JOURNAL ARTICLE

Evaluating the viability of the use of T‐bar and radiotelemetry tags on prespawn Arctic Lampreys.

  • Published In: North American Journal of Fisheries Management, 2023, v. 43, n. 6. P. 1631 1 of 3

  • Database: Environment Complete 2 of 3

  • Authored By: Spanos, Mary C.; Cunningham, Curry J.; Drew, Katie A.; Sutton, Trent M. 3 of 3

Abstract

This article evaluates the effects of externally attached T-bar anchor tags and internally implanted dummy radio telemetry tags on prespawn Arctic Lampreys (Lethenteron camtschaticum) from the Yukon River in Alaska to determine their suitability for future population and movement studies. The laboratory study found that all lampreys survived tagging procedures through 14 weeks, with high tag retention rates (96.2% for T-bar tags and ≥96.2% for radio tags), but higher radio tag burdens (ratio of tag weight to fish weight) increased long-term mortality risk (up to 35 weeks) and reduced swim endurance shortly after tagging. Incision sites exhibited prolonged inflammation regardless of treatment, and antenna protrusion sites showed increasing skin erosion over time. The study concludes that T-bar tags are acceptable for mark–recapture studies if tag loss is accounted for, and radio tag burdens should not exceed 1.3% of fish body weight for short-term (≤14 weeks) studies or 0.5% for long-term (≥14 weeks) studies to minimize impacts on lamprey survival and behavior.

Additional Information

  • Source:North American Journal of Fisheries Management. 2023/12, Vol. 43, Issue 6, p1631
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2023
  • ISSN:0275-5947
  • DOI:10.1002/nafm.10939
  • Accession Number:174780364
  • 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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