Machine learning reveals cryptic dialects that explain mate choice in a songbird

Daiping Wang (), Wolfgang Forstmeier (), Damien R. Farine (), Adriana A. Maldonado-Chaparro, Katrin Martin, Yifan Pei, Gustavo Alarcón-Nieto, James A. Klarevas-Irby, Shouwen Ma, Lucy M. Aplin and Bart Kempenaers ()
Additional contact information
Daiping Wang: Max Planck Institute for Ornithology
Wolfgang Forstmeier: Max Planck Institute for Ornithology
Damien R. Farine: Max Planck Institute of Animal Behavior
Adriana A. Maldonado-Chaparro: Max Planck Institute of Animal Behavior
Katrin Martin: Max Planck Institute for Ornithology
Yifan Pei: Max Planck Institute for Ornithology
Gustavo Alarcón-Nieto: Max Planck Institute of Animal Behavior
James A. Klarevas-Irby: University of Konstanz
Shouwen Ma: Max Planck Institute for Ornithology
Lucy M. Aplin: University of Konstanz
Bart Kempenaers: Max Planck Institute for Ornithology

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: Abstract Culturally transmitted communication signals – such as human language or bird song – can change over time through cultural drift, and the resulting dialects may consequently enhance the separation of populations. However, the emergence of song dialects has been considered unlikely when songs are highly individual-specific, as in the zebra finch (Taeniopygia guttata). Here we show that machine learning can nevertheless distinguish the songs from multiple captive zebra finch populations with remarkable precision, and that ‘cryptic song dialects’ predict strong assortative mating in this species. We examine mating patterns across three consecutive generations using captive populations that have evolved in isolation for about 100 generations. We cross-fostered eggs within and between these populations and used an automated barcode tracking system to quantify social interactions. We find that females preferentially pair with males whose song resembles that of the females’ adolescent peers. Our study shows evidence that in zebra finches, a model species for song learning, individuals are sensitive to differences in song that have hitherto remained unnoticed by researchers.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-022-28881-w Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28881-w

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-022-28881-w

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().