Adaptive trade-offs in fish energetics and physiology: insights from adaptive differentiation among juvenile salmonids
Bibliographic record
Abstract
Juveniles of different salmonid species often co-exist along environmental gradients, making them a useful model for identifying dominant trade-off axes and their stability within a biological hierarchy (e.g., from individuals to populations to species). In this perspective, we use multivariate trade-offs among juvenile coho salmon (Oncorhynchus kisutch) and rainbow trout (Oncorhynchus mykiss) as a case study to explore broader-scale patterns of trait association. Multivariate ordination identified a dominant trade-off axis between high growth, consumption, and growth efficiency versus high aerobic scope and active metabolism among individual juvenile rainbow trout and three ecologically divergent rainbow trout populations. This pattern suggests a dominant trade-off between growth and active metabolism among individuals and populations, facilitated by simple developmental controls on increased energy intake (larger digestive tract, potentially more risky foraging behavior). In contrast, the adaptive trade-off differentiating species appears to have shifted to maximizing growth and consumption (rainbow trout) versus maximizing growth efficiency (coho), based on evolved differences in foraging strategy and digestive physiology. The generality of these patterns remains uncertain, but trade-offs related to growth efficiency may be an underappreciated dimension of adaptive differentiation in fishes, and their relationship to digestive strategy and active metabolism warrants further investigation.
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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".