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Record W4417076993 · doi:10.1111/ibi.70011

Fight song: variation in singing behaviour and song structure during natural agonistic interactions in a tropical songbird, Adelaide's Warbler ( <i>Setophaga adelaidae</i> )

2025· article· en· W4417076993 on OpenAlexafffund
Peter C. Mower, Juleyska Vazquez‐Cardona, Tyler R. Bonnell, David M. Logue

Bibliographic record

VenueIbis · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsUniversity of CalgaryUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Lethbridge
KeywordsAgonistic behaviourSongbirdWarblerSingingAnimal communicationNatural (archaeology)

Abstract

fetched live from OpenAlex

Birds may use their singing behaviours and song structure as agonistic signals in territorial encounters. We conducted an observational study to test this hypothesis in male Adelaide's Warblers Setophaga adelaidae , a tropical songbird that defends a territory year‐round. We described two singing behaviours and nine song structure variables (including vocal performance measures) around the time of natural territorial encounters. We found that birds decreased their song rate and song type switching around the time of encounters. Our findings allow us to reject the hypotheses that male Adelaide's Warblers use high song type diversity or high song rates as agonistic signals. They are, however, consistent with the hypothesis that repetitive singing may be an agonistic signal. Our results also suggest that song may not be an important agonistic signal in close range encounters and low song rates may provoke aggression. This study demonstrates how an observational approach grounds our understanding of aggressive signalling in the reality of natural agonistic encounters. Interestingly, our findings suggest that male Adelaide's Warblers mediate aggressive encounters with repetitive songs rather than high vocal performance or song diversity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.274
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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