Behavioural evidence of a humidistat: a temperature-compensating mechanism of hydroregulation
Bibliographic record
Abstract
Abstract The ability to control hydration state essential for terrestrial species, especially amphibians, which are highly susceptible to dehydration. Here, we examined how temperature (17°C vs. 22°C) influenced behavioral hydroregulation in spotted salamanders ( Ambystoma maculatum ) using a laboratory humidity gradient. Salamanders defended a constant vapour pressure deficit (VPD) between temperatures by targeting higher RH at 22°C than at 17°C, possibly to compensate for increased evaporative demand at warmer temperatures. Individuals selecting higher VPDs experienced greater evaporative water loss (EWL), with larger salamanders losing more water than smaller ones after accounting for temperature. Together, these results highlight a trade-off among body size, humidity preference, and desiccation tolerance. Salamanders also rehydrated faster at 22°C than 17°C, highlighting temperature-dependent water uptake rates. Our finding that salamanders regulated a constant driving force of evaporation between temperatures suggests the ability to detect rates of EWL. Local cooling of the skin arising from evaporation is a plausible mechanism: if moist-skinned ectotherms allow for a specific, fixed decline in skin relative to core temperature, they can effectively detect the drive for evaporation. Ultimately, our study underscores the complexity of amphibian hydroregulation and emphasises the role of behaviour in maintaining hydration state.
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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".