About the Project
Objectives: This project aims to meet the challenge of integrating microbial molecular data with ecosystem measures by extracting microbial traits from soil metagenomics datasets, and representing these traits as life history strategies in individual-based models predicting SOC change. Growth yield (Y), resource acquisition (A), and stress tolerance (S) strategies encompass the key traits that regulate microbial community functioning and appear in microbial-explicit models. Y-A-S framework will be used by the student to organize trait-based life history strategies (Malik et al., 2019). Key objectives of the project are: 1) to extract traits of soil microbes from publicly available metagenomics datasets and assign life history strategies, 2) to link trait-based life history strategies to environmental and SOC metadata through a rigorous statistical framework and 3) to incorporate life history data into an individual-based model predicting SOC change.
Methods and training: Relevant soil metagenomics datasets will be mined from public databases along with environmental and soil properties metadata. The student will extract traits in the form of relative abundances of genes. For growth yield (Y), trait index can be generated from the richness and composition of genes in central carbon metabolism and amino acid, nucleotide and fatty acid synthesis. Resource acquisition traits can be defined by motility (resource discovery), catabolism (mining through extracellular enzymes) and membrane transport (uptake), which will be annotated using COG and CAZy databases. Stress tolerance can be represented by various gene families, but global stress regulators that respond to cellular damage, including molecular chaperons and sigma factors, will be used to estimate a trait value. Various statistical approaches such as correlations, effect size analysis, variation partition analysis and structural equation modelling under R environment will be used to identify trade-offs between traits and to link those to environmental factors and SOC concentrations.
The extracted trait data will be incorporated into an individual-based model called DEMENT (Allison, 2012) to reveal how these trade-offs structure microbial communities and their resulting carbon cycle functions. Relationships between Y-A-S traits will be used as a mechanistic basis for predictions. The model will be used to project community responses and carbon cycling consequences under simulated environmental conditions. Model outputs will be validated with ecosystem measures like SOC concentrations. The modelling tasks will be performed in collaboration with Steven Allison (a pioneer in microbial soil carbon modelling) at University of California, Irvine. Such a combination of metagenomics, bioinformatics, ecological statistics and trait-based modelling approaches will allow the student to gain unique technical skills across disciplines.
Candidates should have (or expect to achieve) a minimum of a 2.1 Honours degree in a relevant subject. Applicants with a minimum of a 2.2 Honours degree may be considered providing they have a Distinction at Master’s level.
• Apply for Degree of Doctor of Philosophy in Biological Sciences
• State name of the lead supervisor as ‘Name of Proposed Supervisor’ on application
• State ‘QUADRAT DTP’ as Intended Source of Funding
• Select the ‘Visit Website’ to apply now
The studentship provides funding for tuition fees, stipend and a research training and support grant subject to eligibility.
Allison, S.D. A trait-based approach for modelling microbial litter decomposition. Ecol Lett 2012; 15: 1058-1070. doi:10.1111/j.1461-0248.2012.01807.x
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