Evolution of Swarming Behavior Is Shaped by How Predators Attack

Interaction Rules Supporting Effective Flocking Behavior

Several simulation models have demonstrated how flocking behavior emerges from the interaction among individuals that react to the relative orientation of their neighbors based on simple rules. However, the precise nature of these rules and the relationship between the characteristics of the rules and the efficacy of the resulting collective behavior are unknown. In this article, we analyze the effect of the strength with which individuals react to the orientation of neighbors located in different sectors of their visual fields and the benefit that could be obtained by using control rules that are more elaborate than those normally used. Our results demonstrate that considering only neighbors located on the frontal side of the visual field permits an increase in the aggregation level of the swarm. Using more complex rules and/or additional sensory information does not lead to better performance.

Sensing Enhancement on Social Networks: The Role of Network Topology

Sensing and processing information from dynamically changing environments is essential for the survival of animal collectives and the functioning of human society. In this context, previous work has shown that communication between networked agents with some preference towards adopting the majority opinion can enhance the quality of error-prone individual sensing from dynamic environments. In this paper, we compare the potential of different types of complex networks for such sensing enhancement. Numerical simulations on complex networks are complemented by a mean-field approach for limited connectivity that captures essential trends in dependencies. Our results show that, whilst bestowing advantages on a small group of agents, degree heterogeneity tends to impede overall sensing enhancement. In contrast, clustering and spatial structure play a more nuanced role depending on overall connectivity. We find that ring graphs exhibit superior enhancement for large connectivity and that random graphs outperform for small connectivity. Further exploring the role of clustering and path lengths in small-world models, we find that sensing enhancement tends to be boosted in the small-world regime.

Collective predator evasion: Putting the criticality hypothesis to the test

According to the criticality hypothesis, collective biological systems should operate in a special parameter region, close to so-called critical points, where the collective behavior undergoes a qualitative change between different dynamical regimes. Critical systems exhibit unique properties, which may benefit collective information processing such as maximal responsiveness to external stimuli. Besides neuronal and gene-regulatory networks, recent empirical data suggests that also animal collectives may be examples of self-organized critical systems. However, open questions about self-organization mechanisms in animal groups remain: Evolutionary adaptation towards a group-level optimum (group-level selection), implicitly assumed in the "criticality hypothesis", appears in general not reasonable for fission-fusion groups composed of non-related individuals. Furthermore, previous theoretical work relies on non-spatial models, which ignore potentially important self-organization and spatial sorting effects. Using a generic, spatially-explicit model of schooling prey being attacked by a predator, we show first that schools operating at criticality perform best. However, this is not due to optimal response of the prey to the predator, as suggested by the "criticality hypothesis", but rather due to the spatial structure of the prey school at criticality. Secondly, by investigating individual-level evolution, we show that strong spatial self-sorting effects at the critical point lead to strong selection gradients, and make it an evolutionary unstable state. Our results demonstrate the decisive role of spatio-temporal phenomena in collective behavior, and that individual-level selection is in general not a viable mechanism for self-tuning of unrelated animal groups towards criticality.

Evolving flocking in embodied agents based on local and global application of Reynolds' rules

In large scale systems of embodied agents, such as robot swarms, the ability to flock is essential in many tasks. However, the conditions necessary to artificially evolve self-organised flocking behaviours remain unknown. In this paper, we study and demonstrate how evolutionary techniques can be used to synthesise flocking behaviours, in particular, how fitness functions should be designed to evolve high-performing controllers. We start by considering Reynolds' seminal work on flocking, the boids model, and design three components of a fitness function that are directly based on his three local rules to enforce local separation, cohesion and alignment. Results show that embedding Reynolds' rules in the fitness function can lead to the successful evolution of flocking behaviours. However, only local, fragmented flocking behaviours tend to evolve when fitness scores are based on the individuals' conformity to Reynolds' rules. We therefore modify the components of the fitness function so that they consider the entire group of agents simultaneously, and find that the resulting behaviours lead to global flocking. Furthermore, the results show that alignment need not be explicitly rewarded to successfully evolve flocking. Our study thus represents a significant step towards the use of evolutionary techniques to synthesise collective behaviours for tasks in which embodied agents need to move as a single, cohesive group.

Metapopulation model for a prey-predator system: Nonlinear migration due to the finite capacities of patches

Many species live in spatially separated patches, and individuals can migrate between patches through paths. In real ecosystems, the capacities of patches are finite. If a patch is already occupied by the individuals of some species, then the migration into the patch is impossible. In the present paper, we deal with prey-predator system composed of two patches. Each patch contains a limited number of cells, where the cell is either empty or occupied by an individual of prey or predator. We introduce "swapping migration" defined by the exchange between occupied and empty cells. An individual can migrate, only when there are empty cells in the destination patch. Reaction-migration equations in prey-predator system are presented, where the migration term forms nonlinear function of densities. We numerically solve equilibrium densities, and find that the population dynamics are largely affected by nonlinear migration. Not only extinction points but also the responses to the environmental changes crucially depend on the patch capacities.

Deep-Reinforcement Learning-Based Co-Evolution in a Predator-Prey System

Understanding or estimating the co-evolution processes is critical in ecology, but very challenging. Traditional methods are difficult to deal with the complex processes of evolution and to predict their consequences on nature. In this paper, we use the deep-reinforcement learning algorithms to endow the organism with learning ability, and simulate their evolution process by using the Monte Carlo simulation algorithm in a large-scale ecosystem. The combination of the two algorithms allows organisms to use experiences to determine their behavior through interaction with that environment, and to pass on experience to their offspring. Our research showed that the predators' reinforcement learning ability contributed to the stability of the ecosystem and helped predators obtain a more reasonable behavior pattern of coexistence with its prey. The reinforcement learning effect of prey on its own population was not as good as that of predators and increased the risk of extinction of predators. The inconsistent learning periods and speed of prey and predators aggravated that risk. The co-evolution of the two species had resulted in fewer numbers of their populations due to their potentially antagonistic evolutionary networks. If the learnable predators and prey invade an ecosystem at the same time, prey had an advantage. Thus, the proposed model illustrates the influence of learning mechanism on a predator-prey ecosystem and demonstrates the feasibility of predicting the behavior evolution in a predator-prey ecosystem using AI approaches.

Confined System Analysis of a Predator-Prey Minimalistic Model

In nature exists a properly defined food chain- an order of hunting and getting hunted. One such hunter-hunted pair is considered in this context and coordinated escape manoeuvres in response to predation is studied in case of a rarely examined confined system. Both the predator agent and prey agents are considered to be self-propelled particles moving in a viscous fluid. The state of motility when alive and passivity on death has been accounted for. A novel individual-based combination of Vicsek model and Boids flocking model is used for defining the self-propelling action and inter-agent interactions. The regimes observed at differing levels of co-ordination segregated by quantification of global order parameter are found to be in agreement with the extant literature. This study strives to understand the penalty on the collective motion due to the restraints employed by the rigid walls of the confinement and the predator’s hunting tactics. The success of any escape manoeuvre is dependent on the rate of information transfer and the strength of the agitation at the source of the manoeuvre. The rate of information transfer is studied as a function of co-ordination and the size of the influence zone and the source strength is studied as a function of escape acceleration activated on the agitated prey. The role of these factors in affecting survival rate of prey is given due coverage.

Understanding interactions between populations: Individual based modelling and quantification using pair correlation functions

Understanding the underlying mechanisms that produce the huge variety of swarming and aggregation patterns in animals and cells is fundamental in ecology, developmental biology, and regenerative medicine, to name but a few examples. Depending upon the nature of the interactions between individuals (cells or animals), a variety of different large-scale spatial patterns can be observed in their distribution; examples include cell aggregates, stripes of different coloured skin cells, etc. For the case where all individuals are of the same type (i.e., all interactions are alike), a considerable literature already exists on how the collective organisation depends on the inter-individual interactions. Here, we focus on the less studied case where there are two different types of individuals present. Whilst a number of continuum models of this scenario exist, it can be difficult to compare these models to experimental data, since real cells and animals are discrete. In order to overcome this problem, we develop an agent-based model to simulate some archetypal mechanisms involving attraction and repulsion. However, with this approach (as with experiments), each realisation of the model is different, due to stochastic effects. In order to make useful comparisons between simulations and experimental data, we need to identify the robust features of the spatial distributions of the two species which persist over many realisations of the model (for example, the size of aggregates, degree of segregation or intermixing of the two species). In some cases, it is possible to do this by simple visual inspection. In others, the features of the pattern are not so clear to the unaided eye. In this paper, we introduce a pair correlation function (PCF), which allows us to analyse multi-species spatial distributions quantitatively. We show how the differing strengths of inter-individual attraction and repulsion between species give rise to different spatial patterns, and how the PCF can be used to quantify these differences, even when it might be impossible to recognise them visually.

Effect of directional migration on Lotka-Volterra system with desert

Migration is observed across many species. Several authors have studied ecological migration by applying cellular automaton (CA). In this paper, we present a directional migration model with desert on a one-dimensional lattice where a traffic CA model and a lattice Lotka-Volterra system are connected. Here predators correspond to locomotive animals while prey is immobile plants. Predators migrate between deserts and fertile lands repeatedly. Computer simulations reveal the two types of phase transition: coexistence of both species and prey dominance, which is caused by both benefit and cost of migration. In the coexistence phase, the steady-state density of predators usually increases by migration as long as the desert size is small and their mortality rate is low. In contrast, the prey density increases, even if the desert size becomes large. Such a paradox comes from the indirect effect: predators go extinct by the increase of desert size, so that the plant density can increase. Moreover, we find several self-organized spatial patterns: 1) predators form a stripe pattern; namely swarms. 2) The velocity of predators is high on deserts, but very low on fertile land. 3) Predators give birth only on fertile lands.

Birds of a feather flock together: Insights into starling murmuration behaviour revealed using citizen science

Pre-roost murmuration displays by European starlings Sturnus vulgaris are a spectacular example of collective animal behaviour. To date, empirical research has focussed largely on flock movement and biomechanics whereas research on possible causal mechanisms that affect flock size and murmuration duration has been limited and restricted to a small number of sites. Possible explanations for this behaviour include reducing predation through the dilution, detection or predator confusion effects (the “safer together” hypotheses) or recruiting more birds to create larger (warmer) roosts (the “warmer together” hypothesis). We collected data on size, duration, habitat, temperature and predators from >3,000 murmurations using citizen science. Sightings were submitted from 23 countries but UK records predominated. Murmurations occurred across a range of habitats but there was no association between habitat and size/duration. Size increased significantly from October to early February, followed by a decrease until the end of the season in March (overall mean 30,082 birds; maximum 750,000 birds). Mean duration was 26 minutes (± 44 seconds SEM). Displays were longest at the start/end of the season, probably due to a significant positive relationship with day length. Birds of prey were recorded at 29.6% of murmurations. The presence of predators including harrier Circus, peregrine Falco peregrinus, and sparrowhawk Accipiter nisus was positively correlated with murmuration size (R² = 0.401) and duration (R² = 0.258), especially when these species were flying near to, or actively engaging with, starlings. Temperature was negatively correlated with duration but the effect was much weaker than that of day length. When predators were present, murmurations were statistically more likely to end with all birds going down en masse to roost rather than dispersing from the site. Our findings suggest that starling murmurations are primarily an anti-predator adaptation rather than being undertaken to attract larger numbers of individuals to increase roost warmth.

Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems

Collective behaviour is a fascinating and easily observable phenomenon, attractive to a wide range of researchers. In biology, computational models have been extensively used to investigate various properties of collective behaviour, such as: transfer of information across the group, benefits of grouping (defence against predation, foraging), group decision-making process, and group behaviour types. The question ‘why,’ however remains largely unanswered. Here the interest goes into which pressures led to the evolution of such behaviour, and evolutionary computational models have already been used to test various biological hypotheses. Most of these models use genetic algorithms to tune the parameters of previously presented non-evolutionary models, but very few attempt to evolve collective behaviour from scratch. Of these last, the successful attempts display clumping or swarming behaviour. Empirical evidence suggests that in fish schools there exist three classes of behaviour; swarming, milling and polarized. In this paper we present a novel, artificial life-like evolutionary model, where individual agents are governed by linguistic fuzzy rule-based systems, which is capable of evolving all three classes of behaviour.

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

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.