Detection of reduction in population size using data from microsatellite loci

TLDR

The authors introduce M, the mean ratio of allele count to size range, as a statistic to detect recent reductions in population size and develop a test that can identify such changes over more than 100 generations. M is calculated from microsatellite allele counts and sizes, and the authors use it to build a statistical test and compute it across diverse populations and species using published microsatellite data. Simulations show that M decreases with population size reductions, with the magnitude and recovery rate linked to severity, duration, and post‑reduction size, allowing differentiation between recent bottlenecks and long‑term small populations; empirical data confirm that M predicts reported demographic histories and offers a valuable conservation tool.

Abstract

Abstract We demonstrate that the mean ratio of the number of alleles to the range in allele size, which we term M , calculated from a population sample of microsatellite loci, can be used to detect reductions in population size. Using simulations, we show that, for a general class of mutation models, the value of M decreases when a population is reduced in size. The magnitude of the decrease is positively correlated with the severity and duration of the reduction in size. We also find that the rate of recovery of M following a reduction in size is positively correlated with post‐reduction population size, but that recovery occurs in both small and large populations. This indicates that M can distinguish between populations that have been recently reduced in size and those which have been small for a long time. We employ M to develop a statistical test for recent reductions in population size that can detect such changes for more than 100 generations with the post‐reduction demographic scenarios we examine. We also compute M for a variety of populations and species using microsatellite data collected from the literature. We find that the value of M consistently predicts the reported demographic history for these populations. This method, and others like it, promises to be an important tool for the conservation and management of populations that are in need of intervention or recovery.