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Applying stochastic models of geographic evolution to explain species-environment relationships of bats in the New World J. Sebastián Tello and Richard.

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Presentation on theme: "Applying stochastic models of geographic evolution to explain species-environment relationships of bats in the New World J. Sebastián Tello and Richard."— Presentation transcript:

1 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

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

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

4 Strong and frequent species-environment correlations Introduction Field et al. 2009 Climate/ Productivity Heterogeneity Nutrients Primacy adj. R 2

5 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

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

7 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

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

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

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

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

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

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

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

15 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

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

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

18 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

19 3. Create a null model Methods Start

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

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

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

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

24 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

25 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

26 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

27 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

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

29 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

30 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

31 Methods R 2 adj. Frequency 010.5 3. Create a null model of the geographic diversification of Phyllostomid bats

32 Methods 4. Test for effects of environment using null model R 2 adj. Frequency 010.5 R 2 adj. Frequency 0 1 0.5 Significant t-testNon-Significant t-test Environmental effectNo environmental effect

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

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

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

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

37 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

38 Geographical evolution null models produce much more appropriate null hypotheses

39 Conclusions Geographical evolution null models can significantly modify results

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


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