Risk perceptions and conservation ethics among recreational anglers targeting threatened sharks in the subtropical Atlantic
Bibliographic record
Abstract
Recreational fisheries management has traditionally been more concerned with quantifiable, catch-centric goals than angler-centric perceptions. However, the attitudes of fishers affect their behavior, which can alter the effort they make towards conservation actions, and ultimately, the outcome for exploited or threatened species. We conducted a quantitative human dimensions study into the drivers of conservation attitudes and perceptions of recreational fishers towards sharks. This was accomplished through a targeted online snowball survey on a sample of 158 recreational anglers in the state of Florida, a global hotspot for recreational fishing. Subjective knowledge of shark conservation issues was the most consistent driver for pro-shark conservation attitudes. Anglers ranked the great hammerhead and tiger shark as being the most threatened species, a result that is generally consistent with empirical data. Anglers did not identify speciesspecific differences in capture stress as an important factor in determining survivability, a result that somewhat contradicts available empirical data. In general, fishers were more supportive of management actions that would be the least restrictive to fishing, except in the case of highly threatened species. Anglers believed commercial fishing had the largest impact on shark populations, and recreational fishing the least, which is largely consistent with empirical information but could also reflect angler bias. Taken together, our findings suggest anglers generally care about shark conservation, but are unaware of some potential angling threats to sharks and possible conservation solutions. Further, anglers who consider themselves knowledgeable about shark conservation will be more sympathetic to shark management and more likely to adopt fishing practices that reduce shark mortality and sub-lethal impacts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".