Acoustic Features and Recognition of Distress Calls in Rhinolophus ferrumequinum: A Study Combining Machine Learning and Playback Experiments

Vocal signals are the primary medium of intraspecific communication in bats, yet the encoding features and recognition mechanisms of distress calls remain largely unclear. This study aimed to examine sex, age, and individual signatures in noise burst to downward frequency modulation (NB-DFM) distress calls of the Rhinolophus ferrumequinum and verify conspecific recognition ability. We recorded NB-DFM calls from 20 adult and 9 subadult bats in Jilin Province, extracted 18 acoustic parameters, built classification models with support vector machines (SVMs), evaluated feature importance using random forest, and performed habituation-dishabituation playback experiments. SVM yielded classification accuracies of 67%, 89%, and 88% for sex, age, and individual identity, respectively, all significantly above chance levels. Call duration, central minimum frequency, and root mean square (RMS) were the most diagnostic parameters, and key acoustic variables differed significantly among classification levels. Playback tests elicited distinct behavioral responses to calls of different sexes, ages, and individuals, confirming discrimination ability. These findings reveal individual, age, and sex specific acoustic markers in bat distress calls, support the utility of machine learning for bioacoustic classification, and provide empirical insight into stress related acoustic communication in nocturnal mammals.