1. Community Assembly: From Small to Large Spatial Scales Nicholas J. Gotelli Department of Biology University of Vermont Burlington, VT 05405

2. Community Comparisons SpeciesBogForest Amblyopone pallipes -1 Dolichoderus pustulatus 1- Lasius alienus 11 Lasius umbratus -4 Camponotus herculaneus -3 Myrmica lobifrons 82- Myrmica detritinodis -1 Stenamma diecki -1 Aphaenogaster rudis complex -1 Bog sample grid (5 m x 5 m) Forest sample grid (5 m x 5 m)

3. Community Comparisons SpeciesBogForest Amblyopone pallipes -1 Dolichoderus pustulatus 1- Lasius alienus 11 Lasius umbratus -4 Camponotus herculaneus -3 Myrmica lobifrons 82- Myrmica detritinodis -1 Stenamma diecki -1 Aphaenogaster rudis complex -1 1) Species Composition

4. Community Comparisons SpeciesBogForest Amblyopone pallipes -1 Dolichoderus pustulatus 1- Lasius alienus 11 Lasius umbratus -4 Camponotus herculaneus -3 Myrmica lobifrons 82- Myrmica detritinodis -1 Stenamma diecki -1 Aphaenogaster rudis complex -1 1)Species Composition 2)Relative Abundance

5. Community Comparisons SpeciesBogForest Amblyopone pallipes -1 Dolichoderus pustulatus 1- Lasius alienus 11 Lasius umbratus -4 Camponotus herculaneus -3 Myrmica lobifrons 82- Myrmica detritinodis -1 Stenamma diecki -1 Aphaenogaster rudis complex -1 1)Species Composition 2)Relative Abundance 3)Species Richness SPECIES RICHNESS 3 7

6. Community Comparisons SpeciesBogForest Amblyopone pallipes -1 Dolichoderus pustulatus 1- Lasius alienus 11 Lasius umbratus -4 Camponotus herculaneus -3 Myrmica lobifrons 82- Myrmica detritinodis -1 Stenamma diecki -1 Aphaenogaster rudis complex -1 1)Species Composition 2)Relative Abundance 3)Species Richness SPECIES RICHNESS 3 7

7. Determinants of Relative Abundance Invertebrate food web associated with northern pitcher plant Sarracenia Small scale Experimental CONCLUSION: Relative abundance is best explained by food web models

8. Determinants of Species Richness Avifauna of South America Large scale Correlative CONCLUSION: Species richness patterns reflect historical forces, not contemporary climate

9. Food webs Diagrams of “who eats whom” Alternative to competitive-based paradigm

10. Methods for Food Web Analysis Interaction matrices Experimental manipulations

11. New approach Manipulate entire communities in ecological “press” experiment Compare relative abundances to predictions of several biologically realistic models

12. Carnivorous plants: well-known, but poorly studied

13. Aaron M. Ellison Harvard Forest

14. The Northern Pitcher Plant Sarracenia purpurea Perennial plant of low-N peatlands Lifespan 30-50 y Arthropod prey capture in water- filled pitchers Diverse inquiline community in pitchers

17. The Inquilines Wyeomyia smithii Metriocnemus knabiHabrotrocha rosa Blaesoxipha fletcheri Sarraceniopus gibsoni

18. Food web structure

19. Moose Bog

21. Experimental Protocol 5 treatment manipulations applied 10 replicate plants per treatment Treatments applied to old, first, and second leaves of each plant Treatments applied twice/week Inquilines censused once/week Treatments maintained 5/31/00 - 8/23/00

22. Habitat Volume Manipulations C Inquilines & liquid removed, censused, returned (Control) C- Inquilines & liquid removed,censused. Liquid replaced with equal volume of d H 2 0 (Trophic Pruning) A Inquilines & liquid removed, censused, returned, topped with d H 2 0 (Habitat Expansion) A- Inquilines & liquid removed, censused. Liquid replaced and topped with d H 2 0 (Habitat Expansion & Trophic Pruning) E Inquilines & liquid removed, censused (Habitat Contraction & Trophic Pruning)

23. Significant alterations in habitat volume among treatments

24. Mean responses per leaf

25. Responses in abundance to both leaf age and habitat manipulation

26. Idiosyncratic responses of individual taxa to manipulations

27. Responses of prey abundance to treatment and leaf age

28. Summary of Effects of Treatment, Age, and Prey Abundance TreatmentAgeT x APrey Wyeomyia ++ Metriocnemus ++++ Habrotrocha +++++ Protozoa +++ Sarraceniopus + Blaesoxipha ++ Prey ++ Volume ++

29. Detecting Species Associations With Multiple Regression Models Dep Var Independent Variable ProWyeMetSarBlaHabPreyVol Pro ++++++ Wye +—+ Met +++++ Sar + Bla -+ Ha ++—–+ Prey +++

30. Standard Regression Methods Controls for covariation Assumes simple independent-dependent data structure Does not allow for direct testing of different hypotheses of community structure P1P2 P3R

31. Path Analysis Controls for covariation Does not assume simple covariance structure Allows for testing of different models of community structure V1V2 V3 V4

32. Path models of community structure

33. Habitat Volume, Prey Models

34. Keystone Species Models

35. Food Web Models Top-Down Control

36. Food Web Models Bottom-Up Control

37. Habitat Volume Model

40. Comparing Models Aikake’s Information Criterion Index (AIC) Balance between adding parameters and reducing residual sum of squares Provides simple “badness of fit” hypothesis test for path model

43. Wyeomyia Keystone Model

44. Conclusions Invertebrates species of Sarracenia show idiosyncratic responses to habitat volume, leaf age Food web models provide a superior fit to relative abundance data compared to habitat volume models, keystone species models Indirect evidence for strong bacterial links

45. Future Research Taxonomic resolution of protists, microbes Nutrient transfer and interactions between plant and invertebrate food web Effects of food supplementation and atmospheric inputs of nitrogen

46. Determinants of Species Richness “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)

47. 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

48. Major Hypotheses Historical Factors Contemporary Climate Mid-domain Effect

49. Major Hypotheses Historical Factors Contemporary Climate Mid-domain Effect

50. “Current” Perspective: Contemporary Climate Controls Species Richness “Climatic variables were the strongest predictors of richness in 83 of 85 cases, providing strong support for the hypothesis that climate in general has a major influence on diversity gradients across large spatial extents.” Hawkins et al. (2004)

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

52. Minimum: 18 species

53. Minimum: 18 species Maximum: 846 species

54. Minimum: 18 species Maximum: 846 species “Taiwan” ~ 4 grid cells ~ 471 bird species

55. Climate, Habitat Variables Measured at Grid Cell Scale

57. Summary of Simple Regression Statistics PREDICTOR VARIABLER2R2 Topographic surface area (km²)0.21 Net primary productivity (tons/yr)0.67 Precipitation (mm/yr)0.53 Temperature (mean annual, Cº)0.48 Topographic relief (elevational range)0.00 Ecosystem diversity (# ecosystem types)0.07 All variables0.79

58. Conventional analyses mask effects of species geographic range! However…

59. Species vary tremendously in geographic range size (= number of grid cells occupied) Myioborus cardonai 1 grid cell Anas puna 64 grid cells Phalacrocorax brasilianus 1676 grid cells Median range size

61. 1 st quartile 2 nd 3 rd 4 th quartile

62. Species Richness Gradients Depend On Range Size Quartile!

63. Species Richness Correlates For Range Quartiles PREDICTOR VARIABLE 1 st quartile 2 nd quartile 3 rd quartile 4 th quartile Topographic surface area (km²) 0.00 0.020.24 Net primary productivity (tons/yr) 0.00 0.050.82 Precipitation (mm/yr) 0.01 0.070.57 Temperature (mean annual, Cº) 0.00 0.69 Topographic relief (elevational range) 0.310.390.160.14 Ecosystem diversity (# ecosystem types) 0.210.230.190.00 All variables 0.480.580.470.85

64. Failure of Climate Variable to Predict Species Richness of First Three Range Size Quartiles PREDICTOR VARIABLE 1 st quartile 2 nd quartile 3 rd quartile 4 th quartile Topographic surface area (km²) 0.00 0.020.24 Net primary productivity (tons/yr) 0.00 0.050.82 Precipitation (mm/yr) 0.01 0.070.57 Temperature (mean annual, Cº) 0.00 0.69 Topographic relief (elevational range) 0.310.390.160.14 Ecosystem diversity (# ecosystem types) 0.210.230.190.00 All variables 0.480.580.470.85

65. Contrasting Correlates for Restricted vs. Widespread Species PREDICTOR VARIABLE 1 st -3 rd quartiles 4 th quartile Total Richness Topographic surface area (km²) 0.000.240.21 Net primary productivity (tons/yr) 0.010.820.67 Precipitation (mm/yr) 0.040.570.53 Temperature (mean annual, Cº) 0.000.690.48 Topographic relief (elevational range) 0.330.140.00 Ecosystem diversity (# ecosystem types) 0.250.000.07 All variables 0.580.850.79

68. Correlates of Total Species Richness Mirror Patterns for Widespread Species (4 th Quartile)

70. Topographic Relief Maximum elevational range within a grid cell Adjusted for snowline at latitude Correlated with habitat diversity Barriers to dispersal and promoters of speciation

71. Contrasting Correlates for Restricted vs. Widespread Species PREDICTOR VARIABLE 1 st -3 rd quartiles 4 th quartile Total Richness Topographic surface area (km²) 0.000.240.21 Net primary productivity (tons/yr) 0.010.820.67 Precipitation (mm/yr) 0.040.570.53 Temperature (mean annual, Cº) 0.000.690.48 Topographic relief (elevational range) 0.330.140.00 Ecosystem diversity (# ecosystem types) 0.250.000.07 All variables 0.580.850.79

74. Major Hypotheses Historical factors Contemporary Climate Mid-domain Effect

75. One-dimensional geographic domain

76. Species geographic ranges randomly placed line segments within domain

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

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

79. domain

80. geographic range

81. die Pfankuchen Guilde Pancakus spp.

82. Reduced species richness at margins of the domain

83. Mid-domain peak of species richness in the center of the domain © Matt Fitzpatrick, UT

84. 2-dimensional MDE Model Random point of origination within continent (speciation) Random spread of geographic range into contiguous unoccupied cells Spreading dye model predicts peak richness in center of continent (r 2 = 0.17)

85. Realistic Hybrid “Range Cohesion Model” Environmental variation is important (as in CC models) Species geographic ranges are cohesive (as in MDE models)

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

87. 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

88. 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

89. 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)

92. Range cohesion improves model fit for all analyses PREDICTOR VARIABLE Environmental Variable Environmental Variable + Range Cohesion Topographic surface area (km²) 0.200.42 Net primary productivity (tons/yr) 0.790.83 Precipitation (mm/yr) 0.670.80 Temperature (mean annual, Cº) 0.670.74 Topographic relief (elevational range) 0.170.20 Ecosystem diversity (# ecosystem types) 0.000.12 All variables 0.840.86

94. Contemporary climate + range cohesion

95. Elevational range + historical factors

96. Conclusions For most species of South American birds, contemporary climate is uncorrelated with species richness Elevational range and habitat diversity are weakly correlated with species richness for all groups Results implicate importance of historical evolutionary forces in shaping species richness Hybrid models that include geographic range cohesion improve fit

97. Future Research Phylogenetic constraints Mechanisms of speciation Analysis of isolated biomes

98. Community Comparisons SpeciesBogForest Amblyopone pallipes -1 Dolichoderus pustulatus 1- Lasius alienus 11 Lasius umbratus -4 Camponotus herculaneus -3 Myrmica lobifrons 82- Myrmica detritinodis -1 Stenamma diecki -1 Aphaenogaster rudis complex -1 1)Species Composition 2)Relative Abundance 3)Species Richness SPECIES RICHNESS 3 7

99. Invertebrate food web associated with Sarrracenia Small scale Experimental Patterns of relative abundance CONCLUSION: Relative abundance is best explained by food web models

100. Invertebrate food web associated with Sarrracenia Small scale Experimental Patterns of relative abundance CONCLUSION: Relative abundance is best explained by food web models Avifauna of South America Large scale Correlative Patterns of species richness CONCLUSION: Species richness patterns reflect historical forces, not contemporary climate