Levrero, F. | bioRxiv Science

bioRxiv2024

A cooperatively breeding mouse shows flexible use of its vocal repertoire according to social context

Mice exchange information using chemical, visual and acoustic signals. Long ignored, mouse ultrasonic communication is now considered to be an important aspect of their social life, transferring information such as individual identity or stress levels. However, whether and how mice modulate their acoustic communications is largely unknown. Here we show that a wild mouse species with a complex social system controls its vocal production both qualitatively and quantitatively, depending on social context. We found that the African striped mouse Rhabdomys pumilio, a cooperatively breeding species, has a vocal repertoire consisting of seven call types, which it uses differently depending on whether the individuals encounter another mouse that is familiar, unfamiliar, of the same or different sex. Familiar individuals, whether of the same or different sex, vocalize more than two unfamiliar same-sex individuals. The greatest diversity of vocalisations is recorded when a female and a male first encounter, suggesting that certain calls are reserved for courtship. Our results highlight that familiar mice alternate their vocalisations (turn-taking) while unfamiliar individuals tend to overlap one another. These observations suggest that African striped mice control the production and temporal dynamics of their vocalisations, addressing targeted information to specific receivers via the acoustic channel.

animal behavior and cognition↗

bioRxiv2022

Improving the workflow to crack Small, Unbalanced, Noisy, but Genuine (SUNG) datasets in bioacoustics: the case of bonobo calls

Despite the accumulation of data and studies, deciphering animal vocal communication remains highly challenging. While progress has been made with some species for which we now understand the information exchanged through vocal signals, researchers are still left struggling with sparse recordings composing Small, Unbalanced, Noisy, but Genuine (SUNG) datasets. SUNG datasets offer a valuable but distorted vision of communication systems. Adopting the best practices in their analysis is therefore essential to effectively extract the available information and draw reliable conclusions. Here we show that the most recent advances in machine learning applied to a SUNG dataset succeed in unraveling the complex vocal repertoire of the bonobo, and we propose a workflow that can be effective with other animal species. We implement acoustic parameterization in three feature spaces along with three classification algorithms (Support Vector Machine, xgboost, neural networks) and their combination to explore the structure and variability of bonobo calls, as well as the robustness of the individual signature they encode. We underscore how classification performance is affected by the feature set and identify the most informative features. We highlight the need to address data leakage in the evaluation of classification performance to avoid misleading interpretations. Finally, using a Uniform Manifold Approximation and Projection (UMAP), we show that classifiers generate parsimonious data descriptions which help to understand the clustering of the bonobo acoustic space. Our results lead to identifying several practical approaches that are generalizable to any other animal communication system. To improve the reliability and replicability of vocal communication studies with SUNG datasets, we thus recommend: i) comparing several acoustic parameterizations; ii) adopting Support Vector Machines as the baseline classification approach; iii) explicitly evaluating data leakage and possibly implementing a mitigation strategy; iv) visualizing the dataset with UMAPs applied to classifier predictions rather than to raw acoustic features.

animal behavior and cognition↗