Aims and methodology
Permafrost thaw induced by climate change is predicted to make up to 174 Pg of near-surface carbon (less than 3m below the surface) available for microbial degradation by 2100. Despite having major implications for human health, prediction of the magnitude of carbon loss as carbon dioxide (CO2) or methane (CH4) is hampered by our limited knowledge of microbial metabolism of organic matter in these environments. Genome-centric meta-omic analysis of microbial communities provides the necessary information to examine how specific lineages transform organic matter during permafrost thaw. Stordalen Mire in northern Sweden has been subject to a decade of intense molecular and biogeochemical study, and almost 50 years of climate and vegetation research providing a unique opportunity to examine how microbial communities are changing alongside our climate.
The overall aim of this project will be to examine how individual microbial community members and entire communities assemble, adapt and acclimatise to changing environmental conditions.
Location of research
Translational Research Institute
Research activities include:
- literature search and review
- data analysis.
The project will make use of a machine learning tool developed to predict the oxygen concentration of samples taken, based upon the microorganisms inhabiting that sample. This tool will be validated in the mire by comparing its predictions with geochemical measures taken across the span of a decade from the site. Since oxygen is the most powerful electron acceptor available to the microorganisms inhabiting the mire, understanding its relationship with microbial community dynamics is an important step towards improved climate modelling.
Skills and experience
Some knowledge of one or more programming languages (e.g. Python / R) is required. An understanding of microbial physiology would also be advantageous.
Resources and access
The work will be supported by the excellent computational resources available at the Centre for Microbiome Research, comprising >2,100 hyperthreaded CPU cores, >8 TB RAM and an NVIDIA V100 GPU spread across 9 nodes. Hardware and OS maintenance is carried out by QUT’s eResearch arm, and CMR employs a system administrator for technical software support.
The student will sit at the TRI, with access to fellow PhD students and the supervisor Dr Woodcroft, and be given access to the CMR computer cluster.
Contact the supervisor for more information.