Using citizen science data to assess the population genetic structure of the common yellowjacket wasp, Vespula vulgaris.
Iona Cunningham-EurichDanai KontouMonika YordanovaAlejandro Maeda-ObregonEmeline FavreauJinliang WangAdam G HartSeirian SumnerPublished in: Insect molecular biology (2023)
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.
Keyphrases
- genetic diversity
- public health
- genome wide
- electronic health record
- copy number
- healthcare
- quality improvement
- primary care
- cross sectional
- gene expression
- mental health
- climate change
- microbial community
- functional connectivity
- deep learning
- white matter
- current status
- artificial intelligence
- general practice
- drug induced