Changes in thermal quality of the environment along an elevational gradient affect investment in thermoregulation by Yarrow’s spiny lizards
Bibliographic record
Abstract
Abstract Body temperature affects physiological processes and, consequently, is assumed to have a large impact on fitness. Lizards need to thermoregulate behaviourally to maintain their body temperature within a range that maximizes performance, but there are costs associated with thermoregulation. The thermal quality of an environment directly affects the amount of time and energy that must be invested by an individual to maintain an optimal body temperature for performance; time and energy are major costs of thermoregulation. According to Huey and Slatkin’s (Q. Rev. Biol. 1976, 363) cost–benefit model of thermoregulation, lizards should only thermoregulate when the benefits outweigh the costs. Thus, in habitats of poor thermal quality, lizards should invest less into thermoregulation. We tested the hypothesis that the thermal quality of an environment dictates investment in thermoregulation across an elevational gradient. Increases in elevation are accompanied by decreases in temperature and therefore thermal quality. We recorded body temperatures of Yarrow’s spiny lizards (Sceloporus jarrovii) at ten talus slopes along an elevational gradient of over 1000 m. We found a significant positive relationship between elevation and effectiveness of thermoregulation, opposite to the prediction of the cost–benefit model of thermoregulation. This suggests that the disadvantages of thermoconformity may be greater than the costs of thermoregulating as habitats become more thermally challenging.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".