Greater plasticity in CTmax with increased climate variability among populations of tailed frogs
Bibliographic record
Abstract
Temporally variable climates are expected to drive the evolution of thermal physiological traits that enable performance across a wider range of temperatures (i.e. climate variability hypothesis, CVH). Spatial thermal variability, however, may mediate this relationship by providing ectotherms with the opportunity to behaviourally select preferred temperatures (i.e. the Bogert effect). These antagonistic forces on thermal physiological traits may explain the mixed support for the CVH within species despite strong support among species at larger geographical scales. Here, we test the CVH as it relates to plasticity in physiological upper thermal limits (critical thermal maximum—CTmax) among populations of coastal tailed frogs ( Ascaphus truei ). We targeted populations that inhabit spatially homogeneous environments, reducing the potentially confounding effects of behavioural thermoregulation. We found that populations experiencing greater temporal thermal variability exhibited greater plasticity in CTmax, supporting the CVH. Interestingly, we identified only one site with spatial temperature variability and tadpoles from this site demonstrated greater plasticity than expected, suggesting the opportunity for behavioural thermoregulation can reduce support for the CVH. Overall, our results demonstrate one role of climate variability in shaping thermal plasticity among populations and provide a baseline understanding of the impact of the CVH in spatially homogeneous thermal landscapes.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".