DIVA-GIS - CLAS Users [PDF]

DIVA-GIS: a simple GIS and BIOCLIM modeling tool A. Mukherjee & M. Thom GIS 5306: GIS Applications in Environmental Systems Fall 2010 Dr. Michael Binford

Outline of Talk • Biological control overview – Application of ENM in biological control – Tropical Soda Apple & Gratiana boliviana Spaeth

• Modeling Workflow – – – –

Data preparation: Occurrences & Climate data Generation of bioclimatic variables Bioclim Modeling: Current & Future Climate Model Evaluation

Biological

Cultural

IPM

Herbicide

Preventive Adkins (1997)

Classical Weed Biocontrol 20

10 Weed Native Habitat

Biocontrol

Weed Invasive Habitat

10

Weed Invasive Habitat

Goal of Classical Weed BC • Reunite natural enemies with their host plants (Broad Sense) • Introduce or apply natural enemies that suppress and maintain the density of the • Weed at “ACCEPTABLE” levels • Important caveat ‒ Biological Control is NOT Eradication ‒ Creates Opportunity to Combine w/ other tactics Presentation by James P. Cuda

Overseas Surveys

Biswanath Chariali, Assam

Overseas Surveys

Two Happy Pathologists!!

Results of Classical BC

Waterhyacinth Plant

Neochetina eichhorniae Warner Presentation by James P. Cuda

Results of Classical BC

Center & Durden, 1986

Results of Classical BC

Center & Durden, 1986

Results of Classical BC

Center & Durden, 1986

Results of Classical BC

Center & Durden, 1986

Results of Classical BC

Center & Durden, 1986

Application of ENM in CBC • Potential distribution of invasive weed • Prediction of native distribution • Identifying areas climatically most suitable for foreign exploration • Potential spread of biocontrol agents • Testing niche shift hypothesis Presentation by James P. Cuda

Biological Control of TSA

Tropical Soda Apple • Solanum viarum Dunal (Solanaceae) • Invasive weed of pastures & woody areas in the SE US • Native to South America • In Florida, over 1 million acres are currently estimated to be infested • Biocontrol project started in 1994 Medal et al. , EDIS, UF

Tropical Soda Apple BC Tropical soda apple Solanum viarum

Biological control agent Gratiana boliviana

Tropical Soda Apple BC

Class Objective • Predicted distribution of TSA – Current & at 2050 A2a and B2a

• Data Requirement: – Occurrence data: www.eddmaps.org – Climate data: www.worldclim.org

Diva-GIS Desktop • DIVA-GIS: ‒ Open source GIS software http://www.diva-gis.org/

‒ Free spatial data ‒ Compatible with MaxEnt ‒ Can generate ASCII – compatible with MaxEnt & ArcGIS

Predicted Distribution of TSA • Steps: – – – –

Import to grids Make & edit .clm files Prediction & projection datasets Make bioclimatic variables

Medal et al. , EDIS, UF

BIOCLIM: What and why? • BIOCLIM model vs. Bioclimatic variable • BIOCLIM model: an envelope style modeling method - models species space in the environmental hyper volume • Biolimatic variables: – Generated from monthly min. and max. temp. & preci – Biologically more meaningful – Represents annual trends

BIOCLIM: What and why? Apalachicola, Florida (30° N) 40 30

Avg. Max Temperature

20

Avg. Min Temperature

10

Precipitation (inches)

0 Jan Feb Mar Apr May Jun

Jul

Aug Sep Oct Nov Dec

Melo, Uruguay (30°S) 40

30 20

Avg. Max Temperature

10

Avg. Min Temperature

0 Jan Feb Mar Apr May Jun

Jul

Aug Sep Oct Nov Dec

BIOCLIM: What and why? Derived from max & min temp. BIO1 = Annual Mean Temperature BIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp)) BIO3 = Isothermality (P2/P7) (* 100) BIO4 = Temperature Seasonality (standard deviation *100) BIO5 = Max Temperature of Warmest Month BIO6 = Min Temperature of Coldest Month BIO7 = Temperature Annual Range (P5-P6) BIO8 = Mean Temperature of Wettest Quarter BIO9 = Mean Temperature of Driest Quarter BIO10 = Mean Temperature of Warmest Quarter BIO11 = Mean Temperature of Coldest Quarter

BIOCLIM: What and why? Derived from precipitation: BIO12 = Annual Precipitation BIO13 = Precipitation of Wettest Month BIO14 = Precipitation of Driest Month BIO15 = Precipitation Seasonality (Coefficient of Variation) BIO16 = Precipitation of Wettest Quarter BIO17 = Precipitation of Driest Quarter BIO18 = Precipitation of Warmest Quarter BIO19 = Precipitation of Coldest Quarter

BIOCLIM Background • Assume that climate restricts species distributions • Summaries number of climatic variables within known range, generating a ‘bioclimatic envelope’ • Correlative modeling tool that interpolates up to 35 climatic parameters ‒ 19 Bioclimate variables (Bio1 – Bio19) ‒ 7 Solar radiation indices (Bio20 – Bio27) ‒ 8 Pan evaporation indices Beaumont et al. 2005

BIOCLIM Advantages BIOCLIM can be used for three main tasks: 1. Describing the environment in which the species has been recorded, 2. Identifying other locations where the species may currently reside, & 3. identifying where the species may occur under alternate climate scenarios 4. Useful ‘first filters’ for identifying locations and species that may be most at risk Beaumont et al. 2005

BIOCLIM Background Diagrammatic representation of a hypothetical 2 dimensional bioclimatic envelope.

Beaumont et al. 2005

BIOCLIM Background 1600 Current

Annual Precipitation (mm)

1500

2050

1400 1300 1200 1100 1000 900 800 15

17

19

21

23

Anneal Mean Temp (0C)

25

27

Factors Affecting Model Output Related to Bioclim data: 1. Error associated with estimation of primary climatic attributes at a point 2. Relevance of derived bioclimatic indices. 3. Derivation of the bioclimatic envelope. 4. Accuracy and level of resolution of the grid used for predicting potential distribution

Nix 1986; http://fennerschool.anu.edu.au/publications

Factors Affecting Model Output Related to Occurrence localities: 1. Taxonomic uncertainty 2. Accuracy of identification and labeling 3. Accuracy of geocoding 4. Adequacy of point sampling within total distribution 5. Checking of anomalous data points Nix 1986; http://fennerschool.anu.edu.au/publications

BIOCLIM Criticisms • Elith et al. 2006: Worst performing model

Elith et al. 2006