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.