DOI: 10.5061/dryad.34tmpg4sh
title: Simulated trait data for convergence analyses

description: Tests of phenotypic convergence can provide evidence of adaptive
 evolution, and the popularity of such studies has grown in recent years
 due to the development of novel, quantitative methods for identifying and
 measuring convergence. These methods include the commonly applied C1–C4
 measures of Stayton (2015), which measure morphological distances between
 lineages, and Ornstein-Uhlenbeck (OU) model-fitting analyses, which test
 whether lineages converged on shared adaptive peaks. We test the
 performance of C-measures and other convergence measures under various
 evolutionary scenarios and reveal a critical issue with C-measures: they
 often misidentify divergent lineages as convergent. We address this issue
 by developing novel convergence measures— Ct1–Ct4-measures —that calculate
 distances between lineages at specific points in time, minimizing the
 possibility of misidentifying divergent taxa as convergent. Ct-measures
 are most appropriate when focal lineages are of the same or similar
 geologic ages (e.g., extant taxa), meaning that the lineages’ evolutionary
 histories include considerable overlap in time. Beyond C-measures, we find
 that all convergence measures are influenced by the position of focal taxa
 in phenotypic space, with morphological outliers often statistically more
 likely to be measured as strongly convergent. Further, we mimic scenarios
 in which researchers assess convergence using OU models with a priori
 regime assignments (e.g., classifying taxa by ecological traits) and find
 that multiple-regime OU models with phenotypically divergent lineages
 assigned to a shared selective regime often outperform simpler models.
 This highlights that model support for these multiple-regime OU models
 should not be assumed to always reflect convergence among focal lineages
 of a shared regime. Our new Ct1–Ct4-measures provide researchers with an
 improved comparative tool, but we emphasize that all available convergence
 measures are imperfect, and researchers should recognize the limitations
 of these methods and use multiple lines of evidence to test convergence
 hypotheses.
description: This Dryad submission includes many simulated datasets and a
 Wolfram's Mathematica notebook with a custom script used to produce
 the simulations. See the README file, included Mathematica file, and
 associated publication for more information. 
description: # Simulated trait data for convergence & divergence analyses
 [https://doi.org/10.5061/dryad.34tmpg4sh](https://doi.org/10.5061/dryad.34tmpg4sh) This Dryad submission includes many simulated datasets and a Wolfram's Mathematica notebook with a custom script used to produce the simulation. Each dataset includes six simulated phenotypic trait values for 201 taxa. For 13 focal taxa (representing five monophyletic clades) for which we simulated convergence or divergence, some or all traits (between two and six of the six total) were evolved to trait optima (targets) via an Ornstein-Uhlenbeck (OU) process (alpha = 0.1, sigma = 0.1). For convergence simulations, the 13 focal taxa were evolved to the same trait optimum. For divergence simulations (file names start with "divBase"), the five clades were evolved to different optima. See our included Mathematica notebook and our associated publication for more details on how the traits were simulated. ## Description of the data and file structure We produced 15 simulated 'base' datasets in which the six traits of most taxa were evolved by Brownian motion (BM). For each base dataset, we produced 45 spreadsheets in which the traits of 13 focal taxa were sequentially altered. The base dataset number is indicated in the spreadsheet file names by "Base=XX". The traits of the focal taxa vary in two ways. First, they evolved to trait optima (targets) of varying distances away from the ancestral traits (zero). The target distance is indicated in the spreadsheet file name by "Target=XX", with targets varying between 0 and 80 in increments of 10. Second, we altered the number of traits (of six total) that were OU-evolved toward trait optima. This varies between three and six traits, with the value indicated in the file name by "Traits=XX". Any traits that were not OU-evolved were evolved by BM. Each spreadsheet has a column of taxon names (representing 201 mammalian species) and six columns of simulated trait values. ## Sharing/Access information Contact the corresponding authors of the associated publication for more information.  ## Code/Software For scripts to produce similar simulated datasets, see our supplemental Wolfram's Mathematica notebook and sample R script in the Supplemental Information file.