Data for: Diversification models conflate likelihood and prior, and cannot be compared using conventional model-comparison tools

Time-calibrated phylogenetic trees are a tremendously powerful tool for studying evolutionary, eco-logical, and epidemiological phenomena. Such trees are predominantly inferred in a Bayesian framework, with the phylogeny itself treated as a parameter with a prior distribution (a “tree prior”). However, we show that the tree “parameter” consists, in part, of data, in the form of taxon samples. Treating the tree as a parameter fails to account for these data and compromises our ability to compare among models using standard techniques (e.g., marginal likelihoods estimated using path-sampling and stepping-stone sampling algorithms). Since accuracy of the inferred phylogeny strongly depends on how well the tree prior approximates the true diversification process that gave rise to the tree, the inability to accurately compare competing tree priors has broad implica- tions for applications based on time-calibrated trees. We outline potential remedies to this problem, and provide guidance for researchers interested in assessing the fit of tree models.

时间校准系统发育树（time-calibrated phylogenetic trees）是研究演化、生态与流行病学相关现象的功能强大的研究工具。此类系统发育树绝大多数通过贝叶斯框架进行推断，研究中通常将系统发育本身视作带有先验分布的参数（即‘树先验（tree prior）’）。然而本研究表明，该“参数”本质上部分由分类群样本形式的数据构成。将树视作参数的处理方式未能纳入此类数据的影响，进而削弱了我们使用标准技术开展模型间比较的能力——此类标准技术包括通过路径采样（path-sampling）和阶梯式采样（stepping-stone sampling）算法估计的边际似然方法。由于推断得到的系统发育准确性高度依赖树先验对生成该树的真实物种分化过程的拟合程度，无法精准比较不同竞争树先验的优劣，会对基于时间校准系统发育树的各类应用产生广泛影响。本文梳理了该问题的潜在解决方案，并为有意评估树模型拟合效果的研究人员提供了操作指引。