A Model-Based Evaluation of Active Management of Recreational Fishing Effort
Bibliographic record
Abstract
Abstract Recreational fisheries are increasingly faced with the dilemma of “too many people chasing too few fish.” Active management of angler effort promises relief from the pressures of overcrowding and declines in angling quality. In this paper, we develop a regional-scale model of recreational fishery dynamics to evaluate how active effort management policies might affect the cumulative value taken over many fisheries. The model incorporates linear increases in satisfaction per angler-day with increases in catch rate to represent demand, declines in catch rate with increasing effort to represent supply limitations, and angler movement among open-access fisheries to represent the presence of uncontrolled, open-access alternatives to effort-managed fisheries. The management control variables were the proportion of fisheries managed under effort control (Pm) and the proportion of open-access effort allowed on managed lakes (Pe). Unmanaged lakes remained open access. Policies that maximized total regional value favored effort management for all combinations of Pm and Pe. The total increases in value over open-access conditions were greatest under conditions of high demand and low effort movement. When effort movement and demand for improved quality were both high, a large proportion (>50%) of fisheries needed to be included under effort control to offset the value losses on open-access fisheries. Because effort management offers promise for creating above-average fishing opportunities, managers should carefully consider regional-scale impacts and the potential demand for high-quality, low-effort angling opportunities.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".