A Balanced Mixture of Antagonistic Pressures Promotes the Evolution of Parallel Movement

Abstract

A common hypothesis about the origins of collective behaviour suggests that animals might live and move in groups to increase their chances of surviving predator attacks. This hypothesis is supported by several studies that use computational models to simulate natural evolution. These studies, however, either tune an ad-hoc model to ‘reproduce’ collective behaviour, or concentrate on a single type of predation pressure, or infer the emergence of collective behaviour from an increase in prey density. In nature, prey are often targeted by multiple predator species simultaneously and this might have played a pivotal role in the evolution of collective behaviour. We expand on previous research by using an evolutionary rule-based system to simulate the evolution of prey behaviour when prey are subject to multiple simultaneous predation pressures. We analyse the evolved behaviour via prey density, polarization, and angular momentum. Our results suggest that a mixture of antagonistic external pressures that simultaneously steer prey towards grouping and dispersing might be required for prey individuals to evolve dynamic parallel movement.

Collective Motion as an Ultimate Effect in Crowded Selfish Herds

“…For example, instead of the attraction to the nearest neighbour 1 , reasonably complicated movement rules based on group densities and individual positions have been demonstrated as necessary for the emergence of large groups in two-dimensional space, the environment of most terrestrial animals 13–17 . In consideration of the ultimate reasons, a growing number of simulations have demonstrated that under the selfish herd scenario, the evolution of prey agents’ movement strategies can result in stable states where selfish agents assemble large and dense groups 18–22 . These theoretical studies indicate that from both proximate and ultimate aspects, the selfish herd scenario would emerge and persist during evolution.…”

“…Nevertheless, the specific impact of crowding effects on aggregating was less considered and examined in related works. For example, a prey agent in a selfish herd model was often treated as a (volume-less) point 13–19 or assigned a constant speed 18–22 , despite that limited mobility due to the physical embodiment of agents has been an important topic for swarm robotic algorithms 23,24 and human crowd simulations 25,26 . The simplifications in traditional selfish herd models may prevent their outputs from explaining real collective behaviours in more detail.…”

“…Specifically, the limited individual sensing range adopted by most of the related models has captured a typical condition in a dense group 27 . The collision-repulsion 20 or collision-penalty 21,22 settings have also been popular, which implicitly account for the individual private zones or body extensions. However, the influence of limited mobility in crowded selfish herds has been studied less.…”

“…To simulate individual movements under the crowding effect, we implemented an open space for the agents’ interaction using a two-dimensional lattice and determined that movement of an agent is valid only if the target cell (one of the four adjacent cells) is empty; otherwise, an agent remains stuck near its neighbours (see Methods, Interaction Rules). Compared with previous models 18–22 , an agent in the proposed model must make movement decisions under strictly restrained mobility without the option of passing through others and entering the group centre. Our goal was to examine the selfish movement rules selected under a crowded environment.…”

A hybrid model for simulating grazing herds in real time

“…Over the last couple of years, computer models have become an important tool for either studying the behaviour of animal groups and their evolution, or generating spectacular animations for use in computer games and movies . Roughly, computer models of collective behaviour can be classified into three groups—individual‐based models, flow‐based models, and hybrid models …”