Prehibernation swarming in temperate bats: a critical transition between summer activity and hibernation
Bibliographic record
Abstract
In this contribution to Dr. Brock Fenton's Festschrift, we briefly reflect on Dr. Fenton's seminal works examining bat swarming behaviour in Ontario and use these reflections as a launch pad to conduct a global review on autumn swarming in bats, and underlying hypotheses to explain this behaviour. Our review frames the swarming period as a time of critical transitions, during which bats must balance multiple life history trade-offs, and we consider how various intrinsic and extrinsic factors may contribute to inter- and intraspecific differences in autumn behaviour. We discuss the transition away from summer residency, including maternity colony breakup, day roosting, and migration during autumn. We review key life history elements of swarming, including mating behaviours and associated reproductive condition, genetic exchange during swarming, and variation among sexes, ages, and species. Finally, we discuss the behaviours and physiological states of bats transitioning from the swarming period to hibernation. Throughout, we identify common patterns and also exceptions. Over 50 years of research has yielded many insights into autumn swarming, but knowledge gaps remain. Future research focus on a greater diversity of species will reveal general principles underlying the transition from summer active season, through the swarming period, and into winter hibernation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".