DOI:10.1093/sysbio/syr071 Advance Access publication on August 29, 2011 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 se-quence 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 ex-tinct 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 con-duct 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 allowin