Incorporating microbial dormancy dynamics into soil decomposition models to improve quantification of soil carbon dynamics of northern temperate forests

Journal Article · Tue Dec 22 00:00:00 EST 2015 · Journal of Geophysical Research. Biogeosciences

DOI:https://doi.org/10.1002/2015JG003130· OSTI ID:1402309

He, Yujie ^[1]; Yang, Jinyan ^[2]; Zhuang, Qianlai ^[3]; Harden, Jennifer W. ^[4]; McGuire, Anthony D. ^[5]; Liu, Yaling ^[1]; Wang, Gangsheng ^[6]; Gu, Lianhong ^[7]

---

1. Department of Earth, Atmospheric, and Planetary Sciences Purdue University West Lafayette Indiana USA
2. Warnell School of Forestry and Natural Resources University of Georgia Athens Georgia USA, Center for Ecological Research Northeast Forestry University Harbin China
3. Department of Earth, Atmospheric, and Planetary Sciences Purdue University West Lafayette Indiana USA, Department of Agronomy Purdue University West Lafayette Indiana USA
4. U.S. Geological Survey Menlo Park California USA
5. Alaska Cooperative Fish and Wildlife Research Unit, U.S. Geological Survey University of Alaska Fairbanks Fairbanks Alaska USA
6. Climate Change Science Institute and Environmental Sciences Division Oak Ridge National Laboratory Oak Ridge Tennessee USA
7. Environmental Sciences Division Oak Ridge National Laboratory Oak Ridge Tennessee USA

Abstract Soil carbon dynamics of terrestrial ecosystems play a significant role in the global carbon cycle. Microbial‐based decomposition models have seen much growth recently for quantifying this role, yet dormancy as a common strategy used by microorganisms has not usually been represented and tested in these models against field observations. Here we developed an explicit microbial‐enzyme decomposition model and examined model performance with and without representation of microbial dormancy at six temperate forest sites of different forest types. We then extrapolated the model to global temperate forest ecosystems to investigate biogeochemical controls on soil heterotrophic respiration and microbial dormancy dynamics at different temporal‐spatial scales. The dormancy model consistently produced better match with field‐observed heterotrophic soil CO ₂ efflux ( R _H ) than the no dormancy model. Our regional modeling results further indicated that models with dormancy were able to produce more realistic magnitude of microbial biomass (<2% of soil organic carbon) and soil R _H (7.5 ± 2.4 Pg C yr ⁻¹ ). Spatial correlation analysis showed that soil organic carbon content was the dominating factor (correlation coefficient = 0.4–0.6) in the simulated spatial pattern of soil R _H with both models. In contrast to strong temporal and local controls of soil temperature and moisture on microbial dormancy, our modeling results showed that soil carbon‐to‐nitrogen ratio (C:N) was a major regulating factor at regional scales (correlation coefficient = −0.43 to −0.58), indicating scale‐dependent biogeochemical controls on microbial dynamics. Our findings suggest that incorporating microbial dormancy could improve the realism of microbial‐based decomposition models and enhance the integration of soil experiments and mechanistically based modeling.

• Sponsoring Organization: USDOE
• Grant/Contract Number: DE‐FG02‐08ER64599
• OSTI ID: 1402309
• Journal Information: Journal of Geophysical Research. Biogeosciences, Journal Name: Journal of Geophysical Research. Biogeosciences Vol. 120 Journal Issue: 12; ISSN 2169-8953
• Publisher: American Geophysical Union (AGU)
• Country of Publication: United States
• Language: English