Project CETI (Cetacean Translation Initiative) aims at deciphering the communication of one of the world’s most enigmatic ocean species: the sperm whale.
Social learning is key in the development of both human and non-human animal societies. Here, we provide quantitative evidence that supports the existence of social learning in sperm whales across socio-cultural barriers, based on acoustic data from locations in the Pacific and Atlantic Oceans. Sperm whale populations have traditionally been partitioned into clans based on their vocal repertoire (what they say) of rhythmically patterned clicks (codas), and in particular their identity codas, which serve as symbolic markers for each clan. However, identity codas account for between 35% and 60% of all codas vocalized depending on the different clans. We introduce a computational modeling approach that recovers clan structure and shows new evidence of social learning across clans from the internal temporal structure of non-identity codas, the remaining fraction of codas. The proposed method is based on vocal style, which encodes how sperm whales assemble individual clicks into codas. Specifically, we modeled clicking pattern data using generative models based on variable length Markov chains, producing what we term "subcoda trees". Based on our results, we propose here a new concept of vocal identity, which consists of both vocal repertoire and style. We show that (i) style-delimited clans are similar to repertoire-delimited clans, and that (ii) sympatry increases vocal style similarity between clans for non-identity codas, but has no significant effect on identity codas. This implies that different clans who geographically overlap have similar styles for most codas, which in turn implies social learning across cultural boundaries. More broadly, the proposed method provides a new framework for comparing communication systems of other animal species, with potential implications for our understanding of cultural transmission in animal societies.