Data from: Delimiting species-poor datasets using single molecular markers: a study of barcode gaps, haplowebs and GMYC

Most single-locus molecular approaches to species delimitation available to date have been designed and tested on datasets comprising at least tens of species, whereas the opposite case (species-poor datasets for which the hypothesis that all individuals are conspecific cannot by rejected beforehand) has rarely been the focus of such attempts. Here we compare the performance of barcode gap detection, haplowebs and Generalized Mixed Yule Coalescent (GMYC) models to delineate chimpanzees and bonobos using nuclear sequence markers, then apply these single-locus species delimitation methods to datasets of 1, 3 or 6 species simulated under a wide range of population sizes, speciation rates, mutation rates and sampling efforts. Our results show that barcode gap detection and GMYC models are unable to delineate species properly in datasets composed of one or two species, two situations in which haplowebs outperform them. For datasets composed of 3 or 6 species, bGMYC and haplowebs outperform the single-threshold and multiple-threshold versions of GMYC, whereas a clear barcode gap is only observed when population sizes and speciation rates are both small. These conditions represent a “sweet spot” for molecular taxonomy where all the single-locus approaches tested work well; however, the performance of these approaches decreases strongly when population sizes and speciation rates are high, suggesting that multilocus approaches may be necessary to tackle the latter cases.