Arctic lake trout (<scp><i>Salvelinus namaycush</i></scp>) show evidence of seasonal acclimation of cardiac adrenergic sensitivity but not heat tolerance
Bibliographic record
Abstract
Abstract Many Arctic fishes experience prolonged periods of extreme cold and large thermal variation over both rapid and seasonal time scales which challenge critical physiological functions. In the central Canadian Arctic, we caught wild adult lake trout (Salvelinus namaycush) acclimatized to winter and summer temperatures to determine the extent to which they seasonally adjust cardiac thermal performance and adrenergic control. We assessed the intrinsic and maximum heart rate (fHint and fHmax) of anaesthetised fish through cholinergic blockade and either adrenergic blockade (fHint) or stimulation (fHmax) during acute warming. Contrary to expectations, S. namaycush showed largely consistent heart rate responses to acute warming between seasons and adrenergic treatments. The fHmax increased with acute warming and reached a peak (peak fH) of 83 and 93 beats min−1 at temperatures (Tpeak) of 19.6 and 20°C and arrhythmia occurred at temperatures (Tarr) of 22.7 and 22.3°C in winter and summer, respectively. However, in the winter, adrenergic stimulation was important to achieve peak fH at high temperatures; adrenergic blockade in winter did not affect Tpeak (18.3°C) or Tarr (21°C) but lowered peak fH to only 68 beats min−1 (p < 0.05). Despite limited seasonal differences in fH, when compared at common, cool to moderate temperatures, winter acclimated fish did exhibit slower atrioventricular conduction (longer PR interval), slower ventricular depolarization (longer QRS duration) and a shorter overall systolic duration (lower QT interval). Overall, S. namaycush exhibited no seasonal plasticity in cardiac thermal tolerance and modest seasonal changes in adrenergic control at high temperatures and in cardiac electrical activity. This limited thermal plasticity may constrain their ability to cope with seasonally distinct thermal challenges including extreme heat events and increase their reliance on other coping or avoidance mechanisms when thermal refugia are available (e.g. behavioural thermoregulation). An increased need for avoidance behaviour could limit the accessibility of key habit for foraging and spawning which occurs in late summer in this region (Dubos et al., 2024).
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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".