Abstract

Abstract A large microsatellite data set from three species of bear (Ursidae) was used to empirically test the performance of six genetic distance measures in resolving relationships at a variety of scales ranging from adjacent areas in a continuous distribution to species that diverged several million years ago. At the finest scale, while some distance measures performed extremely well, statistics developed specifically to accommodate the mutational processes of microsatellites performed relatively poorly, presumably because of the relatively higher variance of these statistics. At the other extreme, no statistic was able to resolve the close sister relationship of polar bears and brown bears from more distantly related pairs of species. This failure is most likely due to constraints on allele distributions at microsatellite loci. At intermediate scales, both within continuous distributions and in comparisons to insular populations of late Pleistocene origin, it was not possible to define the point where linearity was lost for each of the statistics, except that it is clearly lost after relatively short periods of independent evolution. All of the statistics were affected by the amount of genetic diversity within the populations being compared, significantly complicating the interpretation of genetic distance data.

Keywords

BiologyMicrosatelliteGenetic diversityEvolutionary biologyGenetic distanceStatisticsGenetic variationAlleleGeneticsPopulationMathematicsDemography

MeSH Terms

AllelesAnimalsEvaluation Studies as TopicGenetic VariationMicrosatellite RepeatsModelsGeneticModelsStatisticalPhylogenyUrsidae

Affiliated Institutions

Related Publications

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Publication Info

Year
1997
Type
article
Volume
147
Issue
4
Pages
1943-1957
Citations
430
Access
Closed

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Cite This

David Paetkau, Lisette P. Waits, Peter L. Clarkson et al. (1997). An Empirical Evaluation of Genetic Distance Statistics Using Microsatellite Data From Bear (Ursidae) Populations. Genetics , 147 (4) , 1943-1957. https://doi.org/10.1093/genetics/147.4.1943

Identifiers

DOI
10.1093/genetics/147.4.1943
PMID
9409849
PMCID
PMC1208359

Data Quality

Data completeness: 86%