Foraging ants as liquid brains: Movement heterogeneity shapes collective efficiency - Pol Fernández-López; Daniel Oro; Roger Lloret-Cabot; Meritxell Genovart; Joan Garriga; Frederic Bartumeus (Proceedings of the National Academy of Sciences, 2025)
Liquid brains conceptualize living systems that operate without central control, where collective outcomes emerge from local and dynamic interactions. This concept extends beyond ants and other social insects to include immune systems, slime molds, and microbiomes. In such systems, connectivity scales with population density, facilitating more efficient information transfer as group size increases. However, in sparse conditions, where fewer individuals interact, movement likely plays a crucial role in shaping connectivity, ensuring optimal collective efficiency. We tested this hypothesis during the foraging process of Aphaenogaster senilis , an ant species that does not primarily rely on chemical communication. We empirically measured ant movement behavior and characterized their foraging dynamics across large spatiotemporal scales, closely reflecting the species’ natural ecology. Integrating observed movement heterogeneity into a neuronal-like model, we quantitatively replicated ants foraging efficiency and spatiotemporal dynamics. Our results reveal that a simple feedback mechanism, mediated by local interactions, governs the foraging patterns of A. senilis . Such feedback is modulated by adjusting the proportion of two coexisting movement behaviors: recruits, which facilitated information transfer and food exploitation by aggregating closely to the nest and the food patches, and scouts, which could bypass this feedback and discover alternative food sources. Therefore, distinct movement patterns contributed differently to optimizing each phase of the foraging process, proving an adaptive mechanism to balance exploration and exploitation. Our findings underscore how incorporating specific biologically grounded insights into complex systems frameworks, enhances our understanding of the mechanisms underlying collective intelligence in biological systems.