Analytic dataset informing modeling of winter species distributions of North American bat species
Bibliographic record
Abstract
The fungal pathogen Pseudogymnoascus destructans and resultant white-nose syndrome (WNS) continues to advance across North America, infecting new bat populations, species, and hibernacula. Western North America hosts the highest bat diversity in the U.S. and Canada, yet little is known about hibernacula and hibernation behavior in this region. An improved understanding of where bats hibernate and the conditions that create suitable hibernacula is critical if land managers are to anticipate and address the conservation needs of WNS-susceptible species in regions yet to be infected. We estimated suitability of potential winter hibernaculum sites across the ranges of five bat species occurring in western North America. We estimated winter survival capacity from a mechanistic survivorship model based on bat bioenergetics and climate conditions. Leveraging the Google Earth Engine platform for spatial data processing, we used boosted regression trees to relate these estimates, along with key landscape attributes, to bat occurrence data in a hybrid correlative-mechanistic approach. Winter survival capacity, topography, land cover, and access to caves and mines were important predictors of winter hibernaculum selection, but the shape and relative importance of these relationships varied among species. This suggests that the occurrence of bat hibernacula can, in part, be predicted from readily mapped above-ground features, and is not only dictated by below-ground characteristics for which spatial data are lacking. Furthermore, our mechanistic estimate of winter survivorship was, on average, the third strongest predictor of winter occurrence probability across focal species. Winter distributions of North American bat species were driven by their physiological capacity to survive winter conditions and duration in a given location, as well as selection for topographic and other landscape features, but in species-specific ways. The influence of winter survivorship on several species’ distributions, the underlying influence of climate conditions on winter survivorship, and the anticipated influence of WNS on bats’ hibernation physiology and survivorship together suggest that North American bat distributions may undergo future shifts as these species are exposed to not only WNS, but climate change. We anticipate that the models presented here may offer a valuable baseline for assessing the potential species-level impacts of these stressors.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".