Impact of Variable Speed on Collective Movement of Animal Groups

Abstract

The collective dynamics and structure of animal groups has attracted the attention of scientists across a broad range of fields. A variety of agent-based models have been developed to help understand the emergence of coordinated collective behavior from simple interaction rules. A common, simplifying assumption of such collective movement models, is that individual agents move with a constant speed. In this work we critically re-asses this assumption. First, we discuss experimental data showcasing the omnipresent speed variability observed in different species of live fish and artificial agents (RoboFish). Based on theoretical considerations accounting for inertia and rotational friction, we derive a functional dependence of the turning response of individuals on their instantaneous speed, which is confirmed by experimental data. We then investigate the interplay of variable speed and speed-dependent turning on self-organized collective behavior by implementing an agent-based model which accounts for both these effects. We show that, besides the average speed of individuals, the variability in individual speed can have a dramatic impact on the emergent collective dynamics: a group which differs to another only in a lower speed variability of its individuals (groups being identical in all other behavioral parameters), can be in the polarized state while the other group is disordered. We find that the local coupling between group polarization and individual speed is strongest at the order-disorder transition, and that, in contrast to fixed speed models, the group’s spatial extent does not have a maximum at the transition. Furthermore, we demonstrate a decrease in polarization with group size for groups of individuals with variable speed, and a sudden decrease in mean individual speed at a critical group size (N = 4 for Voronoi interactions) linked to a topological transition from an all-to-all to a distributed spatial interaction network. Overall, our work highlights the importance to account for fundamental kinematic constraints in general, and variable speed in particular, when modeling self-organized collective dynamics.

Aggregation of self-propelled particles with sensitivity to local order

“…Recent advances in living active matter have found that modifications in individual behaviour through the sensing of local densities lead to the formation of regions of orientational disorder and aggregations [52][53][54][55]. Simiobservations are made in experiments with active colloidal systems employing different methods to program the particle motion, like optical feedback loops and field modulations [55][56][57].…”

The impact of individual perceptual and cognitive factors on collective states in a data-driven fish school model

“…H. rhodostomus displays a "burst-andcoast" mode of swimming characterized by sequences of sudden accelerations called "kicks" followed by a quasi-passive deceleration period during which the fish glides along a near straight line until the next kick. Consecutive kicks of a single fish do not necessarily have the same length, the same duration, nor the same speed [61]. When swimming in groups, kicks of different fish are asynchronous and of different length, duration and speed.…”

Randomness in the choice of neighbours promotes cohesion in mobile animal groups

“…However, unlike Ballerini et al [ 24 ], who propose that birds interact with a fixed number of nearest neighbours, we assume that organisms may randomly choose any k of the K (≥ k ) nearest neighbours; we show this randomness plays an important role in maintaining group cohesion. We also assume that individuals move at variable speeds [ 16 , 34 – 36 ] and update their motion asynchronously [ 29 , 30 , 37 ], in line with many recent empirical studies [ 16 , 18 , 27 , 32 ]. We reiterate that, in order to show generality of our conclusions, we also study variations of parameter values as well as variations of the model, including a minimal model that has substantially reduced number of parameters.…”