Preliminary Results From A Computational Multi Agent Modelling Approach To Study Humpback Whale Song Cultural Transmission

Humpback whale (Megaptera novaeangliae) songs are a striking example of cultural transmission in non-humans (Garland et al., 2011). During the migration and mating season of this species, males produce complex, stereotyped sound sequences defined as ‘songs’(Payne & McVay, 1971). Within a population, males conform to a common yet slowly evolving song. Change can also occur more rapidly when a completely new song is adopted by the entire population in a relatively short time (termed ‘revolution’) (Noad, Cato, Bryden, Jenner, & Jenner, 2000). These phenomena can only occur if the whales are learning song from each other. While it is possible to record the shared song within a population and how this evolves in time, the individual mechanisms and learning strategies behind the cultural transmission of song remain unknown. Furthermore, it is not clear how populations maintain conformity in songs that change over variable timescales (evolution vs. revolution). This paper presents a spatially explicit multi-agent model designed to investigate humpback whale song learning and transmission