Do by‐catch reduction devices in longline fisheries reduce capture of sharks and rays? A global meta‐analysis
Bibliographic record
Abstract
Abstract By‐catch in marine fisheries, particularly those using pelagic and demersal longlines, is a major driver of declines in abundance of sharks and rays around the world. A wide variety of by‐catch reduction devices (BRDs), that is, modified gears designed to reduce incidental captures of a variety of marine species while maintaining target catch rates, have been proposed, but the extent to which BRDs actually reduce the risk of catching sharks and rays remains unclear. We performed a meta‐analysis of 27 publications that reported the capture of sharks and rays and, in some cases, of targeted teleosts in longline gear deployed with and without BRDs. The risk of shark and ray capture differed between types of BRDs, but only one BRD type, longlines raised off the bottom, reduced by‐catch significantly. Circle hooks did not reduce the risk of capturing sharks and rays but might improve discard survival and are inexpensive, which might make them effective in reducing the detrimental effects of longlining on these species. In addition to being generally ineffective, some devices, such as electropositive and magnetic repellents, are expensive and have inherent construction drawbacks that are likely to make them unsuitable for commercial use. Overall, most BRDs did not affect the likelihood of catching targeted teleosts, but a substantial number of studies did not adequately assess target catch. We identified two poorly studied classes of BRD gear (i.e. raised demersal longlines, and monofilament nylon leaders), which represent promising directions for future 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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.031 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".