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.Mk模型（Mk model）旨在基于离散形态学数据估算系统发育树，适用于现生或化石类群。与所有模型一致，Mk模型存在多项预设假设。其中一项核心假设为：性状状态间的转换是对称的，即从0转变为1的概率与从1转变为0的概率完全相等。然而，数据矩阵中的部分性状可能并不满足该假设。本研究针对贝叶斯框架下放宽该假设的方法开展测试。通过经验数据集，我们开展模型拟合以展示场景：当对性状间的不对称转换速率进行建模时，其效果优于标准Mk模型。此外，我们利用模拟数据集证明，选择最优拟合的性状状态对称模型，可有效提升模型拟合度与系统发育推断精度。