Abstract
Many animal species divide space into a patchwork of home ranges, yet there is little consensus on the mechanisms individuals use to maintain fidelity to particular locations. Theory suggests that animal movement could be based upon simple behavioural rules that use local information such as olfactory deposits, or global strategies, such as long-range biases toward landmarks. However, empirical studies have rarely attempted to distinguish between these mechanisms. Here, we perform individual tracking experiments on four species of social insects, and find that colonies consist of different groups of workers that inhabit separate but partially-overlapping spatial zones. Our trajectory analysis and simulations suggest that worker movement is consistent with two local mechanisms: one in which workers increase movement diffusivity outside their primary zone, and another in which workers modulate turning behaviour when approaching zone boundaries. Parallels with other organisms suggest that local mechanisms might represent a universal method for spatial partitioning in animal populations.
Original language | English |
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Article number | 6985 |
Number of pages | 15 |
Journal | Nature Communications |
Volume | 13 |
Issue number | 1 |
DOIs | |
Publication status | Published - 15 Nov 2022 |
Bibliographical note
Funding Information:T.O.R. acknowledges funding by an EU Marie Curie Actions Fellowship, no. 30114. N.S. acknowledges funding by the European Research Council (ERC Starting Grant ‘DISEASE’, no. 802628). L.K. acknowledges funding by the Swiss NSF and the European Research Council (ERC Advanced Grants ‘Social Life’, no. 249375, and ‘resiliANT’, no. 741491). The computations were performed at the Vital-IT (http://www.vital-it.ch) Center for high-performance computing and at the Advanced Computing Research Centre of the University of Bristol. The authors thank Enrico Gavagnin, Thomas Gorochowski, Graham Thompson, Yuko Ulrich, Erik Frank, Tomas Kay, Danielle Mersch for useful comments, and Guillaume Cailleau (www.data-partner.ch) for the illustrations.
Funding Information:
T.O.R. acknowledges funding by an EU Marie Curie Actions Fellowship, no. 30114. N.S. acknowledges funding by the European Research Council (ERC Starting Grant ‘DISEASE’, no. 802628). L.K. acknowledges funding by the Swiss NSF and the European Research Council (ERC Advanced Grants ‘Social Life’, no. 249375, and ‘resiliANT’, no. 741491). The computations were performed at the Vital-IT ( http://www.vital-it.ch ) Center for high-performance computing and at the Advanced Computing Research Centre of the University of Bristol. The authors thank Enrico Gavagnin, Thomas Gorochowski, Graham Thompson, Yuko Ulrich, Erik Frank, Tomas Kay, Danielle Mersch for useful comments, and Guillaume Cailleau ( www.data-partner.ch ) for the illustrations.
Publisher Copyright:
© 2022, The Author(s).
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