Factors Influencing Postrelease Predation for a Catch-And-Release Tropical Flats Fishery with a High Predator Burden
Bibliographic record
Abstract
Abstract Postrelease predation (PRP) of fishes released by anglers is a potentially significant contributor to overall mortality in recreational fisheries. We quantified PRP and examined the impacts of handling and release practices on Shortjaw Bonefish Albula glossodonta, a species of shallow-water Pacific bonefish that supports a recreational fishery throughout its range and is emerging as recreationally important to the economy in Anaa Atoll, French Polynesia. We caught, released, and monitored the postrelease movements of Shortjaw Bonefish on the shallow flats of Anaa Atoll via recreational angling gear and small floats attached to the bonefish. Using Cox proportional hazards regression of our observations of PRP we tested how handling practices (air exposure) and release strategies (retained for a short period versus immediate release) influenced the probability of PRP. There was some evidence that air exposure increased susceptibility to PRP. However, retention in a recovery bag for 30 min did not reduce PRP. Actually, retention in the recovery bag increased the number of sharks in the release area after 30 min suggesting that Shortjaw Bonefish should be released quickly to avoid aggregating sharks. In both the handling and release practices components of the study, the number of sharks encountered proximate to the release site was the strongest predictor of PRP. Anglers and guides fishing in areas of high predator density such as at Anaa Atoll should release bonefish quickly to minimize aggregation of sharks that depredate released bonefish. Avoiding fishing flats with high predator densities and frequently rotating fishing flats may be necessary to quell PRP. Acknowledging risks and accounting for PRP and its contribution to overall postrelease mortality is essential for maintaining sustainable recreational fisheries for this species. Received August 17, 2016; accepted May 23, 2017Published online August 22, 2017
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".