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.

中文翻译：

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深海采矿的概率生态风险评估：太平洋查塔姆海峡的贝叶斯网络


人们对海洋海底资源利用的日益关注正在给深海环境带来新的压力，其生态影响需要仔细评估。这些生态系统的复杂性和缺乏全面的数据对预测潜在影响构成了重大挑战。在这项研究中，我们展示了使用贝叶斯网络 （BN） 作为建模框架来应对这些挑战，并加强了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。该方法包括与专家一起构建迭代模型，以及对海底采矿后底栖生物不同功能群丰度的相对减少进行定量概率估计。然后，该模型用于评估两种备选的海底采矿情景，以确定与采矿影响相关的不确定性的主要来源。通过在与潜在采矿活动相关的压力与底栖生态系统的各个组成部分之间建立因果关系，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 Aotearoa/新西兰近海 Chatham Rise 的潜在磷矿结核开采为例来说明这种方法，并研究了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。我们进一步讨论了如何使用与生态学和政策相关的指标来构建生态风险评估以更好地为决策提供信息。这项研究的结果突出了 BN 在评估人类活动的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和实地验证这些模型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的长期影响的理解至关重要。通过利用这些工具，政策制定者、研究人员和利益相关者可以共同努力，促进人类在深海的活动，最大限度地减少生态危害并确保保护这些环境。