Tropical fish in a warming world: thermal tolerance of Nile perch<i>Lates niloticus</i>(L.) in Lake Nabugabo, Uganda
Bibliographic record
Abstract
Key to predicting the response of fishes to climate change is quantifying how close fish are to their critical thermal limits in nature and their ability to adjust their thermal sensitivity to maintain performance. Here, we evaluated the effects of body size and habitat on aerobic scope (AS) and thermal tolerance of Nile perch Lates niloticus (L.), a fish of great economic and food security importance in East Africa, using respirometry and critical thermal maximum (CTmax) trials. Juvenile Nile perch from distinct habitats (high or low dissolved oxygen concentrations) of Lake Nabugabo, Uganda were exposed for 4.6 ± 0.55 days to a temperature treatment (25.5, 27.5, 29.5 or 31.5°C) prior to experimentation, with the lowest temperature corresponding to the mean annual daytime temperature in Lake Nabugabo and the highest temperature being 3°C higher than the maximal monthly average. As expected, metabolic rates increased with body mass. Although resting metabolic rate increased with temperature, maximal metabolic rate showed no change. Likewise, AS did not vary across treatments. The CTmax increased with acclimation temperature. There was no effect of habitat on maximal metabolic rate, AS or CTmax; however, there was a trend towards a lower resting metabolic rate for Nile perch captured in the low-dissolved oxygen habitat than in well-oxygenated waters. This study shows that juvenile Nile perch maintain a large AS at temperatures near the upper limit of their natural thermal range and provides evidence that Nile perch have physiological mechanisms to deal with acute exposure to thermal stress.
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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".