Evaluating Short Openings as a Management Tool to Maximize Catch-Related Utility in Catch-and-Release Fisheries
Bibliographic record
Abstract
Abstract Catch and release (CR) is an increasingly common strategy for recreational fisheries in which sustaining high catch rates is important. The success of this strategy is reduced if the released fish are temporarily invulnerable to capture due to behavioral changes, as recent research suggests. Here, we explore how temporary fishing closures with short openings might be used in CR fisheries to increase the catch-related utility associated with angler satisfaction from catches. We simulated generic fisheries in single-lake and multiple-lake systems and found that regular, temporary closures could increase catch-related utility—but predominately under the key assumption that angler satisfaction increases disproportionately with increasing catch rates. In the multiple-lake case, a strategy of rotating temporary closures could provide greater catch-related utility than continuously open fisheries, but this would depend upon anglers' willingness to redistribute effort from closed waters to open waters. A key implication of these results is that even in CR fisheries, effort limitation may be necessary to provide quality angling opportunities. Our results also emphasize the importance of understanding how vulnerable pool dynamics can differ across fisheries and potentially interact with other processes and mechanisms that drive the observed changes in catchability and catch rates.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".