A major part of my PhD is devoted to the estimation of genetic drift, mutation and migration rates in a structured population of Alpine ibex (Capra ibex) in the Swiss Alps. The history of this population is very well known, which makes this case unique. It is thus possible to condition the inference on a considerable amount of non-genetic information, which in turn 'saves' genetic information for the estimation of the parameters of interest. Methodologically, I use a combination of Approximate Bayesian Computation and machine learning techniques. 

In the context of this project, I have written an individual-based simulation that allows for arbitrarily complex population demography and subdivision. Generations may be overlapping. Migration rates can be specified between any two subpopulations and in both directions. The program implements the single stepwise model, the generalised stepwise model and the k-allele model of mutation. It is available upon E-Mail .