12 November 2025

Microbial responses in soil and litter biogeochemical cycles to climate change

Background

Microorganisms are key players in global biogeochemical cycles. Through their decomposition of soil organic matter and plant litter they produce greenhouse gases feeding back to the global climate. Changes in temperature and water availability directly affect these activities but also affect the competitive dynamics among microbial community members leading to alterations in their ecological functions. This thesis project links to the Gradcatch projectwhich makes use of natural temperature and aridity gradients from Greenland, Europe and South Africa to investigate microbial responses to climate change. The project comprises a large dataset of microbial and soil physicochemical parameters (e.g. greenhouse gas fluxes, enzyme activities, quantification of C- and N-cycling genes by qPCR, soil chemical parameters, litter decomposition rates, marker gene and metagenomics sequencing). Moreover, Gradcatch encompasses a climate change experiment where soil cores and litter samples have successively been transplanted to warmer or drier sites and retrieved after one year field incubation.

Objectives

You will work with soil and litter metagenomes from the transplantation experiment. Thereby you will pinpoint microbial functional genes indicative of microbial adaptation to a warmer/drier climate and link these genes with changes in litter decomposition rates and greenhouse gas fluxes. Moreover, you will reconstruct metagenome assembled genomes (MAGs) to identify microbial adaptation strategies characterized by specific combinations of genomic features as well as trade-offs among them.

Tasks

  • processing metagenomic data (quality control, assembly, binning, functional annotation)
  • Differential expression/abundance analysis
  • Statistical analyses linking gene abundances with environmental parameters and C-cycling activities

Skills involved

  • Bash scripting, establishment of a metagenomic pipeline
  • Submitting computational jobs on a cluster such as genomeDK
  • Statistical analyses in R

Duration of the project

  • 6 – 12 months (30 – 60 ECTS)
  • Possibility to start within a Thesis Preparatory Project and extend it into a full thesis thereafter.

Contact

If you are interested in the project, please contact:

Jonathan Donhauser
Jonathan.donhauser@bio.ku.dk

Anders Priemé
aprieme@bio.ku.dk