[MARMAM] New publication: Using agent-based models to understand the role of individuals in the song evolution of humpback whales (Megaptera novaeangliae) (Luca Lamoni)

Luca Lamoni ll42 at st-andrews.ac.uk
Mon Apr 23 06:15:13 PDT 2018

My co-authors and I are pleased to share our recently published article:

Mcloughlin M, Lamoni L, Garland EC, Ingram S, Kirke A, Noad MJ, Rendell L,
& Miranda E. (2018). Using agent-based models to understand the role of
individuals in the song evolution of humpback whales (*Megaptera
novaeangliae*). Music & Science, 1, 2059204318757021.

This article can be found online at
or by contacting me at ll42 at st-andrews.ac.uk

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.

Best regards
Luca Lamoni
ll42 at st-andrews.ac.uk

Luca Lamoni, PhD Student
E-mail: ll42 at st-andrews.ac.uk <ler4 at st-andrews.ac.uk>
School of Biology, University of St. Andrews
Sir Harold Mitchell Building,
St. Andrews, Fife
KY16 9TH

Mobile number (UK): +44 7873 906537
Mobile number (ITA): +39 333 7329155
Skype: lucaluca446
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