How To Measure The Influence Of Landscape On Population Genetic Structure: Developing Resistance Surfaces Using A Pattern-Oriented Modeling Approach

There are several approaches to understand how a landscape, with its several components, affects the genetic population structure by imposing resistance to gene flow. Here we propose the creation of resistance surfaces using a Pattern-Oriented Modeling approach to explain genetic differentiation, estimated by pairwise FST, among "Baruzeiro" populations (Dipteryx alata), a tree species widely distributed in Brazilian Cerrado. To establish the resistance surface, we used land use layers from the area in which the 25 "Baruzeiro" populations were sampled, generating 10000 resistance surfaces. To establish the resistance surface, we used land use layers from the area in which the 25 "Baru" populations were sampled, generating 10000 resistance surfaces. We randomized the cost values for each landscape component between 0 and 100. We use these surfaces to calculate pairwise matrices of the effective resistance among populations. Mantel test revealed a correlation of pairwise FST with a geographical distance equal to r = 0.48 (P < 0.001), whereas the Mantel correlations between pairwise FST and the generated resistance matrices ranged between r = -0.2019 and r= 0.6736. Partial regression on distance matrices was used to select the resistance matrix that provided the highest correlation with pairwise FST, based on the AIC criterion. The selected models suggest that the areas with lower resistance are characterized as natural savanna habitats of different forms, mainly arboreal dense savannas. In contrast, roads, big rivers, and agricultural lands cause higher resistance to gene flow.