Literature DB >> 34229461

Collective detection based on visual information in animal groups.

Jacob D Davidson1,2,3, Matthew M G Sosna4, Colin R Twomey5,6, Vivek H Sridhar1,2,3, Simon P Leblanc4, Iain D Couzin1,2,3.   

Abstract

We investigate key principles underlying individual, and collective, visual detection of stimuli, and how this relates to the internal structure of groups. While the individual and collective detection principles are generally applicable, we employ a model experimental system of schooling golden shiner fish (Notemigonus crysoleucas) to relate theory directly to empirical data, using computational reconstruction of the visual fields of all individuals. This reveals how the external visual information available to each group member depends on the number of individuals in the group, the position within the group, and the location of the external visually detectable stimulus. We find that in small groups, individuals have detection capability in nearly all directions, while in large groups, occlusion by neighbours causes detection capability to vary with position within the group. To understand the principles that drive detection in groups, we formulate a simple, and generally applicable, model that captures how visual detection properties emerge due to geometric scaling of the space occupied by the group and occlusion caused by neighbours. We employ these insights to discuss principles that extend beyond our specific system, such as how collective detection depends on individual body shape, and the size and structure of the group.

Entities:  

Keywords:  collective behaviour; detection; vision

Year:  2021        PMID: 34229461     DOI: 10.1098/rsif.2021.0142

Source DB:  PubMed          Journal:  J R Soc Interface        ISSN: 1742-5662            Impact factor:   4.118


  2 in total

1.  Both prey and predator features predict the individual predation risk and survival of schooling prey.

Authors:  Jolle Wolter Jolles; Matthew M G Sosna; Geoffrey P F Mazué; Colin R Twomey; Joseph Bak-Coleman; Daniel I Rubenstein; Iain D Couzin
Journal:  Elife       Date:  2022-07-19       Impact factor: 8.713

Review 2.  Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets.

Authors:  Assa Bentzur; Shahar Alon; Galit Shohat-Ophir
Journal:  Int J Mol Sci       Date:  2022-03-30       Impact factor: 5.923

  2 in total

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