Optimal management of recreational fisheries in the presence of hooking mortality and noncompliance — predictions from a bioeconomic model incorporating a mechanistic model of angler behavior
Bibliographic record
Abstract
Using a bioeconomic model, we systematically investigated how hooking mortality and regulatory noncompliance influenced management outcomes across a range of freshwater fish species exploited by diverse angler populations. The model integrated an empirically based submodel describing the behaviour of three angler types with an age-structured fish population submodel calibrated to five life-history types (LHTs). Increased hooking mortality generally undermined regulation effectiveness, decreased socially optimal input (license numbers) and output regulations (minimum-size limits), and eroded the social welfare anglers derived from the fishery. However, the results strongly varied with LHT and angler type. Noncompliance had an isolated effect, primarily affecting fish species with low compensatory reserves when hooking mortality was low. However, in the absence of regulatory constraints on effort, noncompliance facilitated recruitment overfishing and increased the minimum-size limit required to avoid it. Despite added mortality from hooking and noncompliance, the strong dependence of angler utility on catch rates usually meant socially optimal management safeguarded biological sustainability. Yet, ignoring hooking mortality and noncompliance when predicting optimal regulations often led to population collapse. To conclude, models designed to derive recommendations for recreational fisheries management must consider both hooking mortality and noncompliance. Otherwise, dissatisfied anglers or biologically overfished stocks are possible.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".