Algorithmic Single-locus Species Delimitation: Effects of Sampling Effort, Variation and Nonmonophyly in Four Methods and 1870 Species of Beetles

The vast number of undescribed species and the fast rate of biodiversity loss call for new approaches to speed up alpha taxonomy. A plethora of methods for delimiting species or operational taxonomic units (OTUs) based on sequence data have been published in recent years. We test the ability of four delimitation methods (BIN, ABGD, GMYC, PTP) to reproduce established species boundaries on a carefully curated DNA barcode data set of 1870 North European beetle species. We also explore how sampling effort, intraspecific variation, nearest neighbour divergence and nonmonophyly affect the OTU delimitations. All methods produced approximately 90% identity between species and OTUs. The effects of variation and sampling differed between methods. ABGD was sensitive to singleton sequences, while GMYC showed tendencies for oversplitting. The best fit between species and OTUs was achieved using simple rules to find consensus between discordant OTU delimitations. Using several approaches simultaneously allows the methods to compensate for each other's weaknesses. Barcode-based OTU-picking is an efficient way to delimit putative species from large data sets where the use of more sophisticated methods based on multilocus or genomic data is not feasible.