Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho

Individual-based analyses relating landscape structure to genetic distances across complex landscapes enable rigorous evaluation of multiple alternative hypotheses linking landscape structure to gene flow. We utilize two extensions to increase the rigor of the individual-based causal modeling approach to inferring relationships between landscape patterns and gene flow processes. First, we add a univariate scaling analysis to ensure that each landscape variable is represented in the functional form that represents the optimal scale of its association with gene flow. Second, we use a two-step form of the causal modeling approach to integrate model selection with null hypothesis testing in individual-based landscape genetic analysis. This series of causal modeling indicated that gene flow in American marten in northern Idaho was primarily related to elevation, and that alternative hypotheses involving isolation by distance, geographical barriers, effects of canopy closure, roads, tree size class and an empirical habitat model were not supported. Gene flow in the Northern Idaho American marten population is therefore driven by a gradient of landscape resistance that is a function of elevation, with minimum resistance to gene flow at 1500 m.