Reading Evolutionary Biology Group - Current Research
Comparitive Methods
We are working on methods for the analysis discrete data and methods for analysing continuously varying data. The methods can be sued to reconstruct ancestral states, measure rates of evolution, and test for correlated evolution among pairs of traits, or in the case of continuously varying traits it is possible to conduct multiple regressions. The techniques are implemented as Markov Chain Monte Carlo methods (including reversible-jump) that find Bayesian posterior distributions of model parameters. They take into account phylogenetic uncertainty, accommodate unknow states or missing data and give estimates of model uncertainty.
Recently we have devised a Bayesian posterior predictive method that allows investigators to derive predicted values of traits for taxa with unknown values.