Long‐term changes in bat activity in Quebec suggest climatic responses and summer niche partitioning associated with white‐nose syndrome
Bibliographic record
Abstract
Abstract In North America, the greatest and most sudden threat to hibernating bats is white‐nose syndrome (WNS), which has caused massive declines in populations since 2006. Other determinants of bat dynamics, such as the climate, and the effect of reduction in the number of individuals sharing foraging space and summer roosting habitat may have an effect on population dynamics. We analyzed transect acoustic bat surveys conducted with ultrasonic detectors in 16 regions in Quebec, Canada, between 2000 and 2015. We used piecewise regression to describe changes in activity over time for each species and a meta‐analytic approach to measure its association with the North Atlantic Oscillation (NAO). As expected, mouse‐eared bat (Myotis spp.) activity sharply declined after the onset of WNS, down by 79% after 3 years. In contrast, big brown/silver‐haired bat activity increased over the same period, possibly due to a release of competition. Hoary bats and red bats remained present, although their activity did not increase. Myotis activity was positively correlated with a one‐year lag to the NAO index, associated with cold conditions in winter, but warm autumns. Big brown/silver‐haired and hoary bats were also more active during NAO‐positive years but without a lag. We conclude that combinations of threats may create rapid shifts in community compositions and that a more balanced research agenda that integrates a wider range of threats would help better understand and manage those changes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".