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Collective motion, which is ubiquitous in nature, has traditionally been explained by “self-propelled particle” models from theoretical physics. Here we show, through field, lab, and virtual reality experimentation, that classical models of collective behavior cannot account for how collective motion emerges in marching desert locusts, whose swarms affect the livelihood of millions. In contrast to assumptions made by these models, locusts do not explicitly align with neighbors. While individuals respond to moving-dot stimuli through the optomotor response, this innate behavior does not mediate social response to neighbors. Instead, locust marching behavior, across scales, can be explained by a minimal cognitive framework, which incorporates individuals’ neural representation of bearings to neighbors and internal consensus dynamics for making directional choices. Our findings challenge long-held beliefs about how order can emerge from disorder in animal collectives. 

How collectives remain coordinated as they grow in size is a fundamental challenge affecting systems ranging from biofilms to governments. This challenge is particularly apparent in multicellular organisms, where coordination among a vast number of cells is vital for coherent animal behavior. However, the earliest multicellular organisms were decentralized, with indeterminate sizes and morphologies, as exemplified by Trichoplax adhaerens , arguably the earliest-diverged and simplest motile animal. We investigated coordination among cells in T. adhaerens by observing the degree of collective order in locomotion across animals of differing sizes and found that larger individuals exhibit increasingly disordered locomotion. We reproduced this effect of size on order through a simulation model of active elastic cellular sheets and demonstrate that this relationship is best recapitulated across all body sizes when the simulation parameters are tuned to a critical point in the parameter space. We quantify the trade-off between increasing size and coordination in a multicellular animal with a decentralized anatomy that shows evidence of criticality and hypothesize as to the implications of this on the evolution hierarchical structures such as nervous systems in larger organisms. 

Abstract Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation.