Evidence map›Paper›PMID 39586767›Full record

ArticleEcological applications : a publication of the Ecological Society of America2025

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

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

Abstract read
In one paragraph

Article in Ecological applications : a publication of the Ecological Society of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Probabilistic ecological risk assessment for deep-sea mining: A Bayesian network for Chatham Rise, Pacific Ocean.Ecological applications : a publication of the Ecological Society of America · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Laura KaikkonenNational Institute of Water and Atmospheric Research, Wellington, New Zealand.
Malcolm R ClarkNational Institute of Water and Atmospheric Research, Wellington, New Zealand.
Daniel LeducNational Institute of Water and Atmospheric Research, Wellington, New Zealand.
Scott D NodderNational Institute of Water and Atmospheric Research, Wellington, New Zealand.
Ashley A RowdenNational Institute of Water and Atmospheric Research, Wellington, New Zealand.
David A BowdenNational Institute of Water and Atmospheric Research, Wellington, New Zealand.
Jennifer BeaumontNational Institute of Water and Atmospheric Research, Wellington, New Zealand.
Vonda CummingsNational Institute of Water and Atmospheric Research, Wellington, New Zealand.

Funding

Ella ja Georg Ehrnroothin SäätiöMaj ja Tor Nesslingin SäätiöNew Zealand Ministry of Business, Innovation and Employment CO1X1614
6 · The paper itself

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.

Indexed as

EcosystemMiningBayes TheoremEnvironmental MonitoringPacific OceanRisk AssessmentBayesian networksbenthic faunadeep‐sea miningparticipatory modelingquantitative risk assessmentseabed disturbance

Identifiers

PMID39586767
PMCPMC11734116

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.