4.5 Article

Bayesian approach for the genetic tracking of cultured and released individuals

期刊

FISHERIES RESEARCH
卷 77, 期 2, 页码 159-172

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ELSEVIER SCIENCE BV
DOI: 10.1016/j.fishres.2005.10.007

关键词

stock enhancement; genetic tag; familyprinting; red drum; Sciaenops ocellatus

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In all supplemental stocking programs, regardless of scale, at least some of the released animals should be tracked (recaptured and identified) to evaluate and quantify the effect of the release on wild stocks. Often, marking these animals extrinsically can be impractical. Here, a parentage-based (familyprinting), Bayesian approach is presented for genetically tracking individuals produced in captivity and released among wild conspecifics. Any class of autosomal, codominant, molecular markers may be used, provided that loci are independent and population genotype frequencies conform to Mendelian expectations for diploid systems. Incorporating reference allele-frequency data from the recipient stock and genotype data from the captive parents, parentage of tested individuals can be established via likelihood ratios that compare the probability of the genetic evidence for coparentage to the probability for coincidence for individuals whose genotypes are compatible with parental pairs. Given a sufficient number of variable loci, products of these likelihood ratios and appropriate prior probabilities yield sufficiently large posterior probabilities of coparentage, i.e., very low expectations for false-positive assignment. Thus, post-release differences in growth, survivorship, or performance traits may be evaluated among groups, among families, or among genotypes and various stocking practices (e.g., size-at-release, release location) can be studied in vivo. The principal benefit of the approach occurs when family sizes of hatchery breeding pairs are considerably larger than those of wild pairs in the stocked population, as expected during successful enhancement. An application of the method to a large-scale stocking program is described, including results of blind performance testing and mutation rate analyses to investigate program error rates. (C) 2005 Elsevier B.V. All rights reserved.

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