在不确定性下重建全球渔业。
Rebuilding global fisheries under uncertainty.
机构信息
Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720;
Department of Earth System Science, University of California, Irvine, CA 92697.
出版信息
Proc Natl Acad Sci U S A. 2019 Aug 6;116(32):15985-15990. doi: 10.1073/pnas.1902657116. Epub 2019 Jul 22.
Current and future prospects for successfully rebuilding global fisheries remain debated due to uncertain stock status, variable management success, and disruptive environmental change. While scientists routinely account for some of this uncertainty in population models, the mechanisms by which this translates into decision-making and policy are problematic and can lead to unintentional overexploitation. Here, we explicitly track the role of measurement uncertainty and environmental variation in the decision-making process for setting catch quotas. Analyzing 109 well-sampled stocks from all oceans, we show that current practices may attain 55% recovery on average, while richer decision methods borrowed from robotics yield 85% recovery of global stocks by midcentury, higher economic returns, and greater robustness to environmental surprises. These results challenge the consensus that global fisheries can be rebuilt by existing approaches alone, while also underscoring that rebuilding stocks may still be achieved by improved decision-making tools that optimally manage this uncertainty.
由于不确定的种群状况、多变的管理成功和破坏性的环境变化，成功重建全球渔业的现状和未来前景仍存在争议。虽然科学家在种群模型中经常考虑到其中的一些不确定性，但这种不确定性转化为决策和政策的机制存在问题，可能导致无意识的过度开发。在这里，我们明确跟踪了测量不确定性和环境变化在设定捕捞配额决策过程中的作用。通过分析来自所有海洋的 109 个样本充足的种群，我们表明，目前的做法平均可能实现 55%的恢复，而从机器人学借鉴的更丰富的决策方法可以在本世纪中叶使全球鱼类种群恢复 85%，带来更高的经济回报，并对环境意外情况更具鲁棒性。这些结果挑战了这样一种共识，即仅通过现有方法就可以重建全球渔业，同时也强调了通过最佳管理这种不确定性的改进决策工具，仍然可以实现鱼类种群的重建。
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