Simultaneous Estimation of Mixing Rates and Genetic Drift Under Successive Sampling of Genetic Markers With Application to the Mud Crab (Scylla paramamosain) in Japan

Abstract

In stock enhancement programs, it is important to assess mixing rates of released individuals in stocks. For this purpose, genetic stock identification has been applied. The allele frequencies in a composite population are expressed as a mixture of the allele frequencies in the natural and released populations. The estimation of mixing rates is possible, under successive sampling from the composite population, on the basis of temporal changes in allele frequencies. The allele frequencies in the natural population may be estimated from those of the composite population in the preceding year. However, it should be noted that these frequencies can vary between generations due to genetic drift. In this article, we develop a new method for simultaneous estimation of mixing rates and genetic drift in a stock enhancement program. Numerical simulation shows that our procedure estimates the mixing rate with little bias. Although the genetic drift is underestimated when the amount of information is small, reduction of the bias is possible by analyzing multiple unlinked loci. The method was applied to real data on mud crab stocking, and the result showed a yearly variation in the mixing rate.I T is well known that many fisheries stocks have been reduced because of overfishing, habitat loss, and degradation. (Pauly et al. 2002). Some management procedures, such as setting a total allowable catch (TAC), are the usual practices for sustainable use of such fisheries stocks (e.g., Hilborn and Walters 1992). In addition to such management procedures, stock enhancement programs through release of hatchery-reared juveniles are used to supplement reduced natural stocks (Blaxter 2000;Svå sand et al. 2000;Leber et al. 2004;Kitada and Kishino 2006).In stock enhancement programs, the assessment of the contribution of the released population to the commercial catch is one of the important tasks . Also, it is necessary to monitor the genetic impact of release on a natural population. Evaluation of the mixing conditions of a released population into a natural population must then be carried out. However, if juveniles are too small to tag and/or the tag-shedding rate is significant, it is difficult to determine their mixing compositions through experiments with artificial tags. For such cases, there is the possibility of stock identification using genetic tags such as mtDNA haplotypes and microsatellite alleles (e.g., Leber et al. 2004).We now consider a fisheries stock of a random-mating population in a closed fishing ground. Here, we call this population a natural population. Suppose that in each year hatchery-reared juveniles are released before the fishing season. Then, a population after the release of the juveniles composes a composite population, which consists of two source populations, that is, the natural and supplemented populations. The composite population could in turn be naturally reproducing in the next year. We call it a natural population again.The haplotype or allele frequencies in a population in the fishing gro...

Transcriptome Analysis of the Mud Crab (Scylla paramamosain) by 454 Deep Sequencing: Assembly, Annotation, and Marker Discovery

“…Population genetic diversity, differentiation, and phylogenetic relationship of S. paramamosain have been well investigated [4] – [9] . Functional genes and their effects on growth, development, and disease-resistance have also been studied in recent years, in order to seek a new way for increasing aquaculture yield and boosting aquaculture industry.…”

Isolation and Characterization of 54 Polymorphic Microsatellite Markers in Scylla paramamosain by FIASCO Approach

“…Nowadays, the resource of S. paramamosain has been severely decreasing because of over‐fishing and seawater pollution. However, studies about the phylogeography, molecular evolution, and identification of S. paramamosain are limited (Keenan et al 1998; Ma et al 2006, 2011; Kitakado et al 2006; He et al 2010).…”

Estimating Effective Population Size from Temporally Spaced Samples with a Novel, Efficient Maximum-Likelihood Algorithm

“…The probability density function of given under genetic drift is where δ is called the “drift parameter” that controls the amount of drift: The drift parameter is a function of and the sampling interval t . It is derived from the continuous model of genetic drift by Kimura (1955) for sufficiently large and is a popular method to model the change in allele frequency due to genetic drift ( Kitakado et al 2006 ; Song et al 2006 ). For the special case of , δ reduces to …”