Data from: A new phylogenetic method for identifying exceptional phenotypic diversification

Currently available phylogenetic methods for studying the rate of evolution in a continuously valued character assume that the rate is constant throughout the tree or that it changes along specific branches according to an a priori hypothesis of rate variation provided by the user. Herein, we describe a new method for studying evolutionary rate variation in continuously valued characters given an estimate of the phylogenetic history of the species in our study. According to this method, we propose no specific prior hypothesis for how the variation in evolutionary rate is structured throughout the history of the species in our study. Instead, we use a Bayesian Markov Chain Monte Carlo approach to estimate evolutionary rates and the shift-point between rates on the tree. We do this by simultaneously sampling rates and shift-points in proportion to their posterior probability, and then collapsing the posterior sample into an estimate of the parameters of interest. We use simulation to show that the method is quite successful at identifying the phylogenetic position of a shift in the rate of evolution, and that estimated rates are asymptotically unbiased. We also provide an empirical example of the method using data for Anolis lizards.当前用于研究连续值性状演化速率的系统发育方法，通常假设演化速率在整个系统发育树中保持恒定，或是根据用户预先设定的速率变异先验假说，沿特定分支发生变化。本文介绍一种新方法，可在已知研究类群系统发育历史估计值的前提下，分析连续值性状的演化速率变异情况。该方法无需预先设定研究类群演化速率变异的具体先验结构假说。取而代之的是，我们采用贝叶斯马尔可夫链蒙特卡洛（Bayesian Markov Chain Monte Carlo）方法，估计系统发育树上的演化速率及速率转换点。具体实现方式为，按后验概率（posterior probability）的比例同时采样速率与转换点，随后将后验样本整合为目标参数的估计值。我们通过模拟实验证实，该方法可有效识别演化速率转换点的系统发育位置，且估计得到的演化速率具备渐近无偏性。此外，我们还利用安乐蜥（Anolis）的相关数据，给出了该方法的实证应用案例。