Romenskyy Maksym, Herbert-Read James E, Ward Ashley J W, Sumpter David J T
Department of Mathematics, Uppsala University, PO Box 480, Uppsala 75106, Sweden.
Department of Zoology, Stockholm University, Stockholm 10691, Sweden.
R Soc Open Sci. 2017 Apr 26;4(4):161056. doi: 10.1098/rsos.161056. eCollection 2017 Apr.
While a rich variety of self-propelled particle models propose to explain the collective motion of fish and other animals, rigorous statistical comparison between models and data remains a challenge. Plausible models should be flexible enough to capture changes in the collective behaviour of animal groups at their different developmental stages and group sizes. Here, we analyse the statistical properties of schooling fish () through a combination of experiments and simulations. We make novel use of a Boltzmann inversion method, usually applied in molecular dynamics, to identify the effective potential of the mean force of fish interactions. Specifically, we show that larger fish have a larger repulsion zone, but stronger attraction, resulting in greater alignment in their collective motion. We model the collective dynamics of schools using a self-propelled particle model, modified to include varying particle speed and a local repulsion rule. We demonstrate that the statistical properties of the fish schools are reproduced by our model, thereby capturing a number of features of the behaviour and development of schooling fish.
虽然有各种各样的自驱动粒子模型试图解释鱼类和其他动物的集体运动,但模型与数据之间进行严格的统计比较仍然是一项挑战。合理的模型应足够灵活,以捕捉动物群体在不同发育阶段和群体规模下集体行为的变化。在这里,我们通过实验和模拟相结合的方式分析了集群鱼类的统计特性。我们创新性地使用了通常应用于分子动力学的玻尔兹曼反演方法,来确定鱼类相互作用平均力的有效势。具体而言,我们表明较大的鱼具有更大的排斥区,但吸引力更强,这导致它们在集体运动中具有更大的对齐性。我们使用自驱动粒子模型对鱼群的集体动力学进行建模,并进行了修改,以包括变化的粒子速度和局部排斥规则。我们证明我们的模型再现了鱼群的统计特性,从而捕捉到了集群鱼类行为和发育的一些特征。