Rainbow trout in seasonal environments: phenotypic trade‐offs across a gradient in winter duration
Bibliographic record
Abstract
Survival through periods of resource scarcity depends on the balance between metabolic demands and energy storage. The opposing effects of predation and starvation mortality are predicted to result in trade-offs between traits that optimize fitness during periods of resource plenty (e.g., during the growing season) and those that optimize fitness during periods of resource scarcity (e.g., during the winter). We conducted a common environment experiment with two genetically distinct strains of rainbow trout to investigate trade-offs due to (1) the balance of growth and predation risk related to foraging rate during the growing season and (2) the allocation of energy to body size prior to the winter. Fry (age 0) from both strains were stocked into replicate natural lakes at low and high elevation that differed in winter duration (i.e., ice cover) by 59 days. Overwinter survival was lowest in the high-elevation lakes for both strains. Activity rate and growth rate were highest at high elevation, but growing season survival did not differ between strains or between environments. Hence, we did not observe a trade-off between growth and predation risk related to foraging rate. Growth rate also differed significantly between the strains across both environments, which suggests that growth rate is involved in local adaptation. There was not, however, a difference between strains or between environments in energy storage. Hence, we did not observe a trade-off between growth and storage. Our findings suggest that intrinsic metabolic rate, which affects a trade-off between growth rate and overwinter survival, may influence local adaptation in organisms that experience particularly harsh winter conditions (e.g., extended periods trapped beneath the ice in high-elevation lakes) in some parts of their range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 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".