Data from: Is BAMM flawed? Theoretical and practical concerns in the analysis of multi-rate diversification models

BAMM (Bayesian Analysis of Macroevolutionary Mixtures) is a statistical framework that uses reversible jump MCMC to infer complex macroevolutionary dynamics of diversification and phenotypic evolution on phylogenetic trees. A recent article by Moore and coauthors (MEA) reported a number of theoretical and practical concerns with BAMM. Major claims from MEA are that (1) BAMM's likelihood function is incorrect, because it does not account for unobserved rate shifts; (2) the posterior distribution on the number of rate shifts is overly sensitive to the prior; and (3) diversification rate estimates from BAMM are unreliable. Here, we show that these and other conclusions from MEA are generally incorrect or unjustified. We first demonstrate that MEA's numerical assessment of the BAMM likelihood is compromised by their use of an invalid likelihood function. We then show that “unobserved rate shifts” appear to be irrelevant for biologically-plausible parameterizations of the diversification process. We find that the purportedly extreme prior sensitivity reported by MEA cannot be replicated with standard usage of BAMM v2.5, or with any other version, when conventional Bayesian model selection is performed. Finally, we demonstrate that BAMM performs very well at estimating diversification rate variation across the ∼20% of simulated trees in MEA's dataset for which it is theoretically possible to infer rate shifts with confidence. Due to ascertainment bias, the remaining 80% of their purportedly variable-rate phylogenies are statistically indistinguishable from those produced by a constant-rate birth-death process and were thus poorly-suited for the summary statistics used in their performance assessment. We demonstrate that inferences about diversification rates have been accurate and consistent across all major previous releases of the BAMM software. We recognize an acute need to address the theoretical foundations of rate-shift models for phylogenetic trees, and we expect BAMM and other modeling frameworks to improve in response to mathematical and computational innovations. However, we remain optimistic that that the imperfect tools currently available to comparative biologists have provided and will continue to provide important insights into the diversification of life on Earth.宏观演化混合贝叶斯分析（Bayesian Analysis of Macroevolutionary Mixtures, BAMM）是一种统计框架，其借助可逆跳跃马尔可夫链蒙特卡洛（reversible jump MCMC）方法，可对系统发育树上的多样化演化与表型演化这类复杂宏观演化动力学开展推断。Moore及其合作者（下称MEA）近期发表的文章针对BAMM提出了多项理论与实践层面的质疑，其核心主张包括三点：其一，BAMM的似然函数存在错误，因其未考虑未被观测到的速率转移事件；其二，速率转移数量的后验分布对先验分布过度敏感；其三，BAMM得到的多样化速率估计结果不可靠。 本文研究表明，MEA得出的上述及其他结论整体上均存在错误或缺乏依据。我们首先证实，MEA对BAMM似然函数的数值评估存在缺陷，原因是其使用了无效的似然函数。随后我们证明，“未被观测的速率转移”对于多样化过程的生物学合理参数化而言，似乎并不具备相关性。我们发现，当采用常规贝叶斯模型选择方法时，MEA所宣称的极端先验敏感性无法通过BAMM v2.5版本的标准使用流程复现，其他版本的BAMM亦是如此。最后我们证实，在MEA数据集的约20%模拟系统发育树中，从理论上可以可靠推断速率转移事件，而BAMM在这类数据集上对多样化速率变异的估计表现极佳。由于存在 ascertainment偏倚（ascertainment bias），剩余80%的所谓“可变速率系统发育树”在统计上与恒定速率生灭过程生成的树无法区分，因此并不适合用于其性能评估中所使用的统计量。 我们证实，在BAMM软件所有主要过往版本中，多样化速率的推断结果始终准确且一致。我们认为，亟需针对系统发育树的速率转移模型的理论基础展开研究，并预计BAMM及其他建模框架将随着数学与计算技术的革新而不断完善。然而我们仍保持乐观态度：当前比较生物学家所使用的尚不完善的工具，已经并将继续为地球生命的多样化演化研究提供重要洞见。