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Call combinations in chimpanzees: a social tool?

Cite this dataset

Leroux, Maël et al. (2022). Call combinations in chimpanzees: a social tool? [Dataset]. Dryad. https://doi.org/10.5061/dryad.pg4f4qrss

Abstract

A growing body of evidence suggests the capacity for animals to combine calls into larger communicative structures is more common than previously assumed. Despite its cross-taxa prevalence, little is known regarding the evolutionary pressures driving such combinatorial abilities. One dominant hypothesis posits that social complexity and vocal complexity are linked, with changes in social structuring (e.g. group size) driving the emergence of ever-more complex vocal abilities, such as call sequencing. In this paper, we tested this hypothesis through investigating combinatoriality in the vocal system of the highly social chimpanzee. Specifically, we predicted combinatoriality to be more common in socially-driven contexts and in females and lower-ranked males (socially challenging contexts and socially challenged individuals respectively). Firstly, through applying methods from computational linguistics (i.e. collocation analyses), we built an objective repertoire of combinatorial structures in this species. Second, we investigated what potential factors influenced call combination production. We show that combinatoriality is predominant in i) social contexts vs. non-social contexts, ii) females vs. males and iii) negatively correlates with male rank. Together, these results suggest one function of combinatoriality in chimpanzees may be to help individuals navigate their dynamic social world. More generally, we argue these findings provide support for the hypothesised link between social and vocal complexity and can provide insight into the evolution of our own highly combinatorial communication system, language.

Methods

Call combinations recorded in the wild. Investigation of non-random combinations using collocation analyses. Investigation of socio-ecological factors influencing the production of combinations using GLMMs. 

Usage notes

Extraction of the data: Praat

Analyses: R

Funding

Swiss National Science Foundation