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Bats are altricial, gregarious, and highly vocal animals. As such, they are compelling animal models for bioacoustic analyses of language-related traits. Insight into the vocal development of bats ...

For analysis of vocal syntax, accurate classification of call sequence structures in different behavioural contexts is essential. However, an effective, intelligent program for classifying call sequences from numerous recorded sound files is still lacking. Here, we employed three machine learning algorithms (logistic regression, support vector machine and decision trees) to classify call sequences of social vocalizations of greater horseshoe bats (Rhinolophus ferrumequinum) in aggressive and distress contexts. The three machine learning algorithms obtained highly accurate classification rates (logistic regression 98%, support vector machine 97% and decision trees 96%). The algorithms also extracted three of the most important features for the classification: the transition between two adjacent syllables, the probability of occurrences of syllables in each position of a sequence, and the characteristics of a sequence. The results of statistical analysis also supported the classification of the algorithms. The study provides the first efficient method for data mining of call sequences and the possibility of linguistic parameters in animal communication. It suggests the presence of song-like syntax in the social vocalizations emitted within a non-breeding context in a bat species.

Bats are an appealing animal for investigating the biological basis and evolution of speech-related traits in wild mammal systems. We investigated vocal development in Horsfield's leaf-nosed bat pups, Hipposideros larvatus. We found that newborn bats emit many types of isolation calls, including downward frequency modulation (DFM), arched frequency modulation (AFM), upward frequency modulation (UFM), and others. The gradual decrease of Euclidean distance measurements revealed that pup isolation calls (DFM, AFM, UFM) gradually change into adult social calls. Furthermore, one of the isolation call types (UFM type) encoded an individual signature. By contrast, we report that early echolocation calls and isolation calls develop in parallel. As pups develop, early echolocation calls gradually mature and transform into adult-like echolocation calls. The interval and duration of echolocation calls decreases, while the peak frequency and bandwidth of frequency modulation increases.