Katie E. Wharton1, Fred C. Dyer1, Zachary Huang2, and Thomas Getty1. (1) Department of Zoology, Michigan State University, 203 Natural Sciences Building, Michigan State University, East Lansing, MI 48824, (2) Entomology, Michigan State University, 243 Natural Science, East Lansing, MI 48824
A honeybee colony’s payoff for rearing drones (males) is determined by several factors, such as season, colony size, the availability of food, and the number of drones already present in the colony, but little is known about how the decisions of individual colony members enable the colony to regulate its investment in drone production based on those factors. Colony drone production arises from a sequence of actions taken by the workers and the queen: workers build wax cells, the queen lays eggs in these cells, and the workers feed the growing larvae. At each stage in this process, the potential exists for either the queen or the workers to assess colony conditions and modify the colony’s investment in drones accordingly, but whether and how this happens remains unknown. To examine the role of the queen in colony drone regulation, we tested whether queens can alter their production of drone eggs in response to their recent egg-laying history. To examine the potential role of the workers in colony drone regulation, we tested whether workers alter the egg-laying decisions of the queen if foraging conditions change between when the queen laid the eggs and when the workers feed the larvae. Our results show that both the queen and the workers are assessing at least one aspect of colony condition for drone production and are altering the colony’s drone production decisions based on that information. In addition, the results suggest that the coordination of queen and worker decisions gives a colony both flexibility and efficiency regarding its investment in drones. We suggest that colony drone regulation arises through cooperation between queens and workers in a sequential and multi-staged process of drone production with features resembling a supply chain management system.
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