Conflicting objectives in trophy trout recreational fisheries: evaluating trade-offs using an individual-based model
Bibliographic record
Abstract
Standard fisheries models, based on average population metrics, are inadequate for analyzing recreational fisheries where fishing is size-selective and management objectives are related to preserving population size structure. We developed a framework for policy analysis of size-based harvest strategies in recreational fisheries. The framework combines a mixed-effects body growth model and an individual-based harvest model to describe the relationship of growth, mortality, and size structure. Fishery performance is quantified with indicators directly associated to catch-related components of anglers’ satisfaction: yield (kg), population size, and availability of trophy-size fish. We applied our analyses to the steelhead ( Oncorhynchus mykiss ) fishery in the Santa Cruz River (Patagonia, Argentina). Large declines in trophy-size fish are to be expected at fishing mortalities much too low to cause a sizeable decline in recruitment from virgin values. When somatic growth is density-independent, harvest occurs at the expense of other indicators associated with the quality of fishing experienced by individual anglers. Size limits provide a tool to better accommodate harvest without compromising fishing quality. When preserving population size is favored over preserving trophy-size fish, minimum size limits constitute the best policy overall, whereas maximum size limits are best when the emphasis is on preserving trophy-size fish.
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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 | 0.005 | 0.008 |
| 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 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".