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

Area of Science:

• Marine Ecology
• Environmental Modeling
• Risk Assessment

Background:

• Deep-sea resource use increases pressure on vulnerable ecosystems.
• Ecosystem complexity and data scarcity challenge impact prediction.
• Quantitative risk assessment is crucial for sustainable deep-sea activities.

Purpose of the Study:

• To demonstrate Bayesian networks (BNs) for predicting deep-sea mining impacts.
• To develop robust quantitative predictions of ecological effects.
• To inform decision-making for minimizing harm to deep-sea environments.

Main Methods:

• Iterative expert-based model building.
• Quantitative probability estimates of benthic functional group abundance changes.
• Evaluation of alternative seabed mining scenarios to identify uncertainties.

Main Results:

• BNs establish causal links between mining pressures and benthic ecosystem components.
• The model quantifies potential impacts, using phosphorite nodule mining as a case study.
• Uncertainty sources in mining impacts were identified.

Conclusions:

• BNs offer valuable insights for evaluating human activity impacts on deep-sea ecosystems.
• Further research and data are vital for refining models and understanding long-term consequences.
• Collaborative efforts are needed to ensure human activities minimize ecological harm in the deep sea.