Evaporative Water Loss Is a Plausible Explanation for Mortality of Bats from White-Nose Syndrome
Bibliographic record
Abstract
White-nose syndrome (WNS) has caused alarming declines of North American bat populations in the 5 years since its discovery. Affected bats appear to starve during hibernation, possibly because of disruption of normal cycles of torpor and arousal. The importance of hydration state and evaporative water loss (EWL) for influencing the duration of torpor bouts in hibernating mammals recently led to "the dehydration hypothesis," that cutaneous infection of the wing membranes of bats with the fungus Geomyces destructans causes dehydration which in turn, increases arousal frequency during hibernation. This hypothesis predicts that uninfected individuals of species most susceptible to WNS, like little brown bats (Myotis lucifugus), exhibit high rates of EWL compared to less susceptible species. We tested the feasibility of this prediction using data from the literature and new data quantifying EWL in Natterer's bats (Myotis nattereri), a species that is, like other European bats, sympatric with G. destructans but does not appear to suffer significant mortality from WNS. We found that little brown bats exhibited significantly higher rates of normothermic EWL than did other bat species for which comparable EWL data are available. We also found that Natterer's bats exhibited significantly lower rates of EWL, in both wet and dry air, compared with values predicted for little brown bats exposed to identical relative humidity (RH). We used a population model to show that the increase in EWL required to cause the pattern of mortality observed for WNS-affected little brown bats was small, equivalent to a solitary bat hibernating exposed to RH of ∼95%, or clusters hibernating in ∼87% RH, as opposed to typical near-saturation conditions. Both of these results suggest the dehydration hypothesis is plausible and worth pursuing as a possible explanation for mortality of bats from WNS.
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 | 0.001 |
| 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) | 0.001 | 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".