COS 78-6
An empirical assessment of a stoichiometric and microbial explicit nutrient cycling model

Wednesday, August 12, 2015: 3:20 PM
303, Baltimore Convention Center
Robert W. Buchkowski, School of Forestry and Environmental Studies, Yale University, New Haven, CT
Oswald J. Schmitz, School of Forestry and Environmental Studies, Yale University, New Haven, CT
Mark A. Bradford, School of Forestry & Environmental Studies, Yale University, New Haven, CT

Understanding the role of soil microbial communities in coupled carbon and nitrogen cycling is central to managing ecosystems in the face of global change. Stoichiometric and microbial explicit models that consider both carbon and nitrogen have emerged as a powerful tool for predicting feedbacks to climate change mediated by soil microbes. However, the most appropriate methods for characterizing microbial communities in such models are still unclear. Much of this uncertainty can be attributed to the difficulty of measuring parameter values and our insufficient understanding of microbial community function. In light of these knowledge gaps, we compared a well-established microbial model with two data sets from laboratory microcosm experiments in order to evaluate the underlying assumptions. The first experiment manipulated carbon and nitrogen availability within a single soil type, while the second experiment manipulated soil type and temperature. Specifically we asked: (1) Is the microbial explicit model able to predict empirical trends in biomass, carbon, and nitrogen cycling?, (2a) If the model is able to predict the experimental results, what mechanism is responsible? and (2b) If the model is unable to predict the results, what improvements might be made in order to improve the predictive ability?


The model’s predictive ability was variable. The model was able to predict the qualitative trends in carbon and nitrogen cycling in the first data set where carbon and nitrogen availability were manipulated. However, the model failed to capture differences in microbial biomass. Biomass was not different in the experiment, but highly dependent on nutrient amendments in the model. The accurate prediction of nutrient cycling in spite of microbial biomass suggests that the stoichiometric mechanisms, not microbial biomass, determined the model’s predictive ability. Conversely, the model was unable to predict the differences caused by soil type and temperature in the second data set. One explanation is that the model did not include different quality soil organic carbon pools or the effect of soil mineral structure on nutrient fluxes that might vary across sites. Both factors are important determinants of nutrient cycling in soils, and their inclusion in future models would help to increase predictive power. While the best representation of soil microbial communities in nutrient cycling model remains unclear, our results indicate that stoichiometric and microbial explicit models are a powerful tool for understanding carbon and nitrogen cycling in soils that could be improved by including more soil carbon pools.