Vervets revisited: A quantitative analysis of alarm call structure and context specificity.

The alarm calls of vervet monkeys (Chlorocebus pygerythrus) constitute the classic textbook example of semantic communication in nonhuman animals, as vervet monkeys give acoustically distinct calls to different predators and these calls elicit appropriate responses in conspecifics. They also give similar sounding calls in aggressive contexts, however. Despite the central role the vervet alarm calls have played for understanding the evolution of communication, a comprehensive, quantitative analysis of the acoustic structure of these calls was lacking. We used 2-step cluster analysis to identify objective call types and discriminant function analysis to assess context specificity. Alarm calls given in response to leopards, eagles, and snakes could be well distinguished, while the inclusion of calls given in aggressive contexts yielded some overlap, specifically between female calls given to snakes, eagles and during aggression, as well as between male vervet barks (additionally recorded in South Africa) in leopard and aggressive contexts. We suggest that both cognitive appraisal of the situation and internal state contribute to the variation in call usage and structure. While the semantic properties of vervet alarm calls bear little resemblance to human words, the existing acoustic variation, possibly together with additional contextual information, allows listeners to select appropriate responses.

Characterizing Vocal Repertoires--Hard vs. Soft Classification Approaches.

To understand the proximate and ultimate causes that shape acoustic communication in animals, objective characterizations of the vocal repertoire of a given species are critical, as they provide the foundation for comparative analyses among individuals, populations and taxa. Progress in this field has been hampered by a lack of standard in methodology, however. One problem is that researchers may settle on different variables to characterize the calls, which may impact on the classification of calls. More important, there is no agreement how to best characterize the overall structure of the repertoire in terms of the amount of gradation within and between call types. Here, we address these challenges by examining 912 calls recorded from wild chacma baboons (Papio ursinus). We extracted 118 acoustic variables from spectrograms, from which we constructed different sets of acoustic features, containing 9, 38, and 118 variables; as well 19 factors derived from principal component analysis. We compared and validated the resulting classifications of k-means and hierarchical clustering. Datasets with a higher number of acoustic features lead to better clustering results than datasets with only a few features. The use of factors in the cluster analysis resulted in an extremely poor resolution of emerging call types. Another important finding is that none of the applied clustering methods gave strong support to a specific cluster solution. Instead, the cluster analysis revealed that within distinct call types, subtypes may exist. Because hard clustering methods are not well suited to capture such gradation within call types, we applied a fuzzy clustering algorithm. We found that this algorithm provides a detailed and quantitative description of the gradation within and between chacma baboon call types. In conclusion, we suggest that fuzzy clustering should be used in future studies to analyze the graded structure of vocal repertoires. Moreover, the use of factor analyses to reduce the number of acoustic variables should be discouraged.