Adaptive Management for Recreational Fisheries Decisions in the Face of Uncertainty
Bibliographic record
Abstract
Abstract Recreational fisheries management requires making decisions that consider social, economic, and ecological dimensions. The feedbacks among ecological changes and social responses by fishers create complexity and uncertainty. However, uncertainty can impede the integration of ecological and social dimensions, particularly when considering human behavioural responses to management decisions and ecological changes. One of the few ways to reduce these uncertainties is via experimentation at the system level. Adaptive management (and the larger umbrella of decision analysis) provides a framework to implement purposeful management experiments in a structured manner to learn through an iterative decision process, thereby allowing for the reduction in social and ecological uncertainties in response to planned policy interventions. We discuss the theory of adaptive management and conditions for which it is appropriate, as well as the benefits and costs of applying active versus passive adaptive management to reduce social and ecological uncertainties for recreational fisheries. We then provide key examples from previous studies on decisions for zoning in the Great Barrier Reef, Australia, stocking decisions that include stakeholder learning in Germany, and decisions for stocking and harvest management in British Columbia, that demonstrate best practices for implementation of adaptive management to reduce uncertainties surrounding human behaviour and other social and ecological objectives related to recreational fisheries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.017 | 0.001 |
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".