Effects of Deforestation‐Induced Warming on the Thermal Tolerance of an African Clariid Catfish
Bibliographic record
Abstract
ABSTRACT Tropical countries, particularly in Africa, are experiencing more frequent deforestation events, often due to agricultural expansion. Deforestation can increase sunlight penetration in freshwater ecosystems, leading to increased average water temperatures and stronger diel temperature fluctuations. To investigate the effects of deforestation‐induced warming and potential adaptive responses from fish, we compared the upper thermal tolerance limits of smooth‐head catfish (Clarias liocephalus) populations from deforested and forested sections of the Dura and Mpanga river systems in and around Kibale National Park, Uganda. We also incorporated a long‐term acclimation period in simulated forested/deforested water temperatures to test for differences in the thermal acclimation ability of two populations. Populations from forested sites had lower CTmax values relative to populations sampled from deforested sections of the same drainage. However, long‐term acclimation in simulated deforested conditions increased their CTmax to values mirroring individuals from the deforested habitat. Individuals from a regenerating wetland (despite long‐term historical deforestation) exhibited CTmax values similar to populations from a forested site. These results suggest that C. liocephalus may be able to thermally respond to deforestation‐induced warming under current global temperatures; however, results may have also revealed a potential thermal ceiling for the species.
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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".