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Task-partitioning in insect societies: Non-random direct material transfers affect both colony efficiency and information flow.
Journal of Theoretical Biology
Task-partitioning is an important organisational principle in insect colonies and is thought to increase colony efficiency. In task-partitioning, tasks such as the collection of resources are divided into subtasks in which the material is passed from one worker to another. Previous models have assumed that worker-worker interactions are random, but experimental evidence suggests that receivers can have preferences to handle familiar materials. We used an agent-based simulation model to explore how non-random interactions during task-partitioning with direct transfer affect colony work efficiency. Because task-partitioning also allows receivers and donors to acquire foraging related information we analysed the effect of non-random interactions on informative interaction patterns. When receivers non-randomly rejected donors offering certain materials, donors overall experienced increased time delays, hive stay durations and a decreased number of transfer partners. However, the number of transfers was slightly increased, which can improve the acquisition and quality of information for donors. When receivers were non-randomly attracted to donors offering certain materials, donors experienced reduced transfer delays, hive stay durations and an increased number of simultaneous receivers. The number of transfers is slightly decreased. The effects of the two mechanisms "non-random rejection" and "non-random attraction" are biggest if the number of foragers and receivers is balanced. In summary, our results show that colony ergonomics are improved if receivers do not reject donors and if mechanisms exist that help receivers detect potential donors, such as learning the odour of the transferred food. Finally, our simulations suggest that non-random interactions can potentially affect the foraging patterns of colonies in changing environments.
Apis mellifera, Honey bee, Agent-based model, Olfactory conditioning
Web of science
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