🤖 AI Summary
Existing Ornstein–Uhlenbeck (OU) model approaches primarily detect shifts in the optimal trait value (θ), neglecting biologically realistic concurrent shifts in diffusion variance (σ²) under environmental perturbations—leading to inflated false-positive rates.
Method: We propose a multi-optimum–multi-variance OU model that jointly models coordinated shifts in both θ and σ² as an L1-regularized variable selection problem, thereby unifying the evolutionary mechanisms underlying adaptive rate and diffusion intensity and their joint impact on phylogenetic covariance structure.
Contribution/Results: Implemented in the ShiVa R package, our method demonstrates superior performance over state-of-the-art alternatives (e.g., l1ou, PhylogeneticEM) in both simulations and empirical lizard data: it achieves higher log-likelihood, lower BIC, and improved prediction accuracy and robustness. This represents the first framework to simultaneously infer adaptive and diffusive regime shifts while preserving statistical identifiability and biological interpretability.
📝 Abstract
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.
