Energy availability influences microclimate selection of hibernating bats
Bibliographic record
Abstract
Many species hibernate to conserve energy during periods of low food and water availability. It has long been assumed that the optimal hibernation strategy involves long, deep bouts of torpor that minimize energy expenditure. However, hibernation has ecological (e.g. decreased predator avoidance) and physiological (e.g. sleep deprivation) costs that must be balanced with energy savings; therefore, individuals possessing sufficient energy reserves may reduce their use of deep torpor. We tested the hypothesis that energy (fat) availability influences temperature selection of two fat-storing bat species during hibernation. We predicted that individuals with small energy reserves would select colder temperatures for hibernation in order to minimize energy expenditure, while individuals with larger energy reserves would choose warmer temperatures to minimize the costs of hibernation. Results from our field experiment indicate that little brown myotis (Myotis lucifugus) hibernating in warm microclimates were significantly heavier than individuals hibernating in cooler microclimates. To determine if energy availability was mediating this relationship, we limited fatty acid availability with mercaptoacetate (MA) and quantified its effect on torpid metabolic rate (TMR) and thermal preference of big brown bats (Eptesicus fuscus). Administration of MA caused a 43% drop in TMR at 10 degrees C and caused bats to choose significantly colder temperatures for hibernation. Our results suggest that fat-storing bats minimize torpor expression using both physiological and behavioral mechanisms.
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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 | 0.000 |
| 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".