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Regional replication of landscape genetics analyses of the Mississippi slimy salamander, Plethodon mississippi

Citation

Burgess, Stephanie; Garrick, Ryan (2020), Regional replication of landscape genetics analyses of the Mississippi slimy salamander, Plethodon mississippi, Dryad, Dataset, https://doi.org/10.5061/dryad.h18931zgh

Abstract

Context

Landscape genetics can identify habitat features that facilitate or resist gene flow, providing a framework for anticipating the impacts of land use changes on dispersal of individuals. To inform management, a better understanding of how inferences derived from one study region are applicable to other regions is needed.

Objectives

We investigated the manner in which five landscape variables correlated with gene flow among Plethodon mississippi populations in two study regions. We compared order of importance, direction (facilitation vs. resistance of gene flow) and scale of effect, and functional relationships of variables within each study area.

Methods

In forests in Mississippi and Alabama, USA, we tested individual-based genetic distances derived from microsatellite genotypes against effective distances caused by agriculture, hardwoods, pine, manmade structures, and wetlands that were optimized for both scale and transformation using maximum likelihood population effects modeling.

Results

Of the landscape variables, agriculture and wetlands ranked at the top of both study areas’ models. In both forest regions, agriculture was consistently associated with resistance, whereas pine was inferred to facilitate gene flow. However, we found region-specific differences in effects of wetlands, hardwoods, and manmade structures. Configuration of the latter landscape variables differed between forest regions, which may explain the contrasting outcomes.

Conclusions

Our results underscore the value of metareplication in revealing which components of landscape genetics models may be consistent across different portions of a species’ range, and those that have context-dependent impacts on gene flow. We also highlight the need to consider habitat configuration when interpreting the results of landscape genetics analyses.