The recent emergence of highly virulent strains of the pathogen causing wheat stem rust has been acknowledged as a threat to global food security. In infected wheat fields, vast amounts of pathogenic fungal spores are produced that can be carried away by wind. For targeted disease surveillance and control it is important to estimate when, where and how many fungal spores are dispersed from infected to susceptible wheat fields. In this study, high-performance computational resources are used to investigate long-distance dispersal revealing atmospheric pathways that connect entire continents.

Mechanistic simulations of turbulent atmospheric spore dispersal are conducted. The analyses bring together a variety of data, including international field disease surveys and finely resolved meteorological model data. The UK Met Office’s Langrangian stochastic particle dispersion model, NAME, is applied, extended and coupled to other models in a set of case studies.

In the first case study, spore dispersal is analysed across Southern/East Africa, the Middle East, and Central/South Asia by simulating billions of stochastic trajectories of fungal spores over dynamically changing host and environmental landscapes. The circumstances under which virulent strains, such as Ug99, pose a risk to globally important wheat producing areas are identified. Simulation results indicate a negligible risk for dispersal from key wheat producing countries on the East African continent (Ethiopia, Kenya) directly to India and Pakistan. However, there is a considerable risk for atmospheric transport from the Arabian Peninsula to South Asia. Spore dispersal trends are quantified between all countries in the domain providing estimates which can be used to improve targeted sampling and control.

In the second case study, dispersal from southern Africa to Australia is analysed. Simulation results, as well as data from phenotypic and genotypic analyses, support the hypothesis that extremely long-distance airborne dispersal across the Indian Ocean is possible, albeit rare. This indicates that the pathogen populations on the two continents are connected and underlines the importance of sharing surveillance intelligence between continents.

The third case study focusses on Ethiopia, determining likely origins of strain TKTTF that recently caused severe epidemics in East Africa’s largest wheat producing country. The analyses suggest inflow into Ethiopia from the Middle East via Yemen, consistent with field survey data. The risk for inflow of pathogens into Ethiopia from key neighbouring countries is ranked for different months of the wheat season.

In the last results chapter a pilot study is summarized testing the feasibility of an automated short-term forecasting system for spore dispersal from the latest field disease detection sites. Whilst the functionality and practical relevance of the forecasting system is demonstrated, considerable challenges remain for testing the forecasts.

The predictive simulation framework described in this thesis can be applied to any wheat producing area worldwide to assess dispersal risks. The research has broader relevance because long-distance dispersal is a key mechanism for the transmission of several crop and livestock diseases, and also plays an important role in other areas of ecology.