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Abstract The outcomes of speciation across organismal dimensions (e.g., ecological, genetic, phenotypic) are often assessed using phylogeographic methods. At one extreme, reproductively isolated lineages represent easily delimitable species differing in many or all dimensions, and at the other, geographically distinct genetic segments introgress across broad environmental gradients with limited phenotypic disparity. In the ambiguous gray zone of speciation, where lineages are genetically delimitable but still interacting ecologically, it is expected that these lineages represent species in the context of ontology and the evolutionary species concept when they are maintained over time with geographically well‐defined hybrid zones, particularly at the intersection of distinct environments. As a result, genetic structure is correlated with environmental differences and not space alone, and a subset of genes fail to introgress across these zones as underlying genomic differences accumulate. We present a set of tests that synthesize species delimitation with the speciation process. We can thereby assess historical demographics and diversification processes while understanding how lineages are maintained through space and time by exploring spatial and genome clines, genotype‐environment interactions, and genome scans for selected loci. Employing these tests in eight lineage‐pairs of snakes in North America, we show that six pairs represent 12 “good” species and that two pairs represent local adaptation and regional population structure. The distinct species pairs all have the signature of divergence before or near the mid‐Pleistocene, often with low migration, stable hybrid zones of varying size, and a subset of loci showing selection on alleles at the hybrid zone corresponding to transitions between distinct ecoregions. Locally adapted populations are younger, exhibit higher migration, and less ecological differentiation. Our results demonstrate that interacting lineages can be delimited using phylogeographic and population genetic methods that properly integrate spatial, temporal, and environmental data. |
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