Towards Global Ecosystem-Based Management for Benthic Seamount Ecosystems
Bibliographic record
Abstract
Seamounts are prominent underwater mountains rising from the seafloor in ocean basins worldwide. These structures are often recognised in the deep-sea as benthic biodiversity hotspots, offering diverse habitats across depth gradients that support a wide variety of marine species. Seamounts are ecologically significant, and since 2006 the United Nations General Assembly (UNGA) has called for their protection as Vulnerable Marine Ecosystems (VMEs), together with cold-water coral gardens and hydrothermal vents. Despite this, seamounts face escalating threats from human activities, such as destructive fishing practices, the effects of climate change, and the potential impacts of deep-sea mining. These pressures underscore the need for enhanced conservation and management strategies.The Deep-Ocean Stewardship Initiative (DOSI) is a global network of thousands of experts from over 115 countries, which integrates science, technology, policy, law and economics to advise on ecosystem-based management of resource use in the deep ocean. In October 2024, the DOSI Fisheries Working Group with support from the UN Ocean Decade program Challenger 150, hosted the Seamount Science Summit workshop at the University of Hawai‘i, the first major gathering focused on seamount ecology and conservation in over a decade.The summit convened 26 global seamount experts to assess current ecological knowledge, review management practices, and develop strategies to improve resilience in seamount ecosystems amid ongoing anthropogenic pressures. Through presentations, plenary sessions and subgroup discussions, the expert group identified several frameworks to improve management of seamount ecosystems. Here, we present key outputs from the workshop including recommendations for Regional Fisheries Management Organizations (RFMOs) to manage seamounts as VMEs in order to prevent Significant Adverse Impacts (SAIs). We present frameworks to enhance data collection on seamounts fostering industry contributions towards scientific processes within RFMOs and enabling data-informed management of seamount ecosystems. Additionally, the expert group emphasises integrating cumulative impacts, including historical fishing pressures and current and projected climate change effects, to reduce bottom fishing impacts and enhance seamount ecosystem resilience. We also present science priorities and discuss pathways to overcome knowledge gaps in seamount research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".