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Additional Analytical Support for a New Method to Compute the Likelihood of Diversification Models
Molecular phylogenies have been increasingly recognized as an important source of information on species diversification. For many models of macroevolution, analytical likelihood formulas have been derived to infer macroevolutionary parameters from phylogenies. A few years ago, a general framework t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6976549/ https://www.ncbi.nlm.nih.gov/pubmed/31970528 http://dx.doi.org/10.1007/s11538-020-00698-y |
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author | Laudanno, Giovanni Haegeman, Bart Etienne, Rampal S. |
author_facet | Laudanno, Giovanni Haegeman, Bart Etienne, Rampal S. |
author_sort | Laudanno, Giovanni |
collection | PubMed |
description | Molecular phylogenies have been increasingly recognized as an important source of information on species diversification. For many models of macroevolution, analytical likelihood formulas have been derived to infer macroevolutionary parameters from phylogenies. A few years ago, a general framework to numerically compute such likelihood formulas was proposed, which accommodates models that allow speciation and/or extinction rates to depend on diversity. This framework calculates the likelihood as the probability of the diversification process being consistent with the phylogeny from the root to the tips. However, while some readers found the framework presented in Etienne et al. (Proc R Soc Lond B Biol Sci 279(1732):1300–1309, 2012) convincing, others still questioned it (personal communication), despite numerical evidence that for special cases the framework yields the same (i.e., within double precision) numerical value for the likelihood as analytical formulas do that were independently derived for these special cases. Here we prove analytically that the likelihoods calculated in the new framework are correct for all special cases with known analytical likelihood formula. Our results thus add substantial mathematical support for the overall coherence of the general framework. |
format | Online Article Text |
id | pubmed-6976549 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-69765492020-02-03 Additional Analytical Support for a New Method to Compute the Likelihood of Diversification Models Laudanno, Giovanni Haegeman, Bart Etienne, Rampal S. Bull Math Biol Methods and Software Molecular phylogenies have been increasingly recognized as an important source of information on species diversification. For many models of macroevolution, analytical likelihood formulas have been derived to infer macroevolutionary parameters from phylogenies. A few years ago, a general framework to numerically compute such likelihood formulas was proposed, which accommodates models that allow speciation and/or extinction rates to depend on diversity. This framework calculates the likelihood as the probability of the diversification process being consistent with the phylogeny from the root to the tips. However, while some readers found the framework presented in Etienne et al. (Proc R Soc Lond B Biol Sci 279(1732):1300–1309, 2012) convincing, others still questioned it (personal communication), despite numerical evidence that for special cases the framework yields the same (i.e., within double precision) numerical value for the likelihood as analytical formulas do that were independently derived for these special cases. Here we prove analytically that the likelihoods calculated in the new framework are correct for all special cases with known analytical likelihood formula. Our results thus add substantial mathematical support for the overall coherence of the general framework. Springer US 2020-01-22 2020 /pmc/articles/PMC6976549/ /pubmed/31970528 http://dx.doi.org/10.1007/s11538-020-00698-y Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Methods and Software Laudanno, Giovanni Haegeman, Bart Etienne, Rampal S. Additional Analytical Support for a New Method to Compute the Likelihood of Diversification Models |
title | Additional Analytical Support for a New Method to Compute the Likelihood of Diversification Models |
title_full | Additional Analytical Support for a New Method to Compute the Likelihood of Diversification Models |
title_fullStr | Additional Analytical Support for a New Method to Compute the Likelihood of Diversification Models |
title_full_unstemmed | Additional Analytical Support for a New Method to Compute the Likelihood of Diversification Models |
title_short | Additional Analytical Support for a New Method to Compute the Likelihood of Diversification Models |
title_sort | additional analytical support for a new method to compute the likelihood of diversification models |
topic | Methods and Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6976549/ https://www.ncbi.nlm.nih.gov/pubmed/31970528 http://dx.doi.org/10.1007/s11538-020-00698-y |
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