Knowledge of movement patterns and the manner in which wildlife interact with their landscape are crucial to wildlife management. Conventional methods of observing wildlife movement include game cameras and drift fences, which are costly, labor intensive, and difficult to conduct at a large scale. Landscape genetic analyses can be used to identify landscape features that cause resistance to wildlife movement with less sampling while encompassing large areas in a single analysis. However, there are several knowledge gaps within the field of landscape genetics that must be addressed before broad conservation application is possible. Specifically, little is known about the transferability, or applicability to nearby areas, of landscape genetics models. Using multispectral imaging, we created land cover classifications for two forests, Holly Springs National Forest (HSNF) and William B Bankhead National Forest (BNF), within the species range of Plethodon mississippi. We then used microsatellite genotypes from P. mississippi individuals within each forest to create best-fit landscape resistance models. To test the transferability of the models, the parameters of the HSNF model were applied to the BNF landscape and tested for significance against BNF genetic data, and vice versa.
Results/Conclusions
Land cover analysis has shown that although both forests are within the species range for P. mississippi, they have different land cover ratios. While the land cover within both forests is primarily pine and hardwood forest, BNF has 9% more wetlands than HSNF, and HSNF has 8% more agricultural land. Preliminary genetic analyses have shown that individuals in HSNF and BNF share 44% of their microsatellite alleles. If, when applied to the BNF landscape, the best-fit landscape resistance model for HSNF produces significant p-values, it would support the use of amphibian landscape genetics models across the species’ variable range. Conversely, if the model does not generate significant p-values, it would indicate the models are location-specific.