Assessing Significant Adverse Impacts on Deep-Sea Vulnerable Marine Ecosystems in the NAFO Regulatory Area.
Bibliographic record
Abstract
Biodiversity loss due to human activities is a critical ecological challenge, particularly in the High Seas where bottom-contact fishing poses a significant threat to Vulnerable Marine Ecosystems (VMEs). These ecosystems, composed of slow-growing, long-lived benthic organisms like deep-sea corals and sponges, are often found in geomorphological features such as seamounts and canyons. The United Nations General Assembly and the Food and Agriculture Organization (FAO) have developed guidelines to protect these ecosystems from Significant Adverse Impacts (SAI) caused by bottom trawling activities.This study focuses on the Northwest Atlantic Fisheries Organization (NAFO) area, utilizing fishery-independent surveys and Vessel Monitoring System (VMS) data to map fishing intensity and VME biomass. Seven VME functional types were identified: large-sized sponges, sea pens, sea-squirts, bryozoans, black corals, large and small gorgonian corals. Species data were analysed using Kernel Density Estimation (KDE) to model the spatial distribution of VME biomass and to assess SAI risk.Of the VMEs assessed, results indicate that large sponge, black coral, and large gorgonian VME are the most sensitive to bottom trawling activities, with significant biomass loss occurring at very low fishing intensities. The study proposes impact thresholds for each VME functional type and argues that modest reductions in fishing effort in sensitive areas could mitigate SAI whilst having little or no impact on fishing opportunities. The findings emphasize the importance of spatial fisheries management measures, such as defining fishing footprints and establishing closed areas, to protect VMEs, to ensure the long-term sustainability of deep-sea ecosystem functions and diversity.The proposed impact thresholds provide a quantitative basis for assessing SAI and support ecosystem-based fisheries management. The research underscores the need for integrated assessments and data-driven approaches to balance ecosystem and fisheries sustainability objectives.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".