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  Disentangling and modeling interactions in fish with burst-and-coast swimming reveal distinct alignment and attraction behaviors

Calovi, D. S., Litchinko, A., Lecheval, V., Lopez, U., Escudero, A. P., Chate, H., et al. (2018). Disentangling and modeling interactions in fish with burst-and-coast swimming reveal distinct alignment and attraction behaviors. PLoS Computational Biology, 14(1): e1005933. doi:10.1371/journal.pcbi.1005933.

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 Creators:
Calovi, Daniel S.1, Author              
Litchinko, A., Author
Lecheval, V., Author
Lopez, U., Author
Escudero, A. P., Author
Chate, H., Author              
Sire, C., Author
Theraulaz, G., Author
Affiliations:
1Centre de Recherches sur la Cognition Animale,, ou_persistent22              

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 Abstract: The development of tracking methods for automatically quantifying individual behavior and social interactions in animal groups has open up new perspectives for building quantitative and predictive models of collective behavior. In this work, we combine extensive data analyses with a modeling approach to measure, disentangle, and reconstruct the actual functional form of interactions involved in the coordination of swimming in Rummy-nose tetra (Hemi-grammus rhodostomus). This species of fish performs burst-and-coast swimming behavior that consists of sudden heading changes combined with brief accelerations followed by quasi-passive, straight decelerations. We quantify the spontaneous stochastic behavior of a fish and the interactions that govern wall avoidance and the reaction to a neighboring fish, the latter by exploiting general symmetry constraints for the interactions. In contrast with previous experimental works, we find that both attraction and alignment behaviors control the reaction of fish to a neighbor. We then exploit these results to build a model of spontaneous burst-and-coast swimming and interactions of fish, with all parameters being estimated or directly measured from experiments. This model quantitatively reproduces the key features of the motion and spatial distributions observed in experiments with a single fish and with two fish. This demonstrates the power of our method that exploits large amounts of data for disentangling and fully characterizing the interactions that govern collective behaviors in animals groups.

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 Dates: 2018-01-11
 Publication Status: Published in print
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 Table of Contents: -
 Rev. Type: -
 Identifiers: Other: WOS:000423845000030
DOI: 10.1371/journal.pcbi.1005933
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 14 (1) Sequence Number: e1005933 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1