Coupled information networks drive honeybee (Apis mellifera) collective foraging.

Collective behaviour by eusocial insect colonies is typically achieved through multiple communication networks that produce complex behaviour at the group level but often appear to provide redundant or even competing information. A classic example occurs in honeybee (Apis mellifera) colonies, where both the dance communication system and robust scent-based mechanisms contribute to the allocation of a colony's workforce by regulating the flow of experienced foragers among known food sources. Here we analysed social connectivity patterns during the reactivation of experienced foragers to familiar feeding sites to show that these social information pathways are not simply multiple means to achieve the same end but intersect to play complementary roles in guiding forager behaviour. Using artificial feeding stations, we mimicked a natural scenario in which two forager groups were simultaneously collecting from distinct patches containing different flowering species. We then observed the reactivation of these groups at their familiar feeding sites after interrupting their foraging. Social network analysis revealed that temporarily unemployed individuals interacted more often and for longer with foragers that advertised a familiar versus unfamiliar foraging site. Due to such resource-based assortative mixing, network-based diffusion analysis estimated that reactivation events primarily resulted from interactions among bees that had been trained to the same feeding station and less so from different-feeder interactions. Both scent- and dance-based interactions strongly contributed to reactivation decisions. However, each bout of dance-following had an especially strong effect on a follower's likelihood of reactivation, particularly when dances indicated locations familiar to followers. Our findings illustrate how honeybee foragers can alter their social connectivity in ways that are likely to enhance collective outcomes by enabling foragers to rapidly access up-to-date information about familiar foraging sites. In addition, our results highlight how reliance on multiple communication mechanisms enables social insect workers to utilise flexible information-use strategies that are robust to variation in the availability of social information.

Using citizen science data to assess the population genetic structure of the common yellowjacket wasp, Vespula vulgaris.

Monitoring insect genetic diversity and population structure has never been more important to manage the biodiversity crisis. Citizen science has become an increasingly popular tool to gather ecological data affordably across a wide range of spatial and temporal scales. To date, most insect-related citizen science initiatives have focused on occurrence and abundance data. Here, we show that poorly preserved insect samples collected by citizen scientists can yield population genetic information, providing new insights into population connectivity, genetic diversity and dispersal behaviour of little-studied insects. We analysed social wasps collected by participants of the Big Wasp Survey, a citizen science project that aims to map the diversity and distributions of vespine wasps in the UK. Although Vespula vulgaris is a notorious invasive species around the world, it remains poorly studied in its native range. We used these data to assess the population genetic structure of the common yellowjacket V. vulgaris at different spatial scales. We found a single, panmictic population across the UK with little evidence of population genetic structuring; the only possible limit to gene flow is the Irish sea, resulting in significant differentiation between the Northern Ireland and mainland UK populations. Our results suggest that queens disperse considerable distances from their natal nests to found new nests, resulting in high rates of gene flow and thus little differentiation across the landscape. Citizen science data has made it feasible to perform this study, and we hope that it will encourage future projects to adopt similar practices in insect population monitoring.