Abstract
Social insects are one of the best examples of complex self-organized systems exhibiting task allocation. How task allocation is achieved is the most fascinating question in behavioural ecology and complex systems science. However, it is difficult to comprehensively characterize task allocation patterns due to behavioural complexity, such as the individual variation, context dependency and chronological variation. Thus, it is imperative to quantify individual behaviours and integrate them into colony levels. Here, we applied bipartite network analyses to characterize individual-behaviour relationships. We recorded the behaviours of all individuals with verified age in ant colonies and analysed the individual-behaviour relationship at the individual, module and network levels. Bipartite network analysis successfully detected the module structures, illustrating that certain individuals performed a subset of behaviours (i.e. task groups). We confirmed age polyethism by comparing age between modules. Additionally, to test the daily rhythm of the executed tasks, the data were partitioned between daytime and nighttime, and a bipartite network was re-constructed. This analysis supported that there was no daily rhythm in the tasks performed. These findings suggested that bipartite network analyses could untangle complex task allocation patterns and provide insights into understanding the division of labour.
Highlights
How the simple elements at lower levels can evolve to higher-level systems—e.g. the evolution of solitary individuals into social animal groups—through natural selection has been a central question in evolutionary biology [1,2]
Over the past two decades, network analysis has developed as a useful tool for analysing complex systems in various fields, including ecology, social science and animal behaviour [23,24,25,26,27,28,29,30,31,32,33]
We addressed three specific questions that are central to the study of colony organization in social insects: (i) What is the structure of individual-behaviour networks? (ii) How are the task groups distributed among workers? (iii) Does the age of the individual or the time of day affect the task group pattern? This study further applied network analysis to individual-behaviour networks, leading to a comprehensive approach to understanding task allocation in complex social systems
Summary
How the simple elements at lower levels can evolve to higher-level systems—e.g. the evolution of solitary individuals into social animal groups—through natural selection has been a central question in evolutionary biology [1,2] Such systems often exhibit the division of labour through efficient task allocation, in which different elements perform different tasks [3]. Pasquaretta & Jeanson [39] quantified the degree of division of labour at the individual and colony levels by using an information theory approach They demonstrated that a community detection method for bipartite networks is an effective approach to determine the clusters of individuals that are engaged in similar subsets of tasks. Nestedness could be a useful index for describing the robustness of colonies
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