Integrated Salmon Hatcheries Can Pose Less Genetic Risk to Wild Populations Than Segregated Programs, Given Imperfect Implementation

Hatchery programs can provide fishery and conservation benefits, but can also inadvertently threaten wild populations through genetic and ecological interactions. Two common, and non-mutually exclusive, strategies for mitigating the genetic impacts of hatchery programs on wild populations are reducing the number of hatchery-origin fish spawning in the wild and integrating wild-origin individuals into the hatchery broodstock. We compared the robustness of these two strategies to imperfect implementation (variation around target proportions of hatchery-origin spawners in the wild and wild-origin brood stock) using a quantitative population genetic model. Simulations revealed that incorporating wild-origin broodstock was more robust to both short- and long-term implementation errors compared to minimizing hatchery-origin spawners in the wild. Furthermore, relatively low levels of hatchery integration were required to achieve most of the increase in robustness, provided that the average proportion of hatchery-origin spawners was correspondingly low. We checked these findings against empirically observed levels of implementation error by parametrizing the model using data from hatchery programs in Washington and Oregon. These findings suggest that integrated hatchery programs can pose a smaller genetic risk to wild populations than segregated programs, given realistic levels of implementation error.

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Publication Details

Journal
Evolutionary Applications
Published
2025-12-01
DOI
https://doi.org/10.1111/eva.70184
Primary Topic
Fish Ecology and Management Studies
Type
article
Field-Weighted Citation Impact
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article

Integrated Salmon Hatcheries Can Pose Less Genetic Risk to Wild Populations Than Segregated Programs, Given Imperfect Implementation

Marissa L. Baskett, Jack H. Buckner, Michael J. Ford
Evolutionary Applications
Fish Ecology and Management Studies
article

Integrated Salmon Hatcheries Can Pose Less Genetic Risk to Wild Populations Than Segregated Programs, Given Imperfect Implementation

Marissa L. Baskett, Jack H. Buckner, Michael J. Ford
article en

Abstract

Hatchery programs can provide fishery and conservation benefits, but can also inadvertently threaten wild populations through genetic and ecological interactions. Two common, and non-mutually exclusive, strategies for mitigating the genetic impacts of hatchery programs on wild populations are reducing the number of hatchery-origin fish spawning in the wild and integrating wild-origin individuals into the hatchery broodstock. We compared the robustness of these two strategies to imperfect implementation (variation around target proportions of hatchery-origin spawners in the wild and wild-origin brood stock) using a quantitative population genetic model. Simulations revealed that incorporating wild-origin broodstock was more robust to both short- and long-term implementation errors compared to minimizing hatchery-origin spawners in the wild. Furthermore, relatively low levels of hatchery integration were required to achieve most of the increase in robustness, provided that the average proportion of hatchery-origin spawners was correspondingly low. We checked these findings against empirically observed levels of implementation error by parametrizing the model using data from hatchery programs in Washington and Oregon. These findings suggest that integrated hatchery programs can pose a smaller genetic risk to wild populations than segregated programs, given realistic levels of implementation error.

Evolutionary ApplicationsVol. 18(12)
NOAA National Marine Fisheries Service (US), National Oceanic and Atmospheric Administration (US), Oregon State University (US), NOAA National Marine Fisheries Service Northwest Fisheries Science Center (US), University of California, Davis (US)
Openalex Percentile: Top 64%
Fish Ecology and Management Studies
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Integrated Salmon Hatcheries Can Pose Less Genetic Risk to Wild Populations Than Segregated Programs, Given Imperfect Implementation — Marissa L. Baskett, Jack H. Buckner, et al. · Evolutionary Applications (2025) | TGRS Research Map | TGRS