Data from: Comparison of methods for molecular species delimitation across a range of speciation scenarios

Species are fundamental units in biological research and can be defined on
the basis of various operational criteria. There has been growing use of
molecular approaches for species delimitation. Among the most widely used
methods, the generalized mixed Yule-coalescent (GMYC) and Poisson tree
processes (PTP) were designed for the analysis of single-locus data but
are often applied to concatenations of multilocus data. In contrast, the
Bayesian multispecies coalescent approach in the software BPP explicitly
models the evolution of multilocus data. In this study, we compare the
performance of GMYC, PTP, and BPP using synthetic data generated by
simulation under various speciation scenarios. We show that in the absence
of gene flow, the main factor influencing the performance of these methods
is the ratio of population size to divergence time, while number of loci
and sample size per species have smaller effects. Given appropriate priors
and correct guide trees, BPP shows lower rates of species overestimation
and underestimation, and is generally robust to various potential
confounding factors except high levels of gene flow. The single-threshold
GMYC and the best strategy that we identified in PTP generally perform
well for scenarios involving more than a single putative species when gene
flow is absent, but PTP outperforms GMYC when fewer species are involved.
Both methods are more sensitive than BPP to the effects of gene flow and
potential confounding factors. Case studies of bears and bees further
validate some of the findings from our simulation study, and reveal the
importance of using an informed starting point for molecular species
delimitation. Our results highlight the key factors affecting the
performance of molecular species delimitation, with potential benefits for
using these methods within an integrative taxonomic framework.