Reference points for assessing significant adverse impacts on deep sea vulnerable marine ecosystems
Bibliographic record
Abstract
• SAI thresholds for 7 Vulnerable Marine Ecosystems are proposed. • Bottom trawling impact-risks are assessed for 7 Northwest Atlantic VMEs. • Large differences in VME sensitivity to bottom trawling activities are quantified. • Conservation targets to protect 60–70 % of deep-sea VME biomass are proposed. • Rapid loss in VME biomass is observed at a bottom trawling intensities of about 0.10 km·km −2 ·y -1 . Biodiversity loss due to human activities is a critical issue, particularly in the High Seas where bottom-contact fishing poses a significant threat to Vulnerable Marine Ecosystems (VMEs). Deep sea VMEs, tend to be composed of slow-growing, long-lived benthic organisms such as deep-sea corals and sponges. The United Nations Food and Agriculture Organization (FAO) has 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) Regulatory Area, utilizing fishery-independent surveys and fishing Vessel Monitoring System (VMS) data to map fishing intensity and VME functional type biomass. Seven VME types have been assessed, e.g., large-sized sponges, sea pens, sea-squirts, bryozoans, black corals, large and small gorgonian corals, to determine the risk of impact. Results indicate that sponges, black corals, and large gorgonians are the most sensitive VME types to bottom trawling activities, with significant biomass loss occurring at very low fishing intensities. The study defines bottom trawling biomass impact thresholds for each VME type in the range of 0.12–9.43 km·km −2 ·yr −1 and 0.01–0.11 km·km −2 ·yr −1 for upper and lower impact thresholds, respectively. The study determines that rapid losses in VME biomass occurs at bottom trawling intensities of about 0.10 km·km −2 ·y -1 for fisheries operating in the NAFO Regulatory Area. The study concludes that modest reductions in fishing effort in sensitive areas could substantially mitigate SAI whilst having little or no impact on fishing opportunities. The findings also support the target of protecting at least 60 % to 70 % of VME biomass to likely ensure good seabed status; and the importance of implementing spatial fisheries management measures, such as defining a fishing footprint and establishing fishery closed areas, to protect VMEs.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".