multi-scale inference of interaction rules in animal groups using bayesian model selection多尺度的交互推理规则在动物组使用贝叶斯模型选择.pdfVIP

Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection 1 1 ¨ 1 2 3 Richard P. Mann *, Andrea Perna , Daniel Strombom , Roman Garnett , James E. Herbert-Read , David J. T. Sumpter1, Ashley J. W. Ward3 1 Department of Mathematics, Uppsala University, Uppsala, Sweden, 2 Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America, 3 School of Biological Sciences, University of Sydney, Sydney, Australia Abstract Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis). We show that these exhibit a stereotypical ‘phase transition’, whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have ‘memory’ of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture fine scale rules of interaction, which are primarily m