Community Assembly From Small to Large Spatial Scales

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

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 H 20 (Trophic Pruning) A Inquilines & liquid removed, censused, returned, topped with d H 20 (Habitat Expansion) A- Inquilines & liquid removed, censused. Liquid replaced and topped with d H 20 (Habitat Expansion & Trophic Pruning) E Inquilines & liquid removed, censused (Habitat Contraction & Trophic Pruning)

Significant alterations in habitat volume among treatments

Mean responses per leaf

Responses in abundance to both leaf age and habitat manipulation

Idiosyncratic responses of individual taxa to manipulations

Responses of prey abundance to treatment and leaf age

Summary of Effects of Treatment, Age, and Prey Abundance Wyeomyia Metriocnemus Treatment Age ++ + ++ Habrotrocha +++ ++ + Sarraceniopus Blaesoxipha Volume Prey +++ Protozoa Prey Tx. A ++ ++

Detecting Species Associations With Multiple Regression Models Dep Var Pro Independent Variable Wye Met Pro Bla + + Wye Met Sar + Hab Prey Vol ++ +++ — + Sar Bla Ha ++ Prey +++ — – + +++

Standard Regression Methods • Controls for covariation • Assumes simple independent-dependent data structure • Does not allow for direct testing of different hypotheses of community structure P 1 R P 2 P 3

Path Analysis • Controls for covariation • Does not assume simple covariance structure • Allows for testing of different models of community structure V 1 V 4 V 2 V 3

Path models of community structure

Habitat Volume, Prey Models

Keystone Species Models

Food Web Models Top-Down Control

Food Web Models Bottom-Up Control

Habitat Volume Model

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

Wyeomyia Keystone Model

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

“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

Summary of Simple Regression Statistics PREDICTOR VARIABLE R 2 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

1 st quartile 2 nd 3 rd 4 th quartile

Species Richness Gradients Depend On Range Size Quartile!

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. 02 0. 24 Net primary productivity (tons/yr) 0. 00 0. 05 0. 82 Precipitation (mm/yr) 0. 01 0. 07 0. 57 Temperature (mean annual, Cº) 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 1 st quartile 2 nd quartile 3 rd quartile 4 th quartile Topographic surface area (km²) 0. 00 0. 02 0. 24 Net primary productivity (tons/yr) 0. 00 0. 05 0. 82 Precipitation (mm/yr) 0. 01 0. 07 0. 57 Temperature (mean annual, Cº) 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 1 st -3 rd quartiles 4 th 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

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

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 1 st -3 rd quartiles 4 th 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

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,

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)

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 + 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