Landscape genetics provides a valuable framework to understand how landscape features influence gene flow and to disentangle the factors that lead to discrete and/or clinal population structure. Here, we attempt to differentiate between these processes in a forest-dwelling small carnivore [European pine marten (Martes martes)]. Specifically, we used complementary analytical approaches to quantify the spatially explicit genetic structure and diversity and analyse patterns of gene flow for 140 individuals genotyped at 15 microsatellite loci. We first used spatially explicit and nonspatial Bayesian clustering algorithms to partition the sample into discrete clusters and evaluate hypotheses of ‘isolation by barriers’ (IBB). We further characterized the relationships between genetic distance and geographical (‘isolation by distance’, IBD) and ecological distances (‘isolation by resistance’, IBR) obtained from optimized landscape models. Using a reciprocal causal modelling approach, we competed the IBD, IBR and IBB hypotheses with each other to unravel factors driving population genetic structure. Additionally, we further assessed spatially explicit indices of genetic diversity using sGD across potentially overlapping genetic neighbourhoods that matched the inferred population structure. Our results revealed a complex spatial genetic cline that appears to be driven jointly by IBD and partial barriers to gene flow (IBB) associated with poor habitat and interspecific competition. Habitat loss and fragmentation, in synergy with past overharvesting and possible interspecific competition with sympatric stone marten (Martes foina), are likely the main factors responsible for the spatial genetic structure we observed. These results emphasize the need for a more thorough evaluation of discrete and clinal hypotheses governing gene flow in landscape genetic studies, and the potential influence of different limiting factors affecting genetic structure at different spatial scales.
Sample locations, microsatellite data and clustering assignment
Complete genetic profiles at 15 microsatellite loci (Ruiz-Gonzalez et al. 2014), geographic coordinates and Bayesian clustering assignment according to STRUCTURE and GENELAND for the 140 pine marten individuals. The genetic raw data is provided in CONVERT data format (Glaubitz, 2004).
Pine_Marten_140Ind_Loc_Pops_STR_GL.xlsx
Landscape resistance maps
Raw '.asc' files of the optimal resistance models (IBR; i.e. EN, LandFB_100 and LandFb_50) produced with ARCGIS version 9.3 (ESRI 2009), with a raster cell size set to 50 m. The three different landscape resistance models were previously optimized for the study species at the Basque country level (Ruiz-González et al. 2014). Land_Fb100 and Land_Fb50: Binary landscape resistance maps indicating that pine marten gene flow in is facilitated by forests, forestry plantations, scrubland, agroforestry mosaics and pastures and meadows, and that crops have roughly 100 (LandFb_100) or 50 (LandFb_50) times higher resistance than optimal habitat and the existence of a barrier effect of national roads, highways, urban areas, reservoirs and quarries. Ecological Network resistance map; EN: a resistance map analogous to that used in the design of the ecological network of the Basque country (Gurrutxaga et al. 2010a). Land use information was obtained in vector format from the most recent forest map of Spain (Spanish Ministry of the environment, 2006) and from national road network maps (Spanish National Geographic Institute (2008). Full methodological details for model construction are reported in Table 1 and Appendix S2 (Supporting information).
Resistance_optimal_Models_ascii.zip
Genetic_LCD_Matrices
Genetic distance (Rousset’s a) matrix and least-cost distance (LCD) matrices for IBD, IBR and IBB models. 1) IBD-Isolation by distance; 2) IBR-Isolation by landscape resistance models optimized by Ruiz-González et al. (2014); Land_Fb100 and Land_Fb50: Binary landscape resistance maps indicating that pine marten gene flow in is facilitated by forests, forestry plantations, scrubland, agroforestry mosaics and pastures and meadows, and that crops have roughly 100 (LandFb_100) or 50 (LandFb_50) times higher resistance than optimal habitat and the existence of a barrier effect of national roads, highways, urban areas, reservoirs and quarries. Ecological Network resistance map; EN: a resistance map analogous to that used in the design of the ecological network of the Basque country (Gurrutxaga et al. 2010a); and 3) IBB-Isolation by barriers or clusters obtained from both Bayesian clustering approaches (i.e. IBB_Geneland and IBB_Structure). The effective and Euclidean distances between each pair of individuals were calculated with PATHMATRIX 1.1 (Ray 2005), following the resistances values outlined in Table 1. Pair-wise effective distances between individuals were calculated as the accumulated cost through the least cost paths (LCP) throughout each resistance surface (Adriaensen et al. 2003; Ray 2005). Full methodological details for model construction are reported in Appendix S2 (Supporting information).