Data from: Ancestral character estimation under the threshold model from quantitative genetics

Evolutionary biology is a study of life's history on Earth. In
researching this history, biologists are often interested in attempting to
reconstruct phenotypes for the long extinct ancestors of living species.
Various methods have been developed to do this on a phylogeny from the
data for extant taxa. In the present article, I introduce a new approach
for ancestral character estimation for discretely valued traits. This
approach is based on the threshold model from evolutionary quantitative
genetics. Under the threshold model, the value exhibited by an individual
or species for a discrete character is determined by an underlying,
unobserved continuous trait called “liability.” In this new method for
ancestral state reconstruction, I use Bayesian Markov chain Monte Carlo
(MCMC) to sample the liabilities of ancestral and tip species, and the
relative positions of two or more thresholds, from their joint posterior
probability distribution. Using data simulated under the model, I find
that the method has very good performance in ancestral character
estimation. Use of the threshold model for ancestral state reconstruction
relies on a priori specification of the order of the discrete character
states along the liability axis. I test the use of a Bayesian MCMC
information theoretic criterion based approach to choose among different
hypothesized orderings for the discrete character. Finally, I apply the
method to the evolution of feeding mode in centrarchid fishes.