In population biology the estimation of parameters such as effective population size and migration rate is crucial. The rapid increase in the collection of population samples of genetic data allows investigation of patterns and rates of migration among geographically subdivided populations with much greater power. Present estimation methods either assume knowledge of the exact genealogical tree of ancestry of the sampled genes or neglect all the information about this tree. Taking the uncertainty in the estimate of the genealogy into account is the major challenge for a proper statistical analysis of these data. The proposed research will use maximum likelihood to infer these rates and patterns, using the Markov Chain Monte Carlo method to sum over all possible genealogies when computing the likelihood. Methods will be extended to multiple populations: (1) for a number of different models of population structure, and (2) for different kinds of data, such as molecular sequences. Computer programs for estimating patterns of migration will be developed and produced. They will be of interest in ecology, conservation biology, and anthropology.
