A mechanistic understanding of hyperstability in catch per unit effort and density-dependent catchability in a multistock recreational fishery
Bibliographic record
Abstract
Mechanisms resulting in hyperstability (where catch per unit effort (CPUE) remains high as fish density declines) in recreational fisheries are poorly understood owing to a lack of experimental data. We collected data on angler CPUE and fish density to determine whether hyperstability exists in the rainbow trout (Onchorhynchus mykiss) lake fishery of British Columbia. We contrasted the relationship between CPUE and fish density in an open-access recreational fishery with an experimental fishery (a set of lakes that had restricted access, standardized fishing methods, and no heterogeneity in angler experience) to assess the mechanistic cause of hyperstability. We detected no evidence of hyperstability in the experimental fishery, but significant hyperstability in the open-access fishery. In the open-access fishery, the composition of the angler population varied among lakes: anglers who fished at low-density lakes were more experienced than anglers fishing at high-density lakes. This segregation of angler experience across lakes appeared to explain the observed hyperstability in this fishery. Our results provide a mechanistic understanding of hyperstability in an open-access recreational fishery and suggest that CPUE data be used in conjunction with data on angler experience when assessing the status of a fishery.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".