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Modeling Species’ Distributions with Applications to Agriculture

Modeling Species’ Distributions with Applications to Agriculture. Víctor Sánchez-Cordero Instituto de Biología, UNAM, México A. T. Peterson The University of Kansas, USA. Agroecosystems and Biodiversity. Systems of interacting species Crop organisms

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Modeling Species’ Distributions with Applications to Agriculture

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  1. Modeling Species’ Distributions with Applications to Agriculture Víctor Sánchez-Cordero Instituto de Biología, UNAM, México A. T. Peterson The University of Kansas, USA

  2. Agroecosystems and Biodiversity • Systems of interacting species • Crop organisms • Pest organisms (rodents, insects, weeds) • Invasive species • Pollinators • Behavior of such systems can be simulated via detailed understanding of the ecological requirements of each component of the system

  3. Case Studies • Corn ecology, areas of risk for natural areas from advancing agricultural frontier, and climate change effects • Risk assessment of potential for invasion by a new pest, a vector for Xylella fastidiosa • Risk assessment for crop damage by rodent pests in Veracruz, Mexico

  4. Modeling Corn Ecology in Mexico • Points where maize is planted without irrigation are used to create an ecological niche model and geographic projection of potential distribution • Inventario Forestal Nacional is used to locate areas actually (2000) planted in maize and areas holding natural vegetation • Comparisons used to assess areas of potential expansion of the ‘frontera agricola’ • Climate change predictions used to assess how this scenario will change over next 50 yr

  5. Points Where Maize Planted without Irrigation

  6. Ecological Niche Model Based on Points

  7. Ecological Niche Model Without Points

  8. Independent Data: Distribution of Maize

  9. Actual Distribution (GREEN) Overlaid on Potential Distribution (BLUE)

  10. Areas Suitable for Maize but Currently with Natural Vegetation (= Possible Expansion of Agricultural Frontier)

  11. Protected Areas Vulnerable to Expansion

  12. Protected Areas Most Vulnerable ANPs completely within the ecological niche of maize

  13. Protected Areas Least Vulnerable ANPs outside of the ecological niche of maize, or mostly outside

  14. Vegetation Types Most Vulnerable

  15. Maize and Climate Change

  16. Maize and Climate Change: Difference Maps DX scenario then AX scenario Red = worsening for maize Blue = improving for maize

  17. Maize Plantations that Are Becoming Inviable

  18. Case Studies • Corn ecology, areas of risk for natural areas from advancing agricultural frontier, and climate change effects • Risk assessment of potential for invasion by a new pest, a vector for Xylella fastidiosa • Risk assessment for crop damage by rodent pests in Veracruz, Mexico

  19. Glassy-winged SharpshooterHomolodisca coagulata

  20. Homalodisca and Xylella

  21. Known Points – Native Distribution

  22. GARP Model Inferred from Points

  23. Projection to California

  24. Test Model Predictions

  25. Risk Assessment in Brazil

  26. Case Studies • Corn ecology, areas of risk for natural areas from advancing agricultural frontier, and climate change effects • Risk assessment of potential for invasion by a new pest, a vector for Xylella fastidiosa • Risk assessment for crop damage by rodent pests in Veracruz, Mexico

  27. PREDICTIVE DISTRIBUTION OF RODENT PEST SPECIES AGRICULTURAL CENSUS DATA ESTIMATED CROP LOSS IN EACH MUNICIPALITY: PLANTED MINUS HARVESTED AREA IS CROP LOSS RELATED TO THE PREDICTED DISTRIBUTION OF RODENT PEST SPECIES?

  28. CROP Total area Area lost (ha) (ha) RICE 21,920 1,359 BEANS 57,988 9,426 SUGARCANE 213,221 69,670 CORN 514,213 49,189 OAT 1,198 239 COFFEE 175,027 9,427 GRASSES 1’322,985 5,315 SORGHUM 5,676 555 WHEAT 1,822 378

  29. CROPSNATIVE RODENT PEST RICE 1 SQUIRREL BEANS13 RATS AND MICE SUGARCANE3POCKET GOPHERS CORN OAT COFFEE GRASSES SORGHUM WHEAT

  30. Stepwise multiple regression analyses Dependent variable: crop damage in each crop in the 207 municipalities for Veracruz. Independent variable: proportional predicted coverage of that municipality by each of the 17 rodent species

  31. CROP r2 RICE 0.65, P < 0.0001 BEANS 0.07, P < 0.05 SUGARCANE 0.04, P < 0.05 CORN 0.11, P < 0.05 OAT 0.35, P < 0.01 COFFEE 0.04, P < 0.05 GRASSES 0.12, P < 0.001 SORGHUM 0.07, P < 0.1 WHEAT 0.22, NS CONCLUSION: Areas of crop damage are not distributed at random with respect to distributional areas of pest rodents … risk of crop damage can be predicted based on the ecological potential of the species causing the damage ….

  32. Summary • Agroecosystem behavior can be predicted based on the ecological requirements of component species • Risk of crop loss, crop inviability, and other phenomena can be predicted • Economic importance of biodiversity informatics … dead animals in museums can inform human economies!

  33. MuitoObrigado victors@ibiologia.unam.mx

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