Circle Hooks for Pacific Longliners: Not a Panacea for Marlin and Shark Bycatch, but Part of the Solution
Bibliographic record
Abstract
Abstract Blue marlin Makaira nigricans , striped marlin Tetrapturus audax , and pelagic sharks (e.g., blue shark Prionace glauca ) are commonly caught as bycatch by longline fisheries in the central North Pacific Ocean. Recently, concern has increased about depletion of these species. Modifications in longline gear may offer one solution. Here, we test the use of circle hooks, rather than the conventional tuna‐style hooks, on longlines using an ecosystem model of the central North Pacific Ocean. The simulations considered span a range of reasonable circle hook catchability and survival rates for released fish. The results suggest that if circle hooks have higher catchability than the currently used tuna‐style hooks, switching to circle hooks depletes marlin biomass by 25–40% and shark biomass by 15–35% over 30 years. However, these depletions do not occur if circle hook catchability is equal to or lower than that of tuna‐style hooks. When the effects of catch‐and‐release requirements for marlins and sharks were also considered, we found that regardless of assumptions about circle hook catchability and survival rates, a combined policy of using circle hooks and releasing sharks and marlins leads to net increases in marlin and shark abundance. The simulations show a trade‐off between the abundance of marlins and sharks and their prey items, yellowfin tuna Thunnus albacares and small blue sharks. There is also evidence of trophic trade‐offs between yellowfin tuna and small blue sharks and their prey, small scombrids ( Auxis spp.) and mahi mahi Coryphaena hippurus . The results illustrate the importance of understanding catchability and survival rates for circle hooks compared with those for tuna‐style hooks and encourage further research in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".