Bisherige Arbeiten (2014 - 2017)
Wissenschaftliche Bearbeitung durch:
Prof. Dr. Michael Bonkowski
Prof. Dr. Francois Buscot
(Helmholtz Zentrum für Umweltforschung- UFZ, Halle)
Prof. Dr. Rolf Daniel
Prof. Dr. Ellen Kandeler
Prof. Dr. Jörg Overmann
Dr. Bärbel Fösel
Dr. Johannes Sikorski
Dr. Yu Shang
1. Soil microbial diversity is not random but has predictable patterns.
2. Changes in microbial diversity are linked to nutrient cycling, land use, plant diversity and productivity.
3. Genes and enzyme expression control variability in soil biological properties at the cellular level
4. Microbial species composition determines variability at the community level
5. The interaction of the soil organism with their environment occurs at the habitat level
The microbial communities in soil show the highest diversity. The large numbers and high specific physiological activity of soil bacteria render them important determinants of the biogeochemical cycling of nutrient, soil fertility and pathogen control. The substrate affinity, specific turnover rate and metabolic regulation vary considerably between different bacterial species. Soil fungi fulfil central functions in the breakdown and mineralization of refractory organic carbon components. The soil protozoa are the major consumers of bacterial production forming the basis of the heterotrophic eukaryotic food web that channels the energy flow from bacteria to higher trophic levels. Besides the enormous microbial biodiversity, the heterogeneous structure of the soil habitat at the microscale represents an additional methodological and conceptual challenge for the study of the interrelations between microbial diversity and abiotic or biotic variables. Although biodiversity experiments with higher organisms have yielded large sets of multitrophic data and enabled a comprehensive analysis of multitrophic interactions, only little is known about the extent and the mechanisms of functional coupling between different microbial taxa, between different trophic levels of the microbial food chain, ecosystem performance and land use.
The work of our project intends to integrate novel concepts and approaches for bacteria, fungi and protozoa into meta-analysis and modeling. The aim is to arrive at new hypotheses on abiotic-biotic interactions and to generate predictions of diversity patterns. The results will be fed back to the experimentally working research groups to be tested within the Biodiversity Exploratories. Thus the proposed project will pursue the following objectives:
1. Determine the relationships between the a-, ß-, y-diversity of microorganisms and species area relationship
2. Resolve temporal changes of soil microbial community structure and function
3. Gather evidence for the degree of functional redundancy in microbial communities
4. Analyse the effects of microbial species richness on ecosystem functions and land use
5. Integrate the interactions between microbial community and higher organisms, and microbial food webs
6. Identify environmental drivers of belowground microbial community composition and function
7. Assess the links between above and belowground compartments of the exploratory ecosystem
8. Develop strategies to compare molecular data derived from different methods and targets and to relate the date for different taxa and functions of soil microorganisms
9. Develop and text improved bioinformatics, statistical and modelling tools for microbial diversity analysis.
At the beginning, a web-based data survey will be conducted by contacting all groups working in the Biodiversity Exploratories to complete our initial assessment of available datasets and previous multivariate statistical analysis.
As a prerequisite for the subsequent meta-analysis, a coordinated development of bioinformatics pipelines is required to ensure equivalent data qualities for molecular results obtained with different approaches, and when targeting genomic regions of different variability or phylogenetic resolution power.
Based on the expertise of the participating groups, the multivariate statistical analysis will be further developed. In order to combine physiology and ecology into an evolutionary framework, the Price equation will be employed to describe the separate effects of physiological, evolutionary and ecological change in an exact and completely general way. Path analysis and structural equation modeling which represent powerful alternative tools to multiple regression, will be utilized to analysis the correlation and causal structure between the drivers and feedbacks in the soil ecosystems.
An ecological network analysis including the utilization of random Matrix based Molecular Ecological Network approach, analysis of the network topology, module detection, module based eigengene analysis, and the characterization of modular roles will be employed. The aim is to identify key microbial species and reveal unknown interactions between microorganism as well as the mechanistic control of system properties. As one application of the ecological network analysis, food web structure in the exploratory soils will be analysed.