Autumn lipid reserves, overwinter lipid depletion, and high winter mortality of rainbow trout in experimental lakes
Bibliographic record
Abstract
Accumulation of lipid reserves is considered important for the overwinter survival of many animals. Fish are thought to deplete lipids until they reach a critical minimum below which starvation mortality occurs, meaning that lipid-dependent selective overwinter mortality may be a strong selective pressure leading to life-history evolution favouring lipid accumulation and storage. Much of our current knowledge comes from either laboratory studies or field studies that are not well controlled, but rarely is overwinter survival directly estimated to evaluate selective mortality. Here, we studied patterns of lipid storage, overwinter lipid depletion, and subsequent survival of a single strain of rainbow trout (Oncorhynchus mykiss) stocked into experimental lakes that differed in productivity but experienced the same local winter conditions. Productive lakes produced trout with higher lipid content and steeper allometric slopes in contrast with trout in low food lakes; however, these field-based values were all substantially lower than those determined in the lab. Surviving trout from low productivity lakes emerged from winter in poor condition, close to the expected critical minimum needed for survival, in comparison with survivors from higher-productivity lakes. As expected, overwinter mortality was lipid-dependent, with fish in low food lakes nearing 90% mortality and about 60% mortality in high food lakes. Importantly, these estimates are higher than from laboratory (∼70%) and modelling studies (0% to 14%) for this species. These results, though from stocked populations, suggest winter mortality is an even stronger selective pressure than previously thought, creating a tight population bottleneck in young fish cohorts and likely promoting life-history strategies that favour energy storage at the expense of somatic growth.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".