We established a self-sustaining laboratory colony of C. Our results provide data that can be used to benchmark swarm models quantitatively, and that advance our fundamental understanding of collective animal behaviour. At large scales, we show that the swarms display an effective potential well that keeps the individual insects bound to it the shape of this well, however, depends on how it is measured. But we also find evidence that local clusters of correlated motion may exist, as suggested by the presence of long tails in the speed distribution and by measurements of the spatial statistics of the midges. We find, as expected, that the group dynamics of our swarms are qualitatively different from bird flocks and fish schools, as characterized by the overall group shape, isotropy of acceleration, and bulk polarisation. Here, using experimental tools originally developed to study turbulent flows, we report three-dimensional, high-speed measurements of the positions, velocities, and accelerations of all the individual members of laboratory swarms of Chironomus riparius midges. Recent developments in high-speed imaging for fluid dynamics and turbulence, where the challenge of accurately measuring dynamics over wide ranges of length and time scales is likewise unavoidable 30, have now made such measurements possible.
Making more progress on understanding and modelling collective animal behaviour requires measurements of animal aggregations that simultaneously resolve the dynamics of the entire group (typically over large length scales and slow time scales) as well as the kinematics and history of motion of each individual in the group (typically over short length scales and fast time scales). Due to the difficulties inherent to fieldwork, however, the temporal range and resolution of this work was limited thus, the STARFLAG group focused on flock shape measurements and single-time velocity statistics 27, 29. A recent landmark study from the STARFLAG group imaged and tracked wild flocks of starlings numbering in the thousands 5, 6, 27, 28, 29, by far the largest groups of collectively moving animals measured to date. In recent years, such data have begun to become available, particularly for animals that move only in two dimensions 4, 19, 20 or three-dimensional groups of a few individuals 7, 21, 22, 23, 24, 25, 26. Both continuum 15 and discrete 16 models can produce results that resemble observational data.īut qualitatively matching the large-scale emergent behaviour does not demonstrate that a model correctly captures the biology 17, 18 instead, detailed, quantitative comparisons with actual data are required. Models with simple rules have been shown to reproduce, at least qualitatively, patterns and behaviours observed in the wild, including bulk alignment or polarisation 10, milling 11, swarming 12, aggregation 13, and predator avoidance 14. The ubiquity of emergent collective behaviour suggests that it may arise from relatively simple interactions between individuals-and indeed, a vast literature on modelling animal aggregations has developed over the past few decades. Nature has found such self-organized behaviour to be a robust, simple solution to a broad range of biological problems. Spontaneous, collective biological activity-in swarms, flocks, schools, herds, or crowds-has evolved independently across the entire biological size spectrum, from single cells 1, 2, 3 to insects 4, birds 5, 6, or fish 7, 8, 9. Our results provide quantitative data against which the emergent characteristics of animal aggregation models can be benchmarked. We also show that the swarms display an effective large-scale potential that keeps individuals bound together, and we characterize the shape of this potential. Even though the swarms do not show an overall polarisation, we find statistical evidence for local clusters of correlated motion. Here, we report three-dimensional, time-resolved measurements of the positions, velocities, and accelerations of individual insects in laboratory swarms of the midge Chironomus riparius. But determining whether these models accurately capture the biology requires data from real animals, which has historically been difficult to obtain.
It has long been known that models with simple interaction rules can reproduce qualitative features of this complex behaviour. Collective animal behaviour occurs at nearly every biological size scale, from single-celled organisms to the largest animals on earth.
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