Applying stochastic models of geographic evolution to explain species-environment relationships of bats in the New World J. Sebastián Tello and Richard D. Stevens Department of Biological Sciences Louisiana State University Baton Rouge, LA 70803

Variation in species richness at broad geographic extents Introduction Bird richness Hawkins et al. 2007

e.g. NPP Species Richness Deterministic effects of environmental conditions is a prominent hypotheses Introduction

Strong and frequent species-environment correlations Introduction Field et al Climate/ Productivity Heterogeneity Nutrients Primacy adj. R 2

Correlative studies have used simple non- biological null hypotheses Expected by chance? Observed Relationship Environmental variable Richness R 2 =0.513R 2 =0.000 Introduction

Richness gradients are formed by the overlap of individual species distributions Introduction Richness Gradient Species Distributions

Species distributions are the consequence of the geographic diversification of clades Introduction Species Distributions Biogeographic Processes and Constraints a. Confined geographic domain of distribution b. Aggregated distributions c. Geographic range movements d. Cladogenesis: Speciation Extinction

Environment assumed to affects richness via fundamental biogeographic processes Introduction Distributions Biogeographic Processes Richness Environment

Environment assumed to affects richness via fundamental biogeographic processes Introduction Distributions Biogeographic Processes Richness Environment

Biogeographic processes not necessarily driven by environment Introduction Distributions Biogeographic Processes Richness Stochasticity

Biogeographic processes not necessarily driven by environment Introduction Distributions Biogeographic Processes Richness Environment Stochasticity

Introduction Biogeographic Processes Richness Environment Stochasticity How do species-environment relationships change when random biogeographic processes are considered? ? Null Model

Family Phyllostomidae 146 species America divided in cells of 100 by 100 km 1. Species richness of bats by geographic range overlap Methods

Correlation estimated with adjusted R 2 values 2. Empirical richness-environment correlations Methods

Correlation estimated with adjusted R 2 values Richness correlated against three variable sets: a. Energy b. Heterogeneity c. Seasonality 2. Empirical richness-environment correlations Methods

2. Empirical richness-environment correlations: Uncertainty estimation by bootstrapping Methods R 2 adj. Frequency 010.5

Methods R 2 adj. Frequency Empirical richness-environment correlations: Uncertainty estimation by bootstrapping

a. Computer simulations in R b. Random biogeographic processes: 1. Range spread 2. Range movement 3. Speciation 4. Extinction c. Constrained domain: the New World (cells of 100 by 100 kms) 3. Create a null model of the geographic diversification of Phyllostomid bats Methods

3. Create a null model Methods Start

3. Create a null model Methods Start Domain colonization Time = 1

3. Create a null model Methods Start Domain colonization Ranges too small? Range growth Time = 1 Yes

3. Create a null model Methods Start Domain colonization Ranges too small? Range growth Range movement Time = 1 No Yes

3. Create a null model Methods Start Domain colonization Ranges too small? Range growth Range movement Speciations? Speciation Time = 1 No Yes

3. Create a null model Methods Start Domain colonization Ranges too small? Range growth Range movement Speciations? Extinctions? Speciation Extinction Time = 1 No Yes No

3. Create a null model Methods Start Domain colonization Ranges too small? Range growth Range movement Speciations? Extinctions? Time limit reached? Speciation Extinction Time = 1 No Yes No

3. Create a null model Methods Start Domain colonization Ranges too small? Range growth Range movement Speciations? Extinctions? Time limit reached? Speciation Extinction Time + 1 Time = 1 No Yes No

3. Create a null model Methods Start Domain colonization Ranges too small? Range growth Range movement Speciations? Extinctions? Time limit reached? Speciation Extinction Time + 1 Time = 1 End No Yes No

Simulation Model Richness Maps Species Distributions Methods 3. Create a null model of the geographic diversification of Phyllostomid bats

Null richness map Null richness- environment correlation Save results Run model for 12,400 time steps Methods 3. Create a null model of the geographic diversification of Phyllostomid bats START

Null richness map Null richness- environment correlation Save results Run model for 12,400 time steps Methods 1000 null model runs 3. Create a null model of the geographic diversification of Phyllostomid bats START END

Methods R 2 adj. Frequency Create a null model of the geographic diversification of Phyllostomid bats

Methods 4. Test for effects of environment using null model R 2 adj. Frequency R 2 adj. Frequency Significant t-testNon-Significant t-test Environmental effectNo environmental effect

Methods 5. Calculate effect size using null model R 2 adj. Frequency 010.5

Richness of Phyllostomid bats in the New World is strongly associated with the environment adj. R 2 Energy Heterogeneity Seasonality Results

All three environmental predictors have a significant effect Results R 2 adj. Frequency EnergyHeterogeneitySeasonality

However, the relative importance changes significantly when using null model Results adj. R 2 Energy Heterogeneity Seasonality Energy Heterogeneity Seasonality Hedges’ d

Naïve null hypotheses are not appropriate for testing species-environment relationships Expected by chance? Observed Relationship R 2 =0.513 Environmental variable Richness R 2 =0.000 Conclusions

Geographical evolution null models produce much more appropriate null hypotheses

Conclusions Geographical evolution null models can significantly modify results

Jim Cronin Bret Elderd Kyle Harms Eve McCulloch Mercedes Gavilanez Maria Sagot Lori Patrick ? Acknowledgements