Social insects are one of the best examples of complex self-organised 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 characterise 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 characterise 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.

中文翻译：

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蚂蚁-任务关联的双向网络分析显示任务组和殖民地日常活动的缺乏

社交昆虫是展现任务分配的复杂自组织系统的最好例子之一。如何实现任务分配是行为生态学和复杂系统科学中最引人入胜的问题。但是，由于行为复杂性（例如个体差异，上下文依赖性和时间顺序差异），很难全面地表征任务分配模式。因此，必须量化个体行为并将其整合到菌落水平中。在这里，我们应用了两方网络分析来表征个人行为关系。我们记录了所有在蚁群中已验证年龄的个体的行为，并在个体，模块和网络级别分析了个体行为关系。双向网络分析成功检测到模块结构，说明某些人执行了行为的子集（即任务组）。我们通过比较模块之间的年龄来确认年龄多种族主义。此外，为了测试已执行任务的日常节奏，将数据在白天和晚上之间进行了划分，并重建了两方网络。该分析支持所执行的任务中没有日常节奏。这些发现表明，两方网络分析可以解开复杂的任务分配模式，并提供深入了解分工的见解。并重建了两方网络。该分析支持所执行的任务中没有日常节奏。这些发现表明，两方网络分析可以解开复杂的任务分配模式，并提供深入了解劳动分工的见解。并重建了两方网络。该分析支持所执行的任务中没有日常节奏。这些发现表明，两方网络分析可以解开复杂的任务分配模式，并提供深入了解劳动分工的见解。