herrera b - spatial epidemiology and crop pest and diseases mapping 2012
DESCRIPTION
TRANSCRIPT
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.
[email protected] - International Center for Tropical Agriculture