Global fishery dynamics are poorly predicted by classical models
Global fishery dynamics are poorly predicted by classical models
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经典模型对全球渔业动态的预测很差
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
J. Thorson
中科院分区：
文献类型：
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作者：
Cody S. Szuwalski;J. Thorson
渔业动态可以被认为是被开发人口与决定开发模式的渔民和/或管理人员之间的相互关系。这些耦合的人-自然系统的蛋白质的可持续生产需要了解他们的动态。在这里，我们通过应用RAM遗留数据库中的一般加性模型来估计捕捞死亡率和产卵生物量，对全球173个渔场的渔业动态进行了表征。用于模拟生产模型的GAMs和更灵活的GAMs被应用。我们发现观察到的动态并不总是与使用“经典”渔业模型的管理假设相匹配，并且这些假设的适用性根据大型海洋生态系统、栖息地、招募的可变性、物种的最大重量和最小观察到的种群生物量而显着变化。这些结果确定了简单模型可能对管理有用的情况。然而，增加经典模型的灵活性往往不能大大提高性能，这表明在许多情况下，只考虑生物量和清除量不足以模拟渔业动态。在选择模型框架、设定管理目标、测试管理战略和开发管理数据有限的渔业的工具时，应利用对管理中共同假设的适宜性的了解。有效地平衡对捕捞渔业未来蛋白质产量的预期和不良后果（如“渔业崩溃”）的风险，取决于我们对利用当前管理范式预测渔业未来动态的预期程度的理解。
Fisheries dynamics can be thought of as the reciprocal relationship between an exploited population and the fishers and/or managers determining the exploitation patterns. Sustainable production of protein of these coupled human-natural systems requires an understanding of their dynamics. Here, we characterized the fishery dynamics for 173 fisheries from around the globe by applying general additive models to estimated fishing mortality and spawning biomass from the RAM Legacy Database. GAMs specified to mimic production models and more flexible GAMs were applied. We show observed dynamics do not always match assumptions made in management using “classical” fisheries models, and the suitability of these assumptions varies significantly according to large marine ecosystem, habitat, variability in recruitment, maximum weight of a species and minimum observed stock biomass. These results identify circumstances in which simple models may be useful for management. However, adding flexibility to classical models often did not substantially improve performance, which suggests in many cases considering only biomass and removals will not be sufficient to model fishery dynamics. Knowledge of the suitability of common assumptions in management should be used in selecting modelling frameworks, setting management targets, testing management strategies and developing tools to manage data-limited fisheries. Effectively balancing expectations of future protein production from capture fisheries and risk of undesirable outcomes (e.g., “fisheries collapse”) depends on understanding how well we can expect to predict future dynamics of a fishery using current management paradigms.