Testing Classical Species Properties with Contemporary Data: How 'Bad Species' in the Brassy Ringlets (Erebia tyndarus complex, Lepidoptera) Turned Good

摘要

All species concepts are rooted in reproductive, and ultimately genealogical, relations. Genetic data are thus the most important source of information for species delimitation. Current ease of access to genomic data and recent computational advances are blooming a plethora of coalescent-based species delimitation methods. Despite their utility as objective approaches to identify species boundaries, coalescent-based methods (1) rely on simplified demographic models that may fail to capture some attributes of biological species, (2) do not make explicit use of the geographic information contained in the data, and (3) are often computationally intensive. In this article, we present a case of species delimitation in the Erebia tyndarus species complex, a taxon regarded as a classic example of problematic taxonomic resolution. Our approach to species delimitation used genomic data to test predictions rooted in the biological species concept and in the criterion of coexistence in sympatry. We (1) obtained restriction-site associated DNA(RAD) sequencing data from a carefully designed sample, (2) applied two genotype clustering algorithms to identify genetic clusters, and (3) performed within clusters and between-clusters analyses of isolation by distance as a test for intrinsic reproductive barriers. Comparison of our results with those from a Bayes factor delimitation coalescent-based analysis, showed that coalescent-based approaches may lead to overconfident splitting of allopatric populations, and indicated that incorrect species delimitation is likely to be inferred when an incomplete geographic sample is analyzed. While we acknowledge the theoretical justification and practical usefulness of coalescent-based species delimitation methods, our results stress that, even in the phylogenomic era, the toolkit for species delimitation should not dismiss more traditional, biologically grounded, approaches coupling genomic data with geographic information.