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dc.contributor.authorNowak, Michael D.
dc.contributor.authorSmith, Andrew B.
dc.contributor.authorSimpson, Carl
dc.contributor.authorZwickl, Derrick J.
dc.date.accessioned2014-03-19T20:21:58Z
dc.date.available2014-03-19T20:21:58Z
dc.date.issued2013-06-05
dc.identifier.citationNowak, M. D., Smith, A. B., Simpson, C., & Zwickl, D. J. (2013). A Simple Method for Estimating Informative Node Age Priors for the Fossil Calibration of Molecular Divergence Time Analyses. PLoS ONE, 8(6). http://dx.doi.org/10.1371/journal.pone.0066245
dc.identifier.urihttp://hdl.handle.net/1808/13274
dc.description.abstractMolecular divergence time analyses often rely on the age of fossil lineages to calibrate node age estimates. Most divergence time analyses are now performed in a Bayesian framework, where fossil calibrations are incorporated as parametric prior probabilities on node ages. It is widely accepted that an ideal parameterization of such node age prior probabilities should be based on a comprehensive analysis of the fossil record of the clade of interest, but there is currently no generally applicable approach for calculating such informative priors. We provide here a simple and easily implemented method that employs fossil data to estimate the likely amount of missing history prior to the oldest fossil occurrence of a clade, which can be used to fit an informative parametric prior probability distribution on a node age. Specifically, our method uses the extant diversity and the stratigraphic distribution of fossil lineages confidently assigned to a clade to fit a branching model of lineage diversification. Conditioning this on a simple model of fossil preservation, we estimate the likely amount of missing history prior to the oldest fossil occurrence of a clade. The likelihood surface of missing history can then be translated into a parametric prior probability distribution on the age of the clade of interest. We show that the method performs well with simulated fossil distribution data, but that the likelihood surface of missing history can at times be too complex for the distribution-fitting algorithm employed by our software tool. An empirical example of the application of our method is performed to estimate echinoid node ages. A simulation-based sensitivity analysis using the echinoid data set shows that node age prior distributions estimated under poor preservation rates are significantly less informative than those estimated under high preservation rates.
dc.description.sponsorshipThis work was financially supported through a Society of Systematic Biologists Graduate Student Research Award to MN, the National Evolutionary Synthesis Center (NESCent), and Google Summer of Code.
dc.publisherPublic Library of Science
dc.rights© 2013 Nowak et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectBayes theorem
dc.subjectBiostratigraphy
dc.subjectDistributution
dc.subjectFossil calibration
dc.subjectFossil record
dc.subjectFossils
dc.subjectPaleobiology
dc.subjectProbability distribution
dc.titleA Simple Method for Estimating Informative Node Age Priors for the Fossil Calibration of Molecular Divergence Time Analyses
dc.typeArticle
kusw.kuauthorZwickl, Derrick J.
kusw.kudepartmentDepartment of Ecology and Evolutionary Biology
kusw.oastatusfullparticipation
dc.identifier.doi10.1371/journal.pone.0066245
kusw.oaversionScholarly/refereed, publisher version
kusw.oapolicyThis item meets KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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© 2013 Nowak et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Except where otherwise noted, this item's license is described as: © 2013 Nowak et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.