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The Spatial Dynamics of Predators and the Benefits and Costs of Sharing Information

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  • Matthieu Barbier
  • James R Watson

Abstract

Predators of all kinds, be they lions hunting in the Serengeti or fishermen searching for their catch, display various collective strategies. A common strategy is to share information about the location of prey. However, depending on the spatial characteristics and mobility of predators and prey, information sharing can either improve or hinder individual success. Here, our goal is to investigate the interacting effects of space and information sharing on predation efficiency, represented by the expected rate at which prey are found and consumed. We derive a feeding functional response that accounts for both spatio-temporal heterogeneity and communication, and validate this mathematical analysis with a computational agent-based model. This agent-based model has an explicit yet minimal representation of space, as well as information sharing about the location of prey. The analytical model simplifies predator behavior into a few discrete states and one essential trade-off, between the individual benefit of acquiring information and the cost of creating spatial and temporal correlation between predators. Despite the absence of an explicit spatial dimension in these equations, they quantitatively predict the predator consumption rates measured in the agent-based simulations across the explored parameter space. Together, the mathematical analysis and agent-based simulations identify the conditions for when there is a benefit to sharing information, and also when there is a cost.Author Summary: When should we work together and when should we work alone? This question is central to our efforts to understand social and ecological systems alike, from lions hunting in the Serengeti to fishermen searching for their catch. Here, we develop a mathematical modeling framework to identify the essential spatial factors controlling the benefits and costs of sharing information. Our approach marries computation with mathematical analysis, and our results highlight that it is only under certain spatial conditions that information sharing is a useful cooperative strategy. Notably, we find conditions for which fully collective and fully individual search are both attractive.

Suggested Citation

  • Matthieu Barbier & James R Watson, 2016. "The Spatial Dynamics of Predators and the Benefits and Costs of Sharing Information," PLOS Computational Biology, Public Library of Science, vol. 12(10), pages 1-22, October.
  • Handle: RePEc:plo:pcbi00:1005147
    DOI: 10.1371/journal.pcbi.1005147
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    References listed on IDEAS

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    1. Keith Evans & Quinn Weninger, 2014. "Information Sharing and Cooperative Search in Fisheries," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 58(3), pages 353-372, July.
    2. Samraat Pawar & Anthony I. Dell & Van M. Savage, 2012. "Dimensionality of consumer search space drives trophic interaction strengths," Nature, Nature, vol. 486(7404), pages 485-489, June.
    3. Jennifer E. Smith & Russell C. Van Horn & Katherine S. Powning & Alison R. Cole & Katharine E. Graham & Sandra K. Memenis & Kay E. Holekamp, 2010. "Evolutionary forces favoring intragroup coalitions among spotted hyenas and other animals," Behavioral Ecology, International Society for Behavioral Ecology, vol. 21(2), pages 284-303.
    4. Smaldino, Paul E. & Schank, Jeffrey C., 2012. "Movement patterns, social dynamics, and the evolution of cooperation," Theoretical Population Biology, Elsevier, vol. 82(1), pages 48-58.
    5. Joo, Rocío & Bertrand, Sophie & Chaigneau, Alexis & Ñiquen, Miguel, 2011. "Optimization of an artificial neural network for identifying fishing set positions from VMS data: An example from the Peruvian anchovy purse seine fishery," Ecological Modelling, Elsevier, vol. 222(4), pages 1048-1059.
    6. Holland, D.S., 2010. "Markets, pooling and insurance for managing bycatch in fisheries," Ecological Economics, Elsevier, vol. 70(1), pages 121-133, November.
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    Cited by:

    1. Burgess, Matthew G. & Carrella, Ernesto & Drexler, Michael & Axtell, Robert L. & Bailey, Richard M. & Watson, James R. & Cabral, Reniel B. & Clemence, Michaela & Costello, Christopher & Dorsett, Chris, 2018. "Opportunities for agent-based modeling in human dimensions of fisheries," SocArXiv gzhm5, Center for Open Science.

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