Data from: A novel Bayesian method for inferring and interpreting the dynamics of adaptive landscapes from phylogenetic comparative data

Our understanding of macroevolutionary patterns of adaptive evolution has
greatly increased with the advent of large-scale phylogenetic comparative
methods. Widely used Ornstein-Uhlenbeck (OU) models can describe an
adaptive process of divergence and selection. However, inference of the
dynamics of adaptive landscapes from comparative data is complicated by
interpretational difficulties, lack of identifiability among parameter
values and the common requirement that adaptive hypotheses must be
assigned a priori. Here we develop a reversible-jump Bayesian method of
fitting multi-optima OU models to phylogenetic comparative data that
estimates the placement and magnitude of adaptive shifts directly from the
data. We show how biologically informed hypotheses can be tested against
this inferred posterior of shift locations using Bayes Factors to
establish whether our a priori models adequately describe the dynamics of
adaptive peak shifts. Furthermore, we show how the inclusion of
informative priors can be used to restrict models to biologically
realistic parameter space and test particular biological interpretations
of evolutionary models. We argue that Bayesian model-fitting of OU models
to comparative data provides a framework for integrating of multiple
sources of biological data–such as microevolutionary estimates of
selection parameters and paleontological timeseries–allowing inference of
adaptive landscape dynamics with explicit, process-based biological
interpretations.