Evidence map›Paper›PMID 35347115›Full record

ArticleNature communications2022

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 1 more

Abstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Challenges of mismatching timescales in longitudinal studies of collective behaviour.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023
    Article
  11. Article
  12. Article
  13. Article
  14. Responses in adult pied flycatcher males depend on playback song similarity to local population.Behavioral ecology : official journal of the International Society for Behavioral Ecology
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Daiping Wang *Department of Behavioural Ecology and Evolutionary Genetics, Max Planck Institute for Ornithology, 82319, Seewiesen, Germany. wangdaiping@ioz.ac.cn.ORCID http://orcid.org/0000-0002-9045-051X
Wolfgang Forstmeier *Department of Behavioural Ecology and Evolutionary Genetics, Max Planck Institute for Ornithology, 82319, Seewiesen, Germany. forstmeier@orn.mpg.de.ORCID http://orcid.org/0000-0002-5984-8925
Damien R FarineDepartment of Collective Behavior, Max Planck Institute of Animal Behavior, 78457, Konstanz, Germany. damien.farine@ieu.uzh.ch.ORCID http://orcid.org/0000-0003-2208-7613
Adriana A Maldonado-ChaparroDepartment of Collective Behavior, Max Planck Institute of Animal Behavior, 78457, Konstanz, Germany.ORCID http://orcid.org/0000-0003-0853-4966
Katrin MartinDepartment of Behavioural Ecology and Evolutionary Genetics, Max Planck Institute for Ornithology, 82319, Seewiesen, Germany.
Yifan PeiDepartment of Behavioural Ecology and Evolutionary Genetics, Max Planck Institute for Ornithology, 82319, Seewiesen, Germany.ORCID http://orcid.org/0000-0002-2411-4454
Gustavo Alarcón-NietoDepartment of Collective Behavior, Max Planck Institute of Animal Behavior, 78457, Konstanz, Germany.ORCID http://orcid.org/0000-0003-4076-3750
James A Klarevas-IrbyCenter for the Advanced Study of Collective Behaviour, University of Konstanz, Universitätsstrasse 10, 78457, Konstanz, Germany.
Shouwen MaDepartment of Behavioural Neurobiology, Max Planck Institute for Ornithology, Eberhard-Gwinner-Straße, 82319, Seewiesen, Germany.ORCID http://orcid.org/0000-0002-0058-4742
Lucy M AplinCenter for the Advanced Study of Collective Behaviour, University of Konstanz, Universitätsstrasse 10, 78457, Konstanz, Germany.ORCID http://orcid.org/0000-0001-5367-826X
Bart KempenaersDepartment of Behavioural Ecology and Evolutionary Genetics, Max Planck Institute for Ornithology, 82319, Seewiesen, Germany. b.kempenaers@orn.mpg.de.ORCID http://orcid.org/0000-0002-7505-5458

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

FinchesAnimalsFemaleLanguageMachine LearningMaleVocalization, Animal

Identifiers

PMID35347115
PMCPMC8960899

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.