Detection of reduction in population size using data from microsatellite loci

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.

European wildcat populations are subdivided into five main biogeographic groups: consequences of Pleistocene climate changes or recent anthropogenic fragmentation?

“…Two‐tailed Wilcoxon signed rank test was used for determining the significance of the observed deviations. Less recent bottlenecks (up to a few hundred generations ago) were tested with Garza and Williamson's (2001) “ m‐ ratio test” in software M_P_Val. The values of m was computed as the ratio of the number of alleles ( k ) over their range in fragment sizes ( r ), which is predicted to decline in a bottleneck because the number of alleles should decrease faster than the range in fragment sizes.…”

“…The significance of m was determined by comparison with critical values ( M c), calculated from hypothetical populations in mutation‐drift equilibrium using the program Critical_M with 10,000 simulation replicates. We used a microsatellite “two‐phase mutation model” with an average size of multistep mutations Δ g  = 3.5, assuming 90% stepwise mutations ( P s ), as recommended by Garza and Williamson (2001). We set θ  = 5 or 10 (being θ  = 4 Ne μ , where Ne is the effective population size and μ is the mutation rate) to evaluate the sensitivity of the method to this parameter.…”

About Ganoderma boninense in oil palm plantations of Sumatra and peninsular Malaysia: Ancient population expansion, extensive gene flow and large scale dispersion ability

“…The reference table was built with a total of 4 000 000 simulated data points, equally distributed among scenario: 998 670 for Scenario 1, 1 002 348 for Scenario 2, 999 200 for Scenario 3, and 999 782 for Scenario 4. We used mean genetic diversity (Nei 1987), mean allele size variance and mean GarzaeWilliamson's M (Garza & Williamson 2001) as sample statistics to estimate scenario probability, parameter confidence interval, and parameter bias and error as well as to assess confidence in scenario choice. These parameters were chosen because our simulations were based on a single population.…”

The ghost of introduction past: Spatial and temporal variability in the genetic diversity of invasive smallmouth bass

“…This was accomplished by simulating 100,000 data sets and comparing summary statistics for both simulated single‐sample (i.e., mean number of alleles, genetic diversity and allele size variance across loci) and two‐sample statistics (i.e., mean genetic diversity, number of alleles, allele size variance, mean index of classification, shared allele distance, distance between samples and F ST ) to the observed data (Cornuet et al., 2014). As the mean M index across loci (Garza & Williamson, 2001) was initially developed with conservation planning in mind, this statistic does not perform well with small, unequal sampling sizes and small starting population sizes (Garza & Williamson, 2001). Hence, it was excluded from the summary statistics used in the current analyses.…”