Data from: Division of labor as a bipartite network

Bipartite ecological networks are increasingly used to described and model relationships between interacting species (e.g. plant-pollinator or host parasite). Here, we apply network methods developed in community ecology to quantify division of labor in insect societies. We consider two quantitative indices (H2' and d') derived from information theory that inform on how much the actual patterns of task performance deviates from the null expectation that workers perform tasks randomly. In addition, we computed network modularity to identify clusters of specialized individuals that are preferentially engaged in the completion of subset of available tasks. We analyzed both simple synthetic networks, varying in size and degree of specialization, and published datasets to introduce the metrics and to show that a bipartite approach provides useful insights into task allocation. Considering division of labor as a bipartite network offers a conceptual framework that could substantially increase our understanding of division of labor in animal societies.二分生态网络（bipartite ecological networks）正日益被用于描述和建模互作物种间的关联关系，例如植物-传粉者或宿主-寄生物互作。本研究将群落生态学中发展出的网络分析方法，应用于量化昆虫社会的劳动分工现象。我们选取了两个源自信息论的量化指标（H2'与d'），用以衡量实际任务执行模式与“工蜂随机执行任务”这一零假设的偏离程度。此外，我们通过计算网络模块化系数，识别出优先参与完成部分可用任务子集的特化个体集群。为介绍上述量化指标，并阐明二分法分析框架可有效揭示任务分配机制，我们同时分析了两类样本：规模与特化程度各异的简单人工合成网络，以及已公开的数据集。将劳动分工视作二分网络的研究思路，可为理解动物社会的劳动分工现象提供一套全新的概念框架，有望大幅深化相关领域的认知水平。