Division of labor as a bipartite network

Bipartite ecological networks are increasingly used to describe 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 2 quantitative indices (d’ and H₂’) 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.