Minerals are the primary component of most soils, with the mineral composition (i.e. the mineralogy) governed largely by the geology of the local parent material. Acting as a surface for microbial colonization, minerals are understood to host approximately 80 % of soil prokaryotes. It has long been recognised that soil mineralogy is intimately related to the physical and chemical properties of soil, however the role of minerals as the habitat for a diverse microbial ecosystem in soil remains largely unexplored (Uroz et al., 2015). Recent advances in soil mineralogy (high-throughput X-ray powder diffraction) and microbial community genomics (next generation sequencing) provide a unique opportunity to investigate the biological role of minerals in soil.
Detailed and precise mineralogical signatures of soil can be derived by X-ray powder diffraction (XRPD) analysis. Combining high-throughput XRPD analysis with data-driven approaches such as supervised (Butler et al., 2018) or unsupervised (Hillier and Butler, 2018) machine learning, allows for soil property – mineralogy relationships to be identified and interpreted. In other, preliminary, research, combining quantitative mineralogy with bacterial community data from Scottish soils found minerals to contribute significantly to the bacterial community assemblage when tested by canonical covariance analysis. Further application of such approaches therefore displays potential for advancing the understanding mineral – microbe interactions within the soil environment, and how these may contribute to soil properties and functions.
To investigate these interactions, the proposed studentship would use targeted fieldwork campaigns combined with laboratory analysis. Fieldwork would involve selective soil sampling from Scotland’s small but diverse geological landscape, seeking a dataset with substantial mineralogical variation. Laboratory work would involve preparing samples for a range of analysis including: XRPD for soil mineralogy; inductively coupled plasma mass spectrometry for chemical soil properties; and next generation sequencing for microbial (bacteria and/or fungi) community genomics. The results from these analyses will then be combined with the multivariate, data-driven, and quantitative approaches described above.
Aside from a willingness to carry out fieldwork and laboratory analysis, the student would ultimately be required to apply an interdisciplinary combination of soil science, mineralogy, bioinformatics, and data science. Relevant training in each of these disciplines will be provided where necessary. Emphasis will be placed on open source solutions to the data-intensive nature of the project, such as the R and/or Python programming languages, therefore candidates should display strong computational aptitude.
Butler, B.M., O’Rourke, S.M., Hillier, S., 2018. Using rule-based regression models to predict and interpret soil properties from X-ray powder diffraction data. Geoderma 329, 43–53. doi:10.1016/j.geoderma.2018.04.005
Hillier, S., Butler, B.M., 2018. New XRD Data-Based Approaches to Soil Mineralogy. Spectroscopy 33, 34–36.
Uroz, S., Kelly, L.C., Turpault, M.P., Lepleux, C., Frey-Klett, P., 2015. The Mineralosphere Concept: Mineralogical Control of the Distribution and Function of Mineral-associated Bacterial Communities. Trends Microbiol. 23, 751–762. doi:10.1016/j.tim.2015.10.004
If you wish to apply for this project, pleadse go to this link.