ISTA (Institute of Science and Technology Austria), Am Campus 1, AT-3400, Klosterneuburg, Austria.
Department of Mathematical Analysis and Numerics, Faculty of Mathematics, Physics, and Informatics, Comenius University, Mlynska Dolina, SK-84248, Bratislava, Slovakia.
Nat Commun. 2023 Jun 3;14(1):3232. doi: 10.1038/s41467-023-38947-y.
Cooperative disease defense emerges as group-level collective behavior, yet how group members make the underlying individual decisions is poorly understood. Using garden ants and fungal pathogens as an experimental model, we derive the rules governing individual ant grooming choices and show how they produce colony-level hygiene. Time-resolved behavioral analysis, pathogen quantification, and probabilistic modeling reveal that ants increase grooming and preferentially target highly-infectious individuals when perceiving high pathogen load, but transiently suppress grooming after having been groomed by nestmates. Ants thus react to both, the infectivity of others and the social feedback they receive on their own contagiousness. While inferred solely from momentary ant decisions, these behavioral rules quantitatively predict hour-long experimental dynamics, and synergistically combine into efficient colony-wide pathogen removal. Our analyses show that noisy individual decisions based on only local, incomplete, yet dynamically-updated information on pathogen threat and social feedback can lead to potent collective disease defense.
合作性疾病防御表现为群体层面的集体行为，但群体成员如何做出潜在的个体决策还知之甚少。我们以花园蚂蚁和真菌病原体作为实验模型，推导出了支配个体蚂蚁梳理选择的规则，并展示了它们如何产生群体卫生水平。时变行为分析、病原体定量和概率建模揭示，当蚂蚁感知到高病原体负荷时，它们会增加梳理行为，并优先针对高传染性个体，但在被巢内同伴梳理后，梳理行为会短暂抑制。因此，蚂蚁会对他人的传染性和它们自己传染性的社交反馈做出反应。虽然这些行为规则仅仅是根据蚂蚁的瞬间决策推断出来的，但它们能够定量预测长达数小时的实验动态，并协同作用，从而有效地去除群体范围内的病原体。我们的分析表明，基于对病原体威胁和社交反馈的局部、不完整但动态更新的信息的嘈杂个体决策，可能导致有效的集体疾病防御。
