Temperature-induced aggregation of young honeybees

This project investigates the self-organised optimum-finding of young bees in a temperature gradient. In contrast to the current believe, our recent preliminary experiments suggested that young bees don not only follow uphill in a temperature gradient (= positive thermotaxis) to find the optimal spot. In flat temperature gradients, the bees seem to follow a totally different strategy to navigate to the optimal spot: The bees do not approach the optimal spot in the gradient individually by thermotaxis, they prefer to form clusters randomly in the arena instead. These clusters then tend to aggregate slowly around the optimal spot, a process that is achieved by a steady exchange of bees among the clusters. This project investigates the role of social contacts within this process by behavioural observation and by advanced individualbased computer simulation. During the project, we will derive a detailed behavioural description of this novel, yet unknown, behavioural aspects of honeybees. Finally, we will derive an abstract algorithm for decentralized optimum-finding from this behavioural description. Such algorithms are of significance in many technical fields of science, e.g., in multi-robotics and in the field of swarm intelligence science. The approach we chose in this project is to investigate these novel aspects of bee behaviour by performing sophisticated research in laboratory experiments, in full hive observations and in individual-based computer simulation. The behaviours described above are of big importance to understand the navigation principles of young bees within the brood nest, which is the core of the honeybee colony. Inside of the broodnest, there is usually a flat slope gradient, while at the outer rim of the broodnest, a steep gradient of temperature is found. The importance of these novel behavioural aspects is that is can explain how young bees keep their position inside of the broodnest and simultaneously guarantee to roam the inner part of the broodnest in bee clusters what is important for the colony because these bees young usually perform the task of brood-cell preparation. Additionally, these bees depend on intense social contacts to a variety of bees (brood, nurses) to develop fast and fully. In recent years, several mathematical models describing thermoregulation in bee clusters have been published. All these studies assumed navigation principles of bees without considering the novel aspects mentioned above. Therefore the project will give important new input also to this field of honeybee research. Comparable behavioural patterns have been found in ants (e.g., cemetery formation), and in termites recently. Within these animal groups, intensive research has been performed. Our preliminary experiments have suggested that there is such social-clustering behaviour also present in honeybees and this project is the first one that will deliver comparable data also from honeybees.

This project investigates the self-organised optimum-finding of young bees in a temperature gradient. In contrast to the current believe, our recent preliminary experiments suggested that young bees don not only follow uphill in a temperature gradient (= positive thermotaxis) to find the optimal spot. In flat temperature gradients, the bees seem to follow a totally different strategy to navigate to the optimal spot: The bees do not approach the optimal spot in the gradient individually by thermotaxis, they prefer to form clusters randomly in the arena instead. These clusters then tend to aggregate slowly around the optimal spot, a process that is achieved by a steady exchange of bees among the clusters. This project investigates the role of social contacts within this process by behavioural observation and by advanced individualbased computer simulation. During the project, we will derive a detailed behavioural description of this novel, yet unknown, behavioural aspects of honeybees. Finally, we will derive an abstract algorithm for decentralized optimum-finding from this behavioural description. Such algorithms are of significance in many technical fields of science, e.g., in multi-robotics and in the field of "swarm intelligence" science. The approach we chose in this project is to investigate these novel aspects of bee behaviour by performing sophisticated research in laboratory experiments, in full hive observations and in individual-based computer simulation. The behaviours described above are of big importance to understand the navigation principles of young bees within the "brood nest", which is the core of the honeybee colony. Inside of the broodnest, there is usually a flat slope gradient, while at the outer rim of the broodnest, a steep gradient of temperature is found. The importance of these novel behavioural aspects is that is can explain how young bees keep their position inside of the broodnest and simultaneously guarantee to roam the inner part of the broodnest in bee clusters what is important for the colony because these bees young usually perform the task of brood-cell preparation. Additionally, these bees depend on intense social contacts to a variety of bees (brood, nurses) to develop fast and fully. In recent years, several mathematical models describing thermoregulation in bee clusters have been published. All these studies assumed navigation principles of bees without considering the novel aspects mentioned above. Therefore the project will give important new input also to this field of honeybee research. Comparable behavioural patterns have been found in ants (e.g., cemetery formation), and in termites recently. Within these animal groups, intensive research has been performed. Our preliminary experiments have suggested that there is such social-clustering behaviour also present in honeybees and this project is the first one that will deliver comparable data also from honeybees.