The Genetic Structure of Microbial Communities as a Signature of their Stability

Global change due to human activities causes an increasing number of disturbed ecosystems and poses a challenge to humanity because human life on earth depends on the stability of ecosystem services. Microorganisms are main drivers of element cycling, they contribute largely to the global organic carbon budget and are therefore fundamental for all biological processes and relevant for ecosystem services. They furthermore represent model organisms to test ecological theory, as they are small and have short generation times what facilitates the generation of comprehensive datasets for statistical evaluation.

Earlier research points to the following mechanisms that support the stability of community functioning in fluctuating environments: first, there has been a long debate about the link of community structures, such as diversity patterns but also the architecture of species interactions to the vulnerability of these communities to environmental change. Secondly, the tolerance of individual community members against environmental change impacts also the community-level robustness and a classification of individual community members reflecting their resistance and resilience to environmental change may be a valuable tool to evaluate the vulnerability of whole communities in a disturbed environment. Third, also species interactions may drive community stability, for instance if the reduction of one species’ fitness causes others to decline.

The genomic material of microbial communities represents a blueprint of their functioning and should contain the information about the tolerance of individual community members as well as information concerning the above-mentioned community structures. However, apart from ongoing advances, there is still a lack in the ability to fully exploit the large amount of information stored in meta-omic data. The overall aim of my research is to improve the prediction of microbial functional as well as compositional dynamics in fluctuating environments using molecular markers obtained from meta-omic datasets as predictor variables.