Enriched rearing environment enhances fitness-related traits of salmonid fishes facing multiple biological interactions
Bibliographic record
Abstract
To improve stocking success in threatened populations, captive-reared animals are often familiarized to natural environment. However, whether such actions improve the ability to cope with multiple biological interactions, such as competition, predation, and parasitism that impose contradicting pressures on decision-making, is not well understood. Here, we explored short-term (4 weeks) and long-term (10 months) effects of enriched rearing on fitness-related traits of Atlantic salmon ( Salmo salar). Salmon with different backgrounds of enrichment and parasite infection (eye fluke Diplostomum pseudospathaceum) were released to semi-natural ponds and monitored for activity, growth, and predation susceptibility. Fish from enriched rearing showed lower short-term activity and higher short-term growth, suggesting that they coped better with novel conditions. However, predation susceptibility and longer-term growth and survival were unaffected by rearing treatment. Importantly, parasitism did not remove the positive effect of enrichment on growth, although the infection decreased both short-term and long-term growth and survival. These results suggest that enriched rearing can enhance fitness-related traits, such as growth, of stocked fish, particularly during the critical early days, which can have important implications for stock enhancement activities.
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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 | 0.000 |
| 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 | 0.000 |
| 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".