Data from: Modeling character change heterogeneity in phylogenetic analyses of morphology through the use of priors

The Mk model was developed for estimating phylogenetic trees from discrete morphological data, whether for living or fossil taxa. Like any model, the Mk model makes a number of assumptions. One assumption is that transitions between character states are symmetric (i.e., the probability of changing from 0 to 1 is the same as 1 to 0). However, some characters in a data matrix may not satisfy this assumption. Here, we test methods for relaxing this assumption in a Bayesian context. Using empirical datasets, we perform model fitting to illustrate cases in which modeling asymmetric transition rates among characters is preferable to the standard Mk model. We use simulated datasets to demonstrate that choosing the best-fit model of transition-state symmetry can improve model fit and phylogenetic estimation.

Usage Notes
READMEExplains the content of various files.EmpiricalDataRaw empirical data, and the results of estimating phylogenetic trees from these data using MrBayes.SimulationsSimulated data files, and the results of phylogenetic estimation from these data.MrBayesBlocksMrBayes instruction sets for each prior.SpreadsheetsProcessed data files used to make the figures in the paper.ScriptsScripts used for data generation and analysis.

Mk模型旨在从离散形态学数据中估计系统发育树，无论是对现存物种还是化石物种。如同任何模型，Mk模型亦基于若干假设。其中之一为特征状态之间的转换是对称的（即，从0变为1的概率与从1变为0的概率相同）。然而，数据矩阵中某些特征可能不满足这一假设。在此，我们测试了在贝叶斯框架下放宽此假设的方法。利用经验数据集，我们进行模型拟合，以展示在建模特征间非对称转换率的情况下，相较于标准Mk模型更为可取。通过模拟数据集，我们证明了选择最佳拟合的转换状态对称模型能够提升模型拟合度和系统发育估计。使用说明：README文件解释了各种文件的内容。经验数据包含原始经验数据和利用MrBayes从这些数据估计系统发育树的结果。模拟数据包含模拟数据文件以及从这些数据中进行的系统发育估计结果。MrBayesBlocks包含针对每个先验的MrBayes指令集。电子表格包含用于制作论文中图表的加工数据文件。脚本包含用于数据生成和分析的脚本。