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Social information use and collective foraging in a pursuit diving seabird

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  • Julian C Evans
  • Colin J Torney
  • Stephen C Votier
  • Sasha R X Dall

Abstract

Individuals of many species utilise social information whilst making decisions. While many studies have examined social information in making large scale decisions, there is increasing interest in the use of fine scale social cues in groups. By examining the use of these cues and how they alter behaviour, we can gain insights into the adaptive value of group behaviours. We investigated the role of social information in choosing when and where to dive in groups of socially foraging European shags. From this we aimed to determine the importance of social information in the formation of these groups. We extracted individuals’ surface trajectories and dive locations from video footage of collective foraging and used computational Bayesian methods to infer how social interactions influence diving. Examination of group spatial structure shows birds form structured aggregations with higher densities of conspecifics directly in front of and behind focal individuals. Analysis of diving behaviour reveals two distinct rates of diving, with birds over twice as likely to dive if a conspecific dived within their visual field in the immediate past. These results suggest that shag group foraging behaviour allows individuals to sense and respond to their environment more effectively by making use of social cues.

Suggested Citation

  • Julian C Evans & Colin J Torney & Stephen C Votier & Sasha R X Dall, 2019. "Social information use and collective foraging in a pursuit diving seabird," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-15, September.
  • Handle: RePEc:plo:pone00:0222600
    DOI: 10.1371/journal.pone.0222600
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    References listed on IDEAS

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    1. Patil, Anand & Huard, David & Fonnesbeck, Christopher J., 2010. "PyMC: Bayesian Stochastic Modelling in Python," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i04).
    2. Sean A. Rands & Guy Cowlishaw & Richard A. Pettifor & J. Marcus Rowcliffe & Rufus A. Johnstone, 2003. "Spontaneous emergence of leaders and followers in foraging pairs," Nature, Nature, vol. 423(6938), pages 432-434, May.
    3. Brian J Dermody & Colby J Tanner & Andrew L Jackson, 2011. "The Evolutionary Pathway to Obligate Scavenging in Gyps Vultures," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-6, September.
    4. Sasha R X Dall & Jonathan Wright, 2009. "Rich Pickings Near Large Communal Roosts Favor ‘Gang’ Foraging by Juvenile Common Ravens, Corvus corax," PLOS ONE, Public Library of Science, vol. 4(2), pages 1-7, February.
    5. Richard P Mann & Andrea Perna & Daniel Strömbom & Roman Garnett & James E Herbert-Read & David J T Sumpter & Ashley J W Ward, 2013. "Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-13, March.
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    Cited by:

    1. Nauta, Johannes & Simoens, Pieter & Khaluf, Yara, 2022. "Group size and resource fractality drive multimodal search strategies: A quantitative analysis on group foraging," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).

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