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Journal article : Review

ManyFishes: a big team science collaboration on fish comparative cognition

Abstract:
Fishes are among the oldest and most diverse groups of vertebrates, encompassing a vast array of species that differ in morphology, ecology, and behavior. While such diversity can pose challenges to comparative cognition research, it can also offer valuable insights into how different ecological and evolutionary pressures shape cognitive abilities. As the earliest diverging extant vertebrates, and the simplest neural architecture, fishes also provide a critical window into the origins and evolution of vertebrate intelligence. However, despite their potential, we have only just begun to scratch the surface of what fish cognition can reveal, in part due to practical limitations that have constrained cross-species comparisons, including the use of small, single-species samples and a lack of standardized testing procedures. To address these challenges, we introduce ManyFishes, the first big team science collaboration dedicated to comparative cognition and behavior in fishes. Here, we discuss the benefits, potential, challenges and solutions, and impact of this large-scale collaborative effort. Like other big team science initiatives, ManyFishes relies on the ability to establish an infrastructure that facilitates communication and coordination among collaborators from diverse backgrounds, while promoting openness, transparency, and reproducibility in fish cognition research. With this paper, we aim to raise awareness of the ManyFishes initiative and invite researchers to join, contribute to, and benefit from this large-scale collaborative effort in future projects.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s10071-025-02031-3

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Author
ORCID:
0000-0002-9936-5506
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Role:
Author
ORCID:
0000-0002-7391-8934
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ORCID:
0000-0001-5849-9869
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ORCID:
0000-0001-5646-8743
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Role:
Author
ORCID:
0000-0003-3939-3852


Publisher:
Springer
Journal:
Animal Cognition More from this journal
Volume:
29
Issue:
1
Article number:
12
Publication date:
2025-12-16
Acceptance date:
2025-12-01
DOI:
EISSN:
1435-9456
ISSN:
1435-9448


Language:
English
Keywords:
Subtype:
Review
Pubs id:
2352882
UUID:
uuid_9b2dd964-05d6-4b8b-95e5-6be6c96224ec
Local pid:
pubs:2352882
Source identifiers:
3677133
Deposit date:
2026-01-20
ARK identifier:
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