Three-dimensional time-resolved trajectories from laboratory insect swarms

Abstract

Aggregations of animals display complex and dynamic behaviour, both at the individual level and on the level of the group as a whole. Often, this behaviour is collective, so that the group exhibits properties that are distinct from those of the individuals. In insect swarms, the motion of individuals is typically convoluted, and swarms display neither net polarization nor correlation. The swarms themselves, however, remain nearly stationary and maintain their cohesion even in noisy natural environments. This behaviour stands in contrast with other forms of collective animal behaviour, such as flocking, schooling, or herding, where the motion of individuals is more coordinated, and thus swarms provide a powerful way to study the underpinnings of collective behaviour as distinct from global order. Here, we provide a data set of threedimensional, time-resolved trajectories, including positions, velocities, and accelerations, of individual insects in laboratory insect swarms. The data can be used to study the collective as a whole as well as the dynamics and behaviour of individuals within the swarm. Design Type(s) observation design • time series design • behavioral data analysis objective Measurement Type(s) group behavior Technology Type(s) digital imaging Factor Type(s) temporal_interval

An equation of state for insect swarms

“…Regions that were highly non-spherical and very large indicated the overlap of the images of multiple midges in the camera's field of view, and so were split into multiple midges (see Ref. 23 ). The 2D midge coordinates were stereo-matched between the cameras by projecting the lines of sight connecting each camera's centre of projection and each midge's 2D location into 3D space using www.nature.com/scientificreports/ the calibrated camera model and then identifying near-intersections.…”

“…1 a). We define appropriate state variables, and empirically deduce their relationship by analysing a large data set of measured swarms 23 . Then, by applying a suitable sequence of external perturbations to the swarms, we show that we can drive them through a thermodynamic cycle in pressure–volume space throughout which our empirical equation of state holds.…”

On the emergence of gravitational-like forces in insect swarms

“…( b ) In accordance with observations [1] velocity distributions of large swarms are predicted to have Gaussian cores and exponential tails. Data (red circles) are taken from [14]. All 17 dusk-time swarms.…”

Similarities between insect swarms and isothermal globular clusters

“…The acceleration of each mass in the simulation is computed by the direct summation of the forces due to the other N − 1 bodies following the form of the force law. The scheme was designed to work efficiently for up to N = 50, well within the range of typical midge swarms [4,18]. For further details about the simulation see the SI.…”

“…We now turn to the spatial distribution of the velocities of the particles in the swarm, as another way to distinguish between the different models and compare to the observations from real swarms. We therefore computed the average speed of midges as a function of the distance r away from the center of mass of the swarm using the dataset from the larger midge enclosure [18]. As shown in Fig.…”