Performance of embryos reared under fluctuating conditions does not conform to predictions based on performance at constant thermal conditions
Bibliographic record
Abstract
Little is known about the impacts of fluctuating temperatures on early development in fishes, as most experiments on the effects of temperature use constant temperature exposures. Here, we asked whether thermal performance curves (TPCs) for development generated at constant temperatures can be used to make predictions about performance at fluctuating temperatures. We incubated embryos of a topminnow (Fundulus heteroclitus) to the same mean temperature with differing extents of diel fluctuation (26±0°C, 26±3°C, 26±5°C, 26±7°C). Based on TPCs from constant temperatures, we predicted that developmental rate and survival would decrease with increasing fluctuation. Consistent with our prediction, embryos incubated at 26±7°C had lower survival, but inconsistent with our prediction, they developed more rapidly than all other groups. In addition, fish in the highest fluctuation regime were longer and had a larger yolk-sac volume at hatch, suggesting that greater fluctuations result in more efficient energy utilization. At the mRNA level, embryos incubated at 26±7°C had higher expression levels of an inducible heat shock protein, hsp70.2, suggesting thermal stress. Once hatched, larvae were raised at a common constant temperature of 26°C to test for persistent effects. Embryos exposed to 26±7°C during development were larger than those reared under constant conditions 1 week post-hatch, and several genes involved in the heat-shock response and DNA methylation exhibited altered mRNA levels. Our data demonstrate that embryos raised under constant and fluctuating temperatures have different phenotypic responses, which highlights the need to incorporate variable thermal regimes into developmental studies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".