Mapping evaporative water loss in desert passerines reveals an expanding threat of lethal dehydration
Bibliographic record
Abstract
Extreme high environmental temperatures produce a variety of consequences for wildlife, including mass die-offs. Heat waves are increasing in frequency, intensity, and extent, and are projected to increase further under climate change. However, the spatial and temporal dynamics of die-off risk are poorly understood. Here, we examine the effects of heat waves on evaporative water loss (EWL) and survival in five desert passerine birds across the southwestern United States using a combination of physiological data, mechanistically informed models, and hourly geospatial temperature data. We ask how rates of EWL vary with temperature across species; how frequently, over what areas, and how rapidly lethal dehydration occurs; how EWL and die-off risk vary with body mass; and how die-off risk is affected by climate warming. We find that smaller-bodied passerines are subject to higher rates of mass-specific EWL than larger-bodied counterparts and thus encounter potentially lethal conditions much more frequently, over shorter daily intervals, and over larger geographic areas. Warming by 4 °C greatly expands the extent, frequency, and intensity of dehydration risk, and introduces new threats for larger passerine birds, particularly those with limited geographic ranges. Our models reveal that increasing air temperatures and heat wave occurrence will potentially have important impacts on the water balance, daily activity, and geographic distribution of arid-zone birds. Impacts may be exacerbated by chronic effects and interactions with other environmental changes. This work underscores the importance of acute risks of high temperatures, particularly for small-bodied species, and suggests conservation of thermal refugia and water sources.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".