Domino-like propagation of collective U-turns in fish schools

Abstract

Moving animal groups such as schools of fish or flocks of birds often undergo sudden collective changes of their travelling direction as a consequence of stochastic fluctuations in heading of the individuals. However, the mechanisms by which these behavioural fluctuations arise at the individual level and propagate within a group are still unclear. In the present study, we combine an experimental and theoretical approach to investigate spontaneous collective U-turns in groups of rummy-nose tetra (Hemigrammus rhodostomus) swimming in a ring-shaped tank. U-turns imply that fish switch their heading between the clockwise and anticlockwise direction. We reconstruct trajectories of individuals moving alone and in groups of different sizes. We show that the group decreases its swimming speed before a collective U-turn. This is in agreement with previous theoretical predictions showing that speed decrease facilitates an amplification of fluctuations in heading in the group, which can trigger U-turns. These collective U-turns are mostly initiated by individuals at the front of the group. Once an individual has initiated a U-turn, the new direction propagates through the group from front to back without amplification or dampening, resembling the dynamics of falling dominoes. The mean time between collective U-turns sharply increases as the size of the group increases. We develop an Ising spin model integrating anisotropic and asymmetrical interactions between fish and their tendency to follow the majority of their neighbours nonlinearly (social conformity). The model quantitatively reproduces key features of the dynamics and the frequency of collective U-turns observed in experiments.

Data-driven modelling of social forces and collective behaviour in zebrafish

“…A pressing open problem is the derivation of computational models able to capture social interaction between zebrafish swimming in a shoal, and reproduce experimentally observed social behaviour [16][17][18][19]. An improved understanding of social interactions can help identifying and quantifying the biological advantages of living in groups, and the role of pharmacological manipulations on group behaviour [20] Formulating an accurate model of zebrafish social behaviour requires the precise quantification of "social forces" between individual fish [21][22][23].…”

Collective gradient sensing and chemotaxis: modeling and recent developments

“…– and possibly more difficult to align. While reorientation dynamics have been extensively explored for natural flocks [76–78], they are not as fully studied in collective cell motility. One nice example of combining reorientation dynamics and gradient sensing dynamics in collective cell migration is that of Lalli et al [39], who find that though clusters of cells more accurately follow the electric field than single cells, the speed of cluster alignment to a change in electric field is slower than that for single cells.…”

idtracker.ai: Tracking all individuals in large collectives of unmarked animals

“…idTracker and further developments in animal identi cation algorithms [2-6] can work for small groups of 2-15 individuals. In larger groups, they only work for particular videos with few animal crossings [7] or with few crossings of particular species-speci c features [5].Here we present idtracker.ai, a system to track all individuals in small or large collectives (up to 100 individuals) at a high identi cation accuracy, often of > 99.9%. The method is species-agnostic and we have tested it in small and large collectives of zebra sh, Danio rerio and ies, Drosophila melanogaster.…”

“…idTracker and further developments in animal identi cation algorithms [2-6] can work for small groups of 2-15 individuals. In larger groups, they only work for particular videos with few animal crossings [7] or with few crossings of particular species-speci c features [5].…”