Bayesian models for comparative analysis integrating phylogenetic uncertainty

de Villemereuil, Pierre, Wells, Jessie A., Edwards, Robert D. and Blomberg, Simon P. (2012) Bayesian models for comparative analysis integrating phylogenetic uncertainty. BMC Evolutionary Biology, 12 1: 102.1-102.16. doi:10.1186/1471-2148-12-102

Author de Villemereuil, Pierre
Wells, Jessie A.
Edwards, Robert D.
Blomberg, Simon P.
Title Bayesian models for comparative analysis integrating phylogenetic uncertainty
Journal name BMC Evolutionary Biology   Check publisher's open access policy
ISSN 1471-2148
Publication date 2012-06
Sub-type Article (original research)
DOI 10.1186/1471-2148-12-102
Open Access Status DOI
Volume 12
Issue 1
Start page 102.1
End page 102.16
Total pages 16
Place of publication London, United Kingdom
Publisher BioMed Central
Collection year 2013
Language eng
Formatted abstract
Background: Uncertainty in comparative analyses can come from at least two sources: a) phylogenetic uncertainty in the tree topology or branch lengths, and b) uncertainty due to intraspecific variation in trait values, either due to measurement error or natural individual variation. Most phylogenetic comparative methods do not account for such uncertainties. Not accounting for these sources of uncertainty leads to false perceptions of precision (confidence intervals will be too narrow) and inflated significance in hypothesis testing (e.g. p-values will be too small). Although there is some application-specific software for fitting Bayesian models accounting for phylogenetic error, more general and flexible software is desirable.

Methods: We developed models to directly incorporate phylogenetic uncertainty into a range of analyses that biologists commonly perform, using a Bayesian framework and Markov Chain Monte Carlo analyses.

Results: We demonstrate applications in linear regression, quantification of phylogenetic signal, and measurement error models. Phylogenetic uncertainty was incorporated by applying a prior distribution for the phylogeny, where this distribution consisted of the posterior tree sets from Bayesian phylogenetic tree estimation programs. The models were analysed using simulated data sets, and applied to a real data set on plant traits, from rainforest plant species in Northern Australia. Analyses were performed using the free and open source software OpenBUGS and JAGS.

Conclusions: Incorporating phylogenetic uncertainty through an empirical prior distribution of trees leads to more precise estimation of regression model parameters than using a single consensus tree and enables a more realistic estimation of confidence intervals. In addition, models incorporating measurement errors and/or individual variation, in one or both variables, are easily formulated in the Bayesian framework. We show that BUGS is a useful, flexible general purpose tool for phylogenetic comparative analyses, particularly for modelling in the face of phylogenetic uncertainty and accounting for measurement error or individual variation in explanatory variables. Code for all models is provided in the BUGS model description language.
Keyword Independent contrasts
Comparative biology
Interspecific data
McMc methods
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article number 102.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2013 Collection
School of Biological Sciences Publications
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Citation counts: TR Web of Science Citation Count  Cited 16 times in Thomson Reuters Web of Science Article | Citations
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