diva-gis: a simple gis and bioclim modeling...

DIVA-GIS: a simple GIS and BIOCLIM modeling tool

A. Mukherjee & M. Thom

GIS 5306: GIS Applications in Environmental SystemsFall 2010

Dr. Michael Binford

• 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

Outline of Talk

IPM

Biological

Cultural

Preventive

Herbicide

Adkins (1997)

Classical Weed Biocontrol

Weed

10

20

Native Habitat

Weed

Invasive Habitat

Biocontrol

Weed

10

Invasive Habitat

• 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

Goal of Classical Weed BC

Presentation by James P. Cuda

Overseas Surveys

Biswanath Chariali, Assam

Overseas Surveys

Two Happy Pathologists!!

Results of Classical BC


Waterhyacinth Plant Neochetina eichhorniae Warner

Center & Durden, 1986

Results of Classical BC

• 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

Application of ENM in CBC


Biological Control of TSA

• 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

Tropical Soda Apple

Medal et al. , EDIS, UF

Tropical soda apple -

Solanum viarum

Biological control agent -Gratiana boliviana

Tropical Soda Apple BC

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

http://www.eddmaps.org/

http://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




• Steps:– Import to grids

– Make & edit .clm files

– Prediction & projection datasets

– Make bioclimatic variables

Predicted Distribution of TSA

Medal et al. , EDIS, UF

• 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?

0

10

20

30

40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Apalachicola, Florida (30° N)

Avg. Max Temperature

Avg. Min Temperature

Precipitation (inches)

0

10

20

30

40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Melo, Uruguay (30°S)

Avg. Max Temperature

Avg. Min Temperature



Derived from max & min temp.

BIO1 = Annual Mean TemperatureBIO2 = 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 MonthBIO6 = Min Temperature of Coldest MonthBIO7 = Temperature Annual Range (P5-P6)BIO8 = Mean Temperature of Wettest QuarterBIO9 = Mean Temperature of Driest QuarterBIO10 = Mean Temperature of Warmest QuarterBIO11 = Mean Temperature of Coldest Quarter


Derived from precipitation:

BIO12 = Annual PrecipitationBIO13 = Precipitation of Wettest MonthBIO14 = Precipitation of Driest MonthBIO15 = Precipitation Seasonality (Coefficient of Variation)BIO16 = Precipitation of Wettest QuarterBIO17 = Precipitation of Driest QuarterBIO18 = Precipitation of Warmest QuarterBIO19 = 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


BIOCLIM Background


Diagrammatic representation of a hypothetical 2 dimensionalbioclimatic envelope.

BIOCLIM Background

800

900

1000

1100

1200

1300

1400

1500

1600

15 17 19 21 23 25 27

An

nu

al P

reci

pit

atio

n (

mm

)

Anneal Mean Temp (0C)

Current

2050

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

diva-gis: a simple gis and bioclim modeling...

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