Rothamsted Research, Harpenden AL5 2JQ, UK.
Centre for Ecology and Conservation, University of Exeter, Penryn, Cornwall TR10 9FE, UK.
J R Soc Interface. 2022 Apr;19(189):20210745. doi: 10.1098/rsif.2021.0745. Epub 2022 Apr 20.
Collective behaviour can be difficult to discern because it is not limited to animal aggregations such as flocks of birds and schools of fish wherein individuals spontaneously move in the same way despite the absence of leadership. Insect swarms are, for example, a form of collective behaviour, albeit one lacking the global order seen in bird flocks and fish schools. Their collective behaviour is evident in their emergent macroscopic properties. These properties are predicted by close relatives of Okubo's 1986 [, 1-94. (doi:10.1016/0065-227X(86)90003-1)] stochastic model. Here, we argue that Okubo's stochastic model also encapsulates the cohesiveness mechanism at play in bird flocks, namely the fact that birds within a flock behave on average as if they are trapped in an elastic potential well. That is, each bird effectively behaves as if it is bound to the flock by a force that on average increases linearly as the distance from the flock centre increases. We uncover this key, but until now overlooked, feature of flocking in empirical data. This gives us a means of identifying what makes a given system collective. We show how the model can be extended to account for intrinsic velocity correlations and differentiated social relationships.
群体行为可能难以识别,因为它不仅限于动物聚集,如鸟类群和鱼群,在这些聚集中,尽管没有领导,个体仍然会自发地以相同的方式移动。昆虫群就是一种群体行为,尽管它缺乏鸟类群和鱼群中看到的全局秩序。它们的群体行为表现在其涌现的宏观特性中。这些特性可以通过与 Okubo 1986 年的随机模型密切相关的模型来预测。在这里,我们认为 Okubo 的随机模型也包含了鸟类群体中起作用的内聚机制,即群体中的鸟类平均表现得好像它们被困在弹性势阱中。也就是说,每只鸟实际上都表现得好像它被一股力束缚在鸟群中,这股力平均随着与鸟群中心距离的增加而线性增加。我们在经验数据中发现了这种关键但迄今为止被忽视的集群特征。这为我们提供了一种识别给定系统集体性的方法。我们展示了如何扩展模型以解释内在的速度相关性和差异化的社会关系。