Water loss and temperature interact to compound amphibian vulnerability to climate change
Bibliographic record
Abstract
Abstract Ectotherm thermal physiology is frequently used to predict species responses to changing climates, but for amphibians, water loss may be of equal or greater importance. Using physical models, we estimated the frequency of exceeding the thermal optimum (Topt) or critical evaporative water loss (EWLcrit) limits, with and without shade‐ or water‐seeking behaviours. Under current climatic conditions (2002–2012), we predict that harmful thermal (>Topt) and hydric (>EWLcrit) conditions limit the activity of amphibians during ~70% of snow‐free days in sunny habitats. By the 2080s, we estimate that sunny and dry habitats will exceed one or both of these physiological limits during 95% of snow‐free days. Counterintuitively, we find that while wet environments eliminate the risk of critical EWL, they do not reduce the risk of exceeding Topt (+2% higher). Similarly, while shaded dry environments lower the risk of exceeding Topt, critical EWL limits are still exceeded during 63% of snow‐free days. Thus, no single environment that we evaluated can simultaneously reduce both physiological risks. When we forecast both temperature and EWL into the 2080s, both physiological thresholds are exceeded in all habitats during 48% of snow‐free days, suggesting that there may be limited opportunity for behaviour to ameliorate climate change. We conclude that temperature and water loss act synergistically, compounding the ecophysiological risk posed by climate change, as the combined effects are more severe than those predicted individually. Our results suggest that predictions of physiological risk posed by climate change that do not account for water loss in amphibians may be severely underestimated and that there may be limited scope for facultative behaviours to mediate rapidly changing environments.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".