Linking Fish and Angler Dynamics to Assess Stocking Strategies for Hatchery-Dependent, Open-Access Recreational Fisheries
Bibliographic record
Abstract
Abstract Optimization of stocking practices regarding release size and density requires an understanding of how dynamic ecological and angler effort processes interact. We used experimental data on size and density-dependent fish recruitment processes and combined these with empirical fishery data to model the outcome of different stocking strategies. The model is based on the British Columbia Rainbow Trout Oncorhynchus mykiss fishery, which is an open-access recreational fishery where fish recruitment is entirely derived from hatchery production. Under this scenario, changes to stocking practices primarily influence angler effort densities, whereas angling quality, defined as a function of catch rate and fish size, remain relatively constant. In light of this, we suggest that the primary performance measure for open-access recreational fisheries management be sustainable angler effort. We modeled the effort response to stocking changes under contrasting biological (lake productivity) and fishery (remoteness, harvest regulations) characteristics. Lake-specific effort is maximized by stocking larger fish into productive lakes that are harvested. However, the strategy to maximize regional effort across multiple lakes was dependent on the ratio of hatchery production to total lake area and the mix of individual lake productivities within the total lake area. Received July 10, 2012; accepted March 10, 2013
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".