Modeling coupled pesticide degradation and organic matter turnover: From gene abundance to process rates. (December 2016) - Pagel, Holger; Poll, Christian; Ingwersen, Joachim; Kandeler, Ellen; Streck, Thilo
Abstract: The mechanistic integration of microbial behavior remains a major challenge in biogeochemical modeling of organic matter turnover in soil. We recently introduced dynamic feedbacks between specific microbial groups and their micro-environment in a biogeochemical model (Pagel et al., 2014). Here, the model was applied in a case study to simulate pesticide degradation coupled to carbon (C) turnover in the detritusphere. We aimed at unravelling the effects of litter-derived substrate supply on the spatiotemporal dynamics of the microbial community and the resulting biogeochemical processes at the mm-scale in soil. We linked genetic information on abundances of bacteria, fungi and specific pesticide degraders to the biogeochemical dynamics of C and a generic model compound (MCPA, 4-chloro-2-methylphenoxyacetic acid) in soil by multiobjective calibration. We observed and simulated increased dissolved organic and microbial C as well as accelerated MCPA degradation in soil up to a 6 mm distance to litter. We found that, whereas transport and sorption processes act as extrinsic control on the encounter of microorganisms and substrates, microbial traits such as substrate preference or metabolic capabilities intrinsically determine turnover rates triggering feedback effects on physicochemical processes such as diffusion. A process analysis revealed that C cycling and pesticide degradation in the detritusphere were strongly controlled by fungal dynamics. Our study demonstrates that integrating mathematical modeling with experiments provides comprehensive insight into the microbial regulation of matter cycling in soil. Highlights: Microbial dynamics regulate coupled carbon and pesticide turnover in soil. Physicochemical processes controlled the encounter of microbes and substrates. Microbial dynamics triggered feedbacks on the diffusion of substrates. Genetic information has a high potential to parameterize biogeochemical models.