Tests of Genetic Stock Identification Using Coded Wire Tagged Fish

1992 ◽  
Vol 49 (7) ◽  
pp. 1507-1517 ◽  
Author(s):  
Jon Brodziak ◽  
Boyd Bentley ◽  
Devin Bartley ◽  
Graham A. E. Gall ◽  
Richard Gomulkiewicz ◽  
...  
Keyword(s):  
Computer Experiments ◽  
Allozyme Variation ◽  
Estimation Accuracy ◽  
Stock Identification ◽  
Estimation Errors ◽  
Genetic Stock ◽  
Blind Test ◽  
Genetic Stock Identification ◽  
Fishery Data ◽  
Sampling Procedures

Genetic Stock Identification (GSI) uses allozyme variation to determine the composition of mixed-stock fisheries. The GSI method was tested using real fishery data. We report, for the first time in the primary literature, results of tests of GSI in which source stock and ocean-caught mixture samples were separately obtained and the mixture composition was known exactly because the fish used were marked by Coded Wire Tags (CWTs). The accuracy of GSI and its dependence on the quality of genetic data were studied by computer experiments. Rare alleles, which could result from poor sampling procedures, can lead to significant estimation errors. Estimation accuracy depended on the concordance between stocks present in the baseline data and the mixture sample and on the number of loci used in the analysis. Two methods for computing the contributions of groups of source stocks were found to be comparable under most, but not all, conditions. In a blind test of GSI, stock group composition estimates had absolute errors of less than 3%. This suggests that the GSI method can produce accurate stock contribution estimates using real fishery data.

Biochemical Genetic Survey of Sockeye Salmon (Oncorhynchus nerka) in Canada

1994 ◽  
Vol 51 (S1) ◽  
pp. 114-131 ◽  
Author(s):  
Chris C. Wood ◽  
Brian E. Riddell ◽  
Dennis T. Rutherford ◽  
Ruth E. Withler
Keyword(s):  
Sockeye Salmon ◽  
Gene Diversity ◽  
Oncorhynchus Nerka ◽  
River System ◽  
Allozyme Variation ◽  
Stock Identification ◽  
River Systems ◽  
Genetic Stock ◽  
Genetic Stock Identification ◽  
Spawning Sites

Allozyme variation was examined in sockeye salmon (Oncorhynchus nerka) from 83 distinct spawning sites representing all major sockeye-producing river systems in Canada. Of 33 nonselected loci examined, only 14 were highly polymorphic (q > 0.05) and 10 were less polymorphic (0 < q ≤ 0.05). No two populations were fixed for different alleles at any locus, but allele frequencies ranged from 0.01 to 0.86 at PGM-1* and from 0.07 to 0.89 at ALAT*, the two most variable loci. Mean heterozygosity ranged from 2.3 to 5.6% (mean 4.1%) across all sites. Hierarchical analysis was used to partition relative gene diversity among river systems (6.3%), major drainages within a river system (2.9%), nursery lakes within drainages (7.0%), spawning sites within lakes (1.0%), and individuals within spawning sites (82.8%). Extensive differentiation among nursery lakes affords excellent opportunities for genetic stock identification within river systems, but the relatively weak regional structuring limits opportunities for coast-wide stock identification. Genetic variation at highly structured loci corroborates the view that modern populations in Canada originated from sockeye that survived the late Wisconsin Glaciation in the Bering and Columbia refuges, and also suggests the existence of coastal refuges in British Columbia.

Comprehensive microsatellite baseline for genetic stock identification of Atlantic salmon (Salmo salar L.) in northernmost Europe

2017 ◽  
Vol 74 (8) ◽  
pp. 2159-2169 ◽  
Author(s):  
Mikhail Ozerov ◽  
Juha-Pekka Vähä ◽  
Vidar Wennevik ◽  
Eero Niemelä ◽  
Martin-A. Svenning ◽  
...  
Keyword(s):  
Atlantic Salmon ◽  
Salmo Salar ◽  
Stock Identification ◽  
Genetic Stock ◽  
Atlantic Salmon Salmo Salar ◽  
Genetic Stock Identification

Genetic Stock Identification of Steelhead in the Columbia River Basin: An Evaluation of Different Molecular Markers

2004 ◽  
Vol 24 (2) ◽  
pp. 672-685 ◽  
Author(s):  
Gary A. Winans ◽  
Melanie M. Paquin ◽  
Donald M. Van Doornik ◽  
Bruce M. Baker ◽  
Perry Thornton ◽  
...  
Keyword(s):  
Molecular Markers ◽  
River Basin ◽  
Columbia River ◽  
Columbia River Basin ◽  
Stock Identification ◽  
Genetic Stock ◽  
Genetic Stock Identification

Genetic Stock Identification Reveals That Angler Harvest Is Representative of Cryptic Stock Proportions in a High‐Profile Kokanee Fishery

2019 ◽  
Vol 39 (3) ◽  
pp. 415-425
Author(s):  
Hillary G. M. Ward ◽  
Paul J. Askey ◽  
Tyler Weir ◽  
Karen K. Frazer ◽  
Michael A. Russello
Keyword(s):  
Stock Identification ◽  
Genetic Stock ◽  
High Profile ◽  
Genetic Stock Identification

Genetic Stock Identification of Russian Honey Bees

2010 ◽  
Vol 103 (3) ◽  
pp. 917-924 ◽  
Author(s):  
Lelania Bourgeois ◽  
Walter S. Sheppard ◽  
H. Allen Sylvester ◽  
Thomas E. Rinderer
Keyword(s):  
Honey Bees ◽  
Stock Identification ◽  
Genetic Stock ◽  
Genetic Stock Identification ◽  
Russian Honey Bees

scholarly journals Development of molecular markers for genetic stock identification of chum salmon

2002 ◽  
Vol 68 (sup1) ◽  
pp. 353-356 ◽  
Author(s):  
SYUITI ABE ◽  
SHUNPEI SATO ◽  
HIROYUKI KOJIMA ◽  
JUNKO ANDO ◽  
HIRONORI ANDO ◽  
...  
Keyword(s):  
Molecular Markers ◽  
Chum Salmon ◽  
Stock Identification ◽  
Genetic Stock ◽  
Genetic Stock Identification

scholarly journals Genetic stock identification of Atlantic salmon and its evaluation in a large population complex

2017 ◽  
Vol 74 (3) ◽  
pp. 327-338 ◽  
Author(s):  
Juha-Pekka Vähä ◽  
Jaakko Erkinaro ◽  
Morten Falkegård ◽  
Panu Orell ◽  
Eero Niemelä
Keyword(s):  
Atlantic Salmon ◽  
Large Population ◽  
Identification Accuracy ◽  
Lower Sensitivity ◽  
Stock Identification ◽  
Genetic Structuring ◽  
Genetic Stock ◽  
Factors Affecting ◽  
Genetic Stock Identification ◽  
Genetic Structure And Diversity

Addressing biocomplexity in fisheries management is a challenge requiring an ability to differentiate among distinct populations contributing to fisheries. We produced extensive genetic baseline data involving 36 sampling locations and 33 microsatellite markers, which allowed characterization of the genetic structure and diversity in a large Atlantic salmon (Salmo salar) population complex of the River Teno system, northernmost Europe. Altogether, we identified 28 hierarchically structured and genetically distinct population segments (global FST = 0.065) corresponding exceptionally well with their geographical locations. An assessment of factors affecting the stock identification accuracy indicated that the identification success is largely defined by the interaction of genetic divergence and the baseline sample sizes. The choice between the two statistical methods tested for performance in genetic stock identification, ONCOR and cBAYES, was not critical, albeit the latter demonstrated slightly higher identification accuracy and lower sensitivity to population composition of the mixture sample. The strong genetic structuring among populations together with a powerful marker system allowed for accurate stock identification of individuals and enabled assessment of stock compositions contributing to mixed-stock fisheries.

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