Elevated temperature and acclimation time affect metabolic performance in the heavily exploited Nile perch of Lake Victoria
Bibliographic record
Abstract
Increasing water temperatures due to anthropogenic climate change are predicted to negatively impact the aerobic metabolic performance of aquatic ectotherms. Specifically, it has been hypothesized that thermal increases result in reductions in aerobic scope (AS), which lead to decreases in energy available for essential fitness and performance functions. Consequences of warming are anticipated to be especially severe for warm-adapted tropical species as they are thought to have narrow thermal windows and limited plasticity for coping with elevated temperatures. In this study we test how predicted warming may affect the aerobic performance of Nile perch (Lates niloticus), a commercially-harvested fish species in the Lake Victoria basin of East Africa. We measured critical thermal maxima (CTmax) and key metabolic variables such as AS and excess post-exercise oxygen consumption (EPOC) across a range of temperatures, and compared responses between acute (3-day) exposures and 3-week acclimations. CTmax increased with acclimation temperature, however 3-week acclimated fish had higher overall CTmax than acutely-exposed individuals. Nile perch also showed the capacity to increase or maintain high AS even at temperatures well beyond their current range, however acclimated Nile perch had lower AS compared to acutely-exposed fish. These changes were accompanied by lower EPOC, suggesting that drops in AS may reflect improved energy utilization after acclimation, a finding that is supported by improvements in growth at high temperatures over the acclimation period. Overall, the results challenge predictions that tropical species have limited thermal plasticity, and that high temperatures will be detrimental due to limitations in AS.
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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".