Hybridization effects on phenotypic plasticity: experimental compensatory growth in farmed‐wild Atlantic salmon
Bibliographic record
Abstract
Compensatory growth (CG) is a means by which organisms can increase their growth rate above their routine growth rate after a period of environmentally induced growth depression. Despite a focus on the implications of CG for aquaculture, little research has evaluated the effect of domesticated-wild hybridization on CG. Any deviation in the mean compensatory ability of hybrids relative to their wild progenitors, or any notable costs to compensation in terms of body morphology, could affect the ability of hybrids to persist in changing environments. We compared CG of farmed, wild and hybrid (F1, F2, wild backcross) juvenile Atlantic salmon (Salmo salar). Wild salmon experienced both lower routine and CG rates relative to farmed salmon, while hybrids were intermediate. However, the compensatory responses (slopes of the reaction norms) for each cross were parallel, indicating that hybridization did not affect the CG response itself. Morphological costs to compensation were not detected. In addition to contributing to risk assessments of the consequences of interbreeding between wild and escaped domesticated organisms, we conclude that plasticity studies on domesticated-wild hybrids and their progenitors are useful for testing basic predictions about the evolution of phenotypic plasticity, as well as understanding the evolutionary significance of hybrids.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".