Three-Dimensional Dynamics Simulation of Fish Schooling Integrating Physiological Characteristics, Movement Patterns, and Multi-Source Perception
DOI:
https://doi.org/10.54097/j4e21955Keywords:
Fish school simulation; Self-organizing behavior; Energy metabolism; Multi-source perception; Ecological response.Abstract
During a long evolutionary process, fish schools in nature have developed complex self-organizing collective behaviors. These behaviors not only rely on local information exchange between individuals, but are also strictly limited by specific hydrological environments, individual physiological differences, and hydrodynamic effects. This article establishes a three-dimensional (3D) simulation of fish ecological dynamics, combining physiological metabolic constraints, composite perception, and heterogeneity characteristics. Based on classical local behavior interaction rules, a fluid dynamics based lateral line perception field and dynamic visual area calculation were introduced, and the process of energy recovery from wake vortices by clusters during swimming was quantified. Finally, three ecological scenarios were provided: biomimetic target induction, predator driven recombination, and hydrodynamic disturbance. This provides a reliable visual analysis tool for further exploring the ecological mechanisms of collective behavior in aquatic animals.
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[1] Zhang, H., Yang, X., & Zhang, R. (2026). Detection of dense fish schools in sonar imagery with a novel YOLOv11-SAS model. Ecological Informatics, 103646.
[2] Mao, X., Xin, Z., & Mao, X. (2026). A coupled framework for modeling fish schooling. Bioinspiration & Biomimetics.
[3] Takahashi, Y., Yoshida, T., & Yamazaki, Y. (2026). An aquaculture simulator for rainbow trout (Oncorhynchus mykiss) based on a fish schooling behavioral model and a dynamic energy budget. Scientific Reports.
[4] Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. In Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques (pp. 25–34).
[5] Couzin, I. D., Krause, J., & James, R. (2002). Collective memory and spatial sorting in animal groups. Journal of Theoretical Biology, 218(1), 1–11.
[6] Romey, W. L. (1996). Individual differences make a difference in the trajectories of simulated schools of fish. Ecological Modelling, 92(1), 65–77.
[7] Videler, J. J., & Weihs, D. (1982). Energetic advantages of burst-and-coast swimming of fish at high speeds. Journal of Experimental Biology, 97(1), 169–178.
[8] Weihs, D. (1973). Hydromechanics of fish schooling. Nature, 241(5387), 290–291.
[9] Hemelrijk, C. K., & Hildenbrandt, H. (2012). Schools of fish and flocks of birds: Their shape and internal structure by self-organization. Interface Focus, 2(6), 726–737.
[10] Partridge, B. L., & Pitcher, T. J. (1980). The sensory basis of fish schools: Relative roles of lateral line and vision. Journal of Comparative Physiology, 135(4), 315–325.
[11] Jolles, J. W., Boogert, N. J., & Sridhar, V. H. (2017). Consistent individual differences drive collective behavior and group functioning of schooling fish. Current Biology, 27(18), 2862–2868.
[12] Kieffer, J. D. (2000). Limits to exhaustive exercise in fish. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 126(2), 161–179.
[13] Scarabello, M., Heigenhauser, G. J. F., & Wood, C. M. (1991). The oxygen debt hypothesis in juvenile rainbow trout after exhaustive exercise. Respiration Physiology, 84(2), 245–259.
[14] Kramer, D. L., & McLaughlin, R. L. (2001). The behavioral ecology of intermittent locomotion. American Zoologist, 41(2), 137–153.
[15] Strandburg-Peshkin, A., Twomey, C. R., & Bode, N. W. F. (2013). Visual sensory networks and effective information transfer in animal groups. Current Biology, 23(17), R709–R711.
[16] Domenici, P., & Blake, R. W. (1997). The kinematics and performance of fish fast-start swimming. Journal of Experimental Biology, 200(8), 1165–1178.
[17] Filella, A., Nadal, F., & Sire, C. (2018). Model of collective fish behavior with hydrodynamic interactions. Physical Review Letters, 120(19), 198101.
[18] Zhang, Y., & Lauder, G. V. (2024). Energy conservation by collective movement in schooling fish. eLife, 12, RP90352.
[19] Hemelrijk, C. K., & Hildenbrandt, H. (2012). Schools of fish and flocks of birds: Their shape and internal structure by self-organization. Interface Focus, 2(6), 726–737.
[20] Papageorgiou, D., & Farine, D. R. (2020). Group size and composition influence collective movement in a highly social terrestrial bird. eLife, 9, e59902.
[21] Calovi, D. S., Lopez, U., & Ngo, S. (2014). Swarming, schooling, milling: Phase diagram of a data-driven fish school model. New Journal of Physics, 16(1), 015026.
[22] Butail, S., Bartolini, T., & Porfiri, M. (2013). Collective response of zebrafish shoals to a free-swimming robotic fish. PLOS ONE, 8(10), e76123.
[23] Klamser, P. P., & Romanczuk, P. (2021). Collective predator evasion: Putting the criticality hypothesis to the test. PLOS Computational Biology, 17(3), e1008832.
[24] Thandiackal, R., & Lauder, G. (2023). In-line swimming dynamics revealed by fish interacting with a robotic mechanism. eLife, 12, e81392.
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