Survey-Derived Angler Characteristics and Perspectives in the Shore-Based Shark Fishery in Florida
Bibliographic record
Abstract
Abstract Shore-based shark fishing in Florida is rarely monitored as it largely occurs at night on remote beaches and has received a questionable reputation after recent exposure of illegal activity. While these events have led to calls for better management and enforcement, the characteristics of the fishery itself remain largely unknown. Our study, therefore, provides the first comprehensive profile of the Florida shore-based shark fishery to inform fisheries management and conservation. We distributed an online survey to all Florida Fish and Wildlife shore-based shark fishing permit holders to gather data on angler sociodemographics, fishing preferences, habits, motivations, and perceptions of shark conservation. We identified three angler typologies that differed primarily by shark fishing experience and frequency: (1) experienced infrequent anglers, (2) skilled frequent anglers, and (3) novice infrequent anglers. Our results revealed that the Florida recreational shore-based shark fishery itself has increased in participation fivefold since 2010 and generates approximately US$7.8 million (95% CI = $7.2– 8.5 million) annually in equipment expenditures and $34.3 million ($30.4–38.1 million) annually in fishing trips. Surveyed anglers caught a total of 9,617 sharks within a 12-month period, averaging 11 sharks/angler, and the most preferred target species was the Blacktip Shark Carcharhinus limbatus. Angler motivations for participating in this fishery were grouped into the following categories: leisure and well-being, experience of the catch, and consumption. Perceptions of shark conservation and management were generally positive; however, many anglers did not believe that recreational fishing negatively impacts shark populations. Most anglers expressed a desire to learn more about handling practices that benefited sharks, which may help managers implement more educational opportunities and communication efforts. Understanding the characteristics and perspectives of anglers from the shore-based shark fishery in Florida is crucial for highlighting potential management pathways and estimating angler acceptance of management.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".