Search NASA⌕ Search

Engineering topics

Campbell, Matthew R.

Publications and source records attributed to Campbell, Matthew R..

Efficient population representation with more genetic markers increases performance of a steelhead (Oncorhynchus mykiss) genetic stock identification baseline

Abstract Genetic stock identification (GSI) is an important fisheries management tool to identify the origin of fish harvested in mixed stock fisheries. Periodic updates of genetic baselines can improve performance via the addition of unsampled or under‐sampled populations and the inclusion of more informative markers. We used a combination of baselines to evaluate how population representation, marker number, and marker type affected the performance and accuracy of genetic stock assignments (self‐assignment, bias, and holdout group tests) for steelhead ( Oncorhynchus mykiss ) in the Snake River basin. First, we compared the performance of an existing genetic baseline with a newly developed one which had a reduced number of individuals from more populations using the same set of markers. Self‐assignment rates were significantly higher ( p < 0.001; +5.4%) for the older, larger baseline, bias did not differ significantly between the two, but there was a significant improvement in performance for the new baseline in holdout results ( p < 0.001; mean increase of 25.0%). Second, we compared the performance of the new baseline with increased numbers of genetic markers (~2x increase of single‐nucleotide polymorphisms; SNPs) for the same set of baseline individuals. In this comparison, results produced significantly higher rates of self‐assignment ( p < 0.001; +9.7%) but neither bias nor leave‐one‐out were significantly affected. Third, we compared 334 SNPs versus opportunistically discovered microhaplotypes from the same amplicons for the new baseline, and showed the latter produced significantly higher rates of self‐assignment ( p < 0.01; +2.6%), similar bias, but slightly lower holdout performance (−0.1%). Combined, we show the performance of genetic baselines can be improved via representative and efficient sampling, that increased marker number consistently improved performance over the original baseline, and that opportunistic discovery of microhaplotypes can lead to small improvements in GSI performance.

Hargrove, John S.↗

Contrasting patterns of sequence variation in steelhead populations reflect distinct evolutionary processes

Abstract Multiple evolutionary processes influence genome‐wide allele frequencies and quantifying effects of genetic drift, and multiple forms of selection remain challenging in natural populations. Here, we investigate variation at major effect loci in contrast to patterns of neutral drift across a wide collection of steelhead ( Oncorhynchus mykiss ) populations that have declined in abundance due to anthropogenic impacts. Whole‐genome resequencing of 74 populations of steelhead revealed genome‐wide patterns (~8 million SNPs) consistent with expected neutral population structure. However, allelic variation at major effect loci associated with adult migration timing (chromosome 28: GREB1L / ROCK1 ) and age at maturity (chromosome 25: SIX6 ) reflected how selection has acted on phenotypic variation in contrast with neutral structure. Variation at major effect loci was influenced by evolutionary processes with differing signals between the strongly divergent Coastal and Inland lineages, while allele frequencies within and among populations within the Inland lineage have been driven by local natural selection as well as recent anthropogenic influences. Recent anthropogenic effects appeared to have influenced the frequency of major effect alleles including artificial selection for specific traits in hatchery stocks with subsequent gene flow into natural populations. Selection from environmental factors at various scales has also likely influenced variation for major effect alleles. These results reveal evolutionary mechanisms that influence allele frequencies at major effect loci that are critical for conservation of phenotypic traits and life history variation of this protected species.

Willis, Stuart↗

Multigeneration Pedigrees to Monitor Hatchery Broodstock Composition and Genetic Variation of Spring/Summer Chinook Salmon in the Columbia River Basin

Abstract Hatchery production of Chinook Salmon Oncorhynchus tshawytscha in the Columbia River basin comprises most of the anadromous salmonid production in this region. Hatchery facilities and programs serve to mitigate for impacts to salmonids due to the construction and operation of hydropower dams and habitat impacts from development in addition to the conservation and restoration of natural populations. A genetic method referred to as parentage-based tagging (PBT) enables highly reliable detection of hatchery-origin fish and inference of multigeneration pedigrees. This study compiles 11 years of PBT data from nearly 125,000 interior stream-type Chinook Salmon from 24 spawning hatcheries located on tributaries of the mid- and upper Columbia River and in the Salmon, Clearwater, and Grande Ronde River subbasins. Multigenerational pedigrees allowed for investigation of the proportions of natural- and hatchery-origin broodstock (pNOB and pHOB, respectively) for each hatchery and enumeration of the scale of production between segregated and integrated programs. We then compared how pHOB and the scale of production influenced the number of stray fish observed, genetic diversity, relatedness, and age-class compositions within broodstocks. Over 91.0% of hatchery broodstock could be assigned back to their parents, and overall less than 1.0% of broodstock consisted of fish that were unintentionally incorporated into nonnatal hatchery programs. We evaluated 11 segregated programs with 0.0–10.0% pNOB, 9 intermediate programs (10.1–50.0% pNOB), and 3 integrated programs (>50.0% pNOB). There was no correlation between the scale of production or pNOB with the level of genetic diversity, but as production size increased, so did the effective number of breeders. This study demonstrates the utility of PBT as a monitoring tool for hatchery broodstocks, and results suggest that segregated and integrated programs have tradeoffs that generally align with their intended broodstock management purpose of providing fish for harvest and/or fish for supplementation or reintroduction.

Horn, Rebekah L.↗

Improving Abundance Estimates of Spring–Summer Snake River Chinook Salmon for Fisheries Management

Abstract The Columbia River basin is home to a run of spring–summer Chinook Salmon Oncorhynchus tshawytscha that returns to the Snake River drainage of Idaho, Oregon, and Washington in the Pacific Northwest. Historically, the run was one of the more productive throughout the Columbia River basin. However, Snake River spring–summer Chinook Salmon have experienced declines in abundance due to overfishing, habitat degradation, and dams. Several stocks are listed as threatened under the U.S. Endangered Species Act and are supported by mitigation hatcheries funded by Idaho Power Company, the Lower Snake River Compensation Plan, and the Bonneville Power Administration. To maximize tribal and state harvest of returning hatchery adults, minimize impacts on wild fish, and ensure that enough hatchery fish return to meet broodstock needs, careful fisheries management is required. Since 2008, managers have used hatchery adults, PIT-tagged as juveniles and detected at Lower Granite Dam, to generate adult abundance estimates. In season, these estimates inform state and tribal harvest shares and ensure that broodstock needs are met. Postseason, they provide smolt-to-adult survival and return rates. Since 2012, parentage-based tagging (PBT) has provided an alternative method to estimate stock- and age-specific returns at Lower Granite Dam, since returning hatchery adults sampled at Lower Granite Dam can be assigned to their parents. We compared stock-specific abundance estimates between PIT- and PBT-derived methodologies for return years 2016–2019. Across all years, PIT tag estimates accounted for 65% of the PBT-based estimates at Lower Granite Dam across all age-groups and release sites combined. This underrepresentation across all groups equated to 49,833 fish that were not accounted for in PIT tag abundance estimates. It is clear that PBT-based estimates should aide in-season harvest management and postseason run reconstruction to avoid the known bias of estimates from PIT tags, especially during years of low returns when increased accuracy is critical.

Coykendall, D. Katharine (ORCID:0000000211482397)↗

Retracted: Monitoring Hatchery Broodstock Composition and Genetic Variation of Spring/Summer Chinook Salmon in the Columbia River Basin with Multigeneration Pedigrees

Abstract Hatchery production of Chinook Salmon Oncorhynchus tshawytscha in the Columbia River basin comprises most of the anadromous salmonid production in this region. Hatchery facilities and programs serve to mitigate for impacts to salmonids due to the construction and operation of hydropower dams and habitat impacts from development in addition to the conservation and restoration of natural populations. A genetic method referred to as parentage-based tagging (PBT) enables highly reliable detection of hatchery-origin fish and inference of multigeneration pedigrees. This study compiles 11 years of PBT data from nearly 125,000 interior stream-type Chinook Salmon from 24 spawning hatcheries located in tributaries of the mid- and upper Columbia River as well as the Salmon, Clearwater, and Grande Ronde River subbasins. Multigenerational pedigrees allowed investigation of the proportion of natural- and hatchery-origin broodstock (pNOB and pHOB, respectively) for each hatchery and enumeration of the scale of production between segregated and integrated programs. We then compared how pHOB and the scale of production influenced the number of stray fish observed, genetic diversity, relatedness, and age-class compositions within broodstocks. Over 91.0% of hatchery broodstock could be assigned back to their parents, and there was an overall rate of less than 1.0% of broodstock that were unintentionally incorporated into nonnatal hatchery programs. We evaluated 14 segregated programs with 0.0–10.0% pNOB, 7 intermediate programs (10.1–50.0% pNOB), and 3 integrated programs (>50.0% pNOB). There was no correlation between the scale of production or pNOB with the level of genetic diversity, but as production size increased, so did the effective number of breeders. This study demonstrates the utility of PBT as a monitoring tool for hatchery broodstocks, and results suggest that segregated and integrated programs have tradeoffs that generally align with their intended broodstock management purpose of providing fish for harvest and/or fish for supplementation or reintroduction.

Horn, Rebekah L.↗

Grandparent Inference from Genetic Data: The Potential for Parentage-Based Tagging Programs to Identify Offspring of Hatchery Strays

Abstract Fisheries managers routinely use hatcheries to increase angling opportunity. Many hatcheries operate as segregated programs where hatchery-origin fish are not intended to spawn with natural-origin conspecifics in order to prevent potential negative effects on the natural-origin population. Currently available techniques to monitor the frequency with which hatchery-origin strays successfully spawn in the wild rely on either genetic differentiation between the hatchery- and natural-origin fish or extensive sampling of fish on the spawning grounds. We present a method to infer grandparent–grandchild trios by using only genotypes from two putative grandparents and one putative grandchild. We developed estimators of false-positive and false-negative error rates and showed that genetic panels containing 500–700 single-nucleotide polymorphisms or 200–300 microhaplotypes are expected to allow application of this technique for monitoring segregated hatchery programs. We discuss the ease with which this technique can be implemented by pre-existing parentage-based tagging programs and provide an R package that applies the method.

Delomas, Thomas A. (ORCID:000000015154759X)↗