Frontiers in Marine Science (Aug 2023)

Evaluating Atlantic bluefin tuna harvest strategies that use conventional genetic tagging data

  • Thomas R. Carruthers,
  • Quang C. Huynh,
  • Matthew V. Lauretta,
  • Ann L. Preece,
  • Douglas S. Butterworth,
  • Adrian R. Hordyk,
  • Nathan G. Taylor

DOI
https://doi.org/10.3389/fmars.2023.1210182
Journal volume & issue
Vol. 10

Abstract

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An individual tagging model was implemented within the spatial, seasonal, multi-stock, multi-fleet operating models of the peer-reviewed Management Strategy Evaluation (MSE) framework for Atlantic bluefin tuna to evaluate the benefits of a harvest strategy that utilizes conventional gene tagging. A multi-year Brownie estimator was developed to test the accuracy and precision of exploitation rate estimates arising from gene tagging programs with various scenarios for spatial release distribution, release numbers and fishery exploitation rates. Harvest strategies that used the Brownie estimator were tested to evaluate yield and resource conservation performance relative to idealized management using perfect information. For the eastern stock, releasing 1,000 fish throughout the Atlantic and genotyping 27% of all landed fish at an estimated cost of US$2M was sufficient to obtain estimates of exploitation rate with a coefficient of variation of 20%. For the western stock, the same precision in exploitation rate estimates required the release of 1,300 fish and genotyping rate of 35% at an estimated cost of US$2.5M. Harvest strategies using the gene tagging data provided expected yield and resource conservation performance that was not substantially lower than a harvest strategy assuming using perfect information regarding vulnerable biomass. Reducing the number of releases most strongly affected the worst-case ‘lower-tail’ outcomes for West area yield and eastern stock biomass. Conventional gene tagging harvest strategies offer a promising basis for calculating management advice for Atlantic bluefin tuna that may be cheaper, simpler, and more robust than the current conventional stock assessment paradigm.

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