Hibernating female big brown bats (<i>Eptesicus fuscus</i>) adjust huddling and drinking behaviour, but not arousal frequency, in response to low humidity
Bibliographic record
Abstract
Many mammals hibernate during winter, reducing energy expenditure via bouts of torpor. The majority of a hibernator's energy reserves are used to fuel brief, but costly, arousals from torpor. Although arousals likely serve multiple functions, an important one is to restore water stores depleted during torpor. Many hibernating bat species require high humidity, presumably to reduce torpid water loss, but big brown bats (Eptesicus fuscus) appear tolerant of a wide humidity range. We tested the hypothesis that hibernating female E. fuscus use behavioural flexibility during torpor and arousals to maintain water balance and reduce energy expenditure. We predicted: (1) E. fuscus hibernating in dry conditions would exhibit more compact huddles during torpor and drink more frequently than bats in high humidity conditions; and (2) the frequency and duration of torpor bouts and arousals, and thus total loss of body mass would not differ between bats in the two environments. We housed hibernating E. fuscus in temperature- and humidity-controlled incubators at 50% or 98% relative humidity (8°C, 110 days). Bats in the dry environment maintained a more compact huddle during torpor and drank more frequently during arousals. Bats in the two environments had a similar number of arousals, but arousal duration was shorter in the dry environment. However, total loss of body mass over hibernation did not differ between treatments, indicating that the two groups used similar amounts of energy. Our results suggest that behavioural flexibility allows hibernating E. fuscus to maintain water balance and reduce energy costs across a wide range of hibernation humidities.
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 | 0.000 | 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".