Data from: The efficacy of consensus tree methods for summarising phylogenetic relationships from a posterior sample of trees estimated from morphological data

Consensus trees are required to summarise trees obtained through MCMC sampling of a posterior distribution, providing an overview of the distribution of estimated parameters such as topology, branch lengths and divergence times. Numerous consensus tree construction methods are available, each presenting a different interpretation of the tree sample. The rise of morphological clock and sampled-ancestor methods of divergence time estimation, in which times and topology are co-estimated, has increased the popularity of the maximum clade credibility (MCC) consensus tree method. The MCC method assumes that the sampled, fully resolved topology with the highest clade credibility contains an adequate summary of the most probable clades, with parameter estimates from compatible sampled trees used to obtain the marginal distributions of parameters such as clade ages and branch lengths. Using both simulated and empirical data, we demonstrate that MCC trees, and trees constructed using the similar maximum a posteriori (MAP) method, often include poorly supported and incorrect clades when summarising diffuse posterior samples of trees. We demonstrate that the paucity of information in morphological datasets contributes to the inability of MCC and MAP trees to present an accurate summary of the posterior distribution. Conversely, majority-rule consensus (MRC) trees report a lower proportion of incorrect nodes when summarising the same posterior samples of trees. Thus, we advocate the use of MRC trees, in place of MCC or MAP trees, in attempts to summarise the results of Bayesian phylogenetic analyses of morphological data.

共识树（consensus tree）是用于汇总通过马尔可夫链蒙特卡洛（Markov Chain Monte Carlo, MCMC）后验分布采样得到的系统发育树的工具，可概览拓扑结构、分支长度、分化时间等估计参数的分布情况。目前已有多种共识树构建方法，每种方法对树样本的解读视角各不相同。随着分化时间估计领域中形态钟（morphological clock）与采样祖先（sampled-ancestor）方法的兴起——这类方法可同时估计分化时间与拓扑结构——最大分支可信度（maximum clade credibility, MCC）共识树方法的应用愈发广泛。MCC方法假设，采样得到的具有最高分支可信度的完全解析拓扑结构，可充分概括最具可信度的演化支；其通过兼容采样树的参数估计值，可获取演化支年龄、分支长度等参数的边际分布。我们通过模拟数据与实证数据均证明，在汇总分散的树后验样本时，MCC树以及采用类似最大后验（maximum a posteriori, MAP）方法构建的树，往往会包含支持度较低且不正确的演化支。研究表明，形态学数据集的信息匮乏，是导致MCC与MAP树无法准确概括后验分布的重要原因。与之相反，在汇总相同的树后验样本时，多数规则共识树（majority-rule consensus, MRC）所包含的错误节点比例更低。因此，在对形态学数据开展贝叶斯系统发育分析的结果汇总工作中，我们建议采用MRC树替代MCC或MAP树。