Data from: Species delimitation using Bayes factors: simulations and application to the Sceloporus scalaris species group (Squamata: Phrynosomatidae)

Current molecular methods of species delimitation are limited by the types of species delimitation models and scenarios that can be tested. Bayes factors allow for more flexibility in testing non-nested species delimitation models and hypotheses of individual assignment to alternative lineages. Here, we examined the efficacy of Bayes factors in delimiting species through simulations and empirical data from the Sceloporus scalaris species group. Marginal likelihood scores of competing species delimitation models, from which Bayes factor values were compared, were estimated with four different methods: harmonic mean estimation, smoothed harmonic mean estimation, path-sampling/thermodynamic integration, and stepping-stone analysis. We also performed model selection using a posterior simulation-based analog of the Akaike information criterion through Markov chain Monte Carlo analysis (AICM). Bayes factor species delimitation results from the empirical data were then compared with results from the reversible-jump MCMC (rjMCMC) coalescent-based species delimitation method Bayesian Phylogenetics and Phylogeography (BP&P). Simulation results show that harmonic and smoothed harmonic mean estimators perform poorly compared to path sampling and stepping stone marginal likelihood estimators when identifying the true species delimitation model. Furthermore, Bayes factor species delimitation showed improved performance when species limits are tested by reassigning individuals between species, as opposed to either lumping or splitting lineages. In the empirical data, Bayes factor species delimitation through path sampling and stepping-stone analyses, as well as the rjMCMC method, each provide support for the recognition of all scalaris group taxa as independent evolutionary lineages. Bayes factor species delimitation and BP&P also support the recognition of three previously undescribed lineages. In both simulated and empirical datasets, harmonic and smoothed harmonic mean marginal likelihood estimators provided much higher marginal likelihood estimates than path sampling and stepping-stone estimators. The AICM displayed poor repeatability in both simulated and empirical datasets, and produced inconsistent model rankings across replicate runs with the empirical data. Our results suggest that species delimitation through the use of Bayes factors with marginal likelihood estimates via path-sampling or stepping-stone analyses provide a useful and complementary alternative to existing species delimitation methods.

当前的物种界定（species delimitation）分子方法受限于可检验的物种界定模型与场景类型。贝叶斯因子（Bayes factor）为检验非嵌套物种界定模型以及个体归属至替代支系的假说提供了更高的灵活性。本研究通过模拟实验与来自Sceloporus scalaris物种组的实证数据，评估了贝叶斯因子在物种界定中的应用效能。用于比较贝叶斯因子值的竞争物种界定模型的边际似然（marginal likelihood）得分，通过四种不同方法进行估计：调和均值估计（harmonic mean estimation）、平滑调和均值估计（smoothed harmonic mean estimation）、路径采样/热力学积分（path-sampling/thermodynamic integration）以及阶梯石分析（stepping-stone analysis）。本研究还采用基于马尔可夫链蒙特卡洛（Markov chain Monte Carlo, MCMC）后验模拟的赤池信息准则类似物（AICM）开展模型选择。随后，将实证数据中的贝叶斯因子物种界定结果，与基于可逆跳跃马尔可夫链蒙特卡洛（reversible-jump MCMC, rjMCMC）的溯祖物种界定方法——贝叶斯系统发育与种群遗传学（Bayesian Phylogenetics and Phylogeography, BP&P）的结果进行了对比。

模拟结果显示，在识别真实物种界定模型时，调和均值与平滑调和均值估计器的表现远差于路径采样与阶梯石边际似然估计器。此外，相较于通过合并或拆分支系来检验物种界限，当通过在物种间重新分配个体来检验物种界限时，贝叶斯因子物种界定的表现得到了提升。在实证数据中，通过路径采样与阶梯石分析开展的贝叶斯因子物种界定，以及rjMCMC方法，均支持将Sceloporus scalaris物种组的所有分类单元认定为独立的演化支系。贝叶斯因子物种界定与BP&P方法同样支持识别三个此前未被描述的支系。

在模拟与实证数据集当中，调和均值与平滑调和均值边际似然估计器所给出的边际似然估计值远高于路径采样与阶梯石估计器。AICM在模拟与实证数据集当中均表现出较差的可重复性，且在实证数据的重复运行中产生了不一致的模型排序结果。本研究结果表明，通过路径采样或阶梯石分析获取边际似然估计值并结合贝叶斯因子开展物种界定，可为现有的物种界定方法提供一种实用且互补的替代方案。