A quantitative risk assessment approach for longline fishing gear impacts on seafloor habitats
Bibliographic record
Abstract
Abstract Bottom longline fishing gear used worldwide to capture fish and invertebrate species can impact seafloor habitats, leading to increased use of spatial closures (e.g. Marine Protected Areas) in areas where habitat risks are considered high. However, such closures often rely on limited data, because fishing impacts on habitat are rarely quantified and habitat maps are often unavailable. We demonstrate a quantitative risk assessment framework for habitat impacts from bottom longline trap and hook fisheries and develop species distribution models for coral and sponge habitats, using the British Columbia sablefish fishery as a case study. We estimate a 4.5% (95% confidence interval: 2.8%–6.9%) overall reduction in sponge habitats due to sablefish fishing from 1965 to 2024, compared to pre-fishery levels. Habitat status for 1 km × 1 km grid cells showed similar trends to the overall status, with habitat declines <10% for 89% of the historical fishing grounds assessed. Our analysis provides fine-scale information on habitat distribution and the impacts from sablefish longline trap and hook fishing gear, supporting conservation planning and fisheries management. This risk assessment approach provides a quantitative metric (relative benthic status) for ecosystem objectives focused on fishery impacts on habitat. Such habitat metrics can be incorporated into fisheries management strategy evaluation, allowing resource managers to compare performance of alternative strategies against a broader suite of sustainability objectives that include habitat, fish stocks, and fisheries catch.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".