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Division of labour is the key factor leading to higher-order systems such as cooperative animal groups. How division of labour is achieved without a central control is one of the most fascinating questions in behavioural ecology and complex systems science. Social insects are one of the best examples of complex self-organised systems through local interactions. However, it is difficult to comprehensively understand division of labour due to the chronological and individual variation in behaviours, and the differences between social environments. Thus, it is imperative to quantify individual behaviours and integrate them into colony levels. Here, we demonstrate that network analyses on individual-behaviour relationships can be valid for characterising the task allocation patterns. We recorded the behaviours of all individuals with verified age in colonies of monomorphic ant and analysed the individual-behaviour relationship at individual, sub-network, and network-levels. We detected the “task groups” including subsets of individuals and behaviour, and the patterns of task groups were consistent throughout the day. We also found a relationship between the age and the characteristics of individuals within the network. These findings suggest that bipartite network analyses can untangle the complex task allocation patterns and provide novel insights into understanding how division of labour is achieved.

A bstract In biological movements, a special class of random walks, the so-called Lévy walk, has been widely observed. It has been shown that Lévy walks outperform normal random walks in terms of search efficiency because of rare, long, straight movements among short steps, and thus the cause of its prevalence is considered to be a consequence of natural selection. However, recent findings suggest that Lévy walks might also be the result of other mechanisms. This suggests that its origins depend on ecological and physical conditions. Although it is crucial to explore the mechanisms of producing Lévy walks by considering various conditions, the generative mechanisms of Lévy walks based on internal states with nonlinear dynamics are less understood. Here, we develop a generative model composed of simple deterministic nonlinear systems and show that the Lévy walk of an autonomous agent can emerge near the critical point, which is between the synchronous and asynchronous states. Furthermore, we show that Lévy walks have maximized sensitivity to a perturbation and can produce diverse behavior, which might be another advantage of Lévy walks. Our results suggest that the commonly observed Lévy walk can be derived from the critical point hypothesis in which biological systems sitting in a critical point between order and disorder receive benefits. This provides new insights into understandings of how and why Lévy walks are prevalent in biological systems.