Adaptive landscapes on trees
G.G. Simpson's phenotypic adaptive landscape provides a conceptual framework for understanding the dynamics of trait evolution on both micro and macroevolutionary scales. This framework has been formalized at microevolutionary scales in the empirically-validated framework of evolutionary quantitative genetics. Futhermore, hypotheses regarding the macroevolutionary adaptive landscape have been applied to patterns of trait diversification at macroevolutionary scales. We work to better understand when, why and how adaptive landscapes change at macroevolutionary scales, and why these shifts occur.

We have developed an approach that uses Bayesian reversible-jump MCMC to detect shifts in the macroevolutionary adaptive landscape. The method is implemented in the software package bayou, which we are continuing to develop. Besides detecting shifts in adaptive landscapes, the Bayesian approach also enables users to use informative priors for analyzing trait evolution--providing a means to connect macroevolutionary models to biologically realistic parameter estimates. Our goal is to provide a variety of tools for understanding adaptive evolution on trees that integrate data from paleontological studies, neontological time-series, estimates of selection and genetic (co)variation, performance landscapes, and phylogenetic comparative data that can be used to better understand (multivariate) trait evolution.