Abstract: Fast transfer of information in groups can have survival value. An example is the so-called wave of agitationobserved in groups of animals of several taxa under attack. It has been shown to reduce predator success. It usually involves the repetition of a manoeuvre throughout the group, transmitting the information of the attack quickly, faster than the group moves itself. The specific manoeuvre underlying a wave is typically known, but not so in starlings (Sturnus vulgaris). Although waves of agitation in starling flocks have been suggested to reflect density waves, exact escape manoeuvres cannot be distinguished because flocks are spatially too far away. Therefore, waves may also reflect orientation waves (due to escape by rolling). In the present study, we investigate this issue in a computational model based on self-organization, StarDisplay, because its flocks resemble empirical data in many respects. The model comprises moving individuals that fly based on fixed-wings aerodynamics, coordinate with 6-7 of their closest neighbours and try to stay close to the site for sleeping. We use this model because its flocks have been shown to resemble starling flocks in many traits, such as variability of shape, diffusion in the flock and behavior during turning. In the model, we show that agitation waves result from changes in orientation rather than in density. They resemble empirical data both qualitatively in visual appearance and quantitatively in wave speed. In the model, local interactions with only two to seven closest neighbours suffice to generate empirical wave speed. Wave speed increases with the number of neighbours repeated from and the distance to them. It decreases with reaction time and with time to identify the escape manoeuvre of others and is not affected by flock size. Our findings can be used as predictions for empirical studies.

Abstract: Collective animal groups exhibit spontaneous ordered movement patterns with robustness against environmental perturbations (e.g., predator attacks), which would be realized by perpetual propagations of information through interactions. Recently it has been reported that the internal structures of collective animal groups are not fixed in time. Such diverse movements of individuals, which might be expected to be detrimental for collectivity, play a prominent role in facilitating interactions with various neighbors and thereby robust collective motion and information transfer. In the present study, we first address whether there is a balance between excessive movement that is detrimental to the maintenance of the group and movement that is too slight to contribute to collectivity. We previously found that in fish schools even though individuals show schooling behavior with high polarity, each individual movement relative to the center of the mass of the group displays Le?vy walk pattern in which the step-length follows power-law distribution. Here, we carry out a simple simulation of individuals? movement within the school, derived from above experimental data. In this simulation, we set an area whose diameter is the mean school size as an internal area of the school, and distribute agents in the area so as to have same density with the school. Movement pattern of each agent obeys one of the following; Le?vy walk, Brownian walk, and Ballistic movement. When we compare efficiencies to contact with neighbors and of information transfer between movement patterns, we find that Le?vy walk that was observed in real school is optimal to interact with various neighbors and thereby robust information transfer. Second, we propose a new computational model of collective behavior in which diverse individual moves positively contribute to form a group, and show that this model can realize densely collective motion with high polarity, showing inherent turbulent motion appearing as a Le?vy walk pattern.

Abstract: Atta leaf-cutter ant colonies reach sizes of many million individuals. Achieving such colony sizes is owed in part to the capacity of foraging on leaves, a virtually unlimited resource in their natural habitat. Nutrients from this low?quality resource are made available to the colony via a symbiotic fungus.While foraging on leaves always follows approximately 10 hour periods, the timing of their foraging bouts is notoriously perplexing: colonies shift seemingly randomly between diurnal and nocturnal foraging, and even within a colony different patterns may co?occur on different foraging trails. What governs these rhythms of foraging activity within colonies? The mechanisms are poorly known, and we have made observations further adding to the mystery: the periodicity of foraging cycles can vary for harvesting different substrates, and multiple cycles can co?exist on the same foraging trail.We propose the existence of a control mechanism that regulates foraging activity to satisfy a target intake rate. Processing of leaf fragments and integration into the fungus is time-consuming ? rather than matching intake rate to processing rate, leaf-cutter ants allow leaf fragment buildup inside the nest. These leaf caches can function as warehouse levels, and have the potential to serve as an inhibitory regulatory signal in a foraging control mechanism.Some forage material however, like fallen fruit, is directly consumed by the adults and requires less handling time. In our field experiments, we find foraging on fruits to be continuous, implying the absence of inhibitory feedback for this resource type.

Abstract: It has been proposed that artificial swarm design can be tackled from the angle of interaction network design. Network science provides a very powerful framework allowing bridging the gap between local dynamics and interactions at the agents level and global response at the swarm level. In the context of swarm dynamics, identifying emerging patterns and their associate properties?especially for swarms lacking apparent order?are known to be very challenging. Through specific network-theoretic analyses, ?hidden? structures emerging through self-organization can be uncovered. For instance, increasing the network degree usually improves the performance of swarms, but it is known that most natural swarms operate with a limited number of social connections. Here, we focus on studying the influence that the interaction network topology has on the dynamical response of a swarming system. We present the consequences of such excessive social behaviors on some dynamical properties of a wide range of networked systems with different topologies.