DOI: 10.5061/dryad.2k4b7
title: Data from: Isolation by distance, resistance and/or clusters? Lessons learned from a forest-dwelling carnivore inhabiting a heterogeneous landscape

description: 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.
description: Sample locations,
 microsatellite data and clustering assignmentComplete 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.xlsxLandscape resistance mapsRaw '.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.zipGenetic_LCD_MatricesGenetic 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).