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Updated: Jul 8, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
Published on: July 4, 2007
Population continuity or replacement? A novel computer simulation approach and its application to the Numic expansion
Graciela S Cabana1, Keith Hunley, Frederika A Kaestle
1Department of Anthropology, University of Tennessee, Knoxville, TN 37996, USA. gcabana@utk.edu
Abstract:
In a previous study, Kaestle and Smith [Am J Phys Anthropol 115 (2001) 1-12] supported a recent (A.D. 1000) Numic expansion into the Great Basin region based on a molecular and statistical analysis of mitochondrial DNA (mtDNA) of ancient and modern native inhabitants of the region. Their statistical methodology could not rule out the possibility that observed differences in haplogroup frequencies are instead the result of long-term microevolutionary change within a single population. To distinguish more effectively between a Numic expansion versus population continuity, we employed a novel computer simulation approach that incorporates microevolutionary factors likely to affect human population genetic variation. We test whether the observed differences in haplogroup frequencies between ancient and modern Great Basin groups could have been produced solely via in situ microevolutionary change. Our results indicate that for reasonable demographic conditions, the observed genetic differences between the observed samples are consistent with population continuity if gene flow among prehistoric Great Basin local groups was less than 1% of local group size per generation. Our analysis also supports a recent population expansion if gene flow between neighboring groups exceeded 8% of local group size per generation. The simulations demonstrate that relatively low gene flow levels and random genetic drift can produce the observed degree of genetic differences between population samples. Although this study focuses on the Numic expansion, this simulation approach can be applied to any geographic region for which genetic data have been collected to address similar questions of population relationships over time.
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