Chimpanzee pant-hoots encode individual information more reliably than group differences

Abstract

Vocal learning, the ability to modify the acoustic structure of vocalizations based on social experience, is a fundamental feature of speech in humans (Homo sapiens). While vocal learning is common in taxa such as songbirds and whales, the vocal learning capacities of nonhuman primates appear more limited. Intriguingly, evidence for vocal learning has been reported in chimpanzees (Pan troglodytes), for example, in the form of regional variation ("dialects") in the "pant-hoot" calls. This suggests that some capacity for vocal learning may be an ancient feature of the Pan-Homo clade. Nonetheless, reported differences have been subtle, with intercommunity variation representing only a small portion of the total acoustic variation. To gain further insights into the extent of regional variation in chimpanzee vocalizations, we performed an analysis of pant-hoots from chimpanzees in the neighboring Kasekela and Mitumba communities at Gombe National Park, Tanzania, and the geographically distant Kanyawara community at Kibale National Park, Uganda. We did not find any statistically significant differences between the neighboring communities at Gombe or among geographically distant communities. Furthermore, we found differences among individuals in all communities. Hence, the variation in chimpanzee pant-hoots reflected individual differences, rather than group differences. Thus, we did not find evidence of dialects in this population, suggesting that extensive vocal learning emerged only after the lineages of Homo and Pan diverged.

Keywords: chimpanzee; dialects; pant-hoot; vocal learning.

Figure 1

A spectrogram of a typical pant‐hoot call with the four phases and drumming labeled

Figure 2

Directed acyclic graph of the assumed causal relationships among relevant variables

Figure 3

Principal components plots with the 68% normal data ellipses containing 68% of the data points included for each context. (a) Principal components analysis performed on structural features. Pant‐hoots given in different context separate over PC1. (b) Principal components analysis performed on acoustic features of the selected build‐up element. (c) Principal components analysis performed on acoustic features of the selected climax element. (d) Principal components analysis performed on all acoustic features simultaneously from all three communities. (b), (c), and (d) reveal strong overlap between contexts. PC1, principal component 1; PC2, principal component 2.

Figure 4

Principal components plots with the 68% normal data ellipses containing 68% of the data points included for each community. (a) Principal components analysis performed on structural features. (b) Principal components analysis performed on acoustic features of the selected build‐up element. (c) Principal components analysis performed on acoustic features of the selected climax element. Kasekela and geographically distant Kanyawara communities separate to some extent over PC2. (d) Principal components analysis on all acoustic features simultaneously from all three communities. (a), (b), and (d) reveal strong overlap among communities. PC1, principal component 1; PC2, principal component 2.

Figure 5

Principal components plots with the 68% normal data ellipses containing 68% of the data points included for each individual. Principal components analysis was performed on the structural features as well as features of the selected build‐up and climax elements simultaneously from the three communities. The 68% normal data ellipses revealed a lower overlap compared to community identity and context. Plot for (a) Kasekala. Some individuals formed distinct clusters over PC2. (b) Mitumba. Some individuals formed distinct clusters over a combination of PC1 and PC2. (c) Kanyawara. Some individuals formed distinct clusters over PC2 and others over PC1. PC1, principal component 1; PC2, principal component 2.