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

Jadhav, Vivek; Guttal, Vishwesha; M, Danny Raj

doi:10.1098/rsos.220124

Cited by 11 publications

(5 citation statements)

References 65 publications

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“…It has also been shown that sheep select interaction neighbors [62]. To incorporate this cognitive constraint in our model, we introduce a key modification — sheep perceives a limited number of its nearest neighbors, among which only k have an impact on its behavior [63].…”

Section: Resultsmentioning

confidence: 99%

Collective responses of flocking sheep to a herding dog

Jadhav,

Pasqua,

Zanon

et al. 2024

Preprint

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Across taxa, group-living organisms exhibit collective escape responses to stimuli varying from mild stress to predatory pressures. How exactly does information flow among group members leading to a collective escape remains an open question. Here we study the collective responses of a flock of sheep to a shepherd dog in a driving task between well-defined target points. We collected high-resolution spatio-temporal data from 14 sheep and the dog, using Ultra Wide Band tags attached to each individual. Through the time delay analysis of velocity correlations, we identify a hierarchy among sheep in terms of directional influence. Notably, the average spatial position of a sheep along the front-back axis of the group's velocity strongly correlates with its impact on the collective movement. Our findings demonstrate that, counter-intuitively, directional information on shorter time scales propagates from the front of the group towards the rear, and that the dog exhibits adaptive movement adjustments in response to the flock's dynamics. Furthermore, we show that a simple shepherding model can capture key features of the collective response of the sheep flocks. In conclusion, our study reveals novel insights on how directional information propagates in escaping animal groups.

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Section: Resultsmentioning

confidence: 99%

Collective responses of flocking sheep to a herding dog

Jadhav,

Pasqua,

Zanon

et al. 2024

Preprint

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“…In our model, each agent is characterised by its orientation, e i = (cos θ i , sin θ i ), position x i and moves at a constant speed, v. Agents move within a box of length L with periodic boundary conditions, and we update the positions of agents every ∆t. Recent studies have emphasised the role of the probabilistic nature of animal interactions on collective motion [44,69,70]. We incorporate this via asynchronous interactions among agents and choice of neighbours [70], as described below.…”

Section: Local Alignment Models With Asynchronous Update Rules For Co...mentioning

confidence: 99%

“…In other words, the schooling of fish is a consequence of the noise associated with small-sized groups, and hence is termed noise-induced schooling. Similarly, other group characteristics such as mechanical properties [38][39][40][41][42] and cohesion have also been identified as a consequence of stochastic effects [43,44]. Intrinsic noise could also be important in evolutionary dynamics that shapes collective motion of finite flocks [45,46].…”

Section: Introductionmentioning

confidence: 99%

Data-driven discovery of stochastic dynamical equations of collective motion

Nabeel

Jadhav

et al. 2023

Phys. Biol.

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Coarse-grained descriptions of collective motion of flocking systems are often derived for the macroscopic or the thermodynamic limit. However, many real flocks are small sized (10 to 100 individuals), called the mesoscopic scales, where stochasticity arising from the finite flock sizes is important. Developing mesoscopic scale equations, typically in the form of stochastic differential equations, can be challenging even for the simplest of the collective motion models. Here, we take a novel data-driven equation learning approach to construct the stochastic mesoscopic descriptions of a simple self-propelled particle (SPP) model of collective motion. In our SPP model, a focal individual can interact with k randomly chosen neighbours within an interaction radius. We consider k = 1 (called stochastic pairwise interactions), k = 2 (stochastic ternary interactions), and k equalling all available neighbours within the interaction radius (equivalent to Vicsek-like local averaging). The data-driven mesoscopic equations reveal that the stochastic pairwise interaction model produces a novel form of collective motion driven by a multiplicative noise term (hence termed, noise-induced flocking). In contrast, for higher order interactions (k > 1), including Vicsek-like averaging interactions, yield collective motion driven primarily by the deterministic forces. We find that the relation between the parameters of the mesoscopic equations describing the dynamics and the population size are sensitive to the density and to the interaction radius, exhibiting deviations from mean-field theoretical expectations. We provide semi-analytic arguments potentially explaining these observed deviations. In summary, our study emphasizes the importance of mesoscopic descriptions of flocking systems and demonstrates the potential of the data-driven equation discovery methods for complex systems studies.

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“…Some models have been proposed that, by defining a set of interactions between the individuals comprising a school of fish, we are able to reproduce the collective behaviours of schools of fish that are most frequently observed in nature. Such models are commonly referred to as individual-based or agentbased models [5,[7][8][9]: in them, the evolution of position and velocity of each individual is governed by a set of differential equations [10][11][12][13][14][15][16][17] or a set of difference equations [4,[18][19][20][21][22][23][24][25][26][27][28][29][30] at discrete instants of time. Despite the simplicity of the interactions included in these models, they have been successful in replicating collective behaviours with good accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, there are models in which the collective behaviours already mentioned emerge even in the absence of orientation [11,12,14,16,44] or in the presence of attraction alone [16,23,25,27]. Finally, it is worth noting that in many works, the investigation was performed by restricting the school of fish to moving in one plane [9][10][11][12][13][14][15][16][17]22,23,26,27,29,30,45] and setting precise initial conditions to start its movement [10,12,13,29,45].…”

Section: Introductionmentioning

confidence: 99%

Applying collective motion models to study discordant individual behaviours within a school of fish

Trucco

2023

R. Soc. open sci.

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Computational models of collective motion successfully reproduce the most common behaviours of a school of fish, using only a few elementary interactions between individuals. However, their ability to also reproduce individual behaviours that are discordant from those of the group has not yet been adequately investigated. In this paper, a self-propelled particle model using three interaction zones is considered in relation to the counter-rotation of an individual: a phenomenon observable in real schools of fish milling in a torus, when an individual moves in the same torus but in the opposite direction for a certain period of time. This study shows that the interactions of repulsion, orientation and attraction between individuals moving at constant speed in a three-dimensional space, with asynchronous updating, can generate temporary counter-rotations. The analysis of such events sheds light on the mechanisms that start the counter-rotation and those that end it. Although the contribution of the repulsion interaction is often significant to start and terminate the counter-rotation, it does not prove to be decisive. Indeed, it is observed that even when interactions between individuals are limited to attraction alone, temporary counter-rotations of individuals occur, provided the fish density along the circumference is not uniform. Some of these conclusions, deduced from the simulations performed, are visually consistent with what is observed in some underwater video recordings of milling schools of fish.

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

Cited by 11 publications

References 65 publications

Collective responses of flocking sheep to a herding dog

Collective responses of flocking sheep to a herding dog

Data-driven discovery of stochastic dynamical equations of collective motion

Applying collective motion models to study discordant individual behaviours within a school of fish

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