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Beaumont MA, Nielsen R, Robert C, et al. In defence of model-based inference in phylogeography. Mol Ecol. 2010;19(3):436-446doi: 10.1111/j.1365-294X.2009.04515.x.
Beaumont, M. A., Nielsen, R., Robert, C., Hey, J., Gaggiotti, O., Knowles, L., Estoup, A., Panchal, M., Corander, J., Hickerson, M., Sisson, S. A., Fagundes, N., Chikhi, L., Beerli, P., Vitalis, R., Cornuet, J. M., Huelsenbeck, J., Foll, M., Yang, Z., Rousset, F., Balding, D., & Excoffier, L. (2010). In defence of model-based inference in phylogeography. Molecular ecology, 19(3), 436-446. https://doi.org/10.1111/j.1365-294X.2009.04515.x
Beaumont, Mark A, et al. "In defence of model-based inference in phylogeography." Molecular ecology vol. 19,3 (2010): 436-446. doi: https://doi.org/10.1111/j.1365-294X.2009.04515.x
Beaumont MA, Nielsen R, Robert C, Hey J, Gaggiotti O, Knowles L, Estoup A, Panchal M, Corander J, Hickerson M, Sisson SA, Fagundes N, Chikhi L, Beerli P, Vitalis R, Cornuet JM, Huelsenbeck J, Foll M, Yang Z, Rousset F, Balding D, Excoffier L. In defence of model-based inference in phylogeography. Mol Ecol. 2010 Feb;19(3):436-446. doi: 10.1111/j.1365-294X.2009.04515.x. Epub 2010 Jan 11. PMID: 29284924; PMCID: PMC5743441.
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Mol Ecol. 2010 Feb;19(3):436-446. doi: 10.1111/j.1365-294X.2009.04515.x. Epub 2010 Jan 11.

In defence of model-based inference in phylogeography.

Molecular ecology

Mark A Beaumont, Rasmus Nielsen, Christian Robert, Jody Hey, Oscar Gaggiotti, Lacey Knowles, Arnaud Estoup, Mahesh Panchal, Jukka Corander, Mike Hickerson, Scott A Sisson, Nelson Fagundes, Lounès Chikhi, Peter Beerli, Renaud Vitalis, Jean-Marie Cornuet, John Huelsenbeck, Matthieu Foll, Ziheng Yang, Francois Rousset, David Balding, Laurent Excoffier

Affiliations

  1. School of Animal and Microbial Sciences, University of Reading, Whiteknights, PO Box 228, Reading, RG6 6AJ, UK.
  2. Integrative Biology, UC Berkeley, 3060 Valley Life Sciences Bldg #3140, Berkeley, CA 94720-3140, USA.
  3. CEREMADE, Université Paris Dauphine, Paris, France.
  4. Department of Genetics, Rutgers University, 604 Allison Road, Piscataway, NJ 08854, USA.
  5. Laboratoire d'Ecologie Alpine, UMR CNRS 5553, Université Joseph Fourier, BP 53, 38041 GRENOBLE, France.
  6. Department of Ecology and Evolutionary Biology, Museum of Zoology, University of Michigan, Ann Arbor, MI 48109-1079, USA.
  7. INRA UMR Centre de Biologie et de Gestion des Populations (INRA ? IRD ? Cirad ? Montpellier SupAgro), Campus international de Baillarguet, Montferrier-sur-Lez, France.
  8. Max Planck Institute for Evolutionary Biology, August-Thienemann-Str. 2, 24306 Plön, Germany.
  9. Department of Mathematics and statistics, University of Helsinki, Finland.
  10. Biology Department, Queens College, City University of New York, 65-30 Kissena Boulevard, Flushing, NY 11367-1597, USA.
  11. School of Mathematics and Statistics, University of New South Wales, Sydney, Australia.
  12. Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.
  13. Université Paul Sabatier-UMR EDB 5174 118, 31062 Toulouse Cedex 09, France.
  14. Department of Scientific Computing, Florida State University, Tallahassee, FL 32306, USA.
  15. CNRS-INRA, CBGP, Campus International de Baillarguet, CS 30016, 34988 Montferrier-sur-Lez, France.
  16. CMPG, Institute of Ecology and Evolution, University of Berne, 3012 Berne, Switzerland.
  17. Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
  18. Department of Biology, University College London, Gower Street, London WC1E 6BT, UK.
  19. Institut des Sciences de l'Évolution, Universté Montpellier 2, CNRS, Place Eugène Bataillon, CC065, Montpellier, Cedex 5, France.
  20. Institute of Genetics, University College London, 2nd Floor, Kathleen Lonsdale Building, 5 Gower Place, London WC1E 6BT, UK.

PMID: 29284924 PMCID: PMC5743441 DOI: 10.1111/j.1365-294X.2009.04515.x

Abstract

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.

Keywords: molecular evolution; phylogeography; population genetics-empirical; population genetics-theoretical

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