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.

二部生态网络（bipartite ecological networks）正日益被应用于描述和建模互作物种间的关系，例如植物-传粉者或宿主-寄生物的互作模式。本研究将群落生态学（community ecology）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。我们采用两种源自信息论（information theory）的量化指标（H2'与d'），用以衡量实际任务执行模式与“工职个体随机执行任务”这一零假设期望之间的偏离程度。此外，我们通过计算网络模块化（network modularity）指数，以识别偏好性参与特定任务子集的特化职虫集群。我们同时分析了规模与特化程度各异的简单人工合成网络，以及已发表的数据集，旨在介绍上述量化指标，并验证二部网络分析方法能够为任务分配机制提供富有价值的研究视角。将劳动分工以二部网络的形式进行建模，可为动物社会的劳动分工研究提供一套统一的概念框架，有望大幅提升我们对动物社会劳动分工机制的认知水平。