Sample design and population clustering
For landscape-scale analyses it is important to not confound diversity within meta-populations with divergence among historically isolated lineages, as have previously been identified in each of the species considered here (Suppl. Mat. S1). To visualize the population structure and divergence among samples using the SNP data, we first performed a principal coordinate analysis (PCoA) based on the genetic distance matrix between individuals using Euclidean distance to identify discrete clusters. We then ran two different analyses to verify clusters and test for admixture among them — FastStructure (Raj, Stephens, & Pritchard, 2014) and conStruct (Bradburd, Coop, & Ralph, 2018).FastStructure is a model-based clustering method that looks for the probability of an individual belonging to a cluster, considering the population structure, and was calculated with model complexity (K) between 1-7. The admixture proportions were visualized with aDistruct plot. ConStruct infers continuous and discrete patterns of population structure by estimating ancestry proportions for each sampled individual from two-dimensional population layers, where within each layer a rate at which relatedness decays with distance is estimated (Bradburd et al., 2018). In essence, this approach has IBD as a null-model, against which isolated populations are inferred, whereasFastStructure (and related methods) assumes panmixia within genetic clusters. For ConStruct we used spatial data (considering Isolation by distance), with a K between 1 and 7, and compared the predictive performance of the models by running cross-validation with 50 replicates. From here on, we will refer to these populations within described species with strong structuring as ‘lineages’, even if low admixture is observed between them in one or more of the above analyses (Table 1). These groups are well supported in previous studies using mitochondrial DNA and, in most cases, multilocus sequencing (more details in Suppl. Mat. S1).