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

Abstract

In moving animal groups, social interactions play a key role in the ability of individuals to achieve coordinated motion. However, a large number of environmental and cognitive factors are able to modulate the expression of these interactions and the characteristics of the collective movements that result from these interactions. Here, we use a data-driven fish school model (Calovi et al., 2018; Lei et al., 2020) to quantitatively investigate the impact of perceptual and cognitive factors on coordination and collective swimming patterns. The model describes the interactions involved in the coordination of burst-and-coast swimming in groups of Hemigrammus rhodostomus. We perform a comprehensive investigation of the respective impacts of two interactions strategies between fish based on the selection of the most or the two most influential neighbors, of the range and intensity of social interactions, of the intensity of individual random behavioral fluctuations, and of the group size, on the ability of groups of fish to coordinate their movements. We find that fish are able to coordinate their movements when they interact with their most or two most influential neighbors, provided that a minimal level of attraction between fish exist to maintain group cohesion. A minimal level of alignment is also required to allow the formation of schooling and milling. However, increasing the strength of social interactions does not necessarily enhance group cohesion and coordination. When attraction and alignment strengths are too high, or when the heading random fluctuations are too large, schooling and milling can no longer be maintained and the school switches to a swarming phase. Increasing the interaction range between fish has a similar impact on collective dynamics as increasing the strengths of attraction and alignment. Finally, we find that coordination and schooling occurs for a wider range of attraction and alignment strength in small group sizes.

Predicting long-term collective animal behavior with deep learning

“…On the other hand, analytical models supplemented by a procedure to reconstruct social interactions [11,19] provide a concise and explicit description of the system in question. Moreover, varying the parameters of such models allows for investigating their relative impact on the dynamics, and to make predictions for various sets of these parameters [33]. This is not feasible with ML models, unless they are retrained or specifically structured to allow it.…”

“…Indeed, H. rhodostomus [32], like many other groupliving species [7], effectively only interact with a few influential neighbors, at a given time. Thus, for a given agent in a group of N > 2 agents, the DLI for H. rhodostomus should only retain the influence of typically the two agents leading to the highest acceleration [32,33], as predicted by the DLI model. Supplementary Video 4 illustrates this procedure for N = 5 agents, resulting in a cohesive and aligned group, in qualitative agreement with experimental observation [32].…”

A multi-scale review of the dynamics of collective behaviour: from rapid responses to ontogeny and evolution

“…The copying of a neighbour's behaviour can create waves of altered behaviour moving across the group, which can not only transfer information, but deter predators from attacking the group [51]. Experimental work has suggested that explicit consideration of sensory cues greatly improves the ability to predict information flow in fish shoals in contrast with classic models of collective motion [52], which are now being explored in more biologically realistic models of collective movement [53,54].…”