Representing Microbial Dormancy in Soil Decomposition Models Improves Model Performance and Reveals Key Ecosystem Controls on Microbial Activity

Monday, 15 December 2014
Yujie He, Purdue University, Department of Earth, Atmospheric and Planetary Sciences, West Lafayette, IN, United States, Jinyan Yang, University of Georgia, Athens, GA, United States, Qianlai Zhuang, Purdue University, West Lafayette, IN, United States, Gangsheng Wang, Oak Ridge National Laboratory, Oak Ridge, TN, United States and Yaling Liu, Purdue Univ, West Lafayette, IN, United States
Climate feedbacks from soils can result from environmental change and subsequent responses of plant and microbial communities and nutrient cycling. Explicit consideration of microbial life history traits and strategy may be necessary to predict climate feedbacks due to microbial physiology and community changes and their associated effect on carbon cycling. In this study, we developed an explicit microbial-enzyme decomposition model and examined model performance with and without representation of dormancy at six temperate forest sites with observed soil efflux ranged from 4 to 10 years across different forest types. We then extrapolated the model to all temperate forests in the Northern Hemisphere (25-50°N) to investigate spatial controls on microbial and soil C dynamics. Both models captured the observed soil heterotrophic respiration (RH), yet no-dormancy model consistently exhibited large seasonal amplitude and overestimation in microbial biomass. Spatially, the total RH from temperate forests based on dormancy model amounts to 6.88PgC/yr, and 7.99PgC/yr based on no-dormancy model. However, no-dormancy model notably overestimated the ratio of microbial biomass to SOC. Spatial correlation analysis revealed key controls of soil C:N ratio on the active proportion of microbial biomass, whereas local dormancy is primarily controlled by soil moisture and temperature, indicating scale-dependent environmental and biotic controls on microbial and SOC dynamics. These developments should provide essential support to modeling future soil carbon dynamics and enhance the avenue for collaboration between empirical soil experiment and modeling in the sense that more microbial physiological measurements are needed to better constrain and evaluate the models.