Working on bee specimens collected in Spring 2021 and 2022, from both pan-traps and netting, this project looks to create species lists for four different varieties of apple in orchards across two counties in the UK.
Department: Sustainable Land Management
Supervised by: Deepa Senapathi
Apples are highly dependent on insect pollination to produce economically viable yields, both in terms of quality and quantity. Within the UK, apple production is a major component of the horticultural sector, occupying 82% of orchard fruit area with a net value of £63m nationally. Identification of insect species that pollinate apple crops is therfore important to allow for targeted management to encourage optimum pollination. In 2021, bee specimens were collected from four different apple varieties across orchards in two counties - Kent and Suffolk - using both pan-traps and netting. Further specimens are likely to be collected in Spring 2022. This project aims to identify and categorize species found in each orchard into either "definite" (i.e. caught whilst visiting apple flowers) and "possible" (i.e. caught passively in pan-traps). The identified specimens will be compared with historic records of the same species in the two counties to determine if there are any changes in flight timings or occurrence of species from 1970s to the present day.
Lab work mainly with light microscope to identify bee specimens and exploratory data analysis of species occurrence and flight times
The student should have a background in agriculture, ecology, biology or another relevant discipline. Experience using light microscopes and an interest in agricultural ecosystems would be useful, but training will be given in these areas. Ability to follow identification keys and lab protocols is essential. The student should be able to systematically collect data, keep meticulous records and work independently after training.
The student will gain bee identification (& general taxonomic identification) skills; they will also gain skills in systematic data entry, data manipulation and experience of basic statistical analyses in R or another relevant statistical software.
School of Agriculture, Policy & Development - University of Reading
9-5 Mon-Fri but flexible depending on student needs
Monday 11 July 2022 - Sunday 21 August 2022
The post will be advertised centrally on the UROP website between 21st February and 4th April 2022. Students should submit their CV and Cover Letter directly to the Project Supervisor (click on supervisor name at the top of the page for email). Successful candidates will be invited for an interview.