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Mechanistic models for macroecolgy: moving beyond correlation

Mechanistic models for macroecolgy: moving beyond correlation. Nicholas J. Gotelli Department of Biology University of Vermont Burlington, VT 05405. ?? What causes geographic variation in species richness ??. Understanding species richness patterns. Data sources

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Mechanistic models for macroecolgy: moving beyond correlation

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  1. Mechanistic models for macroecolgy: moving beyond correlation Nicholas J. Gotelli Department of Biology University of Vermont Burlington, VT 05405

  2. ??What causes geographic variation in species richness??

  3. Understanding species richness patterns • Data sources • A critique of current methods • Range cohesion and the mid-domain effect • Mechanistic models for species richness • Model selection • Summary

  4. Gary EntsmingerAcquired Intelligence Rob ColwellUniversity of Connecticut Nicholas Gotelli, University of Vermont Thiago Rangel Federal University of Goiás Carsten RahbekUniversity of Copenhagen Gary GravesSmithsonian

  5. Understanding species richness patterns • Data sources • A critique of current methods • Range cohesion and the mid-domain effect • Mechanistic models for species richness • Model selection • Summary

  6. Data sources • Gridded map of domain

  7. Avifauna of South America “There can be no question, I think, that South America is the most peculiar of all the primary regions of the globe as to its ornithology.” P.L. Sclater (1858)

  8. South American Avifauna • 2891 breeding species • 2248 species endemic to South America and associated land-bridge islands

  9. Minimum: 18 species

  10. Maximum: 846 species Minimum: 18 species

  11. Data sources • Gridded map of domain • Species occurrence records within grid cells

  12. Anas puna Geographic Ranges For Individual Species Phalacrocorax brasilianus Myiodoorus cardonai

  13. Geographic Ranges Species Richness

  14. Geographic Ranges Species Richness

  15. Data sources • Gridded map of domain • Species occurrence records within grid cells • Quantitative measures of potential predictor variables within grid cells (NPP, temperature, habitat diversity)

  16. Climate, Habitat Variables Measured at Grid Cell Scale

  17. Understanding species richness patterns • Data sources • A critique of current methods • Range cohesion and the mid-domain effect • Mechanistic models for species richness • Model selection • Summary

  18. How are these macroecological data typically analyzed?

  19. How are these macroecological data typically analyzed?Curve-fitting!

  20. Criticisms of Curve-Fitting • “Correlation does not equal causation”

  21. Criticisms of Curve-Fitting • “Correlation does not equal causation”Common to all of macroecology!

  22. Criticisms of Curve-Fitting • “Correlation does not equal causation”Common to all of macroecology! • Non-linearity & non-normal, spatially correlated errors

  23. Criticisms of Curve-Fitting • “Correlation does not equal causation”Common to all of macroecology! • Non-linearity & non-normal, spatially correlated errorsLOESS, Poisson, Spatial Regression (SAM)

  24. Criticisms of Curve-Fitting • “Correlation does not equal causation”Common to all of macroecology! • Non-linearity & non-normal, spatially correlated errorsLOESS, Poisson, Spatial Regression (SAM) • Choosing among correlated predictor variables

  25. Criticisms of Curve-Fitting • “Correlation does not equal causation”Common to all of macroecology! • Non-linearity & non-normal, spatially correlated errorsLOESS, Poisson, Spatial Regression (SAM) • Choosing among correlated predictor variablesModel selection strategies, stepwise regression, AIC

  26. Criticisms of Curve-Fitting • “Correlation does not equal causation”Common to all of macroecology! • Non-linearity & non-normal, spatially correlated errorsLOESS, Poisson, Spatial Regression (SAM) • Choosing among correlated predictor variablesModel selection strategies, stepwise regression, AIC • Sensitivity to spatial scale, taxonomic resolution, geographic range size

  27. Criticisms of Curve-Fitting • “Correlation does not equal causation”Common to all of macroecology! • Non-linearity & non-normal, spatially correlated errorsLOESS, Poisson, Spatial Regression (SAM) • Choosing among correlated predictor variablesModel selection strategies, stepwise regression, AIC • Sensitivity to spatial scale, taxonomic resolution, geographic range sizeStratify analysis

  28. Predicted Species Richness (S / grid cell) Observed Species Richness (S / grid cell) Conceptual Weakness of Curve-Fitting Paradigm Potential Predictor Variables (tonnes/ha, C°)

  29. Predicted Species Richness (S / grid cell) Observed Species Richness (S / grid cell) Conceptual Weakness of Curve-Fitting Paradigm Potential Predictor Variables (tonnes/ha, C°) minimizeresiduals

  30. Predicted Species Richness (S / grid cell) Observed Species Richness (S / grid cell) Conceptual Weakness of Curve-Fitting Paradigm Potential Predictor Variables (tonnes/ha, C°) minimizeresiduals ??MECHANISM??

  31. Predicted Species Richness (S / grid cell) Observed Species Richness (S / grid cell) Alternative Strategy:Mechanistic Simulation Models Potential Predictor Variables (tonnes/ha, C°) ExplicitSimulationModel

  32. Predicted Species Richness (S / grid cell) Observed Species Richness (S / grid cell) Alternative Strategy:Mechanistic Simulation Models Potential Predictor Variables (tonnes/ha, C°) mechanism ExplicitSimulationModel

  33. How can we build explicit simulation models for macroecology?

  34. Understanding species richness patterns • Data sources • A critique of current methods • Range cohesion and the mid-domain effect • Mechanistic models for species richness • Model selection • Summary

  35. One-dimensional geographic domain

  36. One-dimensional geographic domain Species geographic ranges randomly placed line segments within domain

  37. One-dimensional geographic domain Species geographic ranges randomly placed line segments within domain Peak of species richness in geographic center of domain

  38. Species Number One-dimensional geographic domain Species geographic ranges randomly placed line segments within domain Peak of species richness in geographic center of domain

  39. domain

  40. geographic range domain

  41. Pancakus spp. der Pfankuchen Guild

  42. Reduced species richnessat margins of the domain

  43. Mid-domain peak of species richness in the center of the domain

  44. 2-dimensional MDE Model • Random point of originationwithin continent (speciation) • Random spread of geographicrange into contiguousunoccupied cells • Spreading dye model (Jetz & Rahbek 2001) predicts peak richness incenter of continent (r2 = 0.17)

  45. Assumptions of MDE models • Placement of ranges within domain is random with respect to environmental gradients • Controversial, but logical for a null model for climatic effects

  46. Assumptions of MDE models • Placement of ranges within domain is random with respect to environmental gradients • Controversial, but logical for a null model for climatic effects • Geographic ranges are cohesive within the domain • Rarely discussed, but important as the basis for a mechanistic model of species richness

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