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深海采矿的概率生态风险评估:太平洋查塔姆隆起的贝叶斯网络。

Probabilistic ecological risk assessment for deep-sea mining: A Bayesian network for Chatham Rise, Pacific Ocean.

作者信息

Kaikkonen Laura, Clark Malcolm R, Leduc Daniel, Nodder Scott D, Rowden Ashley A, Bowden David A, Beaumont Jennifer, Cummings Vonda

机构信息

National Institute of Water and Atmospheric Research, Wellington, New Zealand.

Ecosystems and Environment Research Programme, University of Helsinki, Helsinki, Finland.

出版信息

Ecol Appl. 2025 Jan;35(1):e3064. doi: 10.1002/eap.3064. Epub 2024 Nov 25.

Abstract

Increasing interest in seabed resource use in the ocean is introducing new pressures on deep-sea environments, the ecological impacts of which need to be evaluated carefully. The complexity of these ecosystems and the lack of comprehensive data pose significant challenges to predicting potential impacts. In this study, we demonstrate the use of Bayesian networks (BNs) as a modeling framework to address these challenges and enhance the development of robust quantitative predictions concerning the effects of human activities on deep-seafloor ecosystems. The approach consists of iterative model building with experts, and quantitative probability estimates of the relative decrease in abundance of different functional groups of benthos following seabed mining. The model is then used to evaluate two alternative seabed mining scenarios to identify the major sources of uncertainty associated with the mining impacts. By establishing causal connections between the pressures associated with potential mining activities and various components of the benthic ecosystem, our model offers an improved comprehension of potential impacts on the seafloor environment. We illustrate this approach using the example of potential phosphorite nodule mining on the Chatham Rise, offshore Aotearoa/New Zealand, SW Pacific Ocean, and examine ways to incorporate knowledge from both empirical data and expert assessments into quantitative risk assessments. We further discuss how ecological risk assessments can be constructed to better inform decision-making, using metrics relevant to both ecology and policy. The findings from this study highlight the valuable insights that BNs can provide in evaluating the potential impacts of human activities. However, further research and data collection are crucial for refining and ground truthing these models and improving our understanding of the long-term consequences of deep-sea mining and other anthropogenic activities on marine ecosystems. By leveraging such tools, policymakers, researchers, and stakeholders can work together toward human activities in the deep sea that minimize ecological harm and ensure the conservation of these environments.

摘要

海洋中对海底资源利用的兴趣日益增加,给深海环境带来了新的压力,需要仔细评估其生态影响。这些生态系统的复杂性以及缺乏全面的数据,给预测潜在影响带来了重大挑战。在本研究中,我们展示了使用贝叶斯网络(BNs)作为建模框架来应对这些挑战,并加强关于人类活动对深海海底生态系统影响的稳健定量预测的发展。该方法包括与专家进行迭代模型构建,以及对海底采矿后不同底栖生物功能群丰度相对下降的定量概率估计。然后,该模型用于评估两种替代海底采矿方案,以确定与采矿影响相关的主要不确定性来源。通过在与潜在采矿活动相关的压力和底栖生态系统的各个组成部分之间建立因果联系,我们的模型提供了对海底环境潜在影响的更好理解。我们以西南太平洋新西兰奥特亚罗瓦近海查塔姆海隆潜在的磷结核采矿为例来说明这种方法,并研究将经验数据和专家评估中的知识纳入定量风险评估的方法。我们进一步讨论如何构建生态风险评估,以利用与生态和政策相关的指标更好地为决策提供信息。本研究的结果突出了贝叶斯网络在评估人类活动潜在影响方面可以提供的宝贵见解。然而,进一步的研究和数据收集对于完善这些模型并进行实地验证,以及提高我们对深海采矿和其他人为活动对海洋生态系统长期后果的理解至关重要。通过利用此类工具,政策制定者、研究人员和利益相关者可以共同努力,使深海中的人类活动对生态的危害最小化,并确保对这些环境的保护。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/72dd/11734116/70abaf0ce68c/EAP-35-e3064-g003.jpg

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