Using agent-based models to understand the role of individuals in the song evolution of humpback whales ( Megaptera novaeangliae )

Male humpback whales produce hierarchically structured songs, primarily during the breeding season. These songs
gradually change over the course of the breeding season, and are generally population specific. However, instances have
been recorded of more rapid song changes where the song of a population can be replaced by the song of an adjacent
population. The mechanisms that drive these changes are not currently understood, and difficulties in tracking individual
whales over long migratory routes mean field studies to understand these mechanisms are not feasible. In order to help
understand the mechanisms that drive these song changes, we present here a spatially explicit agent-based model inspired
by methods used in computer music research. We model the migratory patterns of humpback whales, a simple song
learning and production method coupled with sound transmission loss, and how often singing occurs during these
migratory cycles. This model is then extended to include learning biases that may be responsible for driving changes in the
song, such as a bias towards novel song, production errors, and the coupling of novel song bias and production errors.
While none of the methods showed population song replacement, our model shows that shared feeding grounds where
conspecifics are able to mix provide key opportunities for cultural transmission, and that production errors facilitated
gradually changing songs. Our results point towards other learning biases being necessary in order for population song
replacement to occur.