Species distribution modeling of Pests and diseases Beatriz Vanessa Herrera

International Center for Tropical Agriculture (CIAT) -Decision and Policy Analysis-2012

Pic:Neil Palmer, CIAT

• Occurrence records related with knowledge about pests behaviour and epidemiology of pathogens

•Variable selection

•Evaluation of niche models

•Consensus distribution maps

Methodology

CLASSIFICATION

Presence records

Environmental variables

Worldclim (current) (30 seconds, 10, 5 and 2.5 arc minutes) http://worldclim.orgClimate change downscaled data http://www.ccafs-climate.org/data/Climate data for DIVA-GIS http://www.diva-gis.org/climate

Limiting Factors

Spatial Production Allocation Model- SPAM -http://mapspam.info/download

http://www.geog.mcgill.ca/~nramankutty/Datasets/Datasets.html

Non-climatic variables

Thematic variables

Dataset selection

•All climatic variables

•Spatial correlation

•PCA- Principal component analysis

•Expert criteria

Several niche approximations

ED- Environmental Distance

SVM- Support Vector Machines

GARP- Genetic Algorithm for Rule-Set Production

A ∩ B ∩ M = RN RN = Realized nicheSoberón & Peterson, 2005, 3

CSM- Climate Space Model

Maxent- Maximum Entropy species distribution model

Evaluation of model performance

ArcGis setnull

Values above 75%

EVALUATION METRICS

EVALUATION SAMPLE

REAL PRESENCE (+) PSEUDOABSENCES (-)

MODEL PREDICTION

PRESENCE (+) a True positiveCORRECT PREDICTION b

Negative falseCOMISION ERRROROVERPREDICTION

ABSENCE (-) c

Negative falseOMISION ERROR

UNDERPREDICTIONd True false

CORRECT PREDICTION

Sensibilidad= (A/A + C) 1-Especificidad= (D/B + D)Error de clasificación= (B +C)/N Kappa= [(a+d) – (((a +c)(a+b) + (b+d)(c+d)/ N)] [N – (((a+c) (a+b) + (b+d) (c+d))/N)]

A y D: correct predictionB: Comission error (POSITIVE FALSE) (overprediction)C: Omission error (NEGATIVE FALSE) (underprediction)

Omission error:records in non-predicted areas

Commission error: (pseudo) absence records in predicted areas

Evaluation metrics and selection criteria

Potential distribution mapping

Weight assignment

Final result

Weighted overlay

New values classification

Some results and comparisons of variable datasetsKappa/ threshold

Specificity/ error rateClimate Space Model

0.83/8-

Environmental distance0.802/63.1 - 0.357/90

Maxent0.842/23 - 0.5/1

Garp0.806/30 - 0.632/30

Support vector Machines0.823/25.1 - 0.73/56

0.269–0.96

0.826 0.902

0.442 0.995

0.903 0.844

0.596 0.956

Whitefly- expert criteria

Whitefly- COR

Source: Herrera et al. 2011. Threats to cassava production: known and potential geographic distribution of four key biotic constraints. Food security: 3:329-345

Problems

Whitefly

Model sensitivity Error rate Weight

GARP All 0.904 0.16 26.1

ED All 0.942 0.05 27.23

ED Exp 0.826 0.09 23.8

CSM all 0.788 0.03 22.7

0.865 0.0825 99.8

Maxent- CORSource: Herrera et al. 2011. Threats to cassava production: known and potential geographic distribution of four key biotic constraints. Food security: 3:329-345

Realized vs potential distribution

Source: Herrera et al. 2011. Threats to cassava production: known and potential geographic distribution of four key biotic constraints. Food security: 3:329-345

Model comparisonsCMD

Model sensitivity Error rate Weight

GARP all 0.722 0.04 50

ED all 0.833 0.02 50

0.7775 0.03 100Examples of Underprediction

Source: Herrera et al. 2011. Threats to cassava production: known and potential geographic distribution of four key biotic constraints. Food security: 3:329-345

Cassava Mosaic Disease

Source: Herrera et al. 2011. Threats to cassava production: known and potential geographic distribution of four key biotic constraints. Food security: 3:329-345

What about the global change?

Climate change scenarios

Steps towards adaptation pathways

Cassava mealybugWhitefly Cassava brown streak virus Cassava mosaic geminivirus

Cassava pests and their natural enemies

Bellotti et al, 2012. Cassava in a changing environment.

How did we get this knowledge?

Cassava pest complex

Prospective cassava pests

Mononychellus mcgregori

Some implications for CWR research

Future research should make full use of the advantages of several species distribution models for global and regional studies.

In CC research complementary models are required in order to better explain expected changes in species responses.

Research in CWR should include pressures due to biotic constraints.

b.v.herrera@CGIAR.orgGeographer - International Center for Tropical Agriculture