Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands.
Centre for Ecology and Conservation, University of Exeter, Penryn, United Kingdom.
PLoS Comput Biol. 2022 Jan 10;18(1):e1009772. doi: 10.1371/journal.pcbi.1009772. eCollection 2022 Jan.
Bird flocks under predation demonstrate complex patterns of collective escape. These patterns may emerge by self-organization from local interactions among group-members. Computational models have been shown to be valuable for identifying what behavioral rules may govern such interactions among individuals during collective motion. However, our knowledge of such rules for collective escape is limited by the lack of quantitative data on bird flocks under predation in the field. In the present study, we analyze the first GPS trajectories of pigeons in airborne flocks attacked by a robotic falcon in order to build a species-specific model of collective escape. We use our model to examine a recently identified distance-dependent pattern of collective behavior: the closer the prey is to the predator, the higher the frequency with which flock members turn away from it. We first extract from the empirical data of pigeon flocks the characteristics of their shape and internal structure (bearing angle and distance to nearest neighbors). Combining these with information on their coordination from the literature, we build an agent-based model adjusted to pigeons' collective escape. We show that the pattern of turning away from the predator with increased frequency when the predator is closer arises without prey prioritizing escape when the predator is near. Instead, it emerges through self-organization from a behavioral rule to avoid the predator independently of their distance to it. During this self-organization process, we show how flock members increase their consensus over which direction to escape and turn collectively as the predator gets closer. Our results suggest that coordination among flock members, combined with simple escape rules, reduces the cognitive costs of tracking the predator while flocking. Such escape rules that are independent of the distance to the predator can now be investigated in other species. Our study showcases the important role of computational models in the interpretation of empirical findings of collective behavior.
鸟类在被捕食时会表现出复杂的集体逃避模式。这些模式可能是通过个体之间的局部相互作用自组织形成的。计算模型已被证明对于确定在集体运动中个体之间的行为规则很有价值。然而，由于缺乏野外捕食下鸟类群体的定量数据，我们对集体逃避的这种规则的了解受到限制。在本研究中，我们分析了在受到机器鹰攻击时，空中鸽群的第一个 GPS 轨迹，以建立一种特定于物种的集体逃避模型。我们使用我们的模型来检验最近发现的一种距离相关的集体行为模式：猎物离捕食者越近，鸟群成员远离捕食者的频率就越高。我们首先从鸽群的经验数据中提取它们的形状和内部结构特征（偏航角和与最近邻居的距离）。将这些特征与文献中关于它们协调的信息相结合，我们构建了一个基于代理的模型，该模型适应于鸽子的集体逃避。我们表明，当捕食者靠近时，远离捕食者的频率增加的模式是由于猎物在捕食者附近时没有优先考虑逃避，而是通过独立于其与捕食者的距离的行为规则自组织产生的。在这个自组织过程中，我们展示了如何随着捕食者的靠近，鸟群成员如何增加它们对逃离方向的共识，并集体转向。我们的结果表明，鸟群成员之间的协调，加上简单的逃避规则，可以降低在集体飞行时跟踪捕食者的认知成本。现在可以在其他物种中研究与捕食者距离无关的这种逃避规则。我们的研究展示了计算模型在解释集体行为的经验发现方面的重要作用。
