Mixed support for the attractiveness of feeding buzzes and distress calls within and across four vesper bat species
Bibliographic record
Abstract
Eavesdropping on acoustic signals plays a crucial role in decision making for many animals. While much research has focused on how bats use sound for orientation and foraging, surprisingly little has focused on how eavesdropping impacts survival and decision making. We examined how four insectivorous bat species in North America, the big brown bat, Eptesicus fuscus , silver-haired bat, Lasionycteris noctivagans , hoary bat, Lasiurus cinereus , and little brown bat, Myotis lucifugus , respond to conspecific and heterospecific call sequences. Between June and August of 2023, we played distress and aerial hawking attack sequences along Battle Creek in southwest Saskatchewan, Canada, and used acoustic recorders to measure any changes in bat activity during and just after the playbacks. We hypothesized that bat activity would increase in response to both conspecific and heterospecific call sequences, with species-specific differences based on size and life history. However, we found that while L. cinereus were attracted to conspecific hawking sequences (= feeding buzzes), they were not attracted to any other conspecific or heterospecific attack or distress sequences. Lasionycteris noctivagans , in turn, were repelled by L. cinereus hawking sequences but did not respond to conspecific or heterospecific distress calls. Myotis lucifugus did not respond to conspecific or heterospecific distress call or hawking attack sequences. This is in contrast with previous studies that have concluded M. lucifugus is attracted to conspecific vocalizations. Our findings underscore our limited understanding of how bats respond to call sequences and, more generally, bat community relationships. • We hypothesized that bat activity would increase in response to other bats' sounds. • Our results instead suggest bats may be attracted, repelled or indifferent. • These differences are based on the sounds heard (feeding buzzes vs distress calls). • And the species listening and the species being listened to.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".