MIXOPEAT is currently offering several opportunities to join the project.
MSc Internship (6 months)
Modelling microbial food webs in peatlands under climate change
Keywords. Machine learning, predictive ecology, climate change,
carbon cycling, microbial community
Summary. Peatlands play an important role in the global carbon cycle. While peatlands only cover 3% of the land surface, 550 Pg of carbon is sequestered in them as peat (Yu et al. 2010). These ecosystems store and equivalent of 1/6 of all terrestrial carbon, which is more than the carbon present in the atmosphere. The capacity of peatlands to accumulate this carbon is due to a disbalance in the uptake by plants and the release through decomposition and respiration. Changing climatic conditions, such as increasing temperature, shifting precipitation regimes and resulting lowering of the water table can alter the carbon fluxes in peatlands and promote carbon loss through increased activity of the microbial food web.
The microbial food web is often considered a black-box as studies often use very general microbial indicators such as bacterial and or fungal biomass. But, numerous trophic interactions exist between phototrophs, decomposers and other microbial groups; interactions which can greatly influence peatland carbon fluxes and determine how peatlands respond to climatic change (Jassey et al. 2013, 2015). The goal of this internship is to model the microbial food webs in peatlands using machine learning techniques using a database with morphological and trophic (who-eats-who) information on the various microorganisms found in peatlands. We will then test how these microbial food webs respond to climatic change using data from earlier experiments and determine how this response influences peatland carbon fluxes.
This internship will be part of the Mixopeat research project (www.mixopeat.cnrs.fr) which is conducted by an international research team. The future candidate is expected to have a good level of English and good working experience with R.