Diversity and complexity of angler behaviour drive socially optimal input and output regulations in a bioeconomic recreational-fisheries model
Bibliographic record
Abstract
In many areas of the world, recreational fisheries are not managed sustainably. This might be related to the omission or oversimplification of angler behaviour and angler heterogeneity in fisheries-management models. We present an integrated bioeconomic modelling approach to examine how differing assumptions about angler behaviour, angler preferences, and composition of the angler population altered predictions about optimal recreational-fisheries management, where optimal regulations were determined by maximizing aggregated angler utility. We report four main results derived for a prototypical northern pike ( Esox lucius ) fishery. First, accounting for dynamic angler behaviour changed predictions about optimal angling regulations. Second, optimal input and output regulations varied substantially among different angler types. Third, the composition of the angler population in terms of angler types was important for determining optimal regulations. Fourth, the welfare measure used to quantify aggregated utility altered the predicted optimal regulations, highlighting the importance of choosing welfare measures that closely reflect management objectives. A further key finding was that socially optimal angling regulations resulted in biological sustainability of the fish population. Managers can use the novel integrated modelling framework introduced here to account, quantitatively and transparently, for the diversity and complexity of angler behaviour when determining regulations that maximize social welfare and ensure biological sustainability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".