The number of markers and samples needed for detecting bottlenecks under realistic scenarios, with and without recovery: a simulation‐based study

Abstract

Detecting bottlenecks is a common task in molecular ecology. While several bottleneck detection methods exist, evaluations of their power have focused only on severe bottlenecks (e.g. to Ne~10). As a component of a recent review, Peery et al. (2012) analysed the power of two approaches, the M-ratio and heterozygote excess tests, to detect moderate bottlenecks (e.g. to Ne~100), which is realistic for many conservation situations. In this Comment, we address three important points relevant to but not considered in Peery et al. Under moderate bottleneck scenarios, we test the (i) relative advantage of sampling more markers vs. more individuals, (ii) potential power to detect the bottleneck when utilizing dozens of microsatellites (a realistic possibility for contemporary studies) and (iii) reduction in power when postbottleneck recovery has occurred. For the realistic situations examined, we show that (i) doubling the number of loci shows equal or better power than tripling the number of individuals, (ii) increasing the number of markers (up to 100) results in continued additive gains in power, and (iii) recovery after a moderate amount of time or gradual change in size reduces power, by up to one-half. Our results provide a practical supplement to Peery et al. and encourage the continued use of bottleneck detection methods in the genomic age, but also emphasize that the power under different sampling schemes should be estimated, using simulation modelling, as a routine component of molecular ecology studies.

Does historical harvesting affect colony size distribution and genetic diversity in Corallium rubrum (Linnaeus, 1758)? Evidences from the Southern Mediterranean commercial banks

“…To be more effective, genetic sampling effort should be done in cooperation with fishermen to increase the sample size. New molecular techniques based on the newgeneration sequencing technologies should be implemented to increase the number of markers (Hoban et al, 2013). Simulations, empirically parameterized and tailored to the specific case, can help test hypotheses and interpret empirical data (Aurelle & Ledoux, 2013;Hoban et al, 2013).…”

“…New molecular techniques based on the newgeneration sequencing technologies should be implemented to increase the number of markers (Hoban et al, 2013). Simulations, empirically parameterized and tailored to the specific case, can help test hypotheses and interpret empirical data (Aurelle & Ledoux, 2013;Hoban et al, 2013). Last but not least, a reference database of genotypes inclusive of all the data collected in different regions of the Mediterranean could be implemented to define the geographic origin of colonies in order to face illegal harvesting, poaching, and trade all along the Mediterranean coasts (Ledoux et al, 2016).…”

Population expansions dominate demographic histories of endemic and widespread Pacific reef fishes

“…Neither neutrality tests nor EBSP provided any evidences of population contraction for any markers in any species. The statistical power to detect a bottleneck is dependent on the number of markers used45. Unfortunately, reconstructing complex demography can be a very difficult task even when analyzing whole genome data (see for example Boitard et al 46…”

Genomic signatures of population bottleneck and recovery in Northwest Atlantic pinnipeds

“…From such simulations, we have learned how factors such as mating systems (Armburster & Pfenninger, 2003), epistasis (Turelli & Barton, 2006), baseline allele frequency distributions (Luikart, Allendorf, Cornuet, & Sherwin, 1998), intensity and length of bottleneck (England et al., 2003), and timing of recovery (Hoban, Gaggiotti, & Bertorelle, 2013) are likely to affect the loss of genetic diversity during bottlenecks. More recent studies that compare the power of traditional and genomic markers to detect bottlenecks also show us that the type and amount of data and the selected model parameters can significantly impact conclusions (Cabrera & Palsbøll, 2017; Elleouet & Aitken, 2018; Hoban et al., 2013; Peery et al., 2012; Shafer, Gattepaille, Stewart, & Wolf, 2015). While advances in computational power have enabled increasingly complex demographic models and the incorporation of Bayesian approaches provide more nuanced ways to draw predictions and interpret uncertainty, simulated datasets inherently lack natural variability that without a doubt influences these processes in the natural environment.…”