Warm northern river temperatures increase post‐exercise fatigue in an Arctic migratory salmonid but not in a temperate relative
Bibliographic record
Abstract
Abstract The Arctic is warming at twice the global average rate; how native and non‐native anadromous fishes will respond remains largely unknown. Some native Arctic salmonids are already experiencing warm (>21°C), physically challenging migratory river conditions and large diurnal temperature fluctuations (>10°C). We conducted field and laboratory experiments to determine how these extreme conditions may affect the capacity for migration in Arctic and temperate salmonids. In adult migratory Arctic char, reflex impairment following a handling challenge increased with temperature, indicating more extensive fatigue. In Arctic char smolts, temperature did not affect initial critical swimming performance (Umax1), however, there was a threshold for repeat swimming performance (Umax2) near 20°C, above which recovery was impaired. Following a simulated diurnal warming scenario (11–21°C),Umax1increased in rainbow trout and remained constant in Arctic char as in the field whileUmax2remained constant in rainbow trout it was drastically reduced in Arctic char. Furthermore, at warm temperatures, Arctic char were unable to recover to routine levels of oxygen uptake after exercise, while rainbow trout were. Warming also had more pronounced effects on blood composition and plasma glucose and lactate concentration in Arctic char than in rainbow trout. In general, rainbow trout, a temperate salmonid, had superior swimming performance, aerobic capacity, and warm tolerance than Arctic char, an Arctic salmonid. The present flow and temperature regimes in some Arctic rivers may restrict migration of native salmonids by limiting their ability to recover from fatiguing exercise. Non‐native, temperate salmonids are likely better suited to overcome these particular physical and thermal challenges. A plain language summary is available for this article.
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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".