No 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 1) social contexts vs. non-social contexts, 2) females vs. males, and 3) 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 hypothesized link between social and vocal complexity and can provide insight into the evolution of our own highly combinatorial communication system, language.

Emerging data in a range of non-human animal species have highlighted a latent ability to combine certain pre-existing calls together into larger structures. Currently, however, the quantification of context-specific call combinations has received less attention. This is problematic because animal calls can co-occur with one another simply through chance alone. One common approach applied in language sciences to identify recurrent word combinations is collocation analysis. Through comparing the co-occurrence of two words with how each word combines with other words within a corpus, collocation analysis can highlight above chance, two-word combinations. Here, we demonstrate how this approach can also be applied to non-human animal signal sequences by implementing it on artificially generated data sets of call combinations. We argue collocation analysis represents a promising tool for identifying non-random, communicatively relevant call combinations and, more generally, signal sequences, in animals. Significance statement Assessing the propensity for animals to combine calls provides important comparative insights into the complexity of animal vocal systems and the selective pressures such systems have been exposed to. Currently, however, the objective quantification of context-specific call combinations has received less attention. Here we introduce an approach commonly applied in corpus linguistics, namely collocation analysis, and show how this method can be put to use for identifying call combinations more systematically. Through implementing the same objective method, so-called call-ocations, we hope researchers will be able to make more meaningful comparisons regarding animal signal sequencing abilities both within and across systems.