Modeling emergent phenomena of complex microbial communities

SOM & Carbon cycling
Global Change

Microbial soil organic matter transformations are traditionally investigated from a bird’s eye view, that means at scales that are considerably larger than those relevant to soil microbes, in both empirical and modeling studies. This approach constrains our understanding of the underlying mechanisms, and consequently hampers the prediction of decomposition rates under changing environmental conditions.

The microbial decomposer system, which is characterized by nonlinear interactions between individual microbes in a spatially structured and chemically heterogenous environment is argueable a complex dynamic system. It is well known that in complex dynamic systems, interactions among individuals at the microscale can lead to an ‘emergent’ system behavior at the macroscale, which cannot be derived directly from the traits of the individual agents. Such an emergent behavior, however, can have a crucial influence on mechanisms of microbial soil organic matter decomposition.

Aiming to capture the complex and dynamic nature of the soil’s decomposer system, I developed an individual-based microbial C and N turnover model, which simulates competitive and synergistic interactions between functionally different microbes in a spatially structured micro-scale environment. Based on this ‘bottom-up’ approach, our model allows to explore possible emergent behaviors of the decomposer system based on individual microbial traits.

Results from this modelling work have led to interesting insights into mechanisms that may drive soil C and N cycling: It showed, for instance, that functional diversity of microbes can alleviate stoichiometric constraints during litter decomposition (Kaiser et al. 2014) and that social interactions among microbes can lead to N retention and organic matter build-up in soils (Kaiser et al,  in press). We also found that the well-known Birch effect (i.e. the sudden release of a large amount of CO2 after rewetting of dry soil) can be explained by a combination of relieving diffusion limitations of labile substrates after rain and physiological responses of microbes to drought (Evans et al, in press).

Taken together, our modeling results show that the soil has a large potential for self-regulation and that the response of the soil system to environmental change may not be as predictable from first-order rate decay equations as is often assumed. We currently apply the model to investigate how microbial physiological traits influence C and N storage at the soil’s steady state, and the underlying mechanisms of the Priming effect. We are aiming on developing experimental approaches that allow us to test hypotheses generated by the model.












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