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

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.

2011 ◽  
Vol 68 (3) ◽  
pp. 550-562 ◽  
Author(s):  
Terry D. Beacham ◽  
B. McIntosh ◽  
C. G. Wallace

We evaluated two questions: (i) do microsatellites require larger population baseline sample sizes than single nucleotide polymorphisms (SNPs) to allow the accuracy provided by the microsatellites in genetic stock identification (GSI) applications to be expressed, and (ii) do less genetically distinct populations require larger population baseline sample sizes than more distinct populations to improve population-specific accuracy in GSI applications? Forty-six SNP loci were surveyed in 40 populations of sockeye salmon ( Oncorhynchus nerka ) over 16 regions from southern and central British Columbia and were split into two groups: the top 23 SNPs evaluated for stock identification for British Columbia sockeye salmon and the poorest 23 nuclear SNPs. Fourteen microsatellites were surveyed and split into two groups, with loci from the top 7 loci for stock identification accuracy assembled in one group, and the remaining 7 microsatellites assigned to a second group. SNPs and microsatellites with lower stock identification power required larger population sample sizes to allow expression of stock identification potential. To achieve the same level of population-specific accuracy, SNPs required fewer individuals to be sampled in a population than did microsatellites. Less genetically distinct populations required larger population sample sizes to achieve a given level of accuracy in estimated stock compositions.


1992 ◽  
Vol 49 (7) ◽  
pp. 1507-1517 ◽  
Author(s):  
Jon Brodziak ◽  
Boyd Bentley ◽  
Devin Bartley ◽  
Graham A. E. Gall ◽  
Richard Gomulkiewicz ◽  
...  

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.


2017 ◽  
Vol 74 (8) ◽  
pp. 2159-2169 ◽  
Author(s):  
Mikhail Ozerov ◽  
Juha-Pekka Vähä ◽  
Vidar Wennevik ◽  
Eero Niemelä ◽  
Martin-A. Svenning ◽  
...  

2004 ◽  
Vol 24 (2) ◽  
pp. 672-685 ◽  
Author(s):  
Gary A. Winans ◽  
Melanie M. Paquin ◽  
Donald M. Van Doornik ◽  
Bruce M. Baker ◽  
Perry Thornton ◽  
...  

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

2010 ◽  
Vol 103 (3) ◽  
pp. 917-924 ◽  
Author(s):  
Lelania Bourgeois ◽  
Walter S. Sheppard ◽  
H. Allen Sylvester ◽  
Thomas E. Rinderer

2002 ◽  
Vol 68 (sup1) ◽  
pp. 353-356 ◽  
Author(s):  
SYUITI ABE ◽  
SHUNPEI SATO ◽  
HIROYUKI KOJIMA ◽  
JUNKO ANDO ◽  
HIRONORI ANDO ◽  
...  

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