PATCH OCCUPANCY MODELS OF METAPOPULATION DYNAMICS: EFFICIENT PARAMETER ESTIMATION USING IMPLICIT STATISTICAL INFERENCE

Abstract

The practical value of a predictive metapopulation model is much affected by the amount of data required for parameter estimation. Some metapopulation models require information on population turnover events for parameterization, whereas other models, such as the incidence function model that is used in this study, can be parameterized with spatial data on patch occupancy. The latter data are more readily available. The original method of using spatial pattern data to parameterize the incidence function and other patch models has been criticized for involving potentially troublesome assumptions, such as the independence of habitat patches and constant colonization probabilities. This study describes an improved parameter estimation method that is not affected by these problems. The proposed method is based on Monte Carlo inference for implicit statistical models, and it can be adapted to any stochastic patch occupancy model of metapopulation dynamics. As an additional advantage, the new method allows the estimation of the amplitude of regional stochasticity. Tested with simulated data, the new method was found to produce substantially more accurate parameter estimates than the original method. The new approach is applied to two empirical metapopulations, the false heath fritillary butterfly in Finland and the American pika at Bodie, California.