  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.
