Exploitation rates of Atlantic salmon and sea trout in recreational fisheries in western Norwegian rivers
Bibliographic record
Abstract
Information on fish population abundance and the factors affecting fisheries exploitation rates is crucial for sustainable fisheries management. However, this knowledge is often challenging for managers to obtain. We evaluated a novel approach to this problem using data from drift diving surveys where Atlantic salmon and sea-run brown trout were counted across 63 rivers and over a period of 20 years in western Norway together with catch data to estimate exploitation rates of the two species. The average exploitation rate was 33.5 % for salmon and 13.9 % for sea trout. For both species, the exploitation rate depended on the duration of the fishing season and the management regulations imposed through catch restrictions and quotas. For salmon, the exploitation rate also varied among size groups, being greater for small (< 3 kg: 35.6 %) than for large salmon (> 7 kg: 31.7 %) and medium sized salmon (3–7 kg: 31.5 %). There were also indications of exploitation rates being negatively associated with fish density, at least in some rivers, raising concern that populations may be susceptible to overexploitation when densities are low. The total catch rates including catch and release were 41.6 % for salmon and 21.4 % for sea trout, and increased significantly with the proportion of released fish, suggesting that some fish may have been caught and reported several times. The study highlights the importance of population monitoring surveys for evaluating and adapting management strategies in response to the critical situation for anadromous salmonid fishes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 teacher head, 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".