Linking anglers, fish, and management in a catch-and-release steelhead trout fishery
Bibliographic record
Abstract
Fisheries are complex social–ecological systems with multiple potential linkages between fish and anglers. Understanding these linkages helps to support effective fisheries management. We examine the social–ecological dynamics of a recreational fishery by assessing relationships between fish, anglers, and a management intervention. We focus on catch-and-release steelhead trout (Oncorhynchus mykiss) fisheries on six rivers within the Skeena River watershed, British Columbia, Canada, the location of a recent management intervention. First, based on analyses of annual steelhead trout abundance and annual angler effort information, we found that years with a higher abundance of returning steelhead trout were associated with years of higher catch rates and angler effort. Second, based on analyses of nonresident angler effort, we discovered that a new management intervention provided periods of lower angler effort, but effort was apparently redistributed to other rivers and time periods. Third, responses from angler interviews post-management intervention revealed that anglers were more satisfied if they caught more fish and experienced less crowding; at higher crowding levels, higher catch rates were required to increase angler satisfaction. In conclusion, we found that this recreational fishery is influenced by both human dimensions and natural ecological dynamics such as fish population fluctuations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".