Projecting the compound effects of climate change and white-nose syndrome on North American bat species
Bibliographic record
Abstract
Climate change and disease are threats to biodiversity that may compound and interact with one another in ways that are difficult to predict. White-nose syndrome (WNS), caused by a cold-loving fungus ( Pseudogymnoascus destructans ), has had devastating impacts on North American hibernating bats, and impact severity has been linked to hibernaculum microclimate conditions. As WNS spreads across the continent and climate conditions change, anticipating these stressors’ combined impacts may improve conservation outcomes for bats. We build on the recent development of winter species distribution models for five North American bat species, which used a hybrid correlative-mechanistic approach to integrate spatially explicit winter survivorship estimates from a bioenergetic model of hibernation physiology. We apply this bioenergetic model given the presence of P. destructans , including parameters capturing its climate-dependent growth as well as its climate-dependent effects on host physiology, under both current climate conditions and scenarios of future climate change. We then update species distribution models with the resulting survivorship estimates to predict changes in winter hibernacula suitability under future conditions. Exposure to P. destructans is generally projected to decrease bats’ winter occurrence probability, but in many areas, changes in climate are projected to lessen the detrimental impacts of WNS. This rescue effect is not predicted for all species or geographies and may arrive too late to benefit many hibernacula. However, our findings offer hope that proactive conservation strategies to minimize other sources of mortality could allow bat populations exposed to P. destructans to persist long enough for conditions to improve.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".