Abstract Evolutionary inferences require reliable phylogenies. Morphological data have traditionally been analyzed using maximum parsimony, but recent simulation studies have suggested that Bayesian analyses yield more accurate trees. This debate is ongoing, in part, because of ambiguity over modes of morphological evolution and a lack of appropriate models. Here, we investigate phylogenetic methods using two novel simulation models—one in which morphological characters evolve stochastically along lineages and another in which individuals undergo selection. Both models generate character data and lineage splitting simultaneously: the resulting trees are an emergent property, rather than a fixed parameter. Standard consensus methods for Bayesian searches (Mki) yield fewer incorrect nodes and quartets than the standard consensus trees recovered using equal weighting and implied weighting parsimony searches. Distances between the pool of derived trees (most parsimonious or posterior distribution) and the true trees—measured using Robinson-Foulds (RF), subtree prune and regraft (SPR), and tree bisection reconnection (TBR) metrics—demonstrate that this is related to the search strategy and consensus method of each technique. The amount and structure of homoplasy in character data differ between models. Morphological coherence, which has previously not been considered in this context, proves to be a more important factor for phylogenetic accuracy than homoplasy. Selection-based models exhibit relatively lower homoplasy, lower morphological coherence, and higher inaccuracy in inferred trees. Selection is a dominant driver of morphological evolution, but we demonstrate that it has a confounding effect on numerous character properties which are fundamental to phylogenetic inference. We suggest that the current debate should move beyond considerations of parsimony versus Bayesian, toward identifying modes of morphological evolution and using these to build models for probabilistic search methods. [Bayesian; evolution; morphology; parsimony; phylogenetics; selection; simulation.]

中文翻译：

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使用新的进化模拟评估形态学系统发育

摘要 进化推论需要可靠的系统发育。传统上使用最大简约法分析形态数据，但最近的模拟研究表明，贝叶斯分析会产生更准确的树。这场辩论正在进行，部分原因是形态进化模式的模糊性和缺乏合适的模型。在这里，我们使用两种新的模拟模型来研究系统发育方法——一种是形态特征沿谱系随机进化，另一种是个体经历选择。两种模型同时生成字符数据和谱系分裂：生成的树是一个紧急属性，而不是一个固定参数。贝叶斯搜索 (Mki) 的标准共识方法比使用相等加权和隐含加权简约搜索恢复的标准共识树产生更少的错误节点和四重奏。派生树池（最简约或后验分布）与真实树之间的距离——使用 Robinson-Foulds (RF)、子树修剪和再移植 (SPR) 以及树二分重新连接 (TBR) 指标测量——证明这是相关的到每种技术的搜索策略和共识方法。字符数据中同质性的数量和结构因模型而异。先前在此上下文中未考虑的形态一致性被证明是比同质性更重要的系统发育准确性因素。基于选择的模型表现出相对较低的同质性，较低的形态一致性，以及推断树的更高不准确度。选择是形态进化的主要驱动力，但我们证明它对许多对系统发育推断至关重要的特征特性具有混杂效应。我们建议当前的辩论应该超越简约与贝叶斯的考虑，转向识别形态进化的模式并使用这些模式构建概率搜索方法的模型。[贝叶斯；进化; 形态学; 简约；系统发育学；选择；模拟。] 我们建议当前的辩论应该超越简约与贝叶斯的考虑，转向识别形态进化的模式并使用这些模式构建概率搜索方法的模型。[贝叶斯；进化; 形态学; 简约；系统发育学；选择；模拟。] 我们建议当前的辩论应该超越简约与贝叶斯的考虑，转向识别形态进化的模式并使用这些模式构建概率搜索方法的模型。[贝叶斯；进化; 形态学; 简约；系统发育学；选择；模拟。]