Molecular and physiological responses predict acclimation limits in juvenile brook trout ( <i>Salvelinus fontinalis</i> )
Bibliographic record
Abstract
Understanding the resilience of ectotherms to high temperatures is essential because of the influence of climate change on aquatic ecosystems. The ability of species to acclimate to high temperatures may determine whether populations can persist in their native ranges. We examined physiological and molecular responses of juvenile brook trout (Salvelinus fontinalis) to six acclimation temperatures (5, 10, 15, 20, 23 and 25°C) that span the thermal distribution of the species to predict acclimation limits. Brook trout exhibited an upregulation of stress-related mRNA transcripts (heat shock protein 90-beta, heat shock cognate 71 kDa protein, glutathione peroxidase 1) and downregulation of transcription factors and osmoregulation-related transcripts (nuclear protein 1, Na+/K+/2Cl- co-transporter-1-a) at temperatures ≥20°C. We then examined the effects of acclimation temperature on metabolic rate (MR) and physiological parameters in fish exposed to an acute exhaustive exercise and air exposure stress. Fish acclimated to temperatures ≥20°C exhibited elevated plasma cortisol and glucose, and muscle lactate after exposure to the acute stress. Fish exhibited longer MR recovery times at 15 and 20°C compared with the 5 and 10°C groups; however, cortisol levels remained elevated at temperatures ≥20°C after 24 h. Oxygen consumption in fish acclimated to 23°C recovered quickest after exposure to acute stress. Standard MR was highest and factorial aerobic scope was lowest for fish held at temperatures ≥20°C. Our findings demonstrate how molecular and physiological responses predict acclimation limits in a freshwater fish as the brook trout in the present study had a limited ability to acclimate to temperatures beyond 20°C.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".