Data from: Probabilistic methods surpass parsimony when assessing clade support in phylogenetic analyses of discrete morphological data

Fossil taxa are critical to inferences of historical diversity and the origins of modern biodiversity, but realizing their evolutionary significance is contingent on restoring fossil species to their correct position within the tree of life. For most fossil species, morphology is the only source of data for phylogenetic inference; this has traditionally been analysed using parsimony, the predominance of which is currently challenged by the development of probabilistic models that achieve greater phylogenetic accuracy. Here, based on simulated and empirical datasets, we explore the relative efficacy of competing phylogenetic methods in terms of clade support. We characterize clade support using bootstrapping for parsimony and Maximum Likelihood, and intrinsic Bayesian posterior probabilities, collapsing branches that exhibit less than 50% support. Ignoring node support, Bayesian inference is the most accurate method in estimating the tree used to simulate the data. After assessing clade support, Bayesian and Maximum Likelihood exhibit comparable levels of accuracy, and parsimony remains the least accurate method. However, Maximum Likelihood is less precise than Bayesian phylogeny estimation, and Bayesian inference recaptures more correct nodes with higher support compared to all other methods, including Maximum Likelihood. We assess the effects of these findings on empirical phylogenies. Our results indicate probabilistic methods should be favoured over parsimony.

化石类群（Fossil taxa）对于历史多样性推断以及现代生物多样性（modern biodiversity）起源的研究至关重要，但要阐明其演化意义，需先将化石物种精准定位在生命之树（tree of life）的正确分支之上。对于绝大多数化石物种而言，形态学数据是开展系统发育推断（phylogenetic inference）的唯一数据源；传统上此类分析多采用简约法（Parsimony），但当前随着可实现更高系统发育准确性的概率模型（probabilistic models）的发展，简约法的主导地位正受到挑战。本研究基于模拟数据集（simulated datasets）与实证数据集（empirical datasets），围绕支系支持度（clade support）维度，对比了不同系统发育方法的相对效能。本研究针对简约法与最大似然法（Maximum Likelihood）采用自举法（Bootstrap）评估支系支持度，针对贝叶斯分析则采用固有贝叶斯后验概率（Bayesian posterior probabilities），并将支持度低于50%的分支进行合并处理。若不考虑节点支持度（node support），贝叶斯推断（Bayesian inference）在模拟数据所用系统发育树的重建上准确度最高。在纳入支系支持度评估后，贝叶斯推断与最大似然法的准确度相当，而简约法仍是准确度最低的方法。但相较贝叶斯系统发育重建，最大似然法的估计精度稍逊一筹；且相较于包括最大似然法在内的所有其他方法，贝叶斯推断可恢复更多支持度更高的正确节点。本研究进一步评估了上述结论对实证系统发育树（empirical phylogenies）研究的影响，结果表明相较于简约法，概率模型类方法更值得被优先采用。