Using physiology to unravel the implications of heatwaves for big brown bats (<i>Eptesicus fuscus</i>)
Bibliographic record
Abstract
Nocturnal endotherms are vulnerable to high ambient temperatures (Ta) during the day when sequestered in retreat sites. Artificial roost design must therefore account for the thermal sensitivity of target species and the potential roost temperatures during heatwave conditions at installation sites. We recorded physiological responses of big brown bats (Eptesicus fuscus) under naturally observed roost temperatures using flow-through respirometry. We used the resulting data to parameterise a biophysical model with which we calculated the evaporative cooling requirements as percent body mass during the hottest day of 2023 and a heatwave during 2021. Our data revealed that the evaporative cooling requirements of bats roosting in certain artificial roosts would have exceeded the lethal dehydration threshold for both females and males during the 2021 heatwave (>22.1% body mass). Regardless of the availability of freestanding water in the environment, bats roosting in artificial roosts prone to overheating are at risk of lethal dehydration during heatwaves, even in high latitude habitats. Therefore, conservation management of small nocturnal endotherms should incorporate both physiological data and roost microclimate data when designing and deploying artificial roosts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".