Title: Individual identity information persists in learned calls of introduced parrot populations
Animals can actively encode different types of identity information in learned communication signals, such as group membership or individual identity. The social environments in which animals interact may favor different types of information, but whether identity information conveyed in learned signals is robust or responsive to social disruption over short evolutionary timescales is not well understood. We inferred the type of identity information that was most salient in vocal signals by combining computational tools, including supervised machine learning, with a conceptual framework of “hierarchical mapping”, or patterns of relative acoustic convergence across social scales. We used populations of a vocal learning species as a natural experiment to test whether the type of identity information emphasized in learned vocalizations changed in populations that experienced the social disruption of introduction into new parts of the world. We compared the social scales with the most salient identity information among native and introduced range monk parakeet (Myiopsitta monachus) calls recorded in Uruguay and the United States, respectively. We also evaluated whether the identity information emphasized in introduced range calls changed over time. To place our findings in an evolutionary context, we compared our results with another parrot species that exhibits well-established and distinctive regional vocal dialects that are consistent with signaling group identity. We found that both native and introduced range monk parakeet calls displayed the strongest convergence at the individual scale and minimal convergence within sites. We did not identify changes in the strength of acoustic convergence within sites over time in the introduced range calls. These results indicate that the individual identity information in learned vocalizations did not change over short evolutionary timescales in populations that experienced the social disruption of introduction. Our findings point to exciting new research directions about the robustness or responsiveness of communication systems over different evolutionary timescales.  more » « less
Award ID(s):
2010982
PAR ID:
10504981
Author(s) / Creator(s):
; ; ; ;
Editor(s):
Simon, Ralph
Publisher / Repository:
PLOS Computational Biology
Date Published:
Journal Name:
PLOS Computational Biology
Volume:
19
Issue:
7
ISSN:
1553-7358
Page Range / eLocation ID:
e1011231
Subject(s) / Keyword(s):
Acoustic communication, identity information encoding, learned vocalizations, machine learning, parrot contact calls, species introduction
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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