Accuracy of phylogenetic reconstructions from continuous characters analyzed under parsimony and its parametric correlates

Quantitative traits are a source of evolutionary information often difficult to handle in cladistics. Tools exist to analyze this kind of data without subjective discretization, avoiding biases in the delimitation of categorical states. Nonetheless, the ability of continuous characters to accurately infer relationships is incompletely understood, particularly under parsimony analysis. This study evaluates the accuracy of phylogenetic reconstructions from simulated matrices of continuous characters evolving under alternative evolutionary processes and analyzed by parsimony. We sampled 100 empirical trees to simulate 9,000 matrices, each containing between 25 and 50 taxa and 50 and 150 continuous characters evolving under three evolutionary processes: Brownian-Motion (BM), Ornstein-Uhlenbeck (OU) and Early-Burst (EB) with variable parametrizations. Our cladogram comparisons revealed that continuous character matrices, when discretized objectively and analyzed by parsimony in TNT, carry phylogenetic signals to infer species relationships, regardless of the evolutionary models and parameterization schemes. Interestingly, implementing Equal Weighting (EW) or Implied Weighting (IW) with varying penalization strengths against homoplasies did not affect cladogram reconstructions on the basis of continuous characters. Finally, the accuracy of continuous characters in resolving species relationships is skewed toward apical nodes of the recovered trees. Our findings provide general insights of the utility of quantitative traits in cladistics and demonstrate that their effectiveness in estimating shallower nodes is independent of the underlying evolutionary model, parameters and weighting schemes.数量性状是分支系统学（cladistics）中常难以处理的一类进化信息。现有工具可无需对其进行主观离散化处理，便能开展分析，规避分类性状状态界定过程中产生的偏差。不过，连续性状用于准确推断类群系统发育关系的能力仍未被完全阐明，尤其是在简约分析法的分析框架下。 本研究针对在不同进化过程下演化的连续性状模拟矩阵，采用简约分析法开展分析，评估其系统发育重建的准确性。我们采样100棵经验系统发育树以模拟9000组性状矩阵，每组矩阵包含25至50个分类单元，以及50至150个连续性状，这些性状分别在布朗运动（Brownian-Motion, BM）、奥恩斯坦-乌伦贝克（Ornstein-Uhlenbeck, OU）和早爆发（Early-Burst, EB）三种进化过程下演化，并设置了可变参数方案。 我们的分支图对比分析结果显示，经客观离散化处理后、使用TNT软件以简约分析法分析的连续性状矩阵，可携带系统发育信号以推断物种类群间的演化关系，且不受进化模型与参数设置方案的影响。值得注意的是，采用等权加权（Equal Weighting, EW）或针对同塑性设置不同惩罚强度的隐含加权（Implied Weighting, IW），均未对基于连续性状的分支图重建产生显著影响。 最后，连续性状在解析物种类群关系时的准确性，更偏向于在重建系统发育树的顶端节点上体现。本研究结果为数量性状在分支系统学中的应用价值提供了普适性认识，并证实其在估算较浅分化节点（即类群顶端节点）时的有效性，不受底层进化模型、参数设置与加权方案的影响。