A Method for Detecting Population Genetic Structure in Diverse, High Gene-Flow Species

Kelly, Ryan P.; Oliver, Thomas A.; Sivasundar, Arjun; Palumbi, Stephen R. (2010). A Method for Detecting Population Genetic Structure in Diverse, High Gene-Flow Species. Journal of heredity, 101(4), pp. 423-436. Oxford: Oxford University Press 10.1093/jhered/esq022

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Detecting small amounts of genetic subdivision across geographic space remains a persistent challenge. Often a failure to detect genetic structure is mistaken for evidence of panmixia, when more powerful statistical tests may uncover evidence for subtle geographic differentiation. Such slight subdivision can be demographically and evolutionarily important as well as being critical for management decisions. We introduce here a method, called spatial analysis of shared alleles (SAShA), that detects geographically restricted alleles by comparing the spatial arrangement of allelic co-occurrences with the expectation under panmixia. The approach is allele-based and spatially explicit, eliminating the loss of statistical power that can occur with user-defined populations and statistical averaging within populations. Using simulated data sets generated under a stepping-stone model of gene flow, we show that this method outperforms spatial autocorrelation (SA) and UST under common real-world conditions: at relatively high migration rates when diversity is moderate or high, especially when sampling is poor. We then use this method to show clear differences in the genetic patterns of 2 nearshore Pacific mollusks, Tegula funebralis (5 Chlorostoma funebralis) and Katharina tunicata, whose overall patterns of within-species differentiation are similar according to traditional population genetics analyses. SAShA meaningfully complements UST/FST, SA, and other existing geographic genetic analyses and is especially appropriate for evaluating species with high gene flow and subtle genetic differentiation.

Item Type:

Journal Article (Original Article)


08 Faculty of Science > Department of Biology > Institute of Ecology and Evolution (IEE)

UniBE Contributor:

Sivasundar, Arjun






Oxford University Press




Factscience Import

Date Deposited:

04 Oct 2013 14:17

Last Modified:

17 Mar 2015 19:22

Publisher DOI:


Web of Science ID:



https://boris.unibe.ch/id/eprint/5254 (FactScience: 209985)

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