JOURNAL ARTICLE

Acoustic telemetry yields stock membership clues for Coho Salmon harvested in coastal fisheries.

  • Published In: Transactions of the American Fisheries Society, 2024, v. 153, n. 5. P. 674 1 of 3

  • Database: Environment Complete 2 of 3

  • Authored By: Henslee, Luke H.; Ivanoff, Renae; Liller, Zachary W.; Westley, Peter A. H.; Seitz, Andrew C. 3 of 3

Abstract

This article focuses on using acoustic telemetry to estimate the stock composition of Coho Salmon (Oncorhynchus kisutch) harvested in the commercial fisheries of the Norton Sound district, Alaska, particularly in the Shaktoolik and Unalakleet subdistricts. By tagging 578 Coho Salmon over two seasons (2020 and 2021) and tracking their movements to natal spawning streams, researchers identified distinct natal and transitory stocks contributing to commercial catches. The study found that while the Unalakleet subdistrict catch was predominantly composed of Unalakleet stock salmon (over 85%), the Shaktoolik subdistrict harvest included a substantial proportion of Unalakleet stock (over 50%) alongside Shaktoolik stock, indicating significant stock mixing. These findings highlight the complexity of managing mixed-stock fisheries and suggest that spatial and temporal characteristics revealed by acoustic telemetry can improve stock-specific harvest assessments and inform sustainable fishery management in regions lacking clear genetic or chemical stock markers.

Additional Information

  • Source:Transactions of the American Fisheries Society. 2024/09, Vol. 153, Issue 5, p674
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2024
  • ISSN:0002-8487
  • DOI:10.1002/tafs.10486
  • Accession Number:179773654
  • Copyright Statement:Copyright of Transactions of the American Fisheries Society 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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