Using Decision Analysis to Balance Angler Utility and Conservation in a Recreational Fishery
Bibliographic record
Abstract
Abstract Recreational fisheries are often managed to provide satisfying fishing experiences for anglers while conserving wild fish stocks. However, managing recreational fisheries is difficult because fish populations are often infrequently monitored and fishing effort is uncontrolled; moreover, a satisfying fishery may draw many anglers, which may lead to enhanced risk of overfishing. Furthermore, external pressures will also affect fisheries, leading to fishery collapses despite the best intentions of management. Any management decision about regulations and habitat alteration will have effects on angler satisfaction and conservation. Decisions should be made with the intention of achieving fisheries objectives despite the uncertainties that arise from sampling data, ecosystem processes, and external factors, yet they must be defensible to stakeholders and the public. We show herein how decision analysis can be used to evaluate and communicate the relative efficacy of management decisions that are made to achieve fisheries management objectives by using a variety of commonly collected field data. We used a wild kokanee population at risk of overfishing as a case study and evaluated the medium-term effects of fishing regulations and habitat alterations on conservation and angler utility objectives. Using a flexible age-structured model, we determined that these two objectives are often at odds, where management actions leading to high angler utility in this fishery also lead to high conservation risk. Overall, decision analysis helps to communicate these tradeoffs and makes it clear how particular decisions were made. Decision analysis is not new, but it is often underused in recreational fisheries. This work demonstrates how it may streamline decisions, even for infrequently monitored fisheries, and lead to better fisheries overall.
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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".