The primary axis of the project focused on the consequences of spatial learning through the dance signal, for neural gene expression. The first step was been to identify genes that are differentially expressed when bees learn through their own experience about food locations at different a) distances b) directions. We developed a protocol through we could manipulate an individual’s perception of the distance and direction that she has flown, and through this manipulation, identified a set of genes that are differentially expressed in the bee mushroom bodies when bees perceive themselves to have flown long or short distances. This provided a candidate set of genes that form the basis for the all-important second step in the project, which was to search for similar gene expression differences in bees that follow dances for such locations. We allowed bees to forage for feeders at long or short-distances and perform dances, sampling these individuals and (critically) those bees that followed the same dances. This identified a particular gene- Apidaecin- that responded to distance in bees that flew long/short distances, bees that perceived themselves to have done the same, and bees that learnt about those distant/close locations through dances. We have disseminated these results through several conference symposia, and are in the process of publication.
The second axis of the project focused upon the use of network-based diffusion analysis (NBDA) to compare the contribution of the different information pathways that are available to honeybees. The core assumption underlying NBDA is that if social transmission is occurring, then the spread of a novel behaviour should follow a social network that reflects opportunities for information transfer between individuals. However, earlier iterations of NBDA had several limitations. The approach that we developed, in order to achieve our primary aim of assaying information flow through bee colonies, built on this by incorporating multiple dynamic networks for comparison within a single model. We published this tool as an R-package and disseminated further through an accompanying extensive “How To” paper to encourage uptake by other ecologists, and through workshops at international conferences. We went on to apply our approach to our own empirical data, comparing the importance of three different honeybee communication systems as drivers of forage discovery. We showed that the waggle dance is critical to the discovery of new food sources when colonies shift focus from one patch to another of the same type, that it is not important for food rediscovery, that reliance on it does not vary with distance forage and that it may be less important for shifts between forage types. These results indicate that within-species forage shifts may have been key to the evolution of this unique communication system, and each has been presented at multiple conference symposia and is either published or in the process of publication.
