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一种用于识别移动动物群体中个体之间不同相互作用规则的统计方法。

A statistical method for identifying different rules of interaction between individuals in moving animal groups.

机构信息

School of Science and Technology, University of New England, Armidale, New South Wales 2351, Australia.

Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK.

出版信息

J R Soc Interface. 2021 Mar;18(176):20200925. doi: 10.1098/rsif.2020.0925. Epub 2021 Mar 31.

Abstract

The emergent patterns of collective motion are thought to arise from application of individual-level rules that govern how individuals adjust their velocity as a function of the relative position and behaviours of their neighbours. Empirical studies have sought to determine such rules of interaction applied by 'average' individuals by aggregating data from multiple individuals across multiple trajectory sets. In reality, some individuals within a group may interact differently from others, and such individual differences can have an effect on overall group movement. However, comparisons of rules of interaction used by individuals in different contexts have been largely qualitative. Here we introduce a set of randomization methods designed to determine statistical differences in the rules of interaction between individuals. We apply these methods to a case study of leaders and followers in pairs of freely exploring eastern mosquitofish (). We find that each of the randomization methods is reliable in terms of: repeatability of -values, consistency in identification of significant differences and similarity between distributions of randomization-based test statistics. We observe convergence of the distributions of randomization-based test statistics across repeat calculations, and resolution of any ambiguities regarding significant differences as the number of randomization iterations increases.

摘要

集体运动的涌现模式被认为是由个体层面的规则所产生的,这些规则支配着个体如何根据相对位置和邻居的行为来调整自身速度。实证研究试图通过聚合多个个体在多个轨迹集合中的数据来确定“平均”个体所应用的交互规则。实际上,群体中的一些个体可能与其他个体的相互作用方式不同,这种个体差异会对整体群体运动产生影响。然而,对不同情境下个体所使用的交互规则的比较在很大程度上是定性的。在这里,我们引入了一组随机化方法,旨在确定个体之间交互规则的统计学差异。我们将这些方法应用于一对自由探索的东部食蚊鱼(Gambusia holbrooki)中领导者和追随者的案例研究。我们发现,对于每个随机化方法来说,以下方面都是可靠的:- 值的可重复性、确定显著差异的一致性以及基于随机化的测试统计量分布之间的相似性。我们观察到,随着重复计算次数的增加,基于随机化的测试统计量分布趋于收敛,并且随着随机化迭代次数的增加,关于显著差异的任何歧义都得到解决。

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3
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4
Deep attention networks reveal the rules of collective motion in zebrafish.
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5
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Proc Biol Sci. 2019 May 29;286(1903):20190891. doi: 10.1098/rspb.2019.0891.
6
Cohesion, order and information flow in the collective motion of mixed-species shoals.
R Soc Open Sci. 2018 Dec 12;5(12):181132. doi: 10.1098/rsos.181132. eCollection 2018 Dec.
7
Data-driven modelling of social forces and collective behaviour in zebrafish.
J Theor Biol. 2018 Apr 14;443:39-51. doi: 10.1016/j.jtbi.2018.01.011. Epub 2018 Jan 31.
8
Disentangling and modeling interactions in fish with burst-and-coast swimming reveal distinct alignment and attraction behaviors.
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