Patch occupancy models of metapopulation dynamics: Efficient parameter estimation using implicit statistical inference

The practical value of a predictive metapopulation model is much affected b y the amount of data required for parameter estimation. Some metapopulation models require information on population turnover events for parameterizat ion, whereas other models, such as the incidence function model that is use d in this study, can be parameterized with spatial data on patch occupancy. The latter data are more readily available. The original method of using s patial pattern data to parameterize the incidence function and other patch models has been criticized for involving potentially troublesome assumption s, such as the independence of habitat patches and constant colonization pr obabilities. This study describes an improved parameter estimation method t hat is not affected by these problems. The proposed method is based on Mont e Carlo inference for implicit statistical models, and it can be adapted to any stochastic patch occupancy model of metapopulation dynamics. As an add itional advantage, the new method allows the estimation of the amplitude of regional stochasticity. Tested with simulated data, the new method was fou nd to produce substantially more accurate parameter estimates than the orig inal method. The new approach is applied to two empirical metapopulations, the false heath fritillary butterfly in Finland and the American pika at Bo die, California.