Abstract
Although properly designed sampling in population genetic studies is of key importance for planning evidence-informed conservation measures, sampling strategies are rarely discussed. This is the case for the European mink Mustela lutreola, a critically endangered species. In order to address this problem, a meta-analysis aiming to examine the completeness of mtDNA haplotype sampling in recent studies of M. lutreola inter-population genetic diversity was conducted. The analysis was performed using the sample-size-based rarefaction and extrapolation sampling curve method for three populations—the Northeastern (Russia, Belarus and Estonia), the Western (France and Spain), and the Southeastern (Romania). The extrapolated values of the Shannon–Wiener index were determined, assuming full sample coverage. The gap between the measured and predicted inter-population genetic diversity was estimated, indicating that the identified level of sample coverage was the lowest for the NE population (87%), followed by the SE population (96%) and the W population (99%). A guide for sampling design and accounting for sampling uncertainty in future population genetic studies on European mink is provided. The relatively low sample coverage for the Russian population clearly indicates an urgent need to take conservation measures for European mink in this country.
Highlights
Sample size is a critical issue for measuring genetic variation, yet for population genetic studies, sampling strategies are rarely discussed [1,2,3]
Since it is postulated that research on critically endangered populations should include measures of genetic diversity, even if the sample size is not optimal [18], our goal was to examine if the sample sizes in recent population genetic studies of M. lutreola revealed the possible numbers of haplotypes present in the examined populations and to ex post quantify the potentially missing haplotypes in the samples, through a meta-analysis approach using the rarefaction method
For the SE population, the sample size was more than half the number of the examined individuals from the NE population; the W population was represented by a sample almost 25% larger than that for the NE population
Summary
Sample size is a critical issue for measuring genetic variation, yet for population genetic studies, sampling strategies are rarely discussed [1,2,3]. Sampling schemes can be validated ex post, by utilizing methods such as jackknifing [12], the Good–Turing frequency estimation [13], regression models [14], or rarefaction analysis [1], to account for unsampled (unmeasured) alleles or haplotypes [15]. Such approaches mainly aim to inform future sampling design [4]
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