Abstract 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.

中文翻译：

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在离散形态数据的系统发育分析中评估进化枝支持时，概率方法超越简约

摘要 化石类群对于历史多样性和现代生物多样性起源的推断至关重要，但实现其进化意义取决于将化石物种恢复到其在生命之树中的正确位置。对于大多数化石物种，形态学是系统发育推断的唯一数据来源；这在传统上是使用简约来分析的，其优势目前受到开发概率模型的挑战，这些模型可实现更高的系统发育准确性。在这里，基于模拟和经验数据集，我们探索了竞争系统发育方法在进化枝支持方面的相对功效。我们使用自举法来描述进化枝支持的简约性和最大似然，以及内在贝叶斯后验概率，支持率低于 50% 的折叠分支。忽略节点支持，贝叶斯推理是估计用于模拟数据的树的最准确方法。在评估进化枝支持后，贝叶斯和最大似然表现出相当的准确度，简约仍然是最不准确的方法。然而，最大似然不如贝叶斯系统发育估计精确，并且与所有其他方法（包括最大似然）相比，贝叶斯推理以更高的支持度重新捕获更正确的节点。我们评估了这些发现对经验系统发育的影响。我们的结果表明概率方法应该比简约方法更受青睐。贝叶斯和最大似然法表现出相当的准确度，而简约法仍然是最不准确的方法。然而，最大似然不如贝叶斯系统发育估计精确，并且与所有其他方法（包括最大似然）相比，贝叶斯推理以更高的支持度重新捕获更正确的节点。我们评估了这些发现对经验系统发育的影响。我们的结果表明概率方法应该比简约方法更受青睐。贝叶斯和最大似然法表现出相当的准确度，而简约法仍然是最不准确的方法。然而，最大似然不如贝叶斯系统发育估计精确，并且与所有其他方法（包括最大似然）相比，贝叶斯推理以更高的支持度重新捕获更正确的节点。我们评估了这些发现对经验系统发育的影响。我们的结果表明概率方法应该比简约方法更受青睐。我们评估了这些发现对经验系统发育的影响。我们的结果表明概率方法应该比简约方法更受青睐。我们评估了这些发现对经验系统发育的影响。我们的结果表明概率方法应该比简约方法更受青睐。