Vision Impairs the Abilities of Bats to Avoid Colliding with Stationary Obstacles
Bibliographic record
Abstract
BACKGROUND: Free-flying insectivorous bats occasionally collide with stationary objects they should easily detect by echolocation and avoid. Collisions often occur with lighted objects, suggesting ambient light may deleteriously affect obstacle avoidance capabilities. We tested the hypothesis that free-flying bats may orient by vision when they collide with some obstacles. We additionally tested whether acoustic distractions, such as "distress calls" of other bats, contributed to probabilities of collision. METHODOLOGY/PRINCIPAL FINDINGS: To investigate the role of visual cues in the collisions of free-flying little brown bats (Myotis lucifugus) with stationary objects, we set up obstacles in an area of high bat traffic during swarming. We used combinations of light intensities and visually dissimilar obstacles to verify that bats orient by vision. In early August, bats collided more often in the light than the dark, and probabilities of collision varied with the visibility of obstacles. However, the probabilities of collisions altered in mid to late August, coincident with the start of behavioural, hormonal, and physiological changes occurring during swarming and mating. Distress calls did not distract bats and increase the incidence of collisions. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that visual cues are more important for free-flying bats than previously recognized, suggesting integration of multi-sensory modalities during orientation. Furthermore, our study highlights differences between responses of captive and wild bats, indicating a need for more field experiments.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".