Statistical inferences in phylogeography

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Standard

Statistical inferences in phylogeography. / Nielsen, Rasmus; Beaumont, Mark A.

In: Molecular Ecology, Vol. 18, No. 6, 2009, p. 1034-47.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Nielsen, R & Beaumont, MA 2009, 'Statistical inferences in phylogeography', Molecular Ecology, vol. 18, no. 6, pp. 1034-47. https://doi.org/10.1111/j.1365-294X.2008.04059.x

APA

Nielsen, R., & Beaumont, M. A. (2009). Statistical inferences in phylogeography. Molecular Ecology, 18(6), 1034-47. https://doi.org/10.1111/j.1365-294X.2008.04059.x

Vancouver

Nielsen R, Beaumont MA. Statistical inferences in phylogeography. Molecular Ecology. 2009;18(6):1034-47. https://doi.org/10.1111/j.1365-294X.2008.04059.x

Author

Nielsen, Rasmus ; Beaumont, Mark A. / Statistical inferences in phylogeography. In: Molecular Ecology. 2009 ; Vol. 18, No. 6. pp. 1034-47.

Bibtex

@article{3b707a70a52e11df928f000ea68e967b,
title = "Statistical inferences in phylogeography",
abstract = "In conventional phylogeographic studies, historical demographic processes are elucidated from the geographical distribution of individuals represented on an inferred gene tree. However, the interpretation of gene trees in this context can be difficult as the same demographic/geographical process can randomly lead to multiple different genealogies. Likewise, the same gene trees can arise under different demographic models. This problem has led to the emergence of many statistical methods for making phylogeographic inferences. A popular phylogeographic approach based on nested clade analysis is challenged by the fact that a certain amount of the interpretation of the data is left to the subjective choices of the user, and it has been argued that the method performs poorly in simulation studies. More rigorous statistical methods based on coalescence theory have been developed. However, these methods may also be challenged by computational problems or poor model choice. In this review, we will describe the development of statistical methods in phylogeographic analysis, and discuss some of the challenges facing these methods.",
author = "Rasmus Nielsen and Beaumont, {Mark A}",
note = "Keywords: Computer Simulation; Genetics, Population; Geography; Models, Statistical; Phylogeny; Population Density",
year = "2009",
doi = "10.1111/j.1365-294X.2008.04059.x",
language = "English",
volume = "18",
pages = "1034--47",
journal = "Molecular Ecology",
issn = "0962-1083",
publisher = "Wiley-Blackwell",
number = "6",

}

RIS

TY - JOUR

T1 - Statistical inferences in phylogeography

AU - Nielsen, Rasmus

AU - Beaumont, Mark A

N1 - Keywords: Computer Simulation; Genetics, Population; Geography; Models, Statistical; Phylogeny; Population Density

PY - 2009

Y1 - 2009

N2 - In conventional phylogeographic studies, historical demographic processes are elucidated from the geographical distribution of individuals represented on an inferred gene tree. However, the interpretation of gene trees in this context can be difficult as the same demographic/geographical process can randomly lead to multiple different genealogies. Likewise, the same gene trees can arise under different demographic models. This problem has led to the emergence of many statistical methods for making phylogeographic inferences. A popular phylogeographic approach based on nested clade analysis is challenged by the fact that a certain amount of the interpretation of the data is left to the subjective choices of the user, and it has been argued that the method performs poorly in simulation studies. More rigorous statistical methods based on coalescence theory have been developed. However, these methods may also be challenged by computational problems or poor model choice. In this review, we will describe the development of statistical methods in phylogeographic analysis, and discuss some of the challenges facing these methods.

AB - In conventional phylogeographic studies, historical demographic processes are elucidated from the geographical distribution of individuals represented on an inferred gene tree. However, the interpretation of gene trees in this context can be difficult as the same demographic/geographical process can randomly lead to multiple different genealogies. Likewise, the same gene trees can arise under different demographic models. This problem has led to the emergence of many statistical methods for making phylogeographic inferences. A popular phylogeographic approach based on nested clade analysis is challenged by the fact that a certain amount of the interpretation of the data is left to the subjective choices of the user, and it has been argued that the method performs poorly in simulation studies. More rigorous statistical methods based on coalescence theory have been developed. However, these methods may also be challenged by computational problems or poor model choice. In this review, we will describe the development of statistical methods in phylogeographic analysis, and discuss some of the challenges facing these methods.

U2 - 10.1111/j.1365-294X.2008.04059.x

DO - 10.1111/j.1365-294X.2008.04059.x

M3 - Journal article

C2 - 19207258

VL - 18

SP - 1034

EP - 1047

JO - Molecular Ecology

JF - Molecular Ecology

SN - 0962-1083

IS - 6

ER -

ID: 21332774