Data from: Cardiac plasticity influences aerobic performance and thermal tolerance in a tropical, freshwater fish at elevated temperatures
Bibliographic record
Abstract
Fishes faced with novel thermal conditions often modify physiological functioning to compensate for elevated temperatures. This physiological plasticity (thermal acclimation) has been shown to improve metabolic performance and extend thermal limits in many species. Adjustments in cardiorespiratory function are often invoked as mechanisms underlying thermal plasticity because limitations in oxygen supply have been predicted to define thermal optima in fishes, however few studies have explicitly linked cardiorespiratory plasticity to metabolic compensation. Here we quantify thermal acclimation capacity in the commercially harvested Nile perch (Lates niloticus) of East Africa, and investigate mechanisms underlying observed changes. We reared juvenile Nile perch for 3 months under two temperature regimes, and then measured a series of metabolic traits (e.g., aerobic scope, AS) and critical thermal maximum (CTmax) upon acute exposure to a range of experimental temperatures. We also measured morphological traits of heart ventricles, gills, and brains to identify potential mechanisms for compensation. We found that long-term (3-months) exposure to elevated temperature induced compensation in upper thermal tolerance (CTmax) and metabolic performance (SMR, MMR and AS), and induced cardiac remodeling in Nile perch. Furthermore, variation in heart morphology influenced variations in metabolic function and thermal tolerance. These results indicate that plastic changes enacted over longer exposures lead to differences in metabolic flexibility when acutely exposed to temperature variation. Furthermore, we established functional links between cardiac plasticity, metabolic performance, and thermal tolerance, providing evidence that plasticity in cardiac capacity may be one mechanism for coping with climate change.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".