Estimating effective population size of large marine populations, is it feasible?

Abstract

Sustainable exploitation of marine populations is a challenging task relying on information about their current and past abundance. Fisheries‐related data can be scarce and unreliable making them unsuitable for quantitative modelling. One fishery independent method that has attracted attention in this context consists in estimating the effective population size (Ne), a concept founded in population genetics. We reviewed recent empirical studies on Ne and carried out a simulation study to evaluate the feasibility of estimating Ne in large fish populations with the currently available methods. The detailed review of 26 studies found that published empirical Ne values were very similar despite differences in species and total population sizes (N). Genetic simulations for an age‐structured fish population were carried out for a range of population and samples sizes, and Ne was estimated using the Linkage Disequilibrium method. The results showed that already for medium‐sized populations (1 million individuals) and common sample sizes (50 individuals), negative estimates were likely to occur which for real applications is commonly interpreted as indicating very large (infinite) Ne. Moreover, on average, Ne estimates were negatively biased. The simulations further indicated that around 1% of the total number of individuals might have to be sampled to ensure sufficiently precise estimates of Ne. For large marine populations, this implies rather large samples (several thousands to millions of individuals). If however such large samples were to be collected, many more population parameters than only Ne could be estimated.

Estimating effective population size using RADseq: Effects of SNP selection and sample size

“…Depending on the combination of threshold values, N e estimates varied by up to a factor of 7.5. In comparison, the well‐known effect of underestimation of N e due to ignoring overlapping generations is only around 30% in the thornback ray, independent of the census population size (Marandel et al, ). Further, the MAF threshold value (tested range 0.01 to 0.1) had a larger effect compared with the NA (tested range 25 to 50%).…”

“…In contrast to SNP selection criteria, the sample size was found to impact estimates only slightly, given at least 50 individuals were used. Negative estimates are expected when sample size is insufficient (Marandel et al, ). Further, the 159 individuals were probably not enough to obtain stabilized estimates.…”

“…Empirical estimates of N e are often biased because all methods rely on strong assumptions which are likely violated in natural populations (R. S. Waples, Antao, & Luikart, ). Numerous recent genetic studies have documented how more realistic simulations or real data, which do not fulfill methods’ assumptions, lead to biased N e estimates (Gilbert & Whitlock, ; Hare et al, ; Luikart, Ryman, Tallmon, Schwartz, & Allendorf, ; Marandel et al, ; Robinson & Moyer, ; Russell & Fewster, ; R. S. Waples et al, ; Robin S. Waples & Do, ). Among the various sources of bias, the assumption of nonoverlapping generations is often violated.…”

“…Among the various sources of bias, the assumption of nonoverlapping generations is often violated. In this case, the amount and the direction of bias as well as the precision of estimates are highly dependent on life history traits, thus species‐specific, but also on the sampling fraction (Marandel et al, ; R. S. Waples et al, ). Other factors such as unequal sex ratio, high level of inbreeding and high variance in family sizes have also been found to bias N e estimates(Montarry et al, ).…”

Genetic structure in the marbled rockfish(Sebastiscus marmoratus)across most of the distribution in the northwestern Pacific

“…Unlike other marine species, such as Fenneropenaeus chinensis and Portunus trituberculatus, which have suffered too much human interference (for example, intensive catching and stock enhancement) in the northwestern Pacific (Liu et al, ; Song, Li, Zhang, & Gao, ), the S. marmoratus population has a large Ne (67.0 ‐∞), low genetic relatedness (<0.01) and a low level of inbreeding (<0.12). Moreover, Marandel et al () reported that negative Ne estimates are commonly occurred due to large population sizes in the marine environment when limited sample sizes were analyzed. Hence, the real Ne of S. marmoratus population seems to be greatly larger than we measured.…”