Can conservation‐oriented, captive breeding limit behavioural and growth divergence between offspring of wild and captive origin <scp>A</scp>tlantic salmon (<i><scp>S</scp>almo salar</i>)?
Bibliographic record
Abstract
Abstract Captive rearing is being used increasingly to maintain demographics and genetic diversity of threatened fish populations and species, but its effectiveness can be hindered by domestication, that is, inadvertent selection for performance in captivity at the cost of that in the wild. Some captive rearing programmes have begun to take steps to limit such domestication, but the results are ambiguous, as the degree of generational exposure to captivity is often difficult to control. Using an endangered population of Atlantic salmon (Salmo salar) currently undergoing conservation‐oriented captive rearing, we tested for domestication effects on dominance (dyadic trials) and growth (seminatural stream channels with differing densities and group proportions) of juvenile offspring of wild and captive origin parents. Pedigree data afforded the ability to compare these effects among three specific study groups: wild, single‐generation captives and two to three generation captives. Our results indicate that, despite conservation breeding practices, a divergence in growth can occur in as little as one generation without divergence in dominance behaviour. Further, evidence suggests that trait divergence did not increase with generations in captivity. Given the experimental design, results and supporting literature, we conclude that this contemporary divergence is likely genetic and driven by a combination of factors, including variation in selective histories influencing behaviour.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".