Statistical analysis reveals the onset of synchrony in sparse swarms of Photinus knulli fireflies

Flash synchrony within firefly swarms is an elegant but elusive manifestation of collective animal behaviour. It has been observed, and sometimes demonstrated, in a few populations across the world, but exactly which species are capable of large-scale synchronization remains unclear, especially in low-density swarms. The underlying question which we address here is: how does one qualify a collective flashing display as synchronous, given that the only information available is the time and location of flashes? We propose different statistical approaches and apply them to high-resolution stereoscopic video recordings of the collective flashing of Photinus knulli fireflies, hence establishing the occurrence of synchrony in this species. These results substantiate detailed visual observations published in the early 1980s and made at the same experimental site: Peña Blanca Canyon, Coronado National Forest, Arizona, USA. We also remark that P. knulli's collective flashing patterns mirror that observed in Photinus carolinus fireflies in the Eastern United States, consisting of synchronous flashes in periodic bursts with rapid accretion and quick decay.

Challenges and solutions for studying collective animal behaviour in the wild

Mobile animal groups provide some of the most compelling examples of self-organization in the natural world. While field observations of songbird flocks wheeling in the sky or anchovy schools fleeing from predators have inspired considerable interest in the mechanics of collective motion, the challenge of simultaneously monitoring multiple animals in the field has historically limited our capacity to study collective behaviour of wild animal groups with precision. However, recent technological advancements now present exciting opportunities to overcome many of these limitations. Here we review existing methods used to collect data on the movements and interactions of multiple animals in a natural setting. We then survey emerging technologies that are poised to revolutionize the study of collective animal behaviour by extending the spatial and temporal scales of inquiry, increasing data volume and quality, and expediting the post-processing of raw data. This article is part of the theme issue ‘Collective movement ecology’.

Collective foraging in spatially complex nutritional environments

Nutrition impinges on virtually all aspects of an animal's life, including social interactions. Recent advances in nutritional ecology show how social animals often trade-off individual nutrition and group cohesion when foraging in simplified experimental environments. Here, we explore how the spatial structure of the nutritional landscape influences these complex collective foraging dynamics in ecologically realistic environments. We introduce an individual-based model integrating key concepts of nutritional geometry, collective animal behaviour and spatial ecology to study the nutritional behaviour of animal groups in large heterogeneous environments containing foods with different abundance, patchiness and nutritional composition. Simulations show that the spatial distribution of foods constrains the ability of individuals to balance their nutrient intake, the lowest performance being attained in environments with small isolated patches of nutritionally complementary foods. Social interactions improve individual regulatory performances when food is scarce and clumpy, but not when it is abundant and scattered, suggesting that collective foraging is favoured in some environments only. These social effects are further amplified if foragers adopt flexible search strategies based on their individual nutritional state. Our model provides a conceptual and predictive framework for developing new empirically testable hypotheses in the emerging field of social nutrition. This article is part of the themed issue ‘Physiological determinants of social behaviour in animals’.

7. The wisdom of crowds

Recent work on collective animal behaviour shows that relatively simple behaviours by each animal acting individually can together produce complex, emergent behaviours larger than the parts. This self-organization occurs despite the limited cognitive abilities of individual animals and despite each animal’s limited access to global information, or the ‘big picture’. ‘The wisdom of crowds’ explains this phenomenon termed swarm intelligence by considering the murmurations of starlings, schools of fish, ant trails, and termite nest-building. The behaviour of individual animals and the rules of interaction, from which the collective behaviours emerge, have evolved under natural selection just like other behaviours. The application of such self-organization models is also discussed.

Collective-Animal-Behaviour-Based Optimized Null Placement in Time-Modulated Linear Antenna Arrays

In this paper optimal design of time modulated linear antenna arrays (TMLAA) with optimal placement of nulls in the desired direction of elevation plane has been dealt with the approach based on evolutionary algorithm like collective animal behaviour (CAB). Analysis has been done in theoretical and practical environment. Firstly the current excitation weights of the linear array of isotropic elements have been optimized by CAB is applied to improve null performance of TMLAA by Radio Frequency (RF) switch in MATLAB environment. The nulls positions of a TMLAA can be reduced significantly by optimizing the static excitation amplitudes and proper design of switch-on time intervals of each element. The CAB adjusts the current excitation amplitude of each element to place deeper nulls in the desired directions. Secondly the obtained optimal current excitation weight of the array factor is practically implemented in computer simulation technology- microwave studio (CST- MWS) environment. The array of microstrip patch antenna has been designed to operate at 5.85 GHz.

Emergent behavioural phenotypes of swarming models revealed by mimicking a frustrated anti-ferromagnet

Self-propelled particle (SPP) models are often compared with animal swarms. However, the collective animal behaviour observed in experiments often leaves considerable unconstrained freedom in the structure of a proposed model. Essentially, multiple models can describe the observed behaviour of animal swarms in simple environments. To tackle this degeneracy, we study swarms of SPPs in non-trivial environments as a new approach to distinguish between candidate models. We restrict swarms of SPPs to circular (periodic) channels where they polarize in one of two directions (like spins) and permit information to pass through windows between neighbouring channels. Co-alignment between particles then couples the channels (anti-ferromagnetically) so that they tend to counter-rotate. We study channels arranged to mimic a geometrically frustrated anti-ferromagnet and show how the effects of this frustration allow us to better distinguish between SPP models. Similar experiments could therefore improve our understanding of collective motion in animals. Finally, we discuss how the spin analogy can be exploited to construct universal logic gates, and therefore swarming systems that can function as Turing machines.

Collective Animal Behaviour Based Optimization Algorithm for IIR System Identification Problem

In this paper a novel optimization technique which is developed on mimicking the collective animal behaviour (CAB) is applied to the infinite impulse response (IIR) system identification problem. Functionality of CAB is governed by occupying the best position of an animal according to its dominance in the group. Enrichment of CAB with the features of randomness, stochastic and heuristic search nature has made the algorithm a suitable tool for finding the global optimal solution. The proposed CAB has alleviated from the defects of premature convergence and stagnation, shown by real coded genetic algorithm (RGA), particle swarm optimization (PSO) and differential evolution (DE) in the present system identification problem. The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using CAB over RGA, PSO and DE in terms of convergence speed, unknown plant coefficients and mean square error (MSE) values produced for IIR system models of both the same order and reduced order.

Red deer synchronise their activity with close neighbours

Models of collective animal behaviour frequently make assumptions about the effects of neighbours on the behaviour of focal individuals, but these assumptions are rarely tested. One such set of assumptions is that the switch between active and inactive behaviour seen in herding animals is influenced by the activity of close neighbours, where neighbouring animals show a higher degree of behavioural synchrony than would be expected by chance. We tested this assumption by observing the simultaneous behaviour of paired individuals within a herd of red deer Cervus elaphus. Focal individuals were more synchronised with their two closest neighbours than with the third closest or randomly selected individuals from the herd. Our results suggest that the behaviour of individual deer is influenced by immediate neighbours. Even if we assume that there are no social relationships between individuals, this suggests that the assumptions made in models about the influence of neighbours may be appropriate.