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In defence of model-based inference in phylogeography

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Date
2010
Dewey
Probabilités et mathématiques appliquées
Sujet
population genetics-empirical; phylogeography; Approximate Bayesian Computation; population genetics-theoretical; molecular evolution
JEL code
C11
Journal issue
Molecular Ecology
Volume
19
Number
3
Publication date
02-2010
Article pages
436-446
Publisher
Blackwell
DOI
http://dx.doi.org/10.1111/j.1365-294X.2009.04515.x
URI
https://basepub.dauphine.fr/handle/123456789/4653
Collections
  • CEREMADE : Publications
Metadata
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Author
Corander, Jukka
Panchal, Mahesh
Gaggiotti, Oscar
Hey, Jody
Estoup, Arnaud
Knowles, Lacey
Robert, Christian P.
Nielsen, Rasmus
Beaumont, Mark A.
Yang, Ziheng
Cornuet, Jean-Marie
Vitalis, Renaud
Foll, Matthieu
Huelsenbeck, John
Fagundes, Nelson
Sisson, Scott A.
Beerli, Peter
Chikhi, Lounès
Hickerson, Mike
Excoffier, Laurent
Balding, David
Rousset, François
Type
Article accepté pour publication ou publié
Abstract (EN)
Recent papers have promoted the view that model-based methods in general, and those based on Approximate Bayesian Computation (ABC) in particular, are flawed in a number of ways, and are therefore inappropriate for the analysis of phylogeographic data. These papers further argue that Nested Clade Phylogeographic Analysis (NCPA) offers the best approach in statistical phylogeography. In order to remove the confusion and misconceptions introduced by these papers, we justify and explain the reasoning behind model-based inference. We argue that ABC is a statistically valid approach, alongside other computational statistical techniques that have been successfully used to infer parameters and compare models in population genetics. We also examine the NCPA method and highlight numerous deficiencies, either when used with single or multiple loci. We further show that the ages of clades are carelessly used to infer ages of demographic events, that these ages are estimated under a simple model of panmixia and population stationarity but are then used under different and unspecified models to test hypotheses, a usage the invalidates these testing procedures. We conclude by encouraging researchers to study and use model-based inference in population genetics.

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