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Journal article

A large-scale acoustic dataset of a passerine with spatially variable vocal behavior: fine-scale annotations of song and call types

Abstract:
The study of bird vocal behavior provides insights into ecology, evolution, and conservation. Prior research has mostly focused on bird songs, with limited attention to calling behavior. Although the origins and functions of these vocalizations differ, no detailed annotated acoustic datasets are currently available including both bird songs and calls. Here, we present an acoustic dataset with standardized recordings and detailed annotations of singing and calling behavior in the Dupont’s lark (Chersophilus duponti), a passerine with spatially variable vocal behavior. Recordings were collected across a large spatial scale (20 populations) to capture geographic variation in vocalizations. The dataset includes 4,297 annotated songs from 191 singing males, representing 401 song types, and 795 annotated calls from 97 calling males, representing 80 call types. Annotations provide precise categorization of song and call types, enabling comparisons of individual and population vocal repertoires, geographic variation, and potential effects of habitat fragmentation on vocal behavior. This dataset is intended for reuse in studies of bird vocal behavior, comparative analyses of song and call patterns, and research on passerine acoustic communication.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s41597-026-07131-4

Authors

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Oxford college:
St Anne's College
Role:
Author
ORCID:
0000-0001-6324-0536


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Funder identifier:
https://ror.org/05r0vyz12
Grant:
MCIN/AEI/10.13039/501100011033/FEDER/UE
RYC2024-048830-I


Publisher:
Springer Nature
Journal:
Scientific Data More from this journal
Volume:
13
Issue:
1
Article number:
770
Publication date:
2026-03-30
Acceptance date:
2026-03-25
DOI:
EISSN:
2052-4463


Language:
English
Pubs id:
2397527
Local pid:
pubs:2397527
Source identifiers:
W7143361897
Deposit date:
2026-03-31
ARK identifier:

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