Managing Dynamic Fisheries with Static Regulations: an Assessment of Size-Graded Bag Limits for Recreational Kokanee Fisheries
Bibliographic record
Abstract
Abstract Recreational fisheries regulations are set as static individual level restrictions, but fish populations and fisheries are dynamic. Therefore, evaluation of any recreational regulatory regime should consider the interaction between static regulations and dynamic fish populations. From this perspective, it is clear that traditional harvest regulations such as bag limits are ineffective as a conservation measure or harvest optimization strategy. The objectives of this study were to: (1) review basic theory on how bag limits influence exploitation rates as fish populations fluctuate; (2) investigate the potential applicability of a different approach, referred to as size-graded bag limits, that sets a schedule of different bag limits for different size thresholds; (3) apply the approach with a realistic model based on actual fishery data for kokanee Oncorhynchus nerka through simulation and discuss relevance to other fisheries. Regulation simulation indicated that there are improved regulatory alternatives when a fish population exhibits density-dependent growth and anglers primarily target a single cohort. Size-graded bag limits or a simple maximum retention size better approximate optimal and sustainable harvest regimes than do bag limits in this scenario. This was most relevant to productive fisheries, which are more likely to be overharvested due to angler affinity to fish size. However, catchability is a highly influential variable that was not well defined by available data. It is plausible that most (or all) kokanee fisheries are self-regulating if catchability is at the low range of estimated values. There are practical limitations to implementing size-graded bag limits as a strategy for individual waterbodies, but the protocol may be well suited to a regional perspective. Received May 27, 2015; accepted October 22, 2015 Published online March 16, 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".