Global exposure risk of frogs to increasing environmental dryness
Bibliographic record
Abstract
Species exposed to prolonged drying are at risk of population declines or extinctions. A key missing element for assessments of climate change risk is the sensitivity of species to water loss and their microhabitat preference, or ecotype, as both dictate the risk of environmental drying. Here, we identified globally where water-sensitive ectotherms, i.e. anurans, are at risk to increasing aridity and drought, examined which ecotypes are more sensitive to water loss from 238 species, and estimated how behavioural activity is impacted by future drought and warming scenarios through biophysical models. Under an intermediate and high emission scenario, 6.6 and 33.5% of areas occupied by anurans will increase to arid-like conditions, and 15.4 and 36.1% are at risk of exposure to a combination of increasing drought intensity, frequency, and duration by 2080¬–2100, respectively. Critically, increasing arid-like conditions will increase water loss rates and anurans in dry regions will almost double the water loss rates under a high emission scenario. Biophysical models showed that during the warmest quarter of the year, the combination of drought and warming reduced an anuran’s potential activity by 17.9% relative to the current conditions compared to warming alone which reduced potential activity by 8%. Our results exemplify the widespread exposure risk of environmental drying for anurans, posing a serious challenge for the lives of water-sensitive species beyond the effects of temperature alone.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".