Genetic data in population viability analysis: case studies with ambystomatid salamanders

Abstract

AbstractParameterization of population viability models is a complicated task for most types of animals, as knowledge of population demography, abundance and connectivity can be incomplete or unattainable. Here I illustrate several ways in which genetic data can be used to inform population viability analysis, via the parameterization of both initial abundance and dispersal matrices. As case studies, I use three ambysomatid salamander datasets to address the following question: how do population viability predictions change when dispersal estimates are based on genetic assignment test data versus a general dispersal–distance function? Model results showed that no local population was large enough to ensure long‐term persistence in the absence of immigration, suggesting a metapopulation structure. Models parameterized with a dispersal–distance function resulted in much more optimistic predictions than those incorporating genetic data in the dispersal estimates. Under the dispersal–distance function scenario all local populations persisted; however, using genetic assignments to infer dispersal revealed local populations at risk of extinction. Viability estimates based on dispersal–distance functions should be interpreted with caution, especially in heterogeneous landscapes. In these situations I promote the idea of model parameterization using genetic assignment tests for a more accurate portrayal of real‐world dispersal patterns.