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Abstract
Biologists frequently attempt to infer the character states at ancestral nodes of
a phylogeny from the distribution of traits observed in contemporary organisms. Because
phylogenies are normally inferences from data, it is desirable to account for the
uncertainty in estimates of the tree and its branch lengths when making inferences
about ancestral states or other comparative parameters. Here we present a general
Bayesian approach for testing comparative hypotheses across statistically justified
samples of phylogenies, focusing on the specific issue of reconstructing ancestral
states. The method uses Markov chain Monte Carlo techniques for sampling phylogenetic
trees and for investigating the parameters of a statistical model of trait evolution.
We describe how to combine information about the uncertainty of the phylogeny with
uncertainty in the estimate of the ancestral state. Our approach does not constrain
the sample of trees only to those that contain the ancestral node or nodes of interest,
and we show how to reconstruct ancestral states of uncertain nodes using a most-recent-common-ancestor
approach. We illustrate the methods with data on ribonuclease evolution in the Artiodactyla.
Software implementing the methods (BayesMultiState) is available from the authors.