Paago Natasha, Zheng Wilson, Nonacs Peter
Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, 90095, USA.
Oecologia. 2025 Apr 30;207(5):73. doi: 10.1007/s00442-025-05720-5.
Foraging ant colonies often face the challenge that food items can appear unpredictably across their territory. This is analogous to traveling salesman/salesperson problems (TSP), wherein solutions to visiting multiple possibly-rewarding sites can vary in cost, travel distance, or site revisits. However, TSP solutions for ants are likely also constrained by cognitive limitations. Rather than envisioning entire routes, ants probably determine their paths by individual-level responses to immediate stimuli, such as nestmate presence or avoiding revisiting an already explored site. Thus, complex group-level search and food retrieval patterns may self-organize from simple individual-level movement rules. Here we derive solutions through simulations that optimize net foraging gains across groups of ant-like agents. Agent search strategies evolve in three spatial networks that differ in travel distances to nests, connectivity, and modularity. We compare patterns from simulations to observed foraging of Argentine ants (Linepithema humile) in identical spatial networks. The simulations and ant experiments find foraging patterns are sensitive to both network characteristics and predictability of food appearance. Simulations are consistent in multiple ways with observed ant behavior, particularly in how network arrangements affect search effort, food encounters, and forager distributions (e.g., clustering in the more connected cells). In some distributions, however, ants find food more successfully than simulations predict. This may reflect a greater premium on encountering food in ants versus increasing find exploitation rates for agents. Overall, the results are encouraging that evolutionary optimization models incorporating relevant ant biology can successfully predict expression of complex group-level behavior.
食物可能在其领地内不可预测地出现。这类似于旅行商问题（TSP），即在访问多个可能有收获的地点时，解决方案在成本、旅行距离或地点重访次数方面可能会有所不同。然而，蚂蚁的旅行商问题解决方案可能也受到认知限制的约束。蚂蚁可能不是设想整个路线，而是通过对即时刺激（如蚁伴的存在或避免重访已经探索过的地点）的个体层面反应来确定它们的路径。因此，复杂的群体层面搜索和食物获取模式可能从简单的个体层面运动规则中自组织形成。在这里，我们通过模拟得出解决方案，这些模拟优化了类似蚂蚁的智能体群体的净觅食收益。智能体搜索策略在三个空间网络中进化，这三个网络在到巢穴的旅行距离、连通性和模块化方面有所不同。我们将模拟的模式与在相同空间网络中观察到的阿根廷蚁（Linepithema humile）的觅食情况进行比较。模拟和蚂蚁实验发现，觅食模式对网络特征和食物出现的可预测性都很敏感。模拟在多个方面与观察到的蚂蚁行为一致，特别是在网络布局如何影响搜索努力、食物相遇和觅食者分布方面（例如，在连接性更强的区域聚集）。然而，在某些分布中，蚂蚁找到食物的成功率比模拟预测的更高。这可能反映出蚂蚁在遇到食物方面比增加智能体的发现利用率更受重视。总体而言，结果令人鼓舞，即纳入相关蚂蚁生物学的进化优化模型能够成功预测复杂群体层面行为的表现。
