Fujioka Haruna, Okada Yasukazu, Abe Masato S
Graduate School of Arts and Sciences, the University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo, Japan.
Graduate School of Science, Osaka City University, 3-3-138 Sugimoto-cho, Sumiyoshi-ku, Osaka 558-8585, Japan.
R Soc Open Sci. 2021 Jan 13;8(1):201637. doi: 10.1098/rsos.201637. eCollection 2021 Jan.
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.
群居昆虫是展现任务分配的复杂自组织系统的最佳例子之一。任务分配是如何实现的，这是行为生态学和复杂系统科学中最引人入胜的问题。然而，由于行为的复杂性，如个体差异、情境依赖性和时间变化，很难全面地描述任务分配模式。因此，必须对个体行为进行量化，并将其整合到群体层面。在这里，我们应用二分网络分析来描述个体行为关系。我们记录了蚁群中所有已确认年龄个体的行为，并在个体、模块和网络层面分析了个体行为关系。二分网络分析成功地检测到了模块结构，表明某些个体执行了一部分行为（即任务组）。我们通过比较模块之间的年龄来确认年龄多态性。此外，为了测试执行任务的日常节律，我们将数据分为白天和夜间，并重新构建了一个二分网络。该分析支持所执行的任务没有日常节律。这些发现表明，二分网络分析可以解开复杂的任务分配模式，并为理解分工提供见解。
