A Model-Based Method for Identifying Species Hybrids Using Multilocus Genetic Data

Corresponding author: E. C. Anderson, University of California, Berkeley, CA 94720-3140., eriq{at}u.washington.edu (E-mail)

Communicating editor: M. K. UYENOYAMA

We present a statistical method for identifying species hybrids using data on multiple, unlinked markers. The method does not require that allele frequencies be known in the parental species nor that separate, pure samples of the parental species be available. The method is suitable for both markers with fixed allelic differences between the species and markers without fixed differences. The probability model used is one in which parentals and various classes of hybrids (F₁'s, F₂'s, and various backcrosses) form a mixture from which the sample is drawn. Using the framework of Bayesian model-based clustering allows us to compute, by Markov chain Monte Carlo, the posterior probability that each individual belongs to each of the distinct hybrid classes. We demonstrate the method on allozyme data from two species of hybridizing trout, as well as on two simulated data sets.

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