How meaningful are Bayesian support values?

In this study, we used an empirical example based on 100 mitochondrial genomes from higher teleost fishes to compare the accuracy of parsimony-based jackknife values with Bayesian support values. Phylogenetic analyses of 366 partitions, using differential taxon and character sampling from the entire data matrix of 100 taxa and 7,990 characters, were performed for both phylogenetic methods. The tree topology and branch-support values from each partition were compared with the tree inferred from all taxa and characters. Using this approach, we quantified the accuracy of the branch-support values assigned by the jackknife and Bayesian methods, with respect to each of 15 basal clades. In comparing the jackknife and Bayesian methods, we found that (1) both measures of support differ significantly from an ideal support index; (2) the jackknife underestimated support values; (3) the Bayesian method consistently overestimated support; (4) the magnitude by which Bayesian values overestimate support exceeds the magnitude by which the jackknife underestimates support; and (5) both methods performed poorly when taxon sampling was increased and character sampling was not increases. These results indicate that (1) the higher Bayesian support values are inappropriate (in magnitude), and (2) Bayesian support values should not be interpreted as probabilities that clades are correctly resolved. We advocate the continued use of the relatively conservative bootstrap and jackknife approaches to estimating branch support rather than the more extreme overestimates provided by the Markov Chain Monte Carlo-based Bayesian methods.

在这项研究中，我们使用了一个基于100个高等硬骨鱼线粒体基因组的实证例子，来比较基于简约法的折刀法值与贝叶斯支持值的准确性。对366个分区进行了系统发育分析，使用了从100个分类单元和7990个特征的整个数据矩阵中进行的不同分类单元和特征抽样，两种系统发育方法均如此。将每个分区的树拓扑结构和分支支持值与从所有分类单元和特征推断出的树进行了比较。通过这种方法，我们针对15个基部支系中的每一个，量化了折刀法和贝叶斯方法所赋予的分支支持值的准确性。在比较折刀法和贝叶斯方法时，我们发现：（1）这两种支持度量都与理想的支持指数显著不同；（2）折刀法低估了支持值；（3）贝叶斯方法一贯高估支持值；（4）贝叶斯值高估支持的程度超过了折刀法低估支持的程度；（5）当分类单元抽样增加而特征抽样没有增加时，两种方法表现都不佳。这些结果表明：（1）较高的贝叶斯支持值（在数值上）是不恰当的，（2）贝叶斯支持值不应被解释为支系被正确解析的概率。我们主张继续使用相对保守的自助法和折刀法来估计分支支持，而不是使用基于马尔可夫链蒙特卡罗的贝叶斯方法所提供的更极端的高估结果。