About the Project
This project opportunity is offered as part of the Queen's Doctoral Training Programme - Multi-dimensional approaches to understanding microbe/host interactions in the context of disease, therapeutics and community resilience. For more information, please visit: https://www.findaphd.com/phds/program/queen-s-doctoral-training-programme-multi-dimensional-approaches-to-understanding-microbe-host-interactions-in-the-context-of-disease-therapeutics-and-community-resilience/?p4840
Honeybees are amongst the most important pollinators of agricultural crops and wildflowers worldwide, contributing significantly to the economy, especially in rural areas. The health of these pollinators underpins sustainable horticultural/agricultural sectors as well as a biodiverse and healthy natural environment. Although some recent declines in honeybee populations in temperate global climates have been attributed to factors including pesticide misuse and a lack of forage/habitat, the most common health problems are associated with parasites/pathogens including protozoa, bacteria, and viruses acting singly or as co-infections. To this end there is a clear need for the development of novel diagnostic/prognostic tools to identify reduced colony vigour at an early stage to facilitate the appropriate interventions to prevent colony damage/loss. Additionally, it is imperative that we move away from the current sampling techniques that involve the removal of significant numbers of sample bees from already weakened colonies for pathogen screening. The molecular analysis of honey to screen for the presence of key pathogens and/or markers for colony stresses, especially at low levels of prevalence, would enable the introduction of mitigatory treatments at an earlier stage, greatly reducing colony damage. This project will combine pathogen and environmental DNA screening from honey samples to develop diagnostic/prognostic fingerprints of honeybee hive health. For the first time such data will be interfaced using machine learning with metadata that encompasses geographical and environmental factors to enhance robustness and sensitivity. The project offers a clear route to impact for bee health surveillance in N. Ireland and globally.
Candidate requirements: Some wet laboratory experience.
Start date: October 2021
Duration: 3.5 years
How to apply:
Applicants for this project must apply to the School of Biological Sciences PhD programme at Queen’s via https://dap.qub.ac.uk/portal/user/u_login.php
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