Catch-and-release and size limit regulations for blue, white, and striped marlin: the role of postrelease survival in effective policy design
Bibliographic record
Abstract
Catch-and-release fishing as a management and conservation tool for billfish (family Istiophoridae) is practiced in many recreational fisheries, and mandatory release of billfish has been implemented for some commercial fisheries. Inherent in these approaches is the observation that survival of released fish is greater than those that are landed. Recent studies using pop-up satellite tags have begun to quantify postrelease survival rates for billfish, yet the efficacy of management measures that require some or all billfish to be released have not been evaluated. Using an age- and size-structured population model that accounts for individual variability in growth, we simulated the effects of postrelease mortality on yield, risk of recruitment overfishing, efficiency (i.e., ratio of harvest to postrelease mortality), and probability of catching trophy-sized individuals for three marlin species. Regulations such as size limits, catch-and-release, and mandatory release are likely to provide some benefit to billfish populations, but our results show that the effectiveness of these strategies is reduced when release survival is less than 100%. The management approaches most likely to benefit billfish populations are ones that focus on maximizing postrelease survival in the recreational fishery and minimize the billfish catch in commercial fisheries.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".