Potential foraging niche release in insectivorous bat species relatively unaffected by white-nose syndrome?
Bibliographic record
Abstract
White-nose syndrome (WNS) has rendered four of Ontario’s species endangered, while leaving the other four species relatively unaffected. The causes and extent of the declines have been widely studied. The influence on remaining bat species has not. Comparing acoustic data recorded ∼10 years apart, we evaluated how species in southeastern Ontario, Canada, use different foraging habitats pre- and post-WNS detection. We observed activity declines in now-endangered species over open fields (small-footed myotis, Myotis leibii (Audubon and Bachman, 1842); little brown bat, Myotis lucifugus (Le Conte, 1831); northern myotis, Myotis septentrionalis (Trouessart, 1897); tricolored bat, Perimyotis subflavus (F. Cuvier, 1832)) and speculate that the reduction of the once most common species (M. lucifugus) may have resulted in other species searching for prey in habitat once dominated by M. lucifugus. That is, these changes may have allowed greater presence in open field and clutter or edge environments by the big brown bat (Eptesicus fuscus (Palisot de Beauvois, 1796)) and three migratory species (silver-haired bat, Lasionycteris noctivagans (Le Conte, 1831); red bat, Lasiurus borealis (Müller, 1776); hoary bat, Lasiurus cinereus (Palisot de Beauvois, 1796)). However, our results also suggest that (i) while the decline of most resident bat species due to WNS may have relaxed competition for relatively unaffected species in some, but not all habitats, that (ii) sensory and biomechanical constraints may limit prey exploitation by these less-affected bat species in these habitats.
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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 | 0.001 |
| Scholarly communication | 0.000 | 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".