The voice of bats: how greater mouse-eared bats recognize individuals based on their echolocation calls

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.
Author Summary 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.