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Community Assembly: From Small to Large Spatial Scales Nicholas J. Gotelli Department of Biology University of Vermont Burlington, VT 05405 Community Comparisons Species Bog Forest Amblyopone pallipes - 1 Dolichoderus pustulatus 1 - Lasius alienus 1 1 Lasius umbratus - 4 Camponotus herculaneus - 3 82 - Myrmica detritinodis - 1 Stenamma diecki - 1 Aphaenogaster rudis complex - 1 Myrmica lobifrons Bog sample grid (5 m x 5 m) Forest sample grid (5 m x 5 m) Community Comparisons Species Bog Forest Amblyopone pallipes - 1 Dolichoderus pustulatus 1 - Lasius alienus 1 1 Lasius umbratus - 4 Camponotus herculaneus - 3 82 - Myrmica detritinodis - 1 Stenamma diecki - 1 Aphaenogaster rudis complex - 1 Myrmica lobifrons 1) Species Composition Community Comparisons Species Bog Forest Amblyopone pallipes - 1 Dolichoderus pustulatus 1 - Lasius alienus 1 1 Lasius umbratus - 4 Camponotus herculaneus - 3 82 - Myrmica detritinodis - 1 Stenamma diecki - 1 Aphaenogaster rudis complex - 1 Myrmica lobifrons 1) Species Composition 2) Relative Abundance Community Comparisons Species Bog Forest Amblyopone pallipes - 1 Dolichoderus pustulatus 1 - Lasius alienus 1 1 Lasius umbratus - 4 Camponotus herculaneus - 3 82 - Myrmica detritinodis - 1 Stenamma diecki - 1 Aphaenogaster rudis complex - 1 SPECIES RICHNESS 3 7 Myrmica lobifrons 1) Species Composition 2) Relative Abundance 3) Species Richness Community Comparisons Species Bog Forest Amblyopone pallipes - 1 Dolichoderus pustulatus 1 - Lasius alienus 1 1 Lasius umbratus - 4 Camponotus herculaneus - 3 82 - Myrmica detritinodis - 1 Stenamma diecki - 1 Aphaenogaster rudis complex - 1 SPECIES RICHNESS 3 7 Myrmica lobifrons 1) Species Composition 2) Relative Abundance 3) Species Richness 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 Determinants of Species Richness • Avifauna of South America • Large scale • Correlative • CONCLUSION: Species richness patterns reflect historical forces, not contemporary climate Food webs • Diagrams of “who eats whom” • Alternative to competitive-based paradigm Methods for Food Web Analysis • Interaction matrices • Experimental manipulations New approach • Manipulate entire communities in ecological “press” experiment • Compare relative abundances to predictions of several biologically realistic models Carnivorous plants: well-known, but poorly studied Aaron M. Ellison Harvard Forest The Northern Pitcher Plant Sarracenia purpurea • Perennial plant of low-N peatlands • Lifespan 30-50 y • Arthropod prey capture in waterfilled pitchers • Diverse inquiline community in pitchers Sarraceniopus gibsoni Wyeomyia smithii The Inquilines Blaesoxipha fletcheri Habrotrocha rosa Metriocnemus knabi Food web structure Moose Bog 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 Habitat Volume Manipulations C Inquilines & liquid removed, censused, returned (Control) C- Inquilines & liquid removed,censused. Liquid replaced with equal volume of d H20 (Trophic Pruning) A Inquilines & liquid removed, censused, returned, topped with d H20 (Habitat Expansion) A- Inquilines & liquid removed, censused. Liquid replaced and topped with d H20 (Habitat Expansion & Trophic Pruning) E Inquilines & liquid removed, censused (Habitat Contraction & Trophic Pruning) Significant alterations in habitat volume among treatments Volume (cc) 40 Old Leaf First Leaf Second Leaf 30 20 10 0 C C- A A- Treatment E Mean responses per leaf Treatment A1 Plant 5 Leaf A Date Species 5/31 6/5 6/7 6/12 6/20 6/26 7/4 7/10 7/17 7/24 7/31 8/7 8/14 8/23 Mean Wyeomyia 0 0 0 0 11 12 5 4 4.000 Metriocnemus 1 4 0 1 0 10 2 3 2.625 Blaesoxipha 0 0 0 0 0 1 1 0 0.250 Sarracenioppus 0 1 0 0 0 0 0 0 0.125 Habrotrocha 1 0 0 0 7 21 15 3 5.875 10 300 0 0 0 200 0 20 66.250 3 0 1 3 8 22 4 5.250 4.0 8.2 0.0 7.0 6.7 6.0 9.6 15.6 7.138 Protozoa Prey Volume 1 Responses in abundance to both leaf age and habitat manipulation Wyeomyia Abundance 10 8 Old Leaf First Leaf Second Leaf 6 4 2 0 C C- A A- Treatment E Idiosyncratic responses of individual taxa to manipulations Habrotrocha Number 20 Old Leaf First Leaf Second Leaf 15 10 5 0 C C- A A- Treatment E Responses of prey abundance to treatment and leaf age 16 Prey Number 14 Old Leaf First Leaf Second Leaf 12 Prey 10 8 6 4 2 0 C C- A A- Treatment E Summary of Effects of Treatment, Age, and Prey Abundance Wyeomyia Metriocnemus Treatment Age ++ + ++ Habrotrocha +++ ++ +++ + Sarraceniopus Blaesoxipha Volume Prey +++ Protozoa Prey TxA ++ ++ ++ ++ Detecting Species Associations With Multiple Regression Models Dep Var Pro Independent Variable Wye Met Pro Bla + + Wye Met Sar + Hab ++ +++ — + Sar Bla Ha ++ Prey +++ Prey Vol - — – + +++ + + + +++ Standard Regression Methods • Controls for covariation • Assumes simple independent-dependent data structure • Does not allow for direct testing of different hypotheses of community structure P1 R P2 P3 Path Analysis • Controls for covariation • Does not assume simple covariance structure • Allows for testing of different models of community structure V1 V4 V2 V3 Path models of community structure Habitat Volume, Prey Models S3 S2 Habitat Volume S6 S4 S1 Keystone Species Models S3 S2 Keystone Species (S1) S6 S4 S5 Food Web Models S1 S3 S2 S6 S4 S5 Top-Down Control Food Web Models S1 S3 S2 S6 S4 S5 Bottom-Up Control Habitat Volume Model Protozoa Habrotrocha (rotifer) Blaesoxipha (flesh fly) 0.099 -0.100 -0.004 0.135 Volume Wyeomyia (mosquito) 0.366 0.432 -0.001 Prey Metriocnemus (midge) Sarracenioppus (mite) Blaesoxipha (flesh fly) 0.258 -0.046 Wyeomyia (mosquito) Habrotrocha (rotifer) -0.264 -0.003 0.005 Protozoa Metriocnemus (midge) 0.176 -0.002 0.155 Prey Sarracenioppus (mite) Blaesoxipha (flesh fly) 0.258 -0.046 Wyeomyia (mosquito) Habrotrocha (rotifer) -0.271 -0.025 0.005 Protozoa Metriocnemus (midge) 0.034 -0.002 0.321 0.155 Prey Sarracenioppus (mite) 0.809 -0.350 Bacteria 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 Cross-Validation Index Sarraceniopus Blaesoxipha Protozoa Metriocnemus Habrotrocha Wyeomyia BOTTOM UP BOTTOM UP (B) TOP DOWN TOP DOWN (B) 0.0 PREY 0.2 VOLUME 0.4 PREY + VOLUME 1.0 Random Colonization Models Keystone Species Models Food Web Models 0.8 0.6 21 15 13 13 13 13 13 13 13 15 18 15 18 Cross-Validation Index Sarraceniopus Blaesoxipha Protozoa Metriocnemus Habrotrocha Wyeomyia BOTTOM UP BOTTOM UP (B) TOP DOWN TOP DOWN (B) 0.0 PREY 0.2 VOLUME 0.4 PREY + VOLUME 1.0 Random Colonization Models Keystone Species Models Food Web Models 0.8 0.6 21 15 13 13 13 13 13 13 13 15 18 15 18 Wyeomyia Keystone Model Protozoa Habrotrocha (rotifer) Blaesoxipha (flesh fly) -0.061 -0.265 0.271 Wyeomyia (mosquito) 0.014 0.228 -0.156 Prey Metriocnemus (midge) Sarracenioppus (mite) 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 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 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) Gary Entsminger Acquired Intelligence Nicholas Gotelli, University of Vermont Gary Graves Smithsonian Rob Colwell University of Connecticut Carsten Rahbek University of Copenhagen Thiago Rangel Federal University of Goiás Major Hypotheses • Historical Factors • Contemporary Climate • Mid-domain Effect Major Hypotheses • Historical Factors • Contemporary Climate • Mid-domain Effect “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) South American Avifauna • 2891 breeding species • 2248 species endemic to South America and associated landbridge islands Minimum: 18 species Maximum: 846 species Minimum: 18 species “Taiwan” ~ 4 grid cells ~ 471 bird species Maximum: 846 species Minimum: 18 species Climate, Habitat Variables Measured at Grid Cell Scale 800 600 600 Species Richness Species Richness 800 400 200 200 0 0 0 2 4 6 8 10 Net Primary Productivity (tons / ha) 12 0 14 800 800 600 600 Species Richness Species Richness 400 400 200 0 0 50 100 Precipitation (mm / yr) 150 200 100 150 200 Mean Annual Temperature (C) x 10 250 300 400 200 0 50 0 50 100 150 Climate PCA 200 250 Summary of Simple Regression Statistics PREDICTOR VARIABLE R2 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 variables 0.79 However… Conventional analyses mask effects of species geographic range! Species vary tremendously in geographic range size (= number of grid cells occupied) Myioborus cardonai 1 grid cell Median range size Anas puna 64 grid cells Phalacrocorax brasilianus 1676 grid cells 1st quartile 2nd 3rd 4th quartile Species Richness Gradients Depend On Range Size Quartile! Species Richness Correlates For Range Quartiles PREDICTOR VARIABLE 1st quartile 2nd quartile 3rd quartile 4th quartile Topographic surface area (km²) 0.00 0.00 0.02 0.24 Net primary productivity (tons/yr) 0.00 0.00 0.05 0.82 Precipitation (mm/yr) 0.01 0.01 0.07 0.57 Temperature (mean annual, Cº) 0.00 0.00 0.00 0.69 Topographic relief (elevational range) 0.31 0.39 0.16 0.14 Ecosystem diversity (# ecosystem types) 0.21 0.23 0.19 0.00 All variables 0.48 0.58 0.47 0.85 Failure of Climate Variable to Predict Species Richness of First Three Range Size Quartiles PREDICTOR VARIABLE 1st quartile 2nd quartile 3rd quartile 4th quartile Topographic surface area (km²) 0.00 0.00 0.02 0.24 Net primary productivity (tons/yr) 0.00 0.00 0.05 0.82 Precipitation (mm/yr) 0.01 0.01 0.07 0.57 Temperature (mean annual, Cº) 0.00 0.00 0.00 0.69 Topographic relief (elevational range) 0.31 0.39 0.16 0.14 Ecosystem diversity (# ecosystem types) 0.21 0.23 0.19 0.00 All variables 0.48 0.58 0.47 0.85 Contrasting Correlates for Restricted vs. Widespread Species PREDICTOR VARIABLE 1st -3rd quartiles 4th quartile Total Richness Topographic surface area (km²) 0.00 0.24 0.21 Net primary productivity (tons/yr) 0.01 0.82 0.67 Precipitation (mm/yr) 0.04 0.57 0.53 Temperature (mean annual, Cº) 0.00 0.69 0.48 Topographic relief (elevational range) 0.33 0.14 0.00 Ecosystem diversity (# ecosystem types) 0.25 0.00 0.07 All variables 0.58 0.85 0.79 500 400 400 Species Richness (Quartiles 1-3) Species Richness (Quartiles 1-3) 500 300 200 100 200 100 0 0 0 2 4 6 8 10 Net Primary Productivity (tons / ha) 12 0 14 500 500 400 400 Species Richness (Quartiles 1-3) Species Richness (Quartiles 1-3) 300 300 200 100 0 50 100 150 200 Mean Annual Temperature (C) x 10 250 300 300 200 100 0 0 50 100 Precipitation (mm / yr) 150 200 50 300 550 800 1050 Climate PCA 1300 1550 1800 800 600 600 Species Richness Species Richness 800 400 200 200 0 0 0 2 4 6 8 10 Net Primary Productivity (tons / ha) 12 0 14 800 800 600 600 Species Richness Species Richness 400 400 200 0 0 50 100 Precipitation (mm / yr) 150 200 100 150 200 Mean Annual Temperature (C) x 10 250 300 400 200 0 50 0 50 100 150 Climate PCA 200 250 Correlates of Total Species Richness Mirror Patterns for Widespread Species (4th Quartile) 500 400 Species Richness (Quartile IV) Species Richness (Quartile IV) 500 300 200 300 100 100 0 -100 0 2 4 6 8 10 Net Primary Productivity (tons / ha) 12 14 0 100 150 200 Mean Annual Temperature (C) x 10 250 300 500 500 400 Species Richness (Quartile IV) Species Richness (Quartile IV) 50 300 200 300 100 100 0 -100 0 50 100 Precipitation (mm / yr) 150 200 0 50 100 150 Climate PCA 200 250 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 Contrasting Correlates for Restricted vs. Widespread Species PREDICTOR VARIABLE 1st -3rd quartiles 4th quartile Total Richness Topographic surface area (km²) 0.00 0.24 0.21 Net primary productivity (tons/yr) 0.01 0.82 0.67 Precipitation (mm/yr) 0.04 0.57 0.53 Temperature (mean annual, Cº) 0.00 0.69 0.48 Topographic relief (elevational range) 0.33 0.14 0.00 Ecosystem diversity (# ecosystem types) 0.25 0.00 0.07 All variables 0.58 0.85 0.79 250 200 80 Species Richness Quartile II) Species Richness (Quartile I) 100 60 40 100 20 50 0 0 0 1000 2000 3000 Elevational Range (m) 4000 5000 6000 0 1000 2000 3000 Elevational Range (m) 4000 5000 6000 0 1000 2000 3000 Elevational Range (m) 4000 5000 6000 500 Species Richness (Quartile IV) 200 Species Richness (Quartile III) 150 150 100 50 400 300 200 100 0 0 0 1000 2000 3000 Elevational Range (m) 4000 5000 6000 Species Richness (Total) 800 600 400 200 0 0 1000 2000 3000 Elevational Range (m) 4000 5000 6000 Major Hypotheses • Historical factors • Contemporary Climate • Mid-domain Effect One-dimensional geographic domain One-dimensional geographic domain Species geographic ranges randomly placed line segments within domain One-dimensional geographic domain Species geographic ranges randomly placed line segments within domain Peak of species richness in geographic center of domain Species Number One-dimensional geographic domain Species geographic ranges randomly placed line segments within domain Peak of species richness in geographic center of domain domain geographic range domain Pancakus spp. die Pfankuchen Guilde Reduced species richness at margins of the domain Mid-domain peak of species richness in the center of the domain © Matt Fitzpatrick, UT 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 (r2 = 0.17) Realistic Hybrid “Range Cohesion Model” • Environmental variation is important (as in CC models) • Species geographic ranges are cohesive (as in MDE models) Conceptual Weakness of Curve-Fitting Paradigm Potential Predictor Variables (tonnes/ha, C°) Predicted Species Richness (S / grid cell) Observed Species Richness (S / grid cell) Conceptual Weakness of Curve-Fitting Paradigm Potential Predictor Variables (tonnes/ha, C°) minimize residuals Predicted Species Richness (S / grid cell) Observed Species Richness (S / grid cell) Conceptual Weakness of Curve-Fitting Paradigm Potential Predictor Variables (tonnes/ha, C°) ?? MECHANISM ?? minimize residuals Predicted Species Richness (S / grid cell) Observed Species Richness (S / grid cell) Alternative Strategy: Mechanistic Simulation Models Potential Predictor Variables (tonnes/ha, C°) Predicted Species Richness (S / grid cell) Explicit Simulation Model Observed Species Richness (S / grid cell) Range cohesion improves model fit for all analyses PREDICTOR VARIABLE Environmental Variable Environmental Variable + Range Cohesion Topographic surface area (km²) 0.20 0.42 Net primary productivity (tons/yr) 0.79 0.83 Precipitation (mm/yr) 0.67 0.80 Temperature (mean annual, Cº) 0.67 0.74 Topographic relief (elevational range) 0.17 0.20 Ecosystem types) 0.00 0.12 0.84 0.86 All variables diversity (# ecosystem Contemporary climate + range cohesion Elevational range + historical factors Contemporary climate + range cohesion 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 Future Research • Phylogenetic constraints • Mechanisms of speciation • Analysis of isolated biomes Community Comparisons Species Bog Forest Amblyopone pallipes - 1 Dolichoderus pustulatus 1 - Lasius alienus 1 1 Lasius umbratus - 4 Camponotus herculaneus - 3 82 - Myrmica detritinodis - 1 Stenamma diecki - 1 Aphaenogaster rudis complex - 1 SPECIES RICHNESS 3 7 Myrmica lobifrons 1) Species Composition 2) Relative Abundance 3) Species Richness • Invertebrate food web associated with Sarrracenia • Small scale • Experimental • Patterns of relative abundance • CONCLUSION: Relative abundance is best explained by food web models • 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