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Testing speciation models with DNA sequence data

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Molecular Approaches to Ecology and Evolution

Summary

This chapter reviews an approach to the study of speciation that is based on patterns of genetic variation within and between closely related species. Historically, research on the genetic mechanisms of speciation, and of species divergence, is very difficult–suffering from both practical difficulties in data collection and from theoretical problems. The method outlined in this paper is based on genealogical models of population divergence. We describe a hierarchy of models, and show how these fit into a hypothesis-testing framework that overcomes some of the theoretical problems of studying speciation. The method also advances the empirical study of speciation. Since testing of the models relies only on comparative DNA sequence data from closely related species, it can be applied to existing species regardless of whether it is practical or possible to generate hybrids.

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Author information

Authors and Affiliations

  1. Department of Ecology, Evolution, and Natural Resources, Nelson Biological Labs, Rutgers University, P.O. Box 1059, Piscataway, NJ, 08855, USA

    John Wakeley & Jody Hey

  2. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA

    John Wakeley

Authors
  1. John Wakeley
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  2. Jody Hey
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Editor information

Editors and Affiliations

  1. American Museum of Natural History, Central Park West at 79th Street, New York, NY, 10024-5192, USA

    Rob DeSalle

  2. Zoologisches Institut, J.W. Goethe-Universität, Siesmayerstr. 70, D-60054, Frankfurt, Germany

    Bernd Schierwater

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© 1998 Springer Basel AG

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Wakeley, J., Hey, J. (1998). Testing speciation models with DNA sequence data. In: DeSalle, R., Schierwater, B. (eds) Molecular Approaches to Ecology and Evolution. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-8948-3_8

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  • DOI: https://doi.org/10.1007/978-3-0348-8948-3_8

  • Publisher Name: Birkhäuser, Basel

  • Print ISBN: 978-3-0348-9840-9

  • Online ISBN: 978-3-0348-8948-3

  • eBook Packages: Springer Book Archive

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