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Constructing a Stochastic Model of Bumblebee Flights from Experimental Data

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  • Friedrich Lenz
  • Aleksei V Chechkin
  • Rainer Klages

Abstract

The movement of organisms is subject to a multitude of influences of widely varying character: from the bio-mechanics of the individual, over the interaction with the complex environment many animals live in, to evolutionary pressure and energy constraints. As the number of factors is large, it is very hard to build comprehensive movement models. Even when movement patterns in simple environments are analysed, the organisms can display very complex behaviours. While for largely undirected motion or long observation times the dynamics can sometimes be described by isotropic random walks, usually the directional persistence due to a preference to move forward has to be accounted for, e.g., by a correlated random walk. In this paper we generalise these descriptions to a model in terms of stochastic differential equations of Langevin type, which we use to analyse experimental search flight data of foraging bumblebees. Using parameter estimates we discuss the differences and similarities to correlated random walks. From simulations we generate artificial bumblebee trajectories which we use as a validation by comparing the generated ones to the experimental data.

Suggested Citation

  • Friedrich Lenz & Aleksei V Chechkin & Rainer Klages, 2013. "Constructing a Stochastic Model of Bumblebee Flights from Experimental Data," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-7, March.
  • Handle: RePEc:plo:pone00:0059036
    DOI: 10.1371/journal.pone.0059036
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    References listed on IDEAS

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    1. Reynolds, A.M., 2010. "Balancing the competing demands of harvesting and safety from predation: Lévy walk searches outperform composite Brownian walk searches but only when foraging under the risk of predation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4740-4746.
    2. G. M. Viswanathan & Sergey V. Buldyrev & Shlomo Havlin & M. G. E. da Luz & E. P. Raposo & H. Eugene Stanley, 1999. "Optimizing the success of random searches," Nature, Nature, vol. 401(6756), pages 911-914, October.
    3. F. Lenz & D. Herde & A. Riegert & H. Kantz, 2009. "Bivariate time-periodic Fokker-Planck model for freeway traffic," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 72(3), pages 467-472, December.
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