Fisheries Assessment and Management: A Synthesis of Common Approaches with Special Reference to Deepwater and Data-Poor Stocks

C. Edwards, R. Hillary, P. Levontin, J. L. Blanchard, K. Lorenzen
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引用次数: 23

Abstract

Deepwater fish populations are often characterized by their life-history as being highly susceptible to overexploitation. Moreover, dependent fisheries often develop rapidly, so overexploitation may occur before resource dynamics are quantified sufficiently to assess safe biological limits. It is therefore crucial to employ assessment methods that make the best use of limited data and management procedures that account for large uncertainties. This review provides a critical synthesis of assessment and management approaches for deepwater fisheries. Given limitations in the data, it is clear that assessments are likely to benefit from the application of derived relationships between life-history characteristics and the sharing of this and other information across stocks. It is important that uncertainty in assessment results is represented adequately, and management methods must in turn ensure that decision mechanisms are robust to an incomplete picture of resource dynamics. This requires construction and testing of harvest control rules within a simulation framework. Harvest control rules themselves, however, need not be complicated, and simple empirical approaches can be adequate for situations in which only relative changes in biomass can be discerned from the data. Development and testing of these control rules is likely to prove a productive area of future research.
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渔业评估和管理:综合常用方法,特别涉及深水和缺乏数据的鱼类
深水鱼类种群的特点往往是其生活史极易受到过度捕捞。此外,依赖渔业往往发展迅速,因此,在资源动态得到充分量化以评估安全的生物限度之前,可能会发生过度开发。因此,至关重要的是采用评价方法,最好地利用有限的数据和管理程序,这些程序说明了很大的不确定性。本文综述了深水渔业的评估和管理方法。鉴于数据的局限性,很明显,评估可能受益于应用生活史特征之间的推导关系以及跨种群共享这种和其他信息。重要的是,评估结果中的不确定性应得到充分体现,而管理方法必须反过来确保决策机制对资源动态的不完整图景具有健壮性。这需要在模拟框架内构建和测试收获控制规则。然而,收获控制规则本身并不需要很复杂,对于只能从数据中辨别生物量的相对变化的情况,简单的经验方法就足够了。这些控制规则的开发和测试很可能被证明是未来研究的一个富有成效的领域。
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来源期刊
Reviews in Fisheries Science
Reviews in Fisheries Science 农林科学-渔业
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