A NEW BAYESIAN METHOD FOR FITTING EVOLUTIONARY MODELS TO COMPARATIVE DATA WITH INTRASPECIFIC VARIATION
Version of Record online: 29 APR 2012
© 2012 The Author(s). Evolution© 2012 The Society for the Study of Evolution.
Volume 66, Issue 9, pages 2697–2707, September 2012
How to Cite
Revell, L. J. and Graham Reynolds, R. (2012), A NEW BAYESIAN METHOD FOR FITTING EVOLUTIONARY MODELS TO COMPARATIVE DATA WITH INTRASPECIFIC VARIATION. Evolution, 66: 2697–2707. doi: 10.1111/j.1558-5646.2012.01645.x
- Issue online: 4 SEP 2012
- Version of Record online: 29 APR 2012
- Accepted manuscript online: 3 APR 2012 06:11AM EST
- Received December 31, 2011 Accepted March 13, 2012 Data Archived: Dryad: doi:10.5061/dryad.7fv08k72
- Comparative method;
- interspecific data;
- phylogenetic tree
Phylogenetic comparative methods that incorporate intraspecific variability are relatively new and, so far, not especially widely used in empirical studies. In the present short article we will describe a new Bayesian method for fitting evolutionary models to comparative data that incorporates intraspecific variability. This method differs from an existing likelihood-based approach in that it requires no a priori inference about species means and variances; rather it takes phenotypic values from individuals and a phylogenetic tree as input, and then samples species means and variances, along with the parameters of the evolutionary model, from their joint posterior probability distribution. One of the most novel and intriguing attributes of this approach is that jointly sampling the species means with the evolutionary model parameters means that the model and tree can influence our estimates of species mean trait values, not just the reverse. In the present implementation, we first apply this method to the most widely used evolutionary model for continuously valued phenotypic trait data (Brownian motion). However, the general approach has broad applicability, which we illustrate by also fitting the λ model, another simple model for quantitative trait evolution on a phylogeny. We test our approach via simulation and by analyzing two empirical datasets obtained from the literature. Finally, we have implemented the methods described herein in a new function for the R statistical computing environment, and this function will be distributed as part of the ‘phytools’ R library.