Consilience Across Multiple, Independent Genomic Data Sets Reveals Species in a Complex with Limited Phenotypic Variation.

Species delimitation in the genomic era has focused predominantly on the application of multiple analytical methodologies to a single massive parallel sequencing (MPS) data set, rather than leveraging the unique but complementary insights provided by different classes of MPS data. In this study we demonstrate how the use of two independent MPS data sets, a sequence capture data set and a single nucleotide polymorphism (SNP) data set generated via genotyping-by-sequencing, enables the resolution of species in three complexes belonging to the grass genus Ehrharta, whose strong population structure and subtle morphological variation limit the effectiveness of traditional species delimitation approaches. Sequence capture data are used to construct a comprehensive phylogenetic tree of Ehrharta and to resolve population relationships within the focal clades, while SNP data are used to detect patterns of gene pool sharing across populations, using a novel approach that visualizes multiple values of K. Given that the two genomic data sets are independent, the strong congruence in the clusters they resolve provides powerful ratification of species boundaries in all three complexes studied. Our approach is also able to resolve a number of single-population species and a probable hybrid species, both of which would be difficult to detect and characterize using a single MPS data set. Overall, the data reveal the existence of 11 and five species in the E. setacea and E. rehmannii complexes, with the E. ramosa complex requiring further sampling before species limits are finalized. Despite phenotypic differentiation being generally subtle, true crypsis is limited to just a few species pairs and triplets. We conclude that, in the absence of strong morphological differentiation, the use of multiple, independent genomic data sets is necessary in order to provide the cross-data set corroboration that is foundational to an integrative taxonomic approach.