Phenotypic integration of behavioural and physiological traits is related to variation in growth among stocks of Chinook salmon
Bibliographic record
Abstract
The selection for a single organismal trait like growth in breeding programs of farmed aquaculture species can counterintuitively lead to lowered harvestable biomass. We outbred a domesticated aquaculture stock of Chinook salmon (Oncorhynchus tshawytscha (Walbaum in Artedi, 1792)) with seven wild stocks from British Columbia, Canada. We then examined how functionally related traits underlying energy management – diel variation in cortisol and foraging, social, and movement behaviours — predicted stock-level variation in growth during the freshwater life history stage, which is a performance metric under aquaculture selection. Outbreeding generated significant variation in diel cortisol secretion and behaviours across stocks, and these traits co-varied, suggesting tight integration despite hybridization. The coupling of nighttime cortisol exposure with the daytime behavioural phenotype was the strongest predictor of stock-level variation in body mass. Our results suggest that selection for an integrated phenotype rather than on a single mechanistic trait alone can generate the greatest effect on aquaculture fish growth under outbreeding practices. Furthermore, selecting for these traits at the stock level may increase efficiency of farming methods designed to consistently maximize fish performance on a large scale.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".