title: Distinguishing migration from isolation: a Markov chain Monte Carlo approach.
abstract: A Markov chain Monte Carlo method for estimating the relative effects of migration and isolation on genetic diversity in a pair of populations from DNA sequence data is developed and tested using simulations. The two populations are assumed to be descended from a panmictic ancestral population at some time in the past and may (or may not) after that be connected by migration. The use of a Markov chain Monte Carlo method allows the joint estimation of multiple demographic parameters in either a Bayesian or a likelihood framework. The parameters estimated include the migration rate for each population, the time since the two populations diverged from a common ancestral population, and the relative size of each of the two current populations and of the common ancestral population. The results show that even a single nonrecombining genetic locus can provide substantial power to test the hypothesis of no ongoing migration and/or to test models of symmetric migration between the two populations. The use of the method is illustrated in an application to mitochondrial DNA sequence data from a fish species: the threespine stickleback (Gasterosteus aculeatus).
authors.affiliation: Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA. rn28@cornell.edu
authors.forename: R
authors.initials: R
authors.lastname: Nielsen
authors.forename: J
authors.initials: J
authors.lastname: Wakeley
mesh_terms: D000818:Animals; D004641:Emigration and Immigration; D005399:Fishes; D016013:Likelihood Functions; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method