Computational Methods for Inference of Population Parameters

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Successful management and conservation of natural populations requires knowledge of population structure and migration rates. The increasing availability of extensive genetic data obtained through relatively non-invasive methods, present new opportunities for monitoring and management at a population level. These markers also make possible the use of archive material. However, there are few computational methods available to fully exploit population data. Methods of Monte Carlo likelihood, using Markov chain Monte Carlo (MCMC) have proven successful on the evolutionary scale in phylogenetic analysis, and on the individual scale in pedigree analysis. This project develops a Monte Carlo likelihood approach to inferences from genetic samples taken in monitoring a population over time. This will permit estimation of the ratio of effective to actual sizes of breeding populations and will also provide likelihoods for the estimation of admixture or migration structure. The general approach is being developed on salmonid populations, for which there exist both good data and archive material, and the project will result in software generally applicable for population studies regardless of the study organism.