Data from: Bayesian species delimitation can be robust to guide tree inference errors

The Bayesian method of species delimitation (Yang and Rannala, 2010) uses a so-called guide tree to reduce the number of models to be evaluated in the reversible-jump Markov chain Monte Carlo (rjMCMC) algorithm (Green, 1995). It has been pointed out that the method tends to over-split if a random population tree is used as the guide tree (Fujita and Leaché, 2011). Here we conduct a simulation study to examine the performance of the method under more realistic scenarios, that is, when the guide tree is inferred from the sequence data. We found that Bayesian species delimitation is in general robust to errors in the inferred guide tree.

物种界定（species delimitation）贝叶斯方法（Yang与Rannala，2010）采用所谓的引导树（guide tree），以降低可逆跳马尔可夫链蒙特卡洛（reversible-jump Markov chain Monte Carlo，简称rjMCMC）算法（Green，1995）中需评估的模型数量。已有研究指出，若将随机种群树用作引导树，该方法易出现物种过度划分的问题（Fujita与Leaché，2011）。本研究开展模拟研究，探究当引导树由序列数据推断得到时，该方法在更贴近真实场景下的性能表现。结果表明，物种界定贝叶斯方法总体上对推断得到的引导树的误差具有鲁棒性。