Accommodating Phylogenetic Uncertainty in Evolutionary Studies

• Published in: Science (American Association for the Advancement of Science) Vol. 288; no. 5475; pp. 2349 - 2350
• Main Authors: Huelsenbeck, John P., Rannala, Bruce, Masly, John P.
• Format: Journal Article
• Language: English
• Published: Washington, DC American Society for the Advancement of Science 30.06.2000; American Association for the Advancement of Science; The American Association for the Advancement of Science
• Subjects: Animals; Aphids - genetics; Base Sequence; Bayes Theorem; Bayesian analysis; Bayesian Statistics; Behavior Patterns; Biological and medical sciences; Biological Evolution; Biological taxonomies; Comparative Analysis; Decision trees; Deoxyribonucleic acid; DNA; DNA, Mitochondrial - genetics; Evidence; Evolution; Evolution (Biology); Evolution, Molecular; Evolutionary biology; Evolutionary studies; Fundamental and applied biological sciences. Psychology; Genetics; Genetics of eukaryotes. Biological and molecular evolution; Inferences; Markov Chains; Markov Processes; Military Personnel; Monte Carlo Method; Parsimony; Personality; Phylogenetics; Phylogeny; Probability; Qualitative comparative analysis; Social Class; Statistical analysis; Stochastic Processes; Statistical analysis; Interspecific comparison; Biological evolution; Statistical decision; Phylogeny; Bayes decision; Natural selection
• ISSN: 0036-8075; 1095-9203
• DOI: 10.1126/science.288.5475.2349

Many evolutionary studies use comparisons across species to detect evidence of natural selection and to examine the rate of character evolution. Statistical analyses in these studies are usually performed by means of a species phylogeny to accommodate the effects of shared evolutionary history. The phylogeny is usually treated as known without error; this assumption is problematic because inferred phylogenies are subject to both stochastic and systematic errors. We describe methods for accommodating phylogenetic uncertainty in evolutionary studies by means of Bayesian inference. The methods are computationally intensive but general enough to be applied in most comparative evolutionary studies.