Connecting the dots: how do microbial networks in soil respond to climate change? (NERC EAO DTP)

One of the major challenges of our time is to predict how managed and natural ecosystems will respond to climate change. Soils, and specifically the microbes that live within them, support aboveground plant growth and diversity through decomposing organic matter and releasing nutrients for plant growth. However, and despite their importance in regulating soil functioning, we still have very little understanding of how soil microbial communities respond to extreme events such as drought and heavy rainfall, which are predicted to increase with climate change. This multi-disciplinary studentship aims to investigate how microbial networks are affected by extreme events, which network attributes confer stability, and how these properties link to ecosystem functioning. This will be done using a combination of controlled glasshouse experiments and computational modelling.

Microbes in the soil don’t live in isolation – they form networks of interactions through competing for resources and collaborating in performing the steps of decomposition and nutrient release. Theory predicts that highly connected networks are least stable under disturbances; however, we have no empirical evidence for this from soil microbial communities. This studentship will test the overall hypothesis that soil microbial networks that consist of many strong interactions are least stable under drought and heavy rainfall. This will be tested on soils collected from grasslands differing in plant community composition and soil properties, and using experimental manipulations of soil microbial communities. The successful candidate will subject these soils to drought and flooding, characterize microbial communities using next-generation sequencing, and measure processes of carbon and nitrogen cycling. The student will receive unique and multi-disciplinary training to quantify the effects of extreme events on microbial networks and identify network properties that confer stability from the multi-disciplinary team of supervisors. Specifically, the student will be trained in the set up and analysis of ecological experiments in, and will be part of, De Vries’ Soil and Ecoystem Ecology Group. The student will gain skills in novel bioinformatics and statistical tools such as network analysis and exponential random graph models to characterize microbial network topography and response to extreme events in the Knight and Koskinen labs, and in the use of sequencing techniques and algorithms like Tax4Fun for predicting metagenomes from amplicon sequencing data in the Griffiths lab.