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Density distribution and size sorting in fish schools: an individual-based model

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  • Charlotte K. Hemelrijk
  • Hanspeter Kunz

Abstract

In fish schools the density varies per location and often individuals are sorted according to familiarity and/or body size. High density is considered advantageous for protection against predators and this sorting is believed to be advantageous not only to avoid predators but also for finding food. In this paper, we list a number of mechanisms and we study, with the help of an individual-based model of schooling agents, which spatial patterns may result from them. In our model, schooling is regulated by the following rules: avoiding those that are close by, aligning to those at intermediate distances, and moving towards others further off. Regarding kinship/familiarity, we study patterns that come about when agents actively choose to be close to related agents (i.e., 'active sorting'). Regarding body size, we study what happens when agents merely differ in size but behave according to the usual schooling rules ('size difference model'), when agents choose to be close to those of similar size, and when small agents avoid larger ones ('risk avoidance'). Several spatial configurations result: during 'active sorting' familiar agents group together anywhere in the shoal, but agents of different size group concentrically, whereby the small agents occupy the center and the large ones the periphery ('size difference model' and 'active sorting'). If small agents avoid the risk of being close to large ones, however, small agents end up at the periphery and large ones occupy the center ('risk avoidance'). Spatial configurations are also influenced by the composition of the group, namely the percentage of agents of each type. Furthermore, schools are usually oblong and their density is always greatest near the front. We explain the way in which these patterns emerge and indicate how results of our model may guide the study of spatial patterns in real animals. Copyright 2005.

Suggested Citation

  • Charlotte K. Hemelrijk & Hanspeter Kunz, 2005. "Density distribution and size sorting in fish schools: an individual-based model," Behavioral Ecology, International Society for Behavioral Ecology, vol. 16(1), pages 178-187, January.
  • Handle: RePEc:oup:beheco:v:16:y:2005:i:1:p:178-187
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    File URL: http://hdl.handle.net/10.1093/beheco/arh149
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    Citations

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    Cited by:

    1. Vabø, Rune & Skaret, Georg, 2008. "Emerging school structures and collective dynamics in spawning herring: A simulation study," Ecological Modelling, Elsevier, vol. 214(2), pages 125-140.
    2. Dkhili, Jamila & Berger, Uta & Idrissi Hassani, Lalla Mina & Ghaout, Saïd & Peters, Ronny & Piou, Cyril, 2017. "Self-organized spatial structures of locust groups emerging from local interaction," Ecological Modelling, Elsevier, vol. 361(C), pages 26-40.
    3. Barbaro, Alethea B.T. & Taylor, Kirk & Trethewey, Peterson F. & Youseff, Lamia & Birnir, Björn, 2009. "Discrete and continuous models of the dynamics of pelagic fish: Application to the capelin," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(12), pages 3397-3414.
    4. Beaudouin, Rémy & Monod, Gilles & Ginot, Vincent, 2008. "Selecting parameters for calibration via sensitivity analysis: An individual-based model of mosquitofish population dynamics," Ecological Modelling, Elsevier, vol. 218(1), pages 29-48.
    5. Ye Yuan & Xuebo Chen & Qiubai Sun & Tianyun Huang, 2017. "Analysis of topological relationships and network properties in the interactions of human beings," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-22, August.
    6. Giacomini, Henrique Corrêa & De Marco, Paulo & Petrere, Miguel, 2009. "Exploring community assembly through an individual-based model for trophic interactions," Ecological Modelling, Elsevier, vol. 220(1), pages 23-39.
    7. Wang, Xin & Liu, Shuo & Yu, Yifan & Yue, Shengzhi & Liu, Ying & Zhang, Fumin & Lin, Yuanshan, 2023. "Modeling collective motion for fish schooling via multi-agent reinforcement learning," Ecological Modelling, Elsevier, vol. 477(C).
    8. Romey, William L. & Vidal, Jose M., 2013. "Sum of heterogeneous blind zones predict movements of simulated groups," Ecological Modelling, Elsevier, vol. 258(C), pages 9-15.
    9. Niizato, Takayuki & Gunji, Yukio-Pegio, 2011. "Metric–topological interaction model of collective behavior," Ecological Modelling, Elsevier, vol. 222(17), pages 3041-3049.
    10. Viscido, Steven V. & Parrish, Julia K. & Grünbaum, Daniel, 2007. "Factors influencing the structure and maintenance of fish schools," Ecological Modelling, Elsevier, vol. 206(1), pages 153-165.
    11. Reuter, Hauke & Kruse, Maren & Rovellini, Alberto & Breckling, Broder, 2016. "Evolutionary trends in fish schools in heterogeneous environments," Ecological Modelling, Elsevier, vol. 326(C), pages 23-35.
    12. Pascal P Klamser & Pawel Romanczuk, 2021. "Collective predator evasion: Putting the criticality hypothesis to the test," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-21, March.

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