A precautionary solution to estimation bias in shaping safe harvest boundariesGoto D, Devine J, Umar I, Fischer S, Oliveira JD, Howell D, Jardim E, Mosqueira I, Ono K
Systematic errors in population status such as overestimated abundance are pervasive conservation problems. These problems have plagued assessments of commercial exploitation of marine species and can threaten its sustainability. We develop a computer-intensive approach to persistent estimation bias in assessments, which may emerge from unknown sources, by optimizing harvest measures with closed-loop simulation of resource–management feedback systems: management strategy evaluation. Using saithe (Pollachius virens), a bottom-water, apex predator in the North Sea, as a real-world case study, we illustrate the approach by first diagnosing robustness of the existing harvest measures and then optimizing the measures through propagation of biases (overestimated stock abundance and underestimated fishing pressure) along with select process and observation uncertainties. Analyses showed that severe biases initially set overly optimistic catch limits and then progressively magnify the amplitude of catch fluctuation, thereby posing unacceptably high overharvest risks. Consistent performance of management strategies can be achieved by developing robust harvest measures that explicitly account for estimation bias through a computational grid search for control parameters that maximize yield while keeping stock abundance above a precautionary level. When the biases become more severe, optimized harvest measures–for saithe, raising threshold abundance that triggers management action and lowering target exploitation rate–would not only safeguard against overharvest risk (<3.5%) but also provide near-term stability in catch limits (<20% year-to-year variation): minimum disruption in fishing communities. The precautionary approach to fine-tuning adaptive risk management through management strategy evaluation offers a powerful tool to better shape sustainable harvest boundaries for exploited marine populations when estimation bias persists. By explicitly accounting for emergent sources of uncertainty, our proposed approach ensures effective conservation and sustainable exploitation of living marine resources even under profound uncertainty.
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