Distribution modelling and statistical phylogeography: an integrative framework for generating and testing alternative biogeographical hypotheses

Authors

  • Corinne L. Richards,

    Corresponding author
      *Corinne L. Richards, Department of Ecology and Evolutionary Biology, 1109 Geddes Ave, Museum of Zoology, University of Michigan, Ann Arbor, MI 48109-1079, USA. E-mail: clrichar@umich.edu
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  • Bryan C. Carstens,

    1. Department of Ecology and Evolutionary Biology, 1109 Geddes Ave, Museum of Zoology, University of Michigan, Ann Arbor, MI 48109-1079, USA. New address Department of Biological Sciences, 202 Life Sciences Building, Louisiana State University, Baton Rouge, LA 70803, USA
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  • L. Lacey Knowles

    1. Department of Ecology and Evolutionary Biology, 1109 Geddes Ave, Museum of Zoology, University of Michigan, Ann Arbor, MI 48109-1079, USA. New address Department of Biological Sciences, 202 Life Sciences Building, Louisiana State University, Baton Rouge, LA 70803, USA
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*Corinne L. Richards, Department of Ecology and Evolutionary Biology, 1109 Geddes Ave, Museum of Zoology, University of Michigan, Ann Arbor, MI 48109-1079, USA. E-mail: clrichar@umich.edu

Abstract

Statistical phylogeographic studies contribute to our understanding of the factors that influence population divergence and speciation, and that ultimately generate biogeographical patterns. The use of coalescent modelling for analyses of genetic data provides a framework for statistically testing alternative hypotheses about the timing and pattern of divergence. However, the extent to which such approaches contribute to our understanding of biogeography depends on how well the alternative hypotheses chosen capture relevant aspects of species histories. New modelling techniques, which explicitly incorporate spatio-geographic data external to the gene trees themselves, provide a means for generating realistic phylogeographic hypotheses, even for taxa without a detailed fossil record. Here we illustrate how two such techniques – species distribution modelling and its historical extension, palaeodistribution modelling – in conjunction with coalescent simulations can be used to generate and test alternative hypotheses. In doing so, we highlight a few key studies that have creatively integrated both historical geographic and genetic data and argue for the wider incorporation of such explicit integrations in biogeographical studies.

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