Abrupt environmental changes can lead to evolutionary shifts in not only the optimal trait value, but also the rate of adaptation and the diffusion variance in trait evolution. While several methods exist for detecting shifts in optimal values, few explicitly model shifts in both evolutionary variance and adaptation rates. We use a multi-optima and multi-variance Ornstein-Uhlenbeck (OU) process model to describe trait evolution with shifts in both optimal value and diffusion variance and analyze how covariance between species is affected when shifts in variance occur along the phylogeny. We propose a new method that simultaneously detects shifts in both variance and optimal values by formulating the problem as a variable selection task using an L1-penalized loss function. Our method is implemented in the R package ShiVa (Detection of evolutionary Shifts in Variance). Through simulations, we compare ShiVa with methods that only consider shifts in optimal values (l1ou; PhylogeneticEM), and PCMFit. Our method demonstrates improved predictive ability and significantly reduces false positives in detecting optimal value shifts when variance shifts are present. When only shifts in optimal value occur, our method performs comparably to existing approaches. Applying ShiVa to empirical data from cordylid lizards , we find that it outperforms l1ou and PhylogeneticEM, achieving the highest log-likelihood and lowest BIC.

翻译：环境的急剧变化不仅可能导致最优性状值的演化转变，也可能导致适应速率和性状演化中扩散方差的转变。尽管存在多种检测最优值转变的方法，但很少有方法能同时对演化方差和适应速率的转变进行显式建模。我们采用一个多最优值与多方差的Ornstein-Uhlenbeck（OU）过程模型来描述性状演化中同时发生最优值和扩散方差转变的情况，并分析了当方差转变沿系统发育树发生时物种间协方差受到的影响。我们提出了一种新方法，通过将问题构建为使用L1惩罚损失函数的变量选择任务，同时检测方差和最优值的转变。我们的方法已在R软件包ShiVa（Detection of evolutionary Shifts in Variance）中实现。通过模拟实验，我们将ShiVa与仅考虑最优值转变的方法（l1ou；PhylogeneticEM）以及PCMFit进行了比较。我们的方法在存在方差转变时，展现出更强的预测能力，并显著降低了检测最优值转变的假阳性率。当仅发生最优值转变时，我们的方法与现有方法表现相当。将ShiVa应用于cordylid蜥蜴的经验数据，我们发现其表现优于l1ou和PhylogeneticEM，获得了最高的对数似然值和最低的BIC。