Distinguishing migration from isolation: A Markov chain Monte Carlo approach

A Markov chain Monte Carlo method for estimating the relative effects of mi gration and isolation on genetic diversity in a pair of populations from DN A sequence data is developed and tested using simulations. The two populati ons are assumed to be descended from a panmictic ancestral population at so me time in the past and may (or may not) after that be connected by migrati on. The use of a Markov chain Monte Carlo method allows the joint estimatio n of multiple demographic parameters in either a Bayesian or a likelihood f ramework. The parameters estimated include the migration rate for each popu lation, the time since the two populations diverged from a common ancestral population, and the relative size of each of the two current populations a nd of the common ancestral population. The results show that even a single nonrecombining genetic locus can provide substantial power to test the hypo thesis of no ongoing migration and/or to test models of symmetric migration between the mio populations. The use of the method is illustrated in an ap plication to mitochondrial DNA sequence data from a fish species: the three spine stickleback (Gasterosteus aculeatus).