Pagel, M.
ORCID: https://orcid.org/0000-0001-7287-8865, Meade, A.
ORCID: https://orcid.org/0000-0001-7095-7711 and Barker, D.
(2004)
Bayesian estimation of ancestral character states on phylogenies.
Systematic Biology, 53 (5).
pp. 673-684.
ISSN 1063-5157
doi: 10.1080/10635150490522232
Abstract/Summary
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.
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| Item Type | Article |
| URI | https://reading-clone.eprints-hosting.org/id/eprint/10572 |
| Identification Number/DOI | 10.1080/10635150490522232 |
| Refereed | Yes |
| Divisions | Life Sciences > School of Biological Sciences |
| Uncontrolled Keywords | ancestral states, comparative methods, maximum likelihood, MCMC, phylogeny, MAXIMUM-LIKELIHOOD, DISCRETE CHARACTERS, INFERENCE, EVOLUTION, TREES, SEQUENCE |
| Download/View statistics | View download statistics for this item |
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