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LIKELIHOOD METHODS FOR DETECTING TEMPORAL SHIFTS IN DIVERSIFICATION RATES

Maximum likelihood is a potentially powerful approach for investigating the tempo of diversification using molecular phylogenetic data. Likelihood methods distinguish between rate-constant and rate-variable models of diversification by fitting birth-death models to phylogenetic data. Because model s... Full description

Journal Title: Evolution 2006-06, Vol.60 (6), p.1152-1164
Main Author: Rabosky, Daniel L.
Format: Electronic Article Electronic Article
Language: English
Subjects:
Publisher: Oxford, UK: Blackwell Publishing Ltd
ID: ISSN: 0014-3820
Link: https://www.ncbi.nlm.nih.gov/pubmed/16892966
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recordid: cdi_proquest_miscellaneous_68721911
title: LIKELIHOOD METHODS FOR DETECTING TEMPORAL SHIFTS IN DIVERSIFICATION RATES
format: Article
creator:
  • Rabosky, Daniel L.
subjects:
  • Animals
  • Biodiversity
  • Computer Simulation
  • Crop diversification
  • Diversification rates
  • Evolution
  • Extinction (Biology)
  • Genetics
  • Likelihood Functions
  • Lizards - physiology
  • macroevolution
  • maximum likelihood
  • Maximum likelihood method
  • Modeling
  • Null hypothesis
  • Organisms
  • Parametric models
  • Phylogenetics
  • Phylogeny
  • Population Dynamics
  • Simulations
  • Speciation
  • Taxa
  • Time Factors
ispartof: Evolution, 2006-06, Vol.60 (6), p.1152-1164
description: Maximum likelihood is a potentially powerful approach for investigating the tempo of diversification using molecular phylogenetic data. Likelihood methods distinguish between rate-constant and rate-variable models of diversification by fitting birth-death models to phylogenetic data. Because model selection in this context is a test of the null hypothesis that diversification rates have been constant over time, strategies for selecting best-fit models must minimize Type I error rates while retaining power to detect rate variation when it is present. Here I examine model selection, parameter estimation, and power to reject the null hypothesis using likelihood models based on the birth-death process. The Akaike information criterion (AIC) has often been used to select among diversification models; however, I find that selecting models based on the lowest AIC score leads to a dramatic inflation of the Type I error rate. When appropriately corrected to reduce Type I error rates, the birth-death likelihood approach performs as well or better than the widely used gamma statistic, at least when diversification rates have shifted abruptly over time. Analyses of datasets simulated under a range of rate-variable diversification scenarios indicate that the birth-death likelihood method has much greater power to detect variation in diversification rates when extinction is present. Furthermore, this method appears to be the only approach available that can distinguish between a temporal increase in diversification rates and a rate-constant model with nonzero extinction. I illustrate use of the method by analyzing a published phylogeny for Australian agamid lizards.
language: eng
source:
identifier: ISSN: 0014-3820
fulltext: no_fulltext
issn:
  • 0014-3820
  • 1558-5646
url: Link


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descriptionMaximum likelihood is a potentially powerful approach for investigating the tempo of diversification using molecular phylogenetic data. Likelihood methods distinguish between rate-constant and rate-variable models of diversification by fitting birth-death models to phylogenetic data. Because model selection in this context is a test of the null hypothesis that diversification rates have been constant over time, strategies for selecting best-fit models must minimize Type I error rates while retaining power to detect rate variation when it is present. Here I examine model selection, parameter estimation, and power to reject the null hypothesis using likelihood models based on the birth-death process. The Akaike information criterion (AIC) has often been used to select among diversification models; however, I find that selecting models based on the lowest AIC score leads to a dramatic inflation of the Type I error rate. When appropriately corrected to reduce Type I error rates, the birth-death likelihood approach performs as well or better than the widely used gamma statistic, at least when diversification rates have shifted abruptly over time. Analyses of datasets simulated under a range of rate-variable diversification scenarios indicate that the birth-death likelihood method has much greater power to detect variation in diversification rates when extinction is present. Furthermore, this method appears to be the only approach available that can distinguish between a temporal increase in diversification rates and a rate-constant model with nonzero extinction. I illustrate use of the method by analyzing a published phylogeny for Australian agamid lizards.
editionReceived July 26, 2005. Accepted March 25, 2006.
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subjectAnimals ; Biodiversity ; Computer Simulation ; Crop diversification ; Diversification rates ; Evolution ; Extinction (Biology) ; Genetics ; Likelihood Functions ; Lizards - physiology ; macroevolution ; maximum likelihood ; Maximum likelihood method ; Modeling ; Null hypothesis ; Organisms ; Parametric models ; Phylogenetics ; Phylogeny ; Population Dynamics ; Simulations ; Speciation ; Taxa ; Time Factors
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abstractMaximum likelihood is a potentially powerful approach for investigating the tempo of diversification using molecular phylogenetic data. Likelihood methods distinguish between rate-constant and rate-variable models of diversification by fitting birth-death models to phylogenetic data. Because model selection in this context is a test of the null hypothesis that diversification rates have been constant over time, strategies for selecting best-fit models must minimize Type I error rates while retaining power to detect rate variation when it is present. Here I examine model selection, parameter estimation, and power to reject the null hypothesis using likelihood models based on the birth-death process. The Akaike information criterion (AIC) has often been used to select among diversification models; however, I find that selecting models based on the lowest AIC score leads to a dramatic inflation of the Type I error rate. When appropriately corrected to reduce Type I error rates, the birth-death likelihood approach performs as well or better than the widely used gamma statistic, at least when diversification rates have shifted abruptly over time. Analyses of datasets simulated under a range of rate-variable diversification scenarios indicate that the birth-death likelihood method has much greater power to detect variation in diversification rates when extinction is present. Furthermore, this method appears to be the only approach available that can distinguish between a temporal increase in diversification rates and a rate-constant model with nonzero extinction. I illustrate use of the method by analyzing a published phylogeny for Australian agamid lizards.
copOxford, UK
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pmid16892966
doi10.1111/j.0014-3820.2006.tb01194.x
tpages13
editionReceived July 26, 2005. Accepted March 25, 2006.