Summary
This research demonstrates the integration of molecular diagnostics, bioinformatics and atmospheric modelling into an operational biovigilance toolbox for early detection of cereal rust pathogens across western Canada. By applying ITS2 metabarcoding and trajectory modelling over four growing seasons, the authors identified the Canadian Rocky Mountains as a dispersal barrier, characterised crop and land-use diversification as key drivers of rust distribution, and tracked northeastward spore movement from the Pacific Northwest. The study exemplifies how multi-disciplinary monitoring platforms can support precision disease management under variable climatic and agricultural conditions.
UK applicability
The UK grows cereals (wheat, barley, oats) subject to rust diseases, and similar atmospheric and land-use factors are likely to influence pathogen dispersal. The integrated biovigilance methodology—particularly ITS2 metabarcoding and trajectory modelling—could be adapted to UK contexts; however, the UK's different topography, maritime climate and crop geography would require localised validation and recalibration of predictive models.
Key measures
Aeromycobiota diversity and compositional structure via ITS2 metabarcoding; cereal rust fungal species identification via real-time PCR and bioinformatics classifier; Random Forest modelling of crop, land-use, atmospheric pressure and moisture factors; HYSPLIT trajectory modelling of pathogen dispersal; spatial and temporal rust distribution across four growing seasons (2015–2018)
Outcomes reported
The study optimised and validated an integrated biovigilance platform combining spore sampling, DNA diagnostics (ITS2 metabarcoding), real-time PCR and predictive trajectory modelling to monitor cereal rust fungal pathogens across wheat, oats, barley and rye. The platform identified key environmental and land-use factors driving rust distribution and demonstrated northeastward dispersal patterns of rust urediniospores from the Pacific Northwest.
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