The influence of reproductive condition and concurrent environmental factors on torpor and foraging patterns in female big brown bats (Eptesicus fuscus)
Bibliographic record
Abstract
(Uploaded by Plazi for the Bat Literature Project) Unlike many other mammals, bats in temperate regions employ short bouts of torpor throughout the reproductive period to maintain a positive energy balance. In addition to decreasing energy expenditure during the day, they typically alter foraging patterns as well. It is well known that various environmental conditions influence both torpor and foraging patterns, but studies of these factors often have focussed on one element in isolation thus it is not known how the two behaviours are collectively influencing temperate bats. The objective of our study was to assess how reproductive condition and environmental factors concurrently affect energy balance in female big brown bats (Eptesicus fuscus). We equipped pregnant and lactating bats in southwest Saskatchewan, Canada with temperature-sensitive radio-transmitters. While transmitters were active, skin temperature data were collected and foraging patterns were determined using triangulation. Of the various environmental and physiological parameters used to model torpor characteristics, roost type was the most important factor. Bats roosting in trees used deeper and longer torpor bouts than those roosting in buildings. Lactating bats had a tendency to forage for longer durations than pregnant bats, and often made more foraging trips. When taken together, we found that foraging duration and torpor duration were not directly related during pregnancy, but exhibited an inverse relationship during lactation. This provides support for the hypothesis that there are physiological trade-offs for reproductive bats and suggests that how bats compensate is not entirely predictable based on current environmental conditions.
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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".