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Correlation of Pairwise Genetic and Geographic Distance Measures: Inferring the Relative Influences of Gene Flow and Drift on the Distribution of Genetic Variability

Delbert W. Hutchison and Alan R. Templeton
Evolution
Vol. 53, No. 6 (Dec., 1999), pp. 1898-1914
DOI: 10.2307/2640449
Stable URL: http://www.jstor.org/stable/2640449
Page Count: 17
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Correlation of Pairwise Genetic and Geographic Distance Measures: Inferring the Relative Influences of Gene Flow and Drift on the Distribution of Genetic Variability
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Abstract

Attempts to relate estimates of regional FST to gene flow and drift via Wright's (1931) equation F$_{ST} \backsimeq$ 1/(4Nm + 1) are often inappropriate because most natural sets of populations probably are not at equilibrium (McCauley 1993), as assumed by the island model upon which the equation is based, or ineffective because the influences of gene flow and drift are confounded in the product Nm. Evaluations of the association between genetic (FST) and geographic distances separating all pairwise populations combinations in a region allows one to test for regional equilibrium, to evaluate the relative influences of gene flow and drift on population structure both within and between regions, and to visualize the behavior of the association across all degrees of geographic separation. Tests of the model using microsatellite data from 51 populations of eastern collared lizards (Crotaphytus collaris collaris) collected from four distinct geographical regions gave results highly consistent with predicted patterns of association based on regional differences in various historical and ecological factors that affect the amount of drift and gene flow. The model provides a prerequisite for and an alternative to regional FST analyses, which often simply assume regional equilibrium, thus potentially leading to erroneous and misleading inferences regarding regional population structure.

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