Critically evaluating the theory and performance of Bayesian analyis of macroevolutionary mixtures

Bayesian analysis of macroevolutionary mixtures (BAMM) has recently taken the study of lineage diversification by storm. BAMM estimates the diversification-rate parameters (speciation and extinction) for every branch of a study phylogeny and infers the number and location of diversification-rate shifts across branches of a tree. Our evaluation of BAMM reveals two major theoretical errors: (i) the likelihood function (which estimates the model parameters from the data) is incorrect, and (ii) the compound Poisson process prior model (which describes the prior distribution of diversification-rate shifts across branches) is incoherent. Using simulation, we demonstrate that these theoretical issues cause statistical pathologies; posterior estimates of the number of diversification-rate shifts are strongly influenced by the assumed prior, and estimates of diversification-rate parameters are unreliable. Moreover, the inability to correctly compute the likelihood or to correctly specify the prior for rate-variable trees precludes the use of Bayesian approaches for testing hypotheses regarding the number and location of diversification-rate shifts using BAMM.

宏观演化混合贝叶斯分析（Bayesian analysis of macroevolutionary mixtures, BAMM）近来在谱系分化演化研究领域掀起了热潮。该方法可为研究所用系统发育树的每一分支估算分化速率参数（含物种形成速率与灭绝速率），并推断全树各分支上分化速率转移事件的数量与位置。我们对BAMM的评估揭示了两项重大理论缺陷：其一，用于从数据中估算模型参数的似然函数（likelihood function）存在错误；其二，用于描述分支间分化速率转移事件先验分布的复合泊松过程先验模型（compound Poisson process prior model）不自洽。我们通过模拟实验证实，这些理论问题会引发统计病态现象：分化速率转移事件数量的后验估计会强烈依赖所设定的先验分布，而分化速率参数的估计结果也并不可靠。此外，由于无法为速率可变的系统发育树正确计算似然函数或合理设定先验分布，使用BAMM通过贝叶斯方法检验分化速率转移事件的数量与位置相关假说的路径也被阻断。