title: Evaluation of a bayesian coalescent method of species delimitation.
abstract: A Bayesian coalescent-based method has recently been proposed to delimit species using multilocus genetic sequence data. Posterior probabilities of different species delimitation models are calculated using reversible-jump Markov chain Monte Carlo algorithms. The method accounts for species phylogenies and coalescent events in both extant and extinct species and accommodates lineage sorting and uncertainties in the gene trees. Although the method is theoretically appealing, its utility in practical data analysis is yet to be rigorously examined. In particular, the analysis may be sensitive to priors on ancestral population sizes and on species divergence times and to gene flow between species. Here we conduct a computer simulation to evaluate the statistical performance of the method, such as the false negatives (the error of lumping multiple species into one) and false positives (the error of splitting one species into several). We found that the correct species model was inferred with high posterior probability with only one or two loci when 5 or 10 sequences were sampled from each population, or with 50 loci when only one sequence was sampled. We also simulated data allowing migration under a two-species model, a mainland-island model and a stepping-stone model to assess the impact of gene flow (hybridization or introgression). The behavior of the method was diametrically different depending on the migration rate. Low rates at < 0.1 migrants per generation had virtually no effect, so that the method, while assuming no hybridization between species, identified distinct species despite small amounts of gene flow. This behavior appears to be consistent with biologists' practice. In contrast, higher migration rates at ≥ 10 migrants per generation caused the method to infer one species. At intermediate levels of migration, the method is indecisive. Our results suggest that Bayesian analysis under the multispecies coalescent model may provide important insights into population divergences, and may be useful for generating hypotheses of species delimitation, to be assessed with independent information from anatomical, behavioral, and ecological data.
authors.affiliation: Center for Computational and Evolutionary Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.
authors.forename: Chi
authors.initials: C
authors.lastname: Zhang
authors.forename: De-Xing
authors.initials: DX
authors.lastname: Zhang
authors.forename: Tianqi
authors.initials: T
authors.lastname: Zhu
authors.forename: Ziheng
authors.initials: Z
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D025041:Animal Migration; D000818:Animals; D002080:Butterflies / Q000235:genetics; D002965:Classification / Q000379:methods*; D003198:Computer Simulation; D051456:Gene Flow; D014644:Genetic Variation; D005828:Genetics, Population / Q000379:methods*; D059645:Mutation Rate