Improving the interpretation of fishing effort and pressures in mixed fisheries using spatial overlap metrics
Bibliographic record
Abstract
Managing mixed fisheries requires understanding fishers’ behaviour to allow predicting future fisheries distribution and impact on marine ecosystems. A new approach was developed to compare fine-scale fishing effort distribution of Eastern English Channel (EEC) bottom trawlers to the monthly and spatially resolved abundance distributions of commercial species. First, the added value of using species-specific spatial overlap metric to quantify effective fishing effort and improve the relationship between fishing effort and fishing mortality was assessed. Second, based on the Ideal Free Distribution (IFD) theory, the species-specific weights given by fishers to different species were estimated by maximizing the overlap between target species assemblage and effort distributions in October. At a seasonal scale, our results emphasized the importance of cuttlefish (Sepia officinalis) and red mullet (Mullus surmuletus) for the global distribution of EEC bottom trawlers. In October, cuttlefish and red mullet were clearly more determining fishers’ location choice than historically harvested species and also than the overall expected revenue. This is likely due to external constraints such as low cod (Gadus morhua) quota, causing IFD assumptions violated. This study evidenced the importance of getting good insights into spatiotemporal distributions of stocks and fleets to understand fishers’ behaviour and improve mixed fisheries management advice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".