JOURNAL ARTICLE

The Genetic Composition of Wild Steelhead Based on Spatial Proximity to a Hatchery.

  • Published In: North American Journal of Fisheries Management, 2023, v. 43, n. 2. P. 431 1 of 3

  • Database: Environment Complete 2 of 3

  • Authored By: Bowersox, Brett J.; Hargrove, John S.; Copeland, Timothy; Campbell, Matthew R. 3 of 3

Abstract

This article focuses on the genetic structure of wild steelhead (Oncorhynchus mykiss) populations in the lower Clearwater River, Idaho, and evaluates the extent of genetic influence from the nearby Dworshak National Fish Hatchery (Dworshak NFH). Using genetic markers including single nucleotide polymorphisms (SNPs) and microhaplotypes, the study analyzed 813 juvenile steelhead from 10 tributaries to assess genetic diversity, differentiation, effective population size, and hatchery introgression. Results revealed three zones of hatchery influence: no detectable influence in downstream wild steelhead emphasis tributaries, moderate influence in tributaries near the hatchery, and significant influence in hatchery-supplemented tributaries upstream. The study highlights complex and dynamic genetic interactions shaped by historical, legacy, and contemporary gene flow, with effective population sizes generally above conservation thresholds, and underscores the utility of genetic tools for ongoing monitoring and management of hatchery and wild steelhead coexistence within mixed-use watersheds.

Additional Information

  • Source:North American Journal of Fisheries Management. 2023/04, Vol. 43, Issue 2, p431
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2023
  • ISSN:0275-5947
  • DOI:10.1002/nafm.10861
  • Accession Number:162942903
  • Copyright Statement:Copyright of North American Journal of Fisheries Management is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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