Centre for Ecological Sciences, Indian Institute of Science, Bengaluru 560012, India.
Philos Trans R Soc Lond B Biol Sci. 2020 Sep 14;375(1807):20190381. doi: 10.1098/rstb.2019.0381. Epub 2020 Jul 27.
In animal groups, individual decisions are best characterized by probabilistic rules. Furthermore, animals of many species live in small groups. Probabilistic interactions among small numbers of individuals lead to a so-called at the group level. Theory predicts that the strength of intrinsic noise is not a constant but often depends on the collective state of the group; hence, it is also called a or a . Surprisingly, such noise may produce collective order. However, only a few empirical studies on collective behaviour have paid attention to such effects owing to the lack of methods that enable us to connect data with theory. Here, we demonstrate a method to characterize the role of stochasticity directly from high-resolution time-series data of collective dynamics. We do this by employing two well-studied individual-based toy models of collective behaviour. We argue that the group-level noise may encode important information about the underlying processes at the individual scale. In summary, we describe a method that enables us to establish connections between empirical data of animal (or cellular) collectives and the phenomenon of noise-induced states, a field that is otherwise largely limited to the theoretical literature. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.
在动物群体中，个体决策最好用概率规则来描述。此外，许多物种的动物都生活在小群体中。小群体中个体之间的概率相互作用导致了所谓的群体层面的。理论预测，内在噪声的强度不是一个常数，而是经常取决于群体的集体状态；因此，它也被称为或。令人惊讶的是，这种噪声可能会产生集体秩序。然而，由于缺乏将数据与理论联系起来的方法，只有少数关于集体行为的实证研究关注到了这种效应。在这里，我们展示了一种从集体动力学的高分辨率时间序列数据中直接描述随机性作用的方法。我们通过使用两个经过充分研究的集体行为的基于个体的玩具模型来实现这一点。我们认为，群体层面的噪声可能编码了关于个体尺度下潜在过程的重要信息。总之，我们描述了一种方法，使我们能够在动物（或细胞）群体的经验数据和噪声诱导状态现象之间建立联系，而这个领域在很大程度上仅限于理论文献。本文是主题为“生物系统中集体迁移的多尺度分析和建模”的一部分。
