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Here I focus on the transfer and storage of information in honeybees colonies and how information affects collective behaviour with respect to foraging. The figure shows the different pathways of decision making of bees with respect to foraging in relation to the information they have available. The figure distinguishes between information on currently exploited patches (italic) and information on patches not currently exploited (non-italic). We currently know a great deal about how many of the transitions in the flow diagram affect individual decision-making. For example, the unloading time determines whether new foragers or new receivers (bees that collect nectar from returning foragers) will be recruited. What we do not yet understand is how the transfer and storage of information via individual decision-making leads to a coherent and stable system. To under-stand the role that individual decision-making plays will require a dual approach in which theory and experiment are combined to develop new models of information transfer and storage. These new models should show us how the stability of the foraging system is maintained, and how colonies optimally deploy their foragers among the available foraging patches.

The focus of my research is to determine, both experimentally and theoretically, the importance of information storage and information transfer within the honeybees foraging communication system. Experiments will provide data on: (1) the importance of collective "knowledge" and individual memory with respect to allocating foragers (I define collective knowledge of a colony as the total of the personal memories of individual foragers plus the information shared via the interactions within the colony); (2) the amount of information transferred on the location of foraging patches to nestmates to achieve optimal allocation of foragers over different patches. The experimental outcomes will be used directly to develop new theoretical models and algorithms. These new algorithms, bee algorithms, aim at finding solutions for dynamic multicriterion optimisation problems using knowledge on information transfer and information storage within a colony of foraging honeybeess. I do this in collaboration with Martin Middendorf (Parallel Computing and Complex Systems Group, Faculty of Mathematics and Computer Science University of Leipzig). The mathematical models are developed together with Mary Myerscough (School of Mathematics and Statistics, University of Sydney) and focus on how the honeybees communication system is buffered against perturbations. The models will suggest which component or components are likely to be the most important link in the information flow system with respect to the system's stability and these predictions will be tested experimentally.