Shifts in population density centers of a hibernating mammal driven by conflicting effects of climate change and disease
Bibliographic record
Abstract
Populations wax and wane over time in response to an organism's interactions with abiotic and biotic forces. Numerous studies demonstrate that fluctuations in local populations can lead to shifts in relative population densities across the geographic range of a species over time. Fewer studies attempt to disentangle the causes of such shifts. Over four decades (1983-2022), we monitored populations of hibernating Indiana bats (Myotis sodalis) in two areas separated by ~110 km. The number of bats hibernating in the northern area increased from 1983 to 2011, while populations in the southern area remained relatively constant. We used simulation models and long-term weather data to demonstrate the duration of time bats must rely on stored fat during hibernation has decreased in both areas over that period, but at a faster rate in the northern area. Likewise, increasing autumn and spring temperatures shortened the periods of sporadic prey (flying insect) availability at the beginning and end of hibernation. Climate change thus increased the viability of northern hibernacula for an increasing number of bats by decreasing energetic costs of hibernation. Then in 2011, white-nose syndrome (WNS), a disease of hibernating bats that increases energetic costs of hibernation, was detected in the area. From 2011 to 2022, the population rapidly decreased in the northern area and increased in the southern area, completely reversing the northerly shift in population densities associated with climate change. Energy balance during hibernation is the singular link explaining the northerly shift under a changing climate and the southerly shift in response to a novel disease. Continued population persistence suggests that bats may mitigate many impacts of WNS by hibernating farther south, where insects are available longer each year.
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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 | 0.001 | 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".