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18 results for “Genetic stock identification”
Data from: harnessing the power of regional baselines for broad-scale genetic stock identification: a multistage, integrated, and cost-effective approach
<p>In mixed-stock fishery analyses, genetic stock identification (GSI) estimates the contribution of each population to a mixture and is typically conducted at a regional scale using genetic baselines specific to the stocks expected in that region. Often these regional baselines cannot be combined to produce broader geographical baselines due to non-overlapping populations and genetic markers. In cases where the mixture contains stocks spanning across a wide area, a broad-scale baseline is created, but often at the cost of resolution. Here, we introduce a new GSI method to harness the resolution capabilities of baselines developed for regional applications in the analysis of mixtures containing individuals from a broad geographic range. This method employs a multistage framework that allows disparate baselines to be used in a single integrated process that produces estimates along with the propagated errors from each stage. All individuals in the mixture sample are required to be genotyped for all genetic markers in the baselines used by this model, but the baselines do not require overlap in genetic markers or populations representing the broad-scale or regional baselines.</p> <p>We demonstrate our integrated multistage GSI model using a synthesized data set made up of Chinook salmon, <em>Oncorhynchus tshawytscha</em>, from the North Bering Sea of Alaska. The data set is designed to be run using R package, Ms.GSI, and it does not represent the composition of the real fishery. The results show an improved accuracy for estimates using an integrated multistage framework, compared to the conventional framework of using separate hierarchical steps. The integrated multistage framework allows GSI of a wide geographic area without first developing a large scale, high-resolution genetic baseline or dividing a mixture sample into smaller regions beforehand. This approach is more cost-effective than updating range-wide baselines with all regionally important markers.</p>
A baseline for the genetic stock identification of Atlantic herring, Clupea harengus, in ICES Divisions 6.a, 7.b-c
<p>Atlantic herring in ICES Divisions 6.a, 7.b-c comprises at least three populations, distinguished by temporal and spatial differences in spawning, which have until recently been managed as two stocks defined by geographic delineators. Outside of spawning the populations form mixed aggregations, which are the subject of acoustic surveys. The inability to distinguish the populations has prevented the development of separate survey indices and separate stock assessments. A panel of 45 SNPs, derived from whole genome sequencing, were used to genotype 3,480 baseline spawning samples (2014-2021). A temporally stable baseline comprising 2,316 herring from populations known to inhabit Division 6.a was used to develop a genetic assignment method, with a self-assignment accuracy >90%. The long-term temporal stability of the assignment model was validated by assigning archive (2003-2004) baseline samples (270 individuals) with a high level of accuracy. Assignment of non-baseline samples (1,514 individuals) from Division 6.a, 7.b-c indicated previously unrecognised levels of mixing of populations outside of the spawning season. The genetic markers and assignment models presented constitute a 'toolbox' that can be used for the assignment of herring caught in mixed survey and commercial catches in Division 6.a into their population of origin with a high level of accuracy.</p>
A baseline for the genetic stock identification of Atlantic herring, Clupea harengus, in ICES Divisions 6.a, 7.b-c
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Data from: harnessing the power of regional baselines for broad-scale genetic stock identification: a multistage, integrated, and cost-effective approach
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Data from: Evaluation of a single nucleotide polymorphism baseline for genetic stock identification of Chinook Salmon (Oncorhynchus tshawytscha) in the California Current Large Marine Ecosystem
Chinook Salmon is an economically and ecologically important species, and populations from the west coast of North America are a major component of fisheries in the North Pacific Ocean. The anadromous life history strategy of this species generates populations (or stocks) that typically are differentiated from neighboring populations. In many cases, it is desirable to discern the stock of origin of an individual fish or the stock composition of a mixed sample to monitor the stock-specific effects of anthropogenic impacts and alter management strategies accordingly. Genetic stock identification (GSI) provides such discrimination, and we describe here a novel GSI baseline composed of genotypes from more than 8000 individual fish from 69 distinct populations at 96 single nucleotide polymorphism (SNP) loci. The populations included in this baseline represent the likely sources for more than 99% of the salmon encountered in ocean fisheries of California and Oregon. This new genetic baseline permits GSI with the use of rapid and cost-effective SNP genotyping, and power analyses indicate that it provides very accurate identification of important stocks of Chinook Salmon. In an ocean fishery sample, GSI assignments of more than 1000 fish, with our baseline, were highly concordant (98.95%) at the reporting unit level with information from the physical tags recovered from the same fish. This SNP baseline represents an important advance in the technologies available to managers and researchers of this species.
Data from: Evaluation of a single nucleotide polymorphism baseline for genetic stock identification of Chinook Salmon (Oncorhynchus tshawytscha) in the California Current Large Marine Ecosystem
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Pacific Rim Chinook salmon genetic stock identification baseline of SNP genotypes
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Data From: Parentage‐based tagging combined with genetic stock identification is a cost‐effective and viable replacement for coded‐wire tagging in large‐scale assessments of marine Chinook salmon fisheries in British Columbia, Canada
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Atlantic herring population baseline (genotypes) for genetic stock identification
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Visual and genetic stock identification of a test fishery to forecast Columbia River spring chinook salmon stocks 2 weeks into the future
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Close relatives in population samples: Evaluation of the consequences for genetic stock identification
<p>Determining the origin of individuals in mixed population samples is key in many ecological, conservation and management contexts. Genetic data can be analyzed using Genetic Stock Identification (GSI), where the origin of single individuals is determined using Individual Assignment (IA) and population proportions are estimated with Mixed Stock Analysis (MSA). In such analyses, allele frequencies in a reference baseline are required. Unknown individuals or mixture proportions are assigned to source populations based on the likelihood that their multilocus genotypes occur in a particular baseline sample. Representative sampling of populations included in a baseline is important when designing and performing GSI. Here we investigate the effects of family sampling on GSI, using both simulated and empirical genotypes for Atlantic salmon (<i>Salmo salar</i>). We show that non-representative sampling leading to inclusion of close relatives in a reference baseline may introduce bias in estimated proportions of contributing populations in a mixed sample, and increases the amount of incorrectly assigned individual fish. Simulated data further show that the induced bias increases with increasing family structure, but that it can be partly mitigated by increased baseline population sample sizes. Results from standard accuracy tests of GSI (using only a reference baseline and/or self-assignment) gave a false and elevated indication of the baseline power and accuracy to identify stock proportions and individuals. These findings suggest that family structure in baseline population samples should be quantified and its consequences evaluated, before carrying out GSI.</p>
Data from: Comparison of coded-wire tagging with parentage-based tagging and genetic stock identification in a large-scale coho salmon fisheries application in British Columbia, Canada
Wild Pacific salmon, including Coho salmon Onchorynchus kisutch, have been supplemented with hatchery propagation for over 50 years in support of increased ocean harvest and conservation of threatened populations. In Canada, the Wild Salmon Policy for Pacific salmon was established with the goal of maintaining and restoring healthy and diverse Pacific salmon populations, making conservation of wild salmon and their habitats the highest priority for resource management decision-making. A new approach to the assessment and management of wild coho salmon, and the associated hatchery production and fishery management is needed. Implementation of parentage-based tagging (PBT) may overcome problems associated with coded-wire tag-based (CWT) assessment and management of coho salmon fisheries, providing at a minimum information equivalent to that derived from the CWT program. PBT and genetic stock identification (GSI) were used to identify coho salmon sampled in fisheries (8,006 individuals) and escapements (1,692 individuals) in British Columbia to specific conservation units (CU), populations, and broodyears. Individuals were genotyped at 304 single nucleotide polymorphisms (SNPs) via direct sequencing of amplicons. Very high accuracy of assignment to population (100%) via PBT for 543 jack (age 2) assigned to correct age and collection location and 265 coded-wire tag (CWT, age 3) coho salmon assigned to correct age and release location was observed, with a 40,774–individual, 267–population baseline available for assignment. Coho salmon from un-CWTed enhanced populations contributed 65% of the catch in southern recreational fisheries in 2017. Application of a PBT-GSI system of identification to individuals in 2017 fisheries and escapements provided high-resolution estimates of stock composition, catch, and exploitation rate by CU or population, providing an alternate and more effective method in the assessment and management of Canadian-origin coho salmon relative to CWTs, and an opportunity for a genetic-based system to replace the current CWT system for coho salmon assessment.
Contemporary genetic structure affects genetic stock identification of steelhead trout in the Snake River basin
<p>Genetic stock identification is a widely applied tool for the mixed-stock management of salmonid species throughout the North Pacific Rim. The effectiveness of genetic stock identification is dependent on the level of differentiation among stocks which is often high due to the life history of these species that involves high homing fidelity to their natal streams. However, the utility of this tool can be reduced when natural genetic structuring has been altered by hatchery translocation and/or supplementation. We examined the genetic population structure of ESA-listed steelhead in the Snake River basin of the United States. We analyzed 9,613 natural-origin adult steelhead returning to Passive Integrated Transponder detection sites throughout the basin from 2010 through 2017. Individuals were genotyped at 180 single nucleotide polymorphic genetic markers and grouped into 20 populations based on their return location. While we expected to observe a common pattern of hierarchical genetic structuring due to isolation by distance, we observed low genetic differentiation between populations in the upper Salmon River basin compared to geographically distant populations in the lower Snake River basin. These results were consistent with lower genetic stock assignment probabilities observed for populations in this upper basin. We attribute these patterns of reduced genetic structure to the translocation of lower basin steelhead stocks and ongoing hatchery programs in the upper Salmon River basin. We discuss the implications of these findings on the utility of genetic stock identification in the basin and discuss opportunities for increasing assignment probabilities in the face of low genetic structure.</p>
Data from: Population and individual identification of Coho Salmon in British Columbia through parentage-based tagging and genetic stock identification: an alternative to coded-wire tags
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Data from: Comparison of coded-wire tagging with parentage-based tagging and genetic stock identification in a large-scale coho salmon fisheries application in British Columbia, Canada
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Data from: Comparison of SNPs and microsatellites for fine-scale application of genetic stock identification of Chinook salmon in the Columbia River Basin
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Contemporary genetic structure affects genetic stock identification of steelhead trout in the Snake River basin
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Close relatives in population samples: Evaluation of the consequences for genetic stock identification
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