Wegdell Franziska, Schamberg Isaac, Berthet Mélissa, Rothacher Yannik, Dellwo Volker, Surbeck Martin, Townsend Simon W
Department of Evolutionary Anthropology, University of Zurich, Zurich, Switzerland.
Institute for the Interdisciplinary Study of Language Evolution, University of Zurich, Zurich, Switzerland.
PLoS One. 2025 Sep 10;20(9):e0330250. doi: 10.1371/journal.pone.0330250. eCollection 2025.
Research over the last 20 years has shed important light on the vocal behaviour of our closest living relatives, bonobos and chimpanzees, but mostly relies on qualitative vocal repertoires, for which quantitative validations are absent. Such data are critical for a holistic understanding of a species` communication system and unpacking how these systems compare more broadly with other primate and non-primate species. Here we make key progress by providing the first quantitative validation of a Pan vocal repertoire, specifically for wild bonobos. Using data comprising over 1500 calls from 53 adult individuals collected over 33 months, we employ machine-learning-based random forest analyses and describe 11 acoustically distinguishable call types. We discuss issues associated with resolving vocal repertoires from wild data in great apes and highlight potential future approaches to further capture the complexity and gradedness of the bonobo vocal system.
过去20年的研究为我们现存的近亲倭黑猩猩和黑猩猩的发声行为提供了重要线索，但大多依赖于定性的发声 repertoire，缺乏定量验证。这些数据对于全面理解一个物种的交流系统以及更广泛地比较这些系统与其他灵长类和非灵长类物种至关重要。在这里，我们取得了关键进展，首次对泛猿发声 repertoire 进行了定量验证，特别是针对野生倭黑猩猩。利用33个月内从53只成年个体收集的超过1500个叫声的数据，我们采用基于机器学习的随机森林分析，并描述了11种声学上可区分的叫声类型。我们讨论了从野生大猩猩数据中解析发声 repertoire 相关的问题，并强调了未来进一步捕捉倭黑猩猩发声系统复杂性和分级性的潜在方法。
