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Deciphering Interactions in Moving Animal Groups

Overview of attention for article published in PLoS Computational Biology, September 2012
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Title
Deciphering Interactions in Moving Animal Groups
Published in
PLoS Computational Biology, September 2012
DOI 10.1371/journal.pcbi.1002678
Pubmed ID
Authors

Jacques Gautrais, Francesco Ginelli, Richard Fournier, Stéphane Blanco, Marc Soria, Hugues Chaté, Guy Theraulaz

Abstract

Collective motion phenomena in large groups of social organisms have long fascinated the observer, especially in cases, such as bird flocks or fish schools, where large-scale highly coordinated actions emerge in the absence of obvious leaders. However, the mechanisms involved in this self-organized behavior are still poorly understood, because the individual-level interactions underlying them remain elusive. Here, we demonstrate the power of a bottom-up methodology to build models for animal group motion from data gathered at the individual scale. Using video tracks of fish shoal in a tank, we show how a careful, incremental analysis at the local scale allows for the determination of the stimulus/response function governing an individual's moving decisions. We find in particular that both positional and orientational effects are present, act upon the fish turning speed, and depend on the swimming speed, yielding a novel schooling model whose parameters are all estimated from data. Our approach also leads to identify a density-dependent effect that results in a behavioral change for the largest groups considered. This suggests that, in confined environment, the behavioral state of fish and their reaction patterns change with group size. We debate the applicability, beyond the particular case studied here, of this novel framework for deciphering interactions in moving animal groups.

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Mendeley readers

The data shown below were compiled from readership statistics for 255 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
United Kingdom 3 1%
Switzerland 2 <1%
Germany 1 <1%
Hungary 1 <1%
Brazil 1 <1%
Spain 1 <1%
China 1 <1%
Unknown 241 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 56 22%
Researcher 41 16%
Student > Master 36 14%
Student > Bachelor 24 9%
Professor > Associate Professor 22 9%
Other 50 20%
Unknown 26 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 64 25%
Physics and Astronomy 57 22%
Engineering 30 12%
Computer Science 21 8%
Environmental Science 8 3%
Other 37 15%
Unknown 38 15%