New molecular diagnostics: RT-LAMP assays were developed and validated for BYDV-PAS, BYDV-MAV, and PVY, alongside a Real-time RT-PCR protocol for Yellow Dwarf Virus detection. These tools provide greater strain-level precision than the serological methods previously used in routine surveillance. More than sixty plant samples were tested using the newly developed RT-LAMP assay, confirming sensitivity and specificity. All three protocols were published openly on protocols.io and are freely available to researchers and diagnostic laboratories internationally.
National aphid and virus monitoring: Systematic monitoring was conducted using a network of suction and yellow pan traps across three locations in Ireland. More than 1000 aphids were processed and tested, generating a comprehensive seasonal dataset on aphid flight activity and species composition across Ireland's main arable regions. Monitoring confirmed BYDV-MAV as the dominant strain in Irish cereal crops and revealed that the rose grain aphid consistently occurs in spring barley and winter barley colonization, demonstrating distinct seasonal phenology compared to the English grain aphid.
Vector-virus interactions: Life-history experiments showed that an insecticide-resistant clone of the English grain aphid incurs a significant fitness cost relative to a susceptible clone, a finding with important implications for understanding how resistance evolves in field populations. Transmission efficiency experiments demonstrated that the rose grain aphid is an efficient vector for BYDV-MAV, a moderate vector for BYDV-PAS, and a poor vector for BYDV-PAV. A four-arm olfactometer bioassay was developed to investigate whether aphids prefer virus-infected plants to healthy ones, providing new insight into virus-vector manipulation.
Mathematical modelling: Life history and transmission data generated in MONET contributed to a collaborative modelling study, in which a stage-structured model was built to estimate aphid dispersal and virus spread at the field scale for specific vector-virus combinations. This work, carried out with external collaborators, provides a framework for building future decision support tools for growers.
Scientific outputs: A major output of the work will be four peer-reviewed publications: two are published, one is submitted, and one is under review. Three open diagnostic protocols and one open dataset were also published.
