Schooling is a collective behavior that relies on a fish's ability to sense and respond to the other fish around it. Previous work has identified ‘rules’ of schooling – attraction to neighbors that are far away, repulsion from neighbors that are too close and alignment with neighbors at the correct distance – but we do not understand well how these rules emerge from the sensory physiology and behavior of individual fish. In particular, fish use both vision and their lateral lines to sense each other, but it is unclear how much they rely on information from these sensory modalities to coordinate schooling behavior. To address this question, we studied how the schooling of giant danios (Devario aequipinnatus) changes when they are unable to see or use their lateral lines. We found that giant danios were able to school without their lateral lines but did not school in darkness. Surprisingly, giant danios in darkness had the same attraction properties as fish in light when they were in close proximity, indicating that they could sense nearby fish with their lateral lines. However, they were not attracted to more distant fish, suggesting that long-distance attraction through vision is important for maintaining a cohesive school. These results help us expand our understanding of the roles that vision and the lateral line play in the schooling of some fish species.

Author contributions

Conceptualization: B.K.T., E.D.T.; Methodology: B.K.T., E.D.T.; Software: B.K.T.; Validation: B.K.T., E.D.T.; Formal analysis: B.K.T., A.V.-S.; Investigation: B.K.T., A.V.-S.; Resources: E.D.T.; Data curation: B.K.T., A.V.-S.; Writing - original draft: B.K.T.; Writing - review & editing: B.K.T., E.D.T., A.V.-S.; Visualization: B.K.T.; Supervision: E.D.T.; Project administration: E.D.T.; Funding acquisition: E.D.T.

Funding

This work was funded by National Science Foundation grant IOS 1652582 and Army Research Office grant W911NF-17-1-0234.

Data availability

The data can be found on GitHub along with instructions on how to run the analysis code at: https://github.com/BKTidswell/Tidswell-2024.

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