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Reverse the declining course: A risk assessment for marine and fisheries policy strategies in Europe from current knowledge synthesis

机译:逆转下降的课程:来自当前知识综合的欧洲海洋和渔业政策策略的风险评估

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An ecosystem approach to fisheries management is being developed around the world in an attempt to consider components of marine ecosystems other than just the exploited stocks. While numerous scientific studies on the consequences of fishing on marine ecosystems exist, most of their findings are too uncertain and fail to quantify the magnitude of the effects they describe to be used reliably in an environmental management and marine policy-making realm. To circumvent such a knowledge gap, we built and fitted a Bayesian network (BN), informed by a review of past studies, which integrates the links and direction of effects between socio-ecosystem components. These effects are represented as conditional probabilities, so that missing magnitudes of some ecosystem effects emerge from the numerous past cases that were reviewed. The Bayesian network is informed by a collection of cases extracted from 246 published scientific studies investigating relationships in marine ecosystems. We find that marine ecosystems are likely to be on a declining course under conjugated pressures of both fishing and changes to environmental conditions, e.g. due to ongoing climate change. By querying the fitted BN to obtain posterior probabilities under different scenarios, we showed that increasing fisheries regulation and environmental governance could partly mitigate these effects and decrease the risk of biodiversity loss, decreased profit and social inequity; the three pillars of the EU Common Fishery Policy (CFP). We discuss these findings with regard to particular fisheries across EU Waters, specifically: the North Sea, the Baltic Sea, the North Western and the South Western Waters. Furthermore, we discuss how fishing impacts interact with other ecosystem effects and pressures, caused by, or causing possible far-reaching consequences in marine ecosystem dynamics. The Bayesian network has some limitations regarding the ability to handle feedback loops that can occur in natural marine ecosystems. Nevertheless, such an approach helps to take a holistic view and integrate existing knowledge and new findings from future work, in a coherent, probabilistic risk assessment framework, while identifying what leverage and management actions may help nudge the system toward desired states.
机译:生态系统渔业管理方法正在试图要考虑的不仅仅是开发的种群其他海洋生态系统的组成部分在世界各地的发展。虽然存在于捕捞对海洋生态系统的影响许多科学的研究,大部分的结果都在太不确定和无法量化的,他们描述要在环境管理和海洋政策制定领域可靠使用的影响程度。为了规避这样的知识差距,我们建立并安装一个贝叶斯网络(BN),由过去的研究的回顾,它集成的社会生态系统组件之间的效果的联系,并告知方向。这些影响被表示为条件概率,使一些生态系统的影响缺幅度从进行了审查无数过去的案例出现。贝叶斯网络由从246个公布的科学研究调查海洋生态系统的关系提取法的实施通知。我们发现,海洋生态系统可能是一个既下捕鱼结合过程中的压力下降,改变环境条件,例如由于正在进行的气候变化。通过查询拟合BN获得不同情景下的后验概率,我们发现,提高渔业监管和环境治理可以部分缓解这些影响,并减少生物多样性损失的风险,减少利润和社会不公平;三大支柱的欧盟共同渔业政策(CFP)的。我们讨论了这些发现对于整个欧盟水域渔业特别,特别是:北海,波罗的海,北西部和南部西部水域。此外,我们将讨论如何捕鱼的影响与其他生态系统的影响和压力,造成的相互作用,或在海洋生态系统动力学导致可能产生深远的影响。贝叶斯网络有关于处理可能发生的自然海洋生态系统反馈回路的能力一定的局限性。然而,这样的做法有助于采取全面的观点和整合现有的知识和今后工作的新发现,在一个连贯的,概率风险评估框架,同时确定什么杠杆和管理措施可能有助于微调朝理想状态的系统。

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