The voice of bats: how greater mouse-eared bats recognize individuals based on their echolocation calls.
PLoS Comput Biol 2009;
5:e1000400. [PMID:
19503606 PMCID:
PMC2685012 DOI:
10.1371/journal.pcbi.1000400]
[Citation(s) in RCA: 70] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2009] [Accepted: 05/04/2009] [Indexed: 11/18/2022] Open
Abstract
Echolocating bats use the echoes from their echolocation calls to perceive their
surroundings. The ability to use these continuously emitted calls, whose main
function is not communication, for recognition of individual conspecifics might
facilitate many of the social behaviours observed in bats. Several studies of
individual-specific information in echolocation calls found some evidence for
its existence but did not quantify or explain it. We used a direct paradigm to
show that greater mouse-eared bats (Myotis myotis) can easily
discriminate between individuals based on their echolocation calls and that they
can generalize their knowledge to discriminate new individuals that they were
not trained to recognize. We conclude that, despite their high variability,
broadband bat-echolocation calls contain individual-specific information that is
sufficient for recognition. An analysis of the call spectra showed that
formant-related features are suitable cues for individual recognition. As a
model for the bat's decision strategy, we trained nonlinear statistical
classifiers to reproduce the behaviour of the bats, namely to repeat correct and
incorrect decisions of the bats. The comparison of the bats with the model
strongly implies that the bats are using a prototype classification approach:
they learn the average call characteristics of individuals and use them as a
reference for classification.
Animals must recognize each other in order to engage in social behaviour. Vocal
communication signals could be helpful for recognizing individuals, especially
in nocturnal organisms such as bats. Echolocating bats continuously emit special
vocalizations, known as echolocation calls, and perceive their surroundings by
analyzing the returning echoes. In this work we show that bats can use these
vocalizations for the recognition of individuals, despite the fact that their
main function is not communication. We used a statistical approach to analyze
how the bats could do so. We created a computer model that reproduces the
recognition behaviour of the bats. Our model suggests that the bats learn the
average calls of other individuals and recognize individuals by comparing their
calls with the learnt average representations.
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