BATS LOCATE FLUTTERING INSECTS WITH ECHOLOCATION
Notice bibliographique
Résumé
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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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».