Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A Bayesian Approach for Estimating Branch-Specific Speciation and Extinction Rates

Sebastian Höhna, William A. Freyman, Zachary Nolen, John P. Huelsenbeck, Michael R. May, Brian R. Moore
doi: https://doi.org/10.1101/555805
Sebastian Höhna
1GeoBio-Center, Ludwig-Maximilians-Universität München Richard-Wagner Straße 10, 80333 Munich, Germany
2Division of Evolutionary Biology, Ludwig-Maximilians-Universität München Grosshaderner Straße 2, Planegg-Martinsried 82152, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
William A. Freyman
3Department of Ecology, Evolution, and Behavior, University of Minnesota, Twin Cities 1479 Gortner Avenue, Saint Paul, MM 55108, U.S.A.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Zachary Nolen
2Division of Evolutionary Biology, Ludwig-Maximilians-Universität München Grosshaderner Straße 2, Planegg-Martinsried 82152, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
John P. Huelsenbeck
4Department of Integrative Biology, University of California, Berkeley 3060 VLSB #3140, Berkeley, CA 94720-3140, U.S.A.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Michael R. May
5Department of Evolution and Ecology, University of California, Davis Storer Hall, One Shields Avenue, Davis, CA 95616, U.S.A.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Brian R. Moore
5Department of Evolution and Ecology, University of California, Davis Storer Hall, One Shields Avenue, Davis, CA 95616, U.S.A.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Species richness varies considerably among the tree of life which can only be explained by heterogeneous rates of diversification (speciation and extinction). Previous approaches use phylogenetic trees to estimate branch-specific diversification rates. However, all previous approaches disregard diversification-rate shifts on extinct lineages although 99% of species that ever existed are now extinct. Here we describe a lineage-specific birth-death-shift process where lineages, both extant and extinct, may have heterogeneous rates of diversification. To facilitate probability computation we discretize the base distribution on speciation and extinction rates into k rate categories. The fixed number of rate categories allows us to extend the theory of state-dependent speciation and extinction models (e.g., BiSSE and MuSSE) to compute the probability of an observed phylogeny given the set of speciation and extinction rates. To estimate branch-specific diversification rates, we develop two independent and theoretically equivalent approaches: numerical integration with stochastic character mapping and data-augmentation with reversible-jump Markov chain Monte Carlo sampling. We validate the implementation of the two approaches in RevBayes using simulated data and an empirical example study of primates. In the empirical example, we show that estimates of the number of diversification-rate shifts are, unsurprisingly, very sensitive to the choice of prior distribution. Instead, branch-specific diversification rate estimates are less sensitive to the assumed prior distribution on the number of diversification-rate shifts and consistently infer an increased rate of diversification for Old World Monkeys. Additionally, we observe that as few as 10 diversification-rate categories are sufficient to approximate a continuous base distribution on diversification rates. In conclusion, our implementation of the lineage-specific birth-death-shift model in RevBayes provides biologists with a method to estimate branch-specific diversification rates under a mathematically consistent model.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
Back to top
PreviousNext
Posted February 20, 2019.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A Bayesian Approach for Estimating Branch-Specific Speciation and Extinction Rates
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A Bayesian Approach for Estimating Branch-Specific Speciation and Extinction Rates
Sebastian Höhna, William A. Freyman, Zachary Nolen, John P. Huelsenbeck, Michael R. May, Brian R. Moore
bioRxiv 555805; doi: https://doi.org/10.1101/555805
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
A Bayesian Approach for Estimating Branch-Specific Speciation and Extinction Rates
Sebastian Höhna, William A. Freyman, Zachary Nolen, John P. Huelsenbeck, Michael R. May, Brian R. Moore
bioRxiv 555805; doi: https://doi.org/10.1101/555805

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Evolutionary Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4095)
  • Biochemistry (8786)
  • Bioengineering (6493)
  • Bioinformatics (23386)
  • Biophysics (11766)
  • Cancer Biology (9167)
  • Cell Biology (13290)
  • Clinical Trials (138)
  • Developmental Biology (7422)
  • Ecology (11386)
  • Epidemiology (2066)
  • Evolutionary Biology (15119)
  • Genetics (10413)
  • Genomics (14024)
  • Immunology (9145)
  • Microbiology (22108)
  • Molecular Biology (8793)
  • Neuroscience (47445)
  • Paleontology (350)
  • Pathology (1423)
  • Pharmacology and Toxicology (2483)
  • Physiology (3711)
  • Plant Biology (8063)
  • Scientific Communication and Education (1433)
  • Synthetic Biology (2215)
  • Systems Biology (6021)
  • Zoology (1251)