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Improved estimation of macroevolutionary rates from fossil data using a Bayesian framework

Daniele Silvestro, View ORCID ProfileAlexandre Antonelli, Nicolas Salamin, Xavier Meyer
doi: https://doi.org/10.1101/316992
Daniele Silvestro
1Department of Biological and Environmental Sciences, University of Gothenburg, 413 19 Gothenburg, Sweden
2Global Gothenburg Biodiversity Center, Gothenburg, Sweden
3Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
4Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland
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Alexandre Antonelli
1Department of Biological and Environmental Sciences, University of Gothenburg, 413 19 Gothenburg, Sweden
2Global Gothenburg Biodiversity Center, Gothenburg, Sweden
5Gothenburg Botanical Garden, SE-41319 Goteborg, Sweden
6Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford St., Cambridge, MA 02138 USA
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Nicolas Salamin
3Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
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Xavier Meyer
3Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
4Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland
7Department of Integrative Biology, University of California, Berkeley, CA 94720, USA
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Abstract

The estimation of origination and extinction rates and their temporal variation is central to understanding diversity patterns and the evolutionary history of clades. The fossil record provides the most direct evidence of extinction and biodiversity changes through time and has long been used to infer the dynamics of diversity changes in deep time. The software PyRate implements a Bayesian framework to analyze fossil occurrence data to estimate the rates of preservation, origination and extinction while incorporating several sources of uncertainty. This fully probabilistic approach allows us to explicitly assess the statistical support of alternative macroevolutionary hypotheses and to infer credible intervals around parameter estimates. Here, we present a major update of the software, which implements substantial methodological advancements, including more complex and realistic models of preservation, a reversible jump Markov chain Monte Carlo algorithm to estimate origination and extinction rates and their temporal variation, and a substantial boost in performance. We demonstrate the new functionalities through extensive simulations and with the analysis of a large dataset of Cenozoic marine mammals. We identify several significant shifts in origination and extinction rates of marine mammals, underlying a late Miocene diversity peak and a subsequent 50% diversity decline towards the present. Our analyses indicate that explicit statistical model testing, which is often neglected in fossil-based macroevolutionary analyses, is crucial to obtain accurate and robust results. PyRate provides a flexible, statistically sound analytical framework, which we think can serve as a useful toolkit for many future studies in paleobiology.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted May 09, 2018.
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Improved estimation of macroevolutionary rates from fossil data using a Bayesian framework
Daniele Silvestro, Alexandre Antonelli, Nicolas Salamin, Xavier Meyer
bioRxiv 316992; doi: https://doi.org/10.1101/316992
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Improved estimation of macroevolutionary rates from fossil data using a Bayesian framework
Daniele Silvestro, Alexandre Antonelli, Nicolas Salamin, Xavier Meyer
bioRxiv 316992; doi: https://doi.org/10.1101/316992

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