1. Abrupt environmental changes can lead to evolutionary shifts in not only mean (optimal value), but also variance of descendants in trait evolution. There are some methods to detect shifts in optimal value but few studies consider shifts in variance. 2. We use a multi-optima and multi-variance OU process model to describe the trait evolution process with shifts in both optimal value and variance and provide analysis of how the covariance between species changes when shifts in variance occur along the path. 3. We propose a new method to detect the shifts in both variance and optimal values based on minimizing the loss function with L1 penalty. We implement our method in a new R package, ShiVa (Detection of evolutionary shifts in variance). 4. We conduct simulations to compare our method with the two methods considering only shifts in optimal values (l1ou; PhylogeneticEM). Our method shows strength in predictive ability and includes far fewer false positive shifts in optimal value compared to other methods when shifts in variance actually exist. When there are only shifts in optimal value, our method performs similarly to other methods. We applied our method to the cordylid data, ShiVa outperformed l1ou and phyloEM, exhibiting the highest log-likelihood and lowest BIC.

翻译：1. 环境剧变可导致后代性状进化中不仅均值（最优值）发生进化转变，方差也会出现相应变化。现有若干检测最优值转变的方法，但鲜有研究关注方差转变。2. 我们采用多最优值-多方差的OU过程模型描述同时存在最优值与方差转变的性状进化过程，并分析沿进化路径出现方差转变时物种间协方差的变化模式。3. 我们提出基于L1罚函数最小化的新方法，可同时检测方差与最优值的转变。该方法已在全新R语言包ShiVa（进化方差转变检测）中实现。4. 通过模拟实验，我们将本方法与仅考虑最优值转变的两种方法（l1ou; PhylogeneticEM）进行比较。在存在实际方差转变的情况下，本方法展现出更优的预测能力，且较其他方法显著减少最优值转变的假阳性检测。当仅存在最优值转变时，本方法性能与其他方法相当。将本方法应用于鬣蜥科动物数据时，ShiVa相较于l1ou和phyloEM表现更优，获得最高对数似然值与最低贝叶斯信息准则值。