Illuminating black boxes in the nitrogen cycle
With an increasing world population growth, prediction of climate change on the sustainability and resilience of farming ecosystems is key. Since the nitrogen cycle's role within the farming ecosystem is of utmost importance, and an environmental disturbance leads to farming production loss, understanding the cycle's resilience is crucial. The main actors within this cycle are microorganisms, yet these are ignored when predicting ecological effects to climate change (black box models). Microorganisms do not exist as isolated entities, but are mixed in high numbers, maintaining a diverse number of social interactions created through adaptation and evolution. Microbial-Light will illuminate black box models via the acquisition of a multi-parametric database. Physicochemical disturbances will be applied to a large combinatorial number of mixed microbial populations of well studied nitrifiers. Microbial growth monitoring at microtiter scale will include strain tracking (FISH-flow cytometry), while function (i.e. nitrification) will be determined at a larger scale. The ample data collected will enable elucidation of functional landscapes for each synthetic community under combining environmental stresses. Additionally, the role of microbial interactions (BSocial tool) across disturbance gradients will be sought. Microbial-Light, will engage in modelling individual growth from synthetic mixed population growth, thereby validating the models with previous experimental evidence. Moreover, the optimal social nitrifying community will be coated on tomato seeds, and plant growth efficiency will be compared with uncoated seeds. Parallel to the acquisition multi-parametric database, evaluation of the nitrification potential of poor and rich soils will be tested coupled with microbial diversity (Illumina sequencing). The analysis of synthetic and natural communities, will allow for a more comprehensive ecological model on nitrification.