Thermal Variability Modulates Altitudinal Differences in Metabolic Plasticity of the Asiatic Toad
Bibliographic record
Abstract
ABSTRACT Physiological plasticity is crucial for survival in fluctuating environments. The climate variability hypothesis (CVH) proposes that physiological plasticity scales with climatic variation across geographical gradients, yet its generality remains debated. Furthermore, the mediating role of prior thermal history is largely unexplored. This study examines how altitude and thermal variation shape phenotypic plasticity in resting metabolic rate (RMR) and maximum metabolic rate (MMR) of Asiatic toads ( Bufo gargarizans ). We found that RMR plasticity, but not MMR plasticity, varied altitudinally and was influenced by thermal conditions. Compared to low‐altitude toads, high‐altitude individuals exhibited reduced RMR plasticity, contradicting the CVH. This difference was amplified under higher thermal variability. In contrast, MMR plasticity showed no altitudinal variation or response to thermal variability. However, warm acclimation significantly increased MMR thermal sensitivity. Metabolic substrate choice depended on pre‐acclimation thermal experience. Our results indicate that RMR plasticity, rather than MMR plasticity, primarily underpins altitudinal adaptation, and increased thermal fluctuation may disrupt this adaptive pattern. This research provides novel insights into macrophysiological responses to global warming.
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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".