In the Light of Evolution Volume X: Comparative Phylogeography (2017) / Chapter Skim
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3 Inferring Responses to Climate Dynamics from Historical Demography in Neotropical Forest Lizards - Ivan Prates, Alexander T. Xue, Jason L. Brown, Diego F. Alvarado-Serrano, Miguel T. Rodrigues, Michael J. Hickerson, and Ana C. Carnaval
3 Inferring Responses to Climate Dynamics from Historical Demography in Neotropical Forest Lizards - Ivan Prates, Alexander T. Xue, Jason L. Brown, Diego F. Alvarado-Serrano, Miguel T. Rodrigues, Michael J. Hickerson, and Ana C. Carnaval
Pages 45-66
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Using reduced genomic data to infer * Department of Biology, City College of New York, New York, NY 10031; †Department of Biology, Graduate Center, City University of New York, New York, NY 10016; ‡Cooperative Wildlife Research Laboratory, Department of Zoology, Southern Illinois University, Carbondale, IL 62901; §Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109; Departamento de Zoologia, Instituto de Biociências, Universidade de São Paulo, São Paulo, SP 05508-090, Brazil; and #Division of Invertebrate Zoology, American Museum of Natural History, New York, NY 10024.
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From page 46... ...
and either congruent spatial patterns of genetic diversity across species of broadly similar ecologies (Schneider et al., 1998; Carnaval et al., 2009) or differences in ways largely expected, given their life history traits (Crawford et al., 2007; Nicolas et al., 2008)
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, we develop plausible models of the spatial distribution of genetic diversity under future climates. To do so, we analyze SNP data from Anolis punctatus and Anolis ortonii (Dactyloidae)
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marmoratus occupy a limited extent of the southern Atlantic Forest in the states of Rio de Janeiro and São Paulo) , we focus on concerted demographical shifts between western Amazonia, eastern Amazonia, and the northern Atlantic Forest.
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. Species Show Different Historical Demographic Syndromes Demographic analyses across spatial groups within each species recover signals of population size shifts in western Amazonia, eastern Amazonia, and the northern Atlantic Forest.
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, the only region with sufficient sampling of SNPs per individual to enable historical demographic inference in this species. Shifts in Population Size Are Asynchronous Extending beyond region-specific inferences of population size change within each species, we applied hierarchical demographic models to combine data across regions and species and test for assemblage-wide synchrony in population shifts.
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Genetic Diversity Under Future Climates The distinct responses to environmental shifts observed across these broadly codistributed taxa, as shown by the historical analyses, are also
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that takes into account not only the distribution of suitable habitats under future climates but also species-specific ecological and demographic constraints inferred from molecular data. This framework first uses carefully parameterized species distribution models (SDMs)
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. White dots in maps indicate the localities with empirical genetic data that were used for ABC parameter estimation.
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, we nonetheless find that the demographic processes underscoring those patterns vary across species. This view is supported by tests of synchronous population size shifts that allow for species-specific parameters as well as by plausible models of the distribution of genetic diversity under future carbon emissions.
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. Phylogeographic Structure and Demographic Syndromes We first inferred phylogenetic relationships with singular value decomposition scores for species quartets (SVD quartets)
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, current effective population size change for constant size ~U(50 k, 500 k) , and current effective population size change for contraction ~U(5 k, 100 k)
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In analysis 1, we tested the degree of synchronicity in population size changes at a species level, comparing the three spatial groups within the better-sampled species A punctatus and A
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As in the procedure for the spatial group-specific demographic syndrome analysis, we performed a PCA on the accepted simulations and plotted the simulated data with the empirical aSFS along the first two PCs to assess if the proposed scenarios could have generated the empirical aSFS. Furthermore, to assess hABC performance using the aSFS, we performed 50 leave-oneout cross-validations for the set of simulations per each empirical aSFS, with each cross-validation involving the extraction of a single simulation to act as a pseudo-observed dataset (POD)
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. Species Distribution Models to Inform Analyses of the Future Distribution of Genetic Diversity To parameterize predictive models of future shifts in the spatial distribution of genomic diversity, we estimated migration rates and maximum carrying capacity at occupied sites from spatial demographic simulations that used friction and carrying-capacity layers derived from SDMs projected into former climates.
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. To project species ranges and genetic diversity under future climates, we used bioclimatic variables estimated from the Hadley Center model for the years 2030, 2050, 2070, and 2080 (HadGEN2-ES)
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. Spatial Demographic Simulations for the Past A total of 200,000 forward-in-time simulations of dispersal were performed under different migration rates and maximum carrying capacities.
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, we multiplied the mutation, recombination, and growth rates by 10. Spatial Genetic Simulations for the Past To use the empirical data to validate the simulated demographic scenarios, and hence enable estimation of demographic parameters, each forward-in-time spatial demographic simulation was followed by a corresponding backward-in-time genetic simulation.
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For each past time period, the posterior estimates (modes) of ancestral population size, migration rate, and carrying capacity derived from the historical analyses were used to parameterize the simulations.
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Additional funding included NSF Doctoral Dissertation Improvement Grant DEB 1601271 and a City University of New York (CUNY) Graduate Center Doctoral Student Research Grant (to I.P.)
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