BAYESIAN ANALYSIS OF METAPOPULATION DATA

A Bayesian approach is used to develop a method for fitting a metapopulation model (the incidence function model) to data on habitat patch occupancy, providing esti- mates of the five model parameters. Parameter estimation is carried out using a Markov chain Monte Carlo sampler, and data augmentation is used to include the effect of missing data in the analysis. The Bayesian approach allows us to take into account uncertainty about the parameter estimates when making predictions with the model. We demonstrate the methods of parameter estimation and prediction with simulated data. We first simulated metapopulation dynamics in real habitat patch networks with given parameter values and sampled the simulated data. Parameters were estimated both from full data sets, and from data sets with data for many years treated as missing. These estimates were then used to predict the distribution of time to extinction in modified networks, where patch areas had been reduced so that the real parameter values led to metapopulation extinction within ;30 yr. We were successfully able to fit the model and found that, in some cases, the predictions can be sensitive to one of the parameters.