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. Nicholas Gotelli, University of Vermont Gary Entsminger Acquired Intelligence Rob Colwell University of Connecticut Gary Graves Smithsonian Carsten Rahbek University of Copenhagen Thiago Rangel Federal University of Goiás

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. Minimum: 18 species Maximum: 846 species

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

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

14. Geographic Ranges Species Richness

15. Geographic Ranges Species Richness

16. 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)

17. Climate, Habitat Variables Measured at Grid Cell Scale

18. 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

19. How are these macroecological data typically analyzed?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

38. 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

39. One-dimensional geographic domain

40. Species geographic ranges randomly placed line segments within domain

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

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

43. domain

44. geographic range

45. der Pfankuchen Guild Pancakus spp.

46. Reduced species richness at margins of the domain

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

48. 2-dimensional MDE Model Random point of origination within continent (speciation) Random spread of geographic range into contiguous unoccupied cells Spreading dye model (Jetz & Rahbek 2001) predicts peak richness in center of continent (r 2 = 0.17)

49. 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

50. 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

51. Range CohesionRange Scatter

52. At the 1º x 1º scale, > 95% of species of South American birds have contiguous geographic ranges

53. Causes of Range Cohesion Extrinsic Causes

54. Causes of Range Cohesion Extrinsic Causes –Coarse Spatial Scale –Spatial Autocorrelation in Environments

55. Causes of Range Cohesion Extrinsic Causes –Coarse Spatial Scale –Spatial Autocorrelation in Environments Intrinsic Causes

56. Causes of Range Cohesion Extrinsic Causes –Coarse Spatial Scale –Spatial Autocorrelation in Environments Intrinsic Causes –Limited Dispersal –Philopatry & Site Fidelity –Metapopulation & Source/Sink Structure –Fine-scale Genetic Structure & Local Adaptation –Spatially Mediated Species Interactions

57. Strict Range Cohesion Stepping Stone * The mid-domain effect does not require strict range cohesion. A mid-domain peak in species richness will also arise from stepping stone models with limited dispersal and from neutral model dynamics (Rangel & Diniz-Filho 2005)

58. Homogenous Environment Heterogeneous Environment Almost all MDE models have assumed a homogeneous environment: grid cells are equiprobable

59. Enforced Relaxed HomogeneousHeterogeneous RANGE COHESION ENVIRONMENT

60. Enforced Relaxed HomogeneousHeterogeneous RANGE COHESION ENVIRONMENT Classic MDE Statistical Null (slope = 0)

61. Enforced Relaxed HomogeneousHeterogeneous RANGE COHESION ENVIRONMENT Classic MDE Statistical Null (slope = 0)

62. Enforced Relaxed HomogeneousHeterogeneous RANGE COHESION ENVIRONMENT Classic MDE Statistical Null (slope = 0) Range Scatter Models Range Cohesion Models

63. Enforced Relaxed HomogeneousHeterogeneous RANGE COHESION ENVIRONMENT Classic MDE Statistical Null (slope = 0) Range Scatter Models Range Cohesion Models Range Cohesion Models are a hybrid that describes a stochastic MDE model in a more realistic heterogeneous environment. Range Scatter Models also incorporate environmental heterogeneity, but do not place any constraints on species geographic ranges.

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

65. 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

66. Modeling Strategy Establish simple algorithms that describe P(occupancy) based on environmental variables

67. Modeling Strategy Establish simple algorithms that describe P(occupancy) based on environmental variables Simulate origin and placement of each species geographic range in heterogeneous landscape (with or without range cohesion)

68. Modeling Strategy Establish simple algorithms that describe P(occupancy) based on environmental variables Simulate origin and placement of each species geographic range in heterogeneous landscape (with or without range cohesion) Repeat simulation to estimate predicted species richness per grid cell

69. Geographic Ranges Species Richness

70. What determines P(cell occurrence)? Simple environmental models P(occurrence)  Measured Environmental Variable (NPP, Temperature, etc.)

71. What determines P(cell occurrence)? Simple environmental models P(occurrence)  Measured Environmental Variable (NPP, Temperature, etc.) Formal analytical models

72. What determines P(cell occurrence)? Simple environmental models P(occurrence)  Measured Environmental Variable (NPP, Temperature, etc.) Formal analytical models –Species-Energy Model (Currie et al. 2004) –Temperature Kinetics (Brown et al. 2004)

73. What determines P(cell occurrence)? Simple environmental models P(occurrence)  Measured Environmental Variable (NPP, Temperature, etc.) Formal analytical models –Species-Energy Model (Currie et al. 2004) P(occurrence)  (NPP)(Grid-cell Area) –Temperature Kinetics (Brown et al. 2004) P(occurrence)  e -E/kT

76. 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

77. Model-Selection in Curve-Fitting Analyses Simple tests against the null hypothesis that b=0 No consideration of what expected slope should be with a specific mechanism Least-square and AIC criteria to try and select a subset of variables that best account for variation in S

78. H0: b = 0

79. Model Selection with Mechanistic Simulation Models Models make quantitative predictions of expected species richness Test slope of observed richness versus predicted richness Hypothesis of an acceptable fit H1: b = 1.0 Rank acceptable models according to slope, intercept, and r2 AIC criteria not appropriate

80. Predicted S Observed S Theoretical b = 1.0 Observed b

81. 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

82. Curve-fitting framework does not incorporate explicit mechanisms

83. Summary Curve-fitting framework does not incorporate explicit mechanisms Use mechanistic simulations to define the placement of geographic ranges in a gridded domain

84. Summary Curve-fitting framework does not incorporate explicit mechanisms Use mechanistic simulations to define the placement of geographic ranges in a gridded domain Specify rules for P(occurrence)= f(environmental variables)

85. Summary Curve-fitting framework does not incorporate explicit mechanisms Use mechanistic simulations to define the placement of geographic ranges in a gridded domain Specify rules for P(occurrence)= f(environmental variables) Test model fit against expected slope = 1.0

86. Criticisms & Rejoinders

87. “Each species has a unique and distinctive response to different environmental variables. Species ranges should be modeled independently, not with a single function for all species.”

88. Criticisms & Rejoinders “Each species has a unique and distinctive response to different environmental variables. Species ranges should be modeled independently, not with a single function for all species.” If this is true, why are there widespread repeatable patterns of species richness (e.g., latitude, elevation, area, productivity)?

89. Criticisms & Rejoinders “Each species has a unique and distinctive response to different environmental variables. Species ranges should be modeled independently, not with a single function for all species.” If this is true, why are there widespread repeatable patterns of species richness (e.g., latitude, elevation, area, productivity)? Often not enough data to model each species individually. We need a simple framework for analysing entire floras and faunas at a biogeographic scale.

90. Criticisms & Rejoinders “1:1 scaling of environmental variables with P(occurrence) is unrealistic and arbitrary.”

91. Criticisms & Rejoinders “1:1 scaling of environmental variables with P(occurrence) is unrealistic and arbitrary.” Perhaps, but this is a parsimonious mechanistic model that relates environmental variables to geographic range placement.

92. Criticisms & Rejoinders “1:1 scaling of environmental variables with P(occurrence) is unrealistic and arbitrary.” Perhaps, but this is a parsimonious mechanistic model that relates environmental variables to geographic range placement. Linearity in P(occurrence) is not unreasonable over the empirical ranges of environmental variables measured in South America. (Linearity of P(occurrence) ≠ Linearity of (Species Richness))

93. Criticisms & Rejoinders “1:1 scaling of environmental variables with P(occurrence) is unrealistic and arbitrary” Perhaps, but this is a parsimonious mechanistic model that relates environmental variables to geographic range placement. Linearity in P(occurrence) is not unreasonable over the empirical ranges of environmental variables measured in South America. (Linearity of P(occurrence) ≠ Linearity of (Species Richness)) Mechanistic models are scarce in this literature (n = 2)! We have to begin somewhere!

94. Criticisms & Rejoinders “Many environmental variables, but especially NPP, show non-linear relationships with peaks in richness at intermediate levels. This is not captured by linear models.”

95. Criticisms & Rejoinders “Many environmental variables, but especially NPP, show non-linear relationships with peaks in richness at intermediate levels. This is not captured by linear models.” At least at this spatial scale, no evidence for a diversity hump of avian species richness when plotted with NPP or other variables

96. Criticisms & Rejoinders “Using slopes comparisons will not successfully distinguish between models with intercorrelated predictor variables.”

97. Criticisms & Rejoinders “Using slopes comparisons will not successfully distinguish between models with intercorrelated predictor variables.” Not a problem for these analyses. From an initial set of ~ 100 candidate models (10 variables x 2 algorithms x 5 range size quartiles), we reduced the set down to only 4 or 5 possible contenders.

98. Criticisms & Rejoinders “The model is not truly mechanistic because it does not model the sizes of the geographic ranges, only their placement.”

99. Criticisms & Rejoinders “The model is not truly mechanistic because it does not model the sizes of the geographic ranges, only their placement.” True! Our model takes range sizes as a given and then uses algorithms to place them in a heterogeneous domain. A more realistic model would describe the processes of speciation, dispersal, and extinction of an evolving fauna.

100. Criticisms & Rejoinders “The model is not truly mechanistic because it does not model the sizes of the geographic ranges, only their placement.” True! Our model takes range sizes as a given and then uses algorithms to place them in a heterogeneous domain. A more realistic model would describe the processes of speciation, dispersal, and extinction of an evolving fauna. But how can the parameters of such a model (e.g. speciation and dispersal rates) ever be measured in the real world? Same problems have plagued most empirical evaluations of the neutral model.

101. Criticisms & Rejoinders “The model is not truly mechanistic because it does not model the sizes of the geographic ranges, only their placement.” True! Our model takes range sizes as a given and then uses algorithms to place them in a heterogeneous domain. A more realistic model would describe the processes of speciation, dispersal, and extinction of an evolving fauna. But how can the parameters of such a model (e.g. speciation and dispersal rates) ever be measured in the real world? Same problems have plagued most empirical evaluations of the neutral model. Our models are designed to analyze the data that macroecologists typically have: gridded maps of environmental variables and species geographic ranges.

102. Criticisms & Rejoinders “The range cohesion and range scatter models don’t’ seem like they would give predictions that are any different from just a regression with the underlying variables themselves. What is the added value of these simulation models?”

103. Criticisms & Rejoinders “The range cohesion and range scatter models don’t’ seem like they would give predictions that are any different from just a regression with the underlying variables themselves. What is the added value of these simulation models?” The predictions are not the same. For species with large geographic ranges, the range cohesion models always fit the data better than the range scatter models, regardless of which environmental variable is considered.

104. Key Differences Curve-FittingMechanistic Models Unit of StudySpecies RichnessUnderlying geographic ranges Predicted valuesMinimization of residuals (data dependent) Algorithms for origin and spread of geographic ranges (data independent) Model Selection CriteriaSmallest number of variables that reduce residual sum of squares Quantitative fit to model predictions Statistical TestsH0: (b = 0) tests for any effect that is larger than 0 H0: (b = 1.0) tests for quantitative match between observed and predicted S

105. To Be Continued… Carsten Rahbek. Perception of Species Richness Patterns: The Role of Range Sizes