A Comparison of Stock and Individual Identification for Chinook Salmon in British Columbia Provided by Microsatellites and Single‐Nucleotide Polymorphisms

Abstract

The following questions were addressed in this study: (1) If a suite of 12-15 microsatellites were used in the genetic stock identification (GSI) of Chinook salmon Oncorhynchus tshawytscha, which microsatellites should be in the suite? (2) How many microsatellites are required to provide stock identification resolution equivalent to that of 72 single-nucleotide polymorphisms (SNPs)? (3) How many SNPs are required to replace the current microsatellite baselines used in GSI applications? (4) If additional GSI power is required for microsatellite baselines, what is the incremental increase provided by SNPs and microsatellites? The variation at 29 microsatellite loci and 73 SNP loci was surveyed in 60 populations of Chinook salmon in 16 regions in British Columbia. Microsatellites with more observed alleles provided more accurate estimates of stock composition than those with fewer alleles. The options available for improving the accuracy and precision of stock composition estimates for a 12-locus Fisheries and Oceans Canada (DFO) microsatellite suite range include adding either 4 microsatellites or 25 SNPs to the existing suite to achieve an overall population-specific accuracy of 86% across 60 populations. For the 13-locus Genetic Analysis of Pacific Salmon (GAPS) microsatellites, either 2 microsatellites or 20-25 SNPs can be added to the existing suite to achieve approximately 86% population-specific accuracy in estimated stock composition. The enhanced DFO (16 loci) and GAPS (15 loci) microsatellite baselines were projected to require 179 and 166 SNPs, respectively, for equivalent precision of the population-specific estimates. The level of regional accuracy of individual assignment available from the enhanced DFO and GAPS suites of microsatellites was projected to require 90 and 82 SNPs, respectively. The level of individual assignment to specific populations available from the enhanced DFO and GAPS suites of microsatellites was projected to require 137 and 121 SNPs, respectively.One key aspect of the management of Pacific salmon Oncorhynchus spp. fisheries is estimating the stock composition of mixed-stock fishery samples with enough resolution for effective management decisions, with the constraint that the estimates be timely and cost-effective. Prior to 2002, microsatellites had been successfully applied in sockeye salmon O. nerka fishery management in British Columbia, where the twin management objectives were to restrict exploitation of populations of conservation concern while enabling the harvest of abundant populations (Beacham et al. 2004). In 2002, this was precisely the Subject editor: Timothy King, U.S. Geological Survey, Leetown Science Center, Kearneysville, West Virginia *Corresponding author: terry.beacham@dfo-mpo.gc.ca Received January 10, 2011; accepted August 22, 2011 dilemma confronting Canadian fishery managers in the management of the Chinook salmon O. tshawytscha fisheries off the Queen Charlotte Islands in northern British Columbia and the west coast of Vancouver Island in so...

Population and individual identification of coho salmon in British Columbia through parentage-based tagging and genetic stock identification: an alternative to coded-wire tags

“…Comparisons of the number of SNPs required to equal the accuracy and precision provided by existing microsatellite baselines for both sockeye salmon (Oncorhynchus nerka) (Beacham et al 2010) and Chinook salmon (Beacham et al 2012b) provided no evidence of cost savings in our laboratory by applying SNPs in applications that were handled with microsatellites, when SNP genotyping was conducted using Taqman assays. However, the new method of genotyping individuals via direct sequencing of amplicons has radically changed the cost of genotyping for individual fish in stock identification applications , allowing hundreds of SNPs to be surveyed in a cost-effective manner.…”

Comparison of the accuracy and consistency of likelihood‐based estimation routines for genetic stock identification

“…; fishes: Ruzzante et al . ; Beacham, Jonsen & Wallace ; Ensing et al . ) species and in several contexts including estimating composition of harvest from recreational or commercial fisheries (Pella & Milner ; Begg, Friedland & Pearce ) and forensic identification of possible illegally harvested individuals (Ogden ; Chapman, Pinhal & Shivji ; Lukoschek et al .…”

“…Consequently, the use of SNPs in GSI analyses has become common (Ackerman, Habicht & Seeb ; Bradbury et al . ; Beacham, Jonsen & Wallace ; Clemento et al . ; Larson et al .…”

“…Scribner et al 2003) and aquatic (e.g. reptiles: Bolker et al 2003;Prosdocimi et al 2014;mammals: Baker et al 2000;Lukoschek et al 2009; fishes: Ruzzante et al 2000;Beacham, Jonsen & Wallace Model-based approaches are based on finite-mixture distributions with inferences about population contributions made using either likelihood-or Bayesian-based methods. Modelbased approaches have several benefits, including the ability to assign measures of confidence to contribution estimates, model contributions as functions of ecological or demographic parameters and incorporate prior knowledge as to population contributions (Pella & Masuda 2001;Guo, Dey & Holsinger 2008).…”

“…SNPs generally have two alleles per locus, but markers can be identified quickly and efficiently, so the number of loci that can be incorporated in analyses is greater than with other markers. Consequently, the use of SNPs in GSI analyses has become common (Ackerman, Habicht & Seeb 2011;Bradbury et al 2011;Beacham, Jonsen & Wallace 2012;Clemento et al 2014;Larson et al 2014). However, given their biallelic features as well as the potential for more loci to be incorporated in GSI analyses, there is the possibility for SNPs to affect performance of estimation routines.…”