Molecular and physiological responses predict acclimation limits in juvenile brook trout ( <i>Salvelinus fontinalis</i> )
Bibliographic record
Abstract
Abstract Brook trout ( Salvelinus fontinalis ) populations are at risk of exposure to high water temperatures in the species’ native range in eastern North America. We quantified the physiological and molecular responses of juvenile brook trout to six acclimation temperatures that span the thermal distribution of the species (5, 10, 15, 20, 23, and 25°C). Using quantitative PCR (qPCR), we measured the mRNA transcript abundance of temperature-induced cellular stress genes to identify a potential sub-lethal temperature threshold for brook trout between 20–23°C. Brook trout exhibited an upregulation of stress-related genes ( heat shock protein 90-beta ; heat shock cognate 71 kDa protein ; glutathione peroxidase 1 ) and a downregulation of transcription factors and osmoregulation-related genes ( Na + /K + /2Cl − co-transporter-1-a ; nuclear protein 1 ) at temperatures ≥20°C. We also used respirometry to assess the effects of the acclimation temperatures on oxygen consumption. Standard metabolic rate results indicated that energy expenditure was higher at temperatures ≥20°C. We then examined the effects of acclimation temperature on metabolic rate and blood plasma parameters in fish exposed to an acute exhaustive exercise and air exposure stress. Fish acclimated to temperatures ≥20°C exhibited elevated levels of plasma cortisol, muscle lactate, and plasma glucose after exposure to the acute stressors. After 24 h of recovery, fish showed longer metabolic recovery times at 15 and 20°C and cortisol levels remaining elevated at temperatures ≥20°C. Our findings suggest that brook trout may have a limited ability to acclimate to temperatures >20°C and increases in temperatures beyond 20°C may impact brook trout populations.
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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".