Estimating Correlated Rates of Trait Evolution with Uncertainty

Area of Science:

• Evolutionary biology
• Quantitative genetics
• Phylogenetics

Background:

• Correlated evolution of traits influences phenotypic evolution trajectories.
• Shifts in evolutionary integration can unlock novel morphospace exploration.
• Understanding these patterns is crucial for evolutionary biology.

Purpose of the Study:

• To develop and validate a phylogenetic method for detecting shifts in evolutionary integration.
• To analyze the pace and pattern of trait evolution and correlation across clades.
• To incorporate uncertainty in trait evolution and ancestral states using Bayesian methods.

Main Methods:

• Phylogenetic comparative methods using Bayesian Markov chain Monte Carlo (MCMC).
• Joint estimation of model parameters, including trait evolution and ancestral regimes.
• Extension of Felsenstein's pruning algorithm for multivariate Brownian motion models with multiple rate regimes.
• Use of summary statistics to test for regime shifts.

Main Results:

• The developed method successfully detects shifts in evolutionary integration across phylogenetic trees.
• Simulations confirm the method's performance under various scenarios.
• Case studies on Centrarchidae fishes and Anolis lizards illustrate shifts in evolutionary trajectories.

Conclusions:

• The new method provides a robust framework for studying evolutionary integration and its shifts.
• This approach enhances our understanding of how correlated trait evolution impacts macroevolutionary patterns.
• The findings have implications for studying phenotypic evolution, modularity, and diversification.