75

75

75

75

75

75

75

23

78

78

78

78

78

78

78

78

78

78

78

24

80

80

80

80

80

80

80

80

80

80

80

25

82

82

82

82

82

82

82

82

82

82

82

26

84

84

84

84

84

84

84

84

84

84

84

27

85

85

85

85

85

85

85

85

85

85

85

28

87

87

87

87

87

87

87

87

87

87

87

29

88

88

88

88

88

88

88

88

88

88

88

30

89

89

89

89

89

89

89

89

89

89

89

31

91

91

91

91

91

91

91

91

91

91

91

32

92

92

92

92

92

92

92

92

92

92

92

33

93

93

93

93

93

93

93

93

93

93

93

34

93

93

93

93

93

93

93

93

93

93

93

35

94

94

94

94

94

94

94

94

94

94

94

36

95

95

95

95

95

95

95

95

95

95

95

37

95

95

95

95

95

95

95

95

95

95

95

38

96

96

96

96

96

96

96

96

96

96

96

39

96

96

96

96

96

96

96

96

96

96

96

40

97

97

97

97

97

97

97

97

97

97

97

308

Table A.6.4.3 Sensitivity analysis using the South Australian model including small birds showing changes in predicted community condition according to different values of species richness and proportion small species

Species richness

Proportion small species 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1

10

11

13

15

15

17

19

22

25

28

31

2

11

13

14

16

16

19

21

24

27

30

34

3

12

14

16

18

18

21

23

26

30

33

36

4

14

16

18

20

20

23

26

29

32

36

39

5

15

17

20

22

22

25

28

31

35

39

42

6

17

19

22

24

24

27

31

34

38

41

45

7

19

21

24

27

27

30

33

37

41

44

48

8

21

23

26

29

29

33

36

40

44

48

51

9

23

25

29

32

32

35

39

43

47

51

55

10

25

28

31

35

35

38

42

46

50

54

58

11

27

30

34

37

37

41

45

49

53

57

61

12

30

33

37

40

40

44

48

52

56

60

63

13

32

36

40

43

43

47

51

55

59

63

66

14

35

39

43

46

46

50

54

58

62

66

69

15

38

42

46

49

49

53

57

61

65

68

72

16

41

45

49

53

53

56

60

64

68

71

74

17

44

48

52

56

56

59

63

67

70

73

76

18

47

51

55

59

59

62

66

69

73

76

78

19

50

54

58

62

62

65

69

72

75

78

80

20

53

57

61

64

64

68

71

74

77

80

82

21

56

60

64

67

67

71

74

77

79

82

84

22

59

63

66

70

70

73

76

79

81

84

86

23

62

66

69

72

72

75

78

81

83

85

87

24

65

68

72

75

75

78

80

83

85

87

88

25

68

71

74

77

77

80

82

84

86

88

90

26

70

74

76

79

79

82

84

86

88

89

91

27

73

76

79

81

81

83

85

87

89

90

92

28

75

78

81

83

83

85

87

89

90

91

93

29

77

80

82

85

85

87

88

90

91

92

93

30

80

82

84

86

86

88

89

91

92

93

94

31

81

84

86

88

88

89

91

92

93

94

95

32

83

85

87

89

89

90

92

93

94

95

95

33

85

87

89

90

90

91

93

94

94

95

96

34

86

88

90

91

91

92

93

94

95

96

96

35

88

89

91

92

92

93

94

95

96

96

97

36

89

90

92

93

93

94

95

95

96

97

97

37

90

92

93

94

94

95

95

96

97

97

97

38

91

92

93

94

94

95

96

96

97

97

98

39

92

93

94

95

95

96

96

97

97

98

98

40

93

94

95

96

96

96

97

97

98

98

98

309

Table A.6.4.4 Sensitivity analysis using the temperate south eastern Australia model showing changes in predicted community condition according to different values of species richness and proportion small species

Species richness

Proportion small species 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1

13

14

17

19

21

24

27

30

34

37

41

2

14

16

18

20

23

26

29

32

36

39

43

3

15

17

19

22

25

28

31

34

38

41

45

4

16

18

21

23

26

29

33

36

40

44

48

5

17

20

22

25

28

31

35

38

42

46

50

6

19

21

24

27

30

33

37

41

44

48

52

7

20

23

26

29

32

35

39

43

47

51

54

8

22

24

27

31

34

38

41

45

49

53

57

9

23

26

29

33

36

40

44

47

51

55

59

10

25

28

31

35

38

42

46

50

54

57

61

11

27

30

33

37

40

44

48

52

56

60

63

12

28

32

35

39

43

46

50

54

58

62

65

13

30

34

37

41

45

49

53

57

60

64

68

14

32

36

40

43

47

51

55

59

63

66

69

15

34

38

42

46

49

53

57

61

65

68

71

16

37

40

44

48

52

56

59

63

67

70

73

17

39

42

46

50

54

58

62

65

69

72

75

18

41

45

49

52

56

60

64

67

71

74

77

19

43

47

51

55

59

62

66

69

73

76

78

20

45

49

53

57

61

64

68

71

74

77

80

21

48

52

55

59

63

67

70

73

76

79

81

22

50

54

58

61

65

69

72

75

78

80

83

23

52

56

60

64

67

71

74

77

79

82

84

24

55

58

62

66

69

72

75

78

81

83

85

25

57

61

64

68

71

74

77

80

82

84

86

26

59

63

66

70

73

76

79

81

83

85

87

27

61

65

68

72

75

78

80

83

85

87

88

28

63

67

70

73

76

79

82

84

86

88

89

29

66

69

72

75

78

81

83

85

87

89

90

30

68

71

74

77

80

82

84

86

88

89

91

31

70

73

76

79

81

83

85

87

89

90

92

32

71

75

77

80

82

85

86

88

90

91

92

33

73

76

79

81

84

86

88

89

91

92

93

34

75

78

80

83

85

87

88

90

91

92

93

35

77

79

82

84

86

88

89

91

92

93

94

36

78

81

83

85

87

89

90

92

93

94

95

37

80

82

84

86

88

90

91

92

93

94

95

38

81

84

86

87

89

90

92

93

94

95

95

39

83

85

87

88

90

91

92

93

94

95

96

40

84

86

88

89

91

92

93

94

95

96

96

310

Table A.6.4.5 Sensitivity analysis using the temperate sub-tropical Queensland model showing changes in predicted community condition according to different values of species richness and proportion small species

Species richness

Proportion small species 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1

13

15

17

20

22

25

28

31

34

38

41

2

15

17

19

21

24

27

30

33

37

40

44

3

16

18

21

23

26

29

32

36

39

43

47

4

18

20

23

25

28

31

35

38

42

46

49

5

19

22

24

27

30

34

37

41

45

48

52

6

21

24

27

30

33

36

40

43

47

51

55

7

23

26

29

32

35

39

42

46

50

54

57

8

25

28

31

34

38

41

45

49

53

56

60

9

27

30

33

37

40

44

48

52

55

59

63

10

29

32

36

39

43

47

50

54

58

62

65

11

31

35

38

42

46

49

53

57

61

64

67

12

34

37

41

45

48

52

56

60

63

67

70

13

36

40

44

47

51

55

58

62

66

69

72

14

39

42

46

50

54

57

61

65

68

71

74

15

41

45

49

53

56

60

64

67

70

73

76

16

44

48

52

55

59

63

66

69

72

75

78

17

47

51

54

58

62

65

68

72

75

77

80

18

49

53

57

61

64

68

71

74

77

79

82

19

52

56

60

63

67

70

73

76

78

81

83

20

55

59

62

66

69

72

75

78

80

83

85

21

57

61

65

68

71

74

77

80

82

84

86

22

60

64

67

70

73

76

79

81

83

85

87

23

63

66

69

73

75

78

81

83

85

87

88

24

65

69

72

75

77

80

82

84

86

88

89

25

68

71

74

77

79

82

84

86

87

89

90

26

70

73

76

79

81

83

85

87

89

90

91

27

72

75

78

80

83

85

86

88

90

91

92

28

74

77

80

82

84

86

88

89

91

92

93

29

76

79

81

83

85

87

89

90

91

93

94

30

78

81

83

85

87

88

90

91

92

93

94

31

80

82

84

86

88

89

91

92

93

94

95

32

82

84

86

87

89

90

92

93

94

95

95

33

83

85

87

89

90

91

92

93

94

95

96

34

85

87

88

90

91

92

93

94

95

96

96

35

86

88

89

91

92

93

94

95

95

96

97

36

87

89

90

92

93

94

94

95

96

96

97

37

88

90

91

92

93

94

95

96

96

97

97

38

89

91

92

93

94

95

95

96

97

97

97

39

90

92

93

94

95

95

96

96

97

97

98

40

91

92

93

94

95

96

96

97

97

98

98

311

Table A.6.4.6 R2 values for correlations between regional and Australia-wide models

Comparison South Australia with vs without small birds Australia-wide vs South Australia with small birds Australia-wide vs South Australia without small birds Australia-wide vs temperate south-east Australia-wide vs sub-tropical Queensland

R2 values 0.92 0.98 0.84 1.00 1.00

312

Minerva Access is the Institutional Repository of The University of Melbourne

Author/s: Fraser, Hannah Title: Overcoming inconsistency in woodland bird classification Date: 2017 Persistent Link: http://hdl.handle.net/11343/194698 File Description: Overcoming inconsistency in woodland bird classification Terms and Conditions: Terms and Conditions: Copyright in works deposited in Minerva Access is retained by the copyright owner. The work may not be altered without permission from the copyright owner. Readers may only download, print and save electronic copies of whole works for their own personal non-commercial use. Any use that exceeds these limits requires permission from the copyright owner. Attribution is essential when quoting or paraphrasing from these works.