A statistical method for identifying different rules of interaction between individuals in moving animal groups
Author(s) -
T. M. Schaerf,
James E. HerbertRead,
Ashley J. W. Ward
Publication year - 2021
Publication title -
journal of the royal society interface
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.655
H-Index - 139
eISSN - 1742-5689
pISSN - 1742-5662
DOI - 10.1098/rsif.2020.0925
Subject(s) - resampling , statistics , consistency (knowledge bases) , identification (biology) , set (abstract data type) , statistical hypothesis testing , mathematics , computer science , econometrics , psychology , artificial intelligence , biology , ecology , programming language
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 (Gambusia holbrooki ). We find that each of the randomization methods is reliable in terms of: repeatability ofp -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.
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