Data from: Species delimitation using genome-wide SNP data

The multispecies coalescent has provided important progress for evolutionary inferences, including increasing the statistical rigor and objectivity of comparisons among competing species delimitation models. However, Bayesian species delimitation methods typically require brute force integration over gene trees via Markov chain Monte Carlo (MCMC), which introduces a large computation burden and precludes their application to genomic-scale data. Here we combine a recently introduced dynamic programming algorithm for estimating species trees that bypasses MCMC integration over gene trees with sophisticated methods for estimating marginal likelihoods, needed for Bayesian model selection, to provide a rigorous and computationally tractable technique for genome-wide species delimitation. We provide a critical yet simple correction that brings the likelihoods of different species trees, and more importantly their corresponding marginal likelihoods, to the same common denominator, which enables direct and accurate comparisons of competing species delimitation models using Bayes factors. We test this approach, which we call Bayes factor delimitation (*with genomic data; BFD*), using common species delimitation scenarios with computer simulations. Varying the numbers of loci and the number of samples suggest that the approach can distinguish the true model even with few loci and limited samples per species. Misspecification of the prior for population size θ has little impact on support for the true model. We apply the approach to West African forest geckos (Hemidactylus fasciatus complex) using genome-wide SNP data. This new Bayesian method for species delimitation builds on a growing trend for objective species delimitation methods with explicit model assumptions that are easily tested.多物种溯祖（multispecies coalescent）为进化推断领域带来了重要进展，包括提升了竞争性物种界定模型间比较的统计严谨性与客观性。然而，现有贝叶斯物种界定方法通常需要通过马尔可夫链蒙特卡洛（Markov chain Monte Carlo, MCMC）对基因树进行蛮力积分，这会带来巨大的计算负担，使其无法应用于基因组尺度的数据。本研究将新近提出的、可绕过基因树MCMC积分的物种树估计动态规划算法，与贝叶斯模型选择所需的复杂边际似然估计方法相结合，提出了一种严谨且计算可行的全基因组物种界定技术。我们提出了一项关键且简便的校正方法，可将不同物种树的似然值（更重要的是其对应的边际似然值）统一至同一基准，从而能够利用贝叶斯因子（Bayes factors）对竞争性物种界定模型进行直接且精准的比较。我们借助常见的物种界定场景与计算机模拟对该方法进行了测试，将其命名为贝叶斯因子物种界定（结合基因组数据；Bayes factor delimitation, BFD）。通过改变基因座数量与样本数量的测试表明，即便基因座数量较少、每个物种的样本量有限，该方法仍能识别出真实模型。种群大小θ的先验分布设定错误对真实模型的支持度几乎没有影响。我们利用全基因组单核苷酸多态性（Single Nucleotide Polymorphism, SNP）数据，将该方法应用于西非森林壁虎（Hemidactylus fasciatus复合群）的研究中。这种新型贝叶斯物种界定方法，依托于当前物种界定方法愈发客观化的发展趋势——这类方法具备明确且易于验证的模型假设。