Data from: Vocalizations in the plains zebra (Equus quagga)

Acoustic signals are vital in animal communication, and quantifying these
signals them is fundamental for understanding animal behaviour and
ecology. Vocaliszations can be classified into acoustically and
functionally or contextually distinct categories, but establishing these
categories can be challenging. Newly developed methods, such as machine
learning, can provide solutions for classification tasks. The plains zebra
is known for its loud and specific vocaliszations, yet limited knowledge
exists on the structure and information content of its vocaliszations. In
this study, we employed both feature-based and spectrogram-based
algorithms, incorporating supervised and unsupervised machine learning
methods to enhance robustness in categoriszing zebra vocaliszation types.
Additionally, we implemented a permuted discriminant function analysis
(pDFA) to examine the individual identity information contained in the
identified vocaliszation types. The findings revealed at least four
distinct vocaliszation types he ‘“snort’,” the ‘“soft snort’,” the
‘“squeal’,” and the ‘“quagga quagga’” with individual differences observed
mostly in snorts, and to a lesser extent in squeals. Analyses based on
acoustic features outperformed those based on spectrograms, but each
excelled in characteriszing different vocaliszation types. We thus
recommend the combined use of these two approaches. OuThisr study offers
valuable insights into plains zebra vocaliszation, with implications for
future comprehensive explorations in animal communication.