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

Parameterization 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.