description
- "Nearly 50% of crop yields globally are lost to pests and diseases. Reducing these losses is essential for meeting the United Nations Sustainable Development Goal 2 "to end hunger, achieve food security and improved nutrition, and promote sustainable agriculture". A particular challenge is to predict how crop disease will be affected by climate change. Climate has a strong impact on the growth of crop pathogens via physiological effects of temperature and humidity. But predicting exactly how crop pathogens will respond to climate change is hard. One possible response to climate change is that pathogens shift geographical ranges to track favourable environmental conditions. Another is that pathogens evolve and adapt genetically to changing conditions. New crop pathogens might emerge as a result of the shifting composition of bacterial and fungal communities, invading from the diverse communities of many thousands of environmental microbes surrounding the crops. The interplay of these different responses makes it hard to predict where disease organisms will be in the future, and indeed which organisms will be the major pathogens. Current approaches assume that climatic tolerances are fixed to predict geographical shifts, but they ignore evolution and have not been validated against long-term temporal data. This project will take advantage of a unique historical resource for tracking pathogen evolution over time, namely the CABI culture collection of 28,000 living strains. In particular, cryopreserved global samples from environmental and agricultural systems collected over many decades are available for genera such as Fusarium (fungi that include pathogens of wheat and other monocots) and Pseudomonas (bacteria that include pathogens of a wider range of plant species). Selecting one of these taxa to focus on, the project will measure genetic and phenotypic diversity at high spatial and temporal resolution over ~50 years. High-throughput methods will be used to generate large samples at the required scales, including phenomics, MALDI-TOF, and Illumina marker and whole genome sequencing. The resulting data will provide a unique evolutionary time-series over several decades. The dataset will be combined with historical and projected climate data to answer the following: (1) What is the standing variation in pathogen communities for climatic tolerances and how is it distributed? Are crop pathogens adapted to local climatic conditions? (2) What roles does local evolution versus range shifts play in determining how crop pathogen communities adapt to new conditions? (3) Can we predict successful pathogens of the future based on present-day conditions? Prediction has been successfully applied to influenza epidemics but not previously applied to crop pathogens because of the lack of detailed time-resolved data. Depending on your interests, you could use these results to develop an applied model for predicting future pathogen scenarios or design field experiments in the UK or tropical Africa to test model predictions. You will learn a range of NERCs 'most wanted' skills including microbiology, molecular labwork, bioinformatics, statistical modelling, Geographical Information Systems and/or fieldwork. The exact balance will be targeted to your interests."