Multi-Criteria Decision Analysis for Recreational Trout Fisheries in British Columbia, Canada: A Bayesian Network Implementation
Bibliographic record
Abstract
One of the key challenges in recreational fisheries management is to provide a rational basis for decisions in the face of conflicting objectives, such as improving angling opportunities, maintaining satisfaction across a diverse fishing client base, controlling costs, and conserving wild populations. We developed a multi-criteria decision analysis approach for managing recreational trout fisheries (e.g., Rainbow Trout Oncorhynchus mykiss). The approach was implemented in a Bayesian decision network. The decision support tool, called “Stock-Optim,” provides a user-friendly interface for predicting fishery performance from alternate stocking prescriptions. The tool integrates survey information on angler typology and satisfaction with previously developed models for fish biology and fishery dynamics to more fully consider the biological and social outcomes of management decisions. Specifically, the tool evaluated alternative stocking options given three performance criteria: angler effort, angler satisfaction, and the cost of the stocking program. Predicted effort was highest for fish that were released in the size range of 8–20 g and at stocking densities of 200–500 fish/ha. Effort maximization at these rates and sizes is a result of compromise between the conflicting preferences of Rainbow Trout enthusiasts and occasional anglers toward fish size and harvest. Furthermore, lowering the stocking program’s costs will lead to lower stocking rates and thereby favor the enthusiasts. Currently, stocking levels in British Columbia are lower than levels that would maximize effort and are most consistent with a policy of maximizing satisfaction for Rainbow Trout enthusiasts and minimizing costs. Stock-Optim will allow managers to compare predicted outcomes from the current and alternative regimes with stated lake-specific or region-specific management objectives and regional averages and thereby more closely meet these objectives in the future. Lastly, the model was validated by comparing predicted effort with observed effort in stocked lakes. Received February 1, 2016; accepted July 14, 2016 Published online November 28, 2016
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".