Login / Signup

Redesigning harvest strategies for sustainable fishery management in the face of extreme environmental variability.

Laura K BlameyÉva Elizabeth PlagányiTrevor HuttonRoy A DengJudy UpstonAnnie Jarrett
Published in: Conservation biology : the journal of the Society for Conservation Biology (2021)
Short-lived, fast-growing species that contribute greatly to global capture fisheries are sensitive to fluctuations in the environment. Uncertainties in exact stock-environment relationships have meant that environmental variability and extremes have been difficult to integrate directly into fisheries management. We applied a management strategy evaluation approach for one of Australia's large prawn stocks to test the robustness of harvest control rules to environmental variability. The model ensemble included coupled environmental-population models and an alternative catchability scenario fitted to historical catch per unit effort data. We compared the efficacy of alternative management actions to conserve marine resources under a variable environment while accounting for fisher livelihoods. Model fits to catch per unit effort were reasonably good and similar across operating models (OMs). For models that were coupled to the environment, environmental parameters for El Niño years were estimated with good associated precision, and OM3 had a lower AIC score (77.61)  than the base model (OM1, 80.39), whereas OM2 (AIC 82.41) had a similar AIC score, suggesting the OMs were all plausible model alternatives. Our model testing resulted in a plausible subset of management options, and stakeholders selected a permanent closure of the first fishing season based on overall performance of this option; ability to reduce the risk of fishery closure and stock collapse; robustness to uncertainties; and ease of implementation. Our simulation approach enabled the selection of an optimal yet pragmatic solution for addressing economic and conservation objectives under a variable environment with extreme events.
Keyphrases
  • human health
  • life cycle
  • healthcare
  • randomized controlled trial
  • climate change
  • machine learning
  • risk assessment
  • study protocol
  • quality improvement
  • data analysis