How long does it take grassland fungi to recover after disturbance?

This project is part of the ARIES DTP and the successful candidate will be predominantly based at the Earlham Institute and registered at University of East Anglia.

Unimproved, sensitively grazed lowland grasslands are an ecologically vital but highly threatened habitat. These grasslands support diverse fungal communities, from the iconic and vulnerable waxcaps to a vast, poorly understood diversity of soil microfungi that underpin plant health, nutrient cycling, and carbon storage. 

A key determinant of fungal community recovery is time since last disturbance (e.g. ploughing), because fungi rely on slow-growing, undisturbed hyphal networks. Understanding how long recovery takes, and what drives it, is essential for informing grassland restoration and sustainable land management policy.

This project will use three UK grassland sites in Norfolk, Hertfordshire and Devon that span soil type, climate, and documented management history. In these fields you will sample soils along a gradient of time-since-disturbance at each site and use metabarcoding sequencing to characterise fungal diversity and community composition. 

Collecting soil chemistry data alongside will help disentangle the effects of disturbance history from other environmental drivers. In parallel, sequencing of bacteria from the same samples will allow the comparison of relative impacts of disturbance on fungi versus bacteria. 

Finally, informed by emerging results, you will identify a focal shared grass species present across all sites to sample at finer resolution (roots, rhizosphere, bulk soil) to determine whether recruitment of fungal partners is consistent across sites. 

The integration of field, laboratory and bioinformatics training delivered by this project offers an exciting opportunity to develop a diverse transferable skillset of high value in both academia and industry. 

The project will be based at the Earlham Institute, a world-class BBSRC research centre of excellence in genomics, engineering and computational biology, and AI, in collaboration with the  long-term experiment sites, datasets, and expertise at Rothamsted Research. 

This project suits someone with a degree in ecology, microbiology, environmental science, or related field, and enthusiasm for field, lab and computational work.