The new CRC RESIST aims to understand and explain the mechanisms underlying the degradation of and recovery from multiple stressors in stream ecosystems. My subproject focuses on the role of different fungal and bacterial groups in enzymatic decomposition of coarse particulate organic matter (CPOM) - leaf decompositions - in the presence and absence of stressors. To determine the stressor effect on molecular level we will use metatranscriptome sequencing. In two experimental systems experiments will be run in collaboration with all CRC members. Here we will evaluate the effect of stressor increase and release for both fungi and bacteria. In particular, we will test whether functions recover faster than community composition to a pre‐degradation stage, due to partial functional redundancy among taxa. We expect that functions are shared between bacteria and fungi, and among different taxa within these microbial groups. Thus, if species or other taxonomic groups disappear following stressor exposure, their metabolic activity will be compensated for by other taxa. However, such compensatory mechanisms might be limited if a succession of taxa is necessary for the decomposition of CPOM. First, we will need to determine the bacterial and fungal community composition contributing to the decomposition of leaf litter by stable isotope probing and amplicon and metatranscriptome sequencing in the absence of stressors. Thereupon, we will analyse via metatranscriptomics: i) the taxa involved in the decomposition of leaf litter, ii) their specific functional roles, iii) their interactions and redundancies and iv) the effect of multiple stressors on all of the previous aspects.
The subproject focuses on the bioinformatic analysis of high-throughput sequencing data, development of analysis workflows and the statistical evaluation. Limited help is needed for the experimental setup, but the share of experimental and laboratory work can be increased depending on the interest of the hired PhD student.
The new CRC RESIST aims to understand and explain the mechanisms underlying the degradation of and recovery from multiple stressors in stream ecosystems. The Z-INF project represents the backbone for data management and data integration to facilitate collaboration inside RESIST. Data storage and data exchange between RESIST’s individual projects and with external institutions will be coordinated by Z-INF, for which a respective infrastructure will be established. Central data storage facilities will ensure archiving and accessibility of raw and processed data of RESIST. Management of research data for exchange between projects, for publications and future internal and external use will be provided and maintained. For the provided data, data type descriptors with respect to file format, content, sample and experimental affiliation, generation of the data (hardware, software, versions, parameters) will be developed and applied. With mandatory documentation of the generated data, in form of metadata and the introduction of an electronic laboratory notebook connected to the data management system, we will contribute to ensuring reproducible research. In the scope of further utilisation of the data, pipelines and tools for data integration will be developed to analyse multiple data sources in conjunction with regard to stressor effects across experimental systems and analysed organisms. This involves the development of methods to integrate heterogeneous types.