Data from: Argentine ants (Linepithema humile) use adaptable transportation networks to track changes in resource quality

Transportation networks play a crucial role in human and animal societies. For a transportation network to be efficient, it must have adequate capacity to meet traffic demand. Network design becomes increasingly difficult in situations where traffic demand can change unexpectedly. In humans, network design is often constrained by path dependency because it is difficult to move a road once it is built. A similar issue theoretically faces pheromone-trail laying social insects; once a trail has been laid, positive feedback makes re-routing difficult because new trails cannot compete with continually-reinforced pre-existing trails. In the present study we examine the response of Argentine ant colonies and their trail networks to variable environments where resources differ in quality and change unexpectedly. We found that Argentine ant colonies effectively tracked changes in food quality such that colonies allocated the highest proportion of foragers to the most rewarding feeder. Ant colonies maximised access to high concentration feeders by building additional trails and routes connecting the nest to the feeder. Trail networks appeared to form via a pruning process in which lower traffic trails were gradually removed from the network. At the same time, we observed several instances where new trails appear to have been built to accommodate a surge in demand. The combination of trail building when traffic demand is high and trail pruning when traffic demand is low results in a demand-driven network formation system that allows ants to monopolise multiple dynamic resources.

交通网络在人类与动物社会中均发挥着至关重要的作用。若要保障交通网络的运行效率，其必须具备足够的通行能力以适配交通需求。当交通需求出现无预期变动时，网络设计的难度会显著提升。就人类社会而言，网络设计常受路径依赖的制约——道路一旦建成便难以迁改。依靠信息素痕迹行进的社会性昆虫在理论上也面临类似难题：一旦痕迹被铺设完成，正向反馈便会使得重新规划路线变得困难，因为新痕迹无法与持续获得强化的既有痕迹相抗衡。本研究聚焦于阿根廷蚁（Argentine ant）群落及其信息素痕迹网络，探究其在资源质量各异且变动无规律的可变环境中的响应模式。研究结果显示，阿根廷蚁群落能够有效追踪食物资源的质量变化，将最高比例的觅食蚁分配至收益最优的喂食点。蚁群通过构建连接蚁巢与喂食点的额外痕迹与路径，最大化获取高浓度食物资源的机会。痕迹网络的形成似乎遵循修剪机制：通行量较低的痕迹会逐渐从网络中被移除。与此同时，研究团队观测到多起新痕迹被搭建以应对需求激增的案例。当交通需求高涨时新建痕迹、需求低迷时修剪痕迹的结合机制，催生了一套需求驱动的网络形成系统，使蚂蚁能够垄断多个动态变化的资源。