Viability analyses with habitat-based metapopulation models

Population viability analysis (PVA) models incorporate spatial dynamics in different ways. At one extreme are the occupancy models that are based on t he number of occupied populations. The simplest occupancy models ignore the location of populations. At the other extreme are individual-based models, which describe the spatial structure with the location of each individual in the population, or the location of territories or home ranges. In betwee n these are spatially structured metapopulation models that describe the dy namics of each population with structured demographic models and incorporat e spatial dynamics by modeling dispersal and temporal correlation among pop ulations. Both dispersal and correlation between each pair of populations d epend on the location of the populations, making these models spatially str uctured. In this article, I describe a method that expands spatially struct ured metapopulation models by incorporating information about habitat relat ionships of the species and the characteristics of the landscape in which t he metapopulation exists. This method uses a habitat suitability map to det ermine the spatial structure of the metapopulation, including the number, s ize, and location of habitat patches in which subpopulations of the metapop ulation live. The habitat suitability map can be calculated in a number of different ways, including statistical analyses (such as logistic regression ) that find the relationship between the occurrence (or, density) of the sp ecies and independent variables which describe its habitat requirements. Th e habitat suitability map is then used to calculate the spatial structure o f the metapopulation, based on species-specific characteristics such as the home range size, dispersal distance, and minimum habitat suitability for r eproduction.