Data from: Robustness of compound Dirichlet priors for Bayesian inference of branch lengths

We modified the phylogenetic program MrBayes 3.1.2 to incorporate the compound Dirichlet priors for branch lengths proposed recently by Rannala et al. (2011 Mol. Biol. Evol. in press) as a solution to the problem of branch length overestimation in Bayesian phylogenetic inference. The compound Dirichlet prior specifies a fairly diffuse prior on the tree length (the sum of branch lengths) and uses a Dirichlet distribution to partition the tree length into branch lengths. Six problematic datasets originally analyzed by Brown et al. (2010 Syst. Biol. 59: 145-161) are re-analyzed using the modified version of MrBayes to investigate properties of Bayesian branch length estimation using the new priors. While the default exponential priors for branch lengths produced extremely long trees, the compound Dirichlet priors produced posterior estimates that are much closer to the maximum likelihood estimates. Furthermore, the posterior tree lengths were quite robust to changes in the paramter values in the compound Dirichlet priors, for example, when the prior mean of tree length changed over several orders of magnitude. Our results suggest that the compound Dirichlet priors may be useful for correcting branch length overestimation in phylogenetic analyses of empirical datasets.本研究对系统发育软件MrBayes 3.1.2进行修改，将Rannala等人（2011，《Molecular Biology and Evolution》，待刊）近期提出的用于分支长度的复合狄利克雷先验（compound Dirichlet prior）纳入其中，以解决贝叶斯系统发育推断中分支长度高估的问题。复合狄利克雷先验对树长（即所有分支长度之和）设定了较为宽泛的先验分布，并通过狄利克雷分布将树长拆解为各分支的长度。本研究使用修改后的MrBayes版本，对Brown等人（2010，《Systematic Biology》59: 145-161）最初分析过的6组存在问题的数据集进行重新分析，以探究采用该新型先验时贝叶斯分支长度估计的相关特性。相较于默认的分支长度指数先验会生成极长的树长，复合狄利克雷先验得到的后验估计值与最大似然估计值更为接近。此外，即便复合狄利克雷先验中的参数值发生变化——例如当先验的树长均值跨越数个数量级时，后验树长仍保持相当强的稳健性。本研究结果表明，复合狄利克雷先验可有效修正实证数据集系统发育分析中的分支长度高估问题。