Data from: Estimating morphological diversity and tempo with discrete character-taxon matrices: implementation, challenges, progress, and future directions

Discrete character-taxon matrices are increasingly being used in an attempt to understand the pattern and tempo of morphological evolution; however, methodological sophistication and bespoke software implementations have lagged behind. In the present study, an attempt is made to provide a state-of-the-art description of methodologies and introduce a new R package (Claddis) for performing foundational disparity (morphologic diversity) and rate calculations. Simulations using its core functions show that: (1) of the two most commonly used distance metrics (Generalized Euclidean Distance and Gower's Coefficient), the latter tends to carry forward more of the true signal; (2) a novel distance metric may improve signal retention further; (3) this signal retention may come at the cost of pruning incomplete taxa from the data set; and (4) the utility of bivariate plots of ordination spaces are undermined by their frequently extremely low variances. By contrast, challenges to estimating morphologic tempo are presented qualitatively, such as how trees are time-scaled and changes are counted. Both disparity and rates deserve better time series approaches that could unlock new macroevolutionary analyses. However, these challenges need not be fatal, and several potential future solutions and directions are suggested.

离散特征-分类群矩阵（discrete character-taxon matrices）正日益被用于探索形态演化的模式与速率；然而，相关方法学的精细化程度与定制化软件实现却始终滞后。本研究旨在对相关方法学进行前沿性梳理，并介绍一款全新的R包（Claddis），用于开展基础的形态差异度（morphologic diversity）与演化速率计算工作。基于该包核心函数开展的模拟实验表明：(1) 在两种最常用的距离度量（distance metrics）——广义欧氏距离（Generalized Euclidean Distance）与高沃系数（Gower's Coefficient）中，后者往往能保留更多真实信号；(2) 一款全新的距离度量可进一步提升信号保留能力；(3) 这种信号保留能力的提升，可能需要以从数据集中剔除不完整分类群为代价；(4) 排序空间（ordination spaces）二元绘图的实用价值常因极低的方差而大打折扣。与之相对，形态演化速率的估算在定性层面面临诸多挑战，例如系统发育树的时间校准方式与演化改变的计数规则等问题。形态差异度与演化速率的研究均亟需更完善的时间序列分析方法，以推动全新的宏观演化分析工作。不过，这些挑战并非致命性难题，本研究同时提出了若干可行的未来解决方案与研究方向。