Sugar intake elicits a small-scale search behavior in flies and honey bees that involves capabilities found in large-scale navigation

Abstract

Social insects, particularly bees and ants, show exceptional large-scale navigational skills to find and carry back food to their nests. Honey bees further evolved a symbolic communication to direct nest mates to attractive food sources. Till now it is unclear how these capabilities evolved. Sixty years ago, Vincent Dethier demonstrated that a small-scale sugar-elicited search behavior identified in flies shows remarkable similarities with honey bee dance behavior. Those findings suggested that both behaviors are based on common mechanisms and are likely evolutionary related. We now present for the first time a detailed comparison of the sugar-elicited search behavior in Drosophila melanogaster and Apis mellifera. In both species, intake of sugar elicits a complex of searching responses. The most obvious response was an increase in turning frequency, but more importantly we found that flies and honey bees returned to the location of the sugar drop. They even returned to the food location when we prevented them from using visual and chemosensory cues indicating that this small scale local search involves path integration mechanisms. Finally, we show that visual landmarks presented in the vicinity of the sugar drop affected the search trajectory and in honey bees the sugar intake induced learning of landmarks. Together, our experiments indicate that the sugar-elicited local search exhibits two major behavioral capabilities of large-scale navigation, path integration and landmark orientation.\n\nSignificance StatementTo search for food social insects evolved sophisticated strategies of spatial orientation and large-scale navigation. We now show that even a small-scale local search behavior in solitary flies and social honey bees involves path integration and landmark learning two major mechanisms of large-scale navigation. We propose that in the future sugar-elicited local search can be used to identify neural circuits involved in navigation, path integration, and landmark learning.