Energetic benefits of enhanced summer roosting habitat for little brown bats (<i>Myotis lucifugus</i>) recovering from white-nose syndrome
Bibliographic record
Abstract
Habitat modification can improve outcomes for imperilled wildlife. Insectivorous bats in North America face a range of conservation threats, including habitat loss and white-nose syndrome (WNS). Even healthy bats face energetic constraints during spring, but enhancement of roosting habitat could reduce energetic costs, increase survival and enhance recovery from WNS. We tested the potential of artificial heating of bat roosts as a management tool for threatened bat populations. We predicted that: (i) after hibernation, captive bats would be more likely to select a roost maintained at a temperature near their thermoneutral zone; (ii) bats recovering from WNS at the end of hibernation would show a stronger preference for heated roosts compared with healthy bats; and (iii) heated roosts would result in biologically significant energy savings. We housed two groups of bats (WNS-positive and control) in separate flight cages following hibernation. Over 7.5 weeks, we quantified the presence of individuals in heated vs. unheated bat houses within each cage. We then used a series of bioenergetic models to quantify thermoregulatory costs in each type of roost under a number of scenarios. Bats preferentially selected heated bat houses, but WNS-affected bats were much more likely to use the heated bat house compared with control animals. Our model predicted energy savings of up to 81.2% for bats in artificially heated roosts if roost temperature was allowed to cool at night to facilitate short bouts of torpor. Our results are consistent with research highlighting the importance of roost microclimate and suggest that protection and enhancement of high-quality, natural roosting environments should be a priority response to a range of threats, including WNS. Our findings also suggest the potential of artificially heated bat houses to help populations recover from WNS, but more work is needed before these might be implemented on a large scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".