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Syst Biol. 2016 Nov;65(6):1009-1023. Epub 2016 May 6.

Estimating Bayesian Phylogenetic Information Content.

Author information

1
Department of Ecology and Evolutionary Biology, University of Connecticut, 75 N. Eagleville Road, Unit 3043, Storrs, CT 06269, USA; paul.lewis@uconn.edu.
2
Department of Statistics, University of Connecticut, 215 Glenbrook Road, Unit 4120, Storrs, CT 06269, USA.
3
Department of Ecology and Evolutionary Biology, University of Connecticut, 75 N. Eagleville Road, Unit 3043, Storrs, CT 06269, USA.

Abstract

Measuring the phylogenetic information content of data has a long history in systematics. Here we explore a Bayesian approach to information content estimation. The entropy of the posterior distribution compared with the entropy of the prior distribution provides a natural way to measure information content. If the data have no information relevant to ranking tree topologies beyond the information supplied by the prior, the posterior and prior will be identical. Information in data discourages consideration of some hypotheses allowed by the prior, resulting in a posterior distribution that is more concentrated (has lower entropy) than the prior. We focus on measuring information about tree topology using marginal posterior distributions of tree topologies. We show that both the accuracy and the computational efficiency of topological information content estimation improve with use of the conditional clade distribution, which also allows topological information content to be partitioned by clade. We explore two important applications of our method: providing a compelling definition of saturation and detecting conflict among data partitions that can negatively affect analyses of concatenated data. [Bayesian; concatenation; conditional clade distribution; entropy; information; phylogenetics; saturation.].

PMID:
27155008
PMCID:
PMC5066063
DOI:
10.1093/sysbio/syw042
[Indexed for MEDLINE]
Free PMC Article

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