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Open Access Highly Accessed Methodology article

A Bayesian framework to estimate diversification rates and their variation through time and space

Daniele Silvestro123*, Jan Schnitzler12 and Georg Zizka123

Author affiliations

1 Biodiversity and Climate Research Centre (BiK-F), Senckenberganlage 25, 60325 Frankfurt am Main, Germany

2 Department of Botany and Molecular Evolution, Senckenberg Research Institute, Senckenberganlage 25, 60325 Frankfurt am Main, Germany

3 Diversity and Evolution of Higher Plants, Institute of Ecology, Evolution and Diversity, Goethe University, Senckenberganlage 31, 60325 Frankfurt am Main, Germany

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Citation and License

BMC Evolutionary Biology 2011, 11:311  doi:10.1186/1471-2148-11-311

Published: 21 October 2011

Abstract

Background

Patterns of species diversity are the result of speciation and extinction processes, and molecular phylogenetic data can provide valuable information to derive their variability through time and across clades. Bayesian Markov chain Monte Carlo methods offer a promising framework to incorporate phylogenetic uncertainty when estimating rates of diversification.

Results

We introduce a new approach to estimate diversification rates in a Bayesian framework over a distribution of trees under various constant and variable rate birth-death and pure-birth models, and test it on simulated phylogenies. Furthermore, speciation and extinction rates and their posterior credibility intervals can be estimated while accounting for non-random taxon sampling. The framework is particularly suitable for hypothesis testing using Bayes factors, as we demonstrate analyzing dated phylogenies of Chondrostoma (Cyprinidae) and Lupinus (Fabaceae). In addition, we develop a model that extends the rate estimation to a meta-analysis framework in which different data sets are combined in a single analysis to detect general temporal and spatial trends in diversification.

Conclusions

Our approach provides a flexible framework for the estimation of diversification parameters and hypothesis testing while simultaneously accounting for uncertainties in the divergence times and incomplete taxon sampling.