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Report

Teaser, summary, work performed and final results

Periodic Reporting for period 2 - BeeDanceGap (Honeybee communication: animal social learning at the height of social complexity)

Teaser

Summary

The honeybee (Apis mellifera) waggle dance is one of the most complex and celebrated communication systems in the animal world. Foraging bees use an abstract symbolic code to communicate the distance, direction, and quality of resources to their nest mates, and the information network that results has been shaped by natural selection to produce fine-tuned, rapid, group-level responses from colonies that can contain up to 80 000 individuals. BeeDanceGap addresses two fundamental gaps in our current understanding of this extraordinary behaviour: (1) how is it that bee brains process the complex information provided by the dance (2) why have they evolved to do so? To address the first of these questions, we are assaying the changes in gene expression that occur within the bee mushroom bodies- an area of the brain that is associated with learning and memory- immediately after following a dance. We aim to identify genes that respond to acquisition of spatial information through the dance, and compare them to those that are involved in aquiring the same information through individual experience. We are addressing the second question by assaying the importance of dance-following for food discovery under varying forage distributions. Foragers have multiple information sources available to them in the nest, and we are using network-based diffusion analysis to evaluate the relative contribution of each pathway to real-world food discovery. A better understanding of the extraordinary social communication system of these remarkable insects extends our knowledge of sophisticated collective behavior, but it also provides insight into a critical information pathway that drives one of the worldâ€™s most important and threatened pollinators to food. Our research is important not simply because honeybee foraging behavior is an evolutionary marvel, but because it is a significant contributor to pollination services at a global scale.

Work performed

The primary axis of the project focuses on the consequences of spatial learning through the dance signal, for neural gene expression. The first step has 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 have developed a protocol through we can manipulate an individualâ€™s perception of the distance and direction that she has flown, and through this manipulation, have 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. Thus, we now have a candidate set of genes that form the basis for the all-important second step in the project, which is to search for similar gene expression differences in bees that follow dances for such locations. We have collected an extensive gene expression dataset from dance-followers, and bioinformatic analysis of that dataset is currently underway.

The second axis of the project focuses 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 have developed, in order to achieve our primary aim of assaying information flow through bee colonies, now incorporates multiple networks for comparison within a single model, and incorporates dynamic networks. We have validated this approach using our empirical data, comparing the importance of three different honeybee communication systems as drivers of forage discovery. We also developed techniques to study shifting honeybee forage patterns, with major potential applications in bee conservation, and particularly in studying the consequences of agrochemical use. Finally, we have also used our system to predict the effects of anthropogenic change on bee populations, including the finding that a major emerging pesticide group is a threat to pollinator survival and fitness.

Final results

The incorporation of multiple dynamic networks into established NBDA techniques is a development with potential applications for the study of rapid information flow in any social group, and we intend to publish our approach as an open-source software package and associated journal article in the second half of the project. We will also publish our empirical proof-of-principle data as a separate stand-alone outcome. Moving forward, we can now apply our protocol to the true focus of the project, to establish the ecological circumstances under which the dance drives bees to food, which will be accomplished through food distribution manipulations over the current and the coming field seasons. We are focussing upon the distance to and the distribution of food sources, testing longstanding theory that distant, emphemeral, clumped resources are critical to the evolution of recruitment communication.

We have introduced next-generation transcriptomic approaches to the study of animal social learning- a psychology-based subject area that rarely overlaps with molecular biology. In the coming two field seasons, following bioinformatics analysis that is already in progress, we will characterise the genes that are up- or downregulated when bees learn socially about forage locations. We also plan to manipulate these genes using RNAi techniques that we are currently piloting. By the end of the project, we expect to have a) characterised the genes that are involved in learning about distance to forage sources, through both correlative and manipulative experiments b) established whether the brief signal exposure characterised by dance exposure has consequences at the transcriptomic level, in addition to the known behavioural consequences.