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Tanentzap Group: Invisible world: the biogeography of molecular and microbial diversity in European lakes


Supervisor: Andrew Tanentzap (Plant Sciences
Co-Supervisor: Prof Thorsten Dittmar, University of Oldenburg

Brief Summary

Invisible to the naked eye lies a tremendous diversity of organic molecules that interact with microorganisms to shape the world's biogeochemical cycles.

Importance of research:

Dissolved organic matter (DOM) is central to the functioning of freshwater ecosystems that support Earth's life systems. It does so by increasing the attenuation of solar radiation, altering contaminant toxicity and increasing nutrient pools. Changes in these properties result in major variation in ecosystem metabolism and production, particularly by freshwater microbes that live off DOM. Yet, despite its importance, little is known about how the vast diversity of molecules found in DOM influences its many properties.

Recent advances in analytical chemistry have shown that a handful of lake water contains thousands of different molecular formulas of varying origin and composition (Kellerman et al. 2014). But the biological role of all these different molecules largely remains a mystery.

Project summary:

This project will discover how the composition of dissolved organic matter and microbial communities co-vary across macro-ecological scales. In the first part of the project, you will coordinate tens of monitoring sites worldwide as they collect water samples at high temporal resolution in Global Lake Ecological Observatory Network project. You will then test how abundances of molecules differentially change with abundances of microbes across different timescales. Related, you will test if these associations and co-interaction networks vary with lake and catchment characteristics.

The second part of this project will test how chemical diversity influences ecosystem multifunctionality in 25 British lakes, accounting for variation in microbial diversity. You will apply theory developed for biodiversity to the concept of chemical diversity.

What will the student do?

You will join a large team working on the ERC-funded #sEEIngDOM project. You will learn the latest techniques in chemoinformatics and bioinformatics to process ultra-high resolution mass spectrometry and shotgun metagenomics datasets, including mine existing data generated for 120 lakes spanning the extent of Continental Europe. You will then apply machine learning and statistical modelling techniques to analyse temporal changes in molecular compounds and microbes (e.g. Morton et al. 2019). You will also design a multi-year field study and collect all the associated data, working in remote locations in Scotland and northern England for long periods of time. Data collection will involve the difficult task of paddling to the middle of lakes and collecting water samples that will be analysed in the lab for chemical and microbial diversity. You will then apply similar data analysis techniques as the first part of the project to test how chemical diversity influences multifunctionality.

Training to be provided:

You will be trained in carrying out field work (including limnological sampling), experimental design, and statistical modelling (including computer programming). Training in bio/chemoinformatics, lab-based molecular biology and analytical chemistry techniques will also be provided from our group, visits to European collaborators, and formal courses, but some experience with these techniques is advantageous.


  • Kellerman, A.M., Dittmar, T., Kothawala, D.N. & Tranvik, L.J. 2014. Chemodiversity of dissolved organic matter in lakes driven by climate and hydrology. Nature Communications, vol. 5, pp.3804.
  • Morton, J.T., Aksenov, A.A., Nothias, L.F. et al. 2019. Learning representations of microbe–metabolite interactions. Nature Methods, vol. 16, pp. 1306–1314.
  • Tanentzap, A.J., Fitch, A., Orland, C. et al. 2019. Chemical and microbial diversity covary in fresh water to influence ecosystem functioning. PNAS, vol. 116, pp.24689-24695.

 For details on how to apply to the Cambridge NERC Doctoral Training Partnerships see 


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