BATS LOCATE FLUTTERING INSECTS WITH ECHOLOCATION
Bibliographic record
Abstract
Over the course of evolution, bats have developed two different echolocation strategies. Some species emit single calls and wait for the reflection (echo) to return before calling again; otherwise, they could not hear the echoes above their loud calls. They are low duty cycle or intermittent echolocators. However, other species call more continuously. Brock Fenton from University of Western Ontario, Canada, explains that bats that have opted for this Doppler radar-like (high duty cycle) strategy can distinguish between their calls and weak echoes arriving at the same time because the echoes have changed frequency (pitch). This is due to the Doppler effect, where sound waves emitted by a bat flying toward an object become compressed, raising the frequency of the reflection and allowing the bats to distinguish between their own cries and the returning higher pitched reflection.As well as allowing bats to call continually, this shift in frequency could also help bats to detect fluttering targets: specifically, tasty moths and insects. Fenton explains that a target's fluttering wings could modify the returning echo so that the pitch oscillates up and down like a wailing siren, allowing the bat to pick out a fluttering target against the background of single-toned reflections generated by static objects such as leaves. However, no one had tested whether bats that evolved continual radar-like echolocation can detect fluttering targets against cluttered vegetation. So, Brock Fenton and student Louis Lazure set out to test whether radar-like bats are better at trapping fluttering targets in vegetation than intermittent callers (p. 1131).Requiring a reliable source of flutter to test the bats' responses, the pair built a robot by attaching a piece of masking tape to a wire and spinning the wire to simulate an insect's fluttering wings. Then they tested the robot, affectionately dubbed Robo-moth, by playing simulated bat cries to it while it fluttered at different rates, and recording the reflections to make sure that a bat could detect the robot's echoes. Then Lazure travelled to Taiwan to try Robo-moth out with real bats. Locating Robo-moth in forests that they knew were heavily used by foraging bats, Lazure recorded the echolocation cries of inbound bats and filmed their responses to the fluttering fake.Over 23 nights, Lazure recorded 2727 passing bats and identified that 2382 of them were radar-like echolocators while the rest used intermittent echolocation calls. Of the 446 bats that reacted to Robo-moth and tried to attack, 442 were radar-like echolocating bats: lesser horseshoe bats, great roundleaf bats and woolly horseshoe bats. They also noticed that the horseshoe bats seemed more sensitive to the siren-like echoes than the great roundleaf bats.So, radar-like echolocating bats are better at detecting fluttering prey in cluttered forests than intermittent echolocators and Fenton suspects that the radar-like bats have a better chance of capturing their prey because they continually monitor their victim's progress.Repeating the experiments in Belize – one of the homes of the only radar-like echolocating bat (Parnell's moustached bat) in the Americas – the duo recorded five Parnell's moustached bats and 370 intermittent echolocating species flying past Robo-moth. According to Fenton, the Parnell's moustached bats attacked Robo-moth as often as the Taiwanese radar-like echolocators; however, he was surprised that four intermittent echolocators – woolly bats and tube-nosed bats in Taiwan and two other species of moustached bats in Belize – also succeeded in attacking Robo-moth. Fenton suspects that these bats may be evolutionary intermediates that could help us understand why bats evolved this radar-like strategy for detecting prey in forests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".