JOURNAL ARTICLE

The spatial and seasonal patterns and stability of the Lake Whitefish fishery in Michigan waters of southern Lake Huron.

  • Published In: North American Journal of Fisheries Management, 2024, v. 44, n. 5. P. 1121 1 of 3

  • Database: Environment Complete 2 of 3

  • Authored By: He, Ji X.; Goniea, Thoms M.; Bence, James R.; Wills, Todd C.; Herbst, Seth J.; Briggs, Andrew S.; Fielder, David G. 3 of 3

Abstract

This article focuses on developing an overall abundance index for Lake Whitefish (Coregonus clupeaformis) in Michigan waters of southern Lake Huron by analyzing commercial trap-net fishery catch-and-effort data from five separate fishing grounds. Using linear mixed models and Bayesian information criterion for model selection, the study found that spatial (area) and seasonal (month) interactions significantly influence fishery yields, reflecting seasonal spatial migrations within a single large mixed Lake Whitefish population. The abundance index indicated a decline in Lake Whitefish abundance during the late 2000s, stabilizing after 2012, while continued declines in annual fishery yield since then were primarily due to reduced fishing effort rather than further abundance decreases. The study highlights the importance of accounting for spatial and seasonal variability in fisheries lacking independent surveys and provides insights relevant for managing Lake Whitefish fisheries in southern Lake Huron.

Additional Information

  • Source:North American Journal of Fisheries Management. 2024/10, Vol. 44, Issue 5, p1121
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:0275-5947
  • DOI:10.1002/nafm.11029
  • Accession Number:180775448
  • 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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