Microbial methane cycling in aquatic ecosystems

Aquatic ecosystems are a major source of methane, which is a very potent greenhouse gas. Microorganisms are responsible for the production and the consumption of methane, thereby controlling how much is released to the atmosphere. Human activities, such as the use of agriculture fertilizers, livestock industry and sewage, can lead to excessive nutrient inputs to coastal ocean and lake ecosystems causing algal blooms (=eutrophication). It is currently unclear how such changes in environmental conditions affect the diverse microorganisms involved in methane cycling. Understanding how eutrophication might influence the balance between the production and consumption of methane is of central importance to better predict future climate developments. Microbial methane production has long been thought of as strictly anaerobic process exclusively occurring in the absence of oxygen. However, a fraction of the methane emissions from aquatic ecosystems to the atmosphere also occurs in well-oxygenated surface waters in close proximity to the atmosphere (=methane paradox). One major objective of the project will focus on the fundamental question about the origin of methane in oxic surface waters and the influence of eutrophication on its production. Additionally, we will unravel the identities, activities, and metabolic capabilities of microorganisms involved in methane cycling, in order to understand how community dynamics affect methane production and consumption processes under changing environmental conditions. To tackle these open questions, we will use a combination of sophisticated methodological approaches including environmental measurements, incubation experiments under controlled laboratory conditions, and molecular tools to analyze microbial communities. The expected results of this project promise fundamentally new insights into aquatic methane cycling, particularly due to its broad scope, including measurements in contrasting marine and freshwater environments and across different spatial and temporal gradients.