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Vegetation change of the Arctic tundra due to global warming is a well-known process, but the implication for the belowground microbial communities, key in nutrient cycling and decomposition, is poorly understood. We characterized the fungal and bacterial abundances in litter and soil layers across 16 warming experimental sites at 12 circumpolar locations. We investigated the relationship between microbial abundances and nitrogen (N) and carbon (C) isotopic signatures, indicating shifts in microbial processes with warming. Microbial abundances were 2–3 orders of magnitude larger in litter than in soil. Local, site-dependent responses of microbial abundances were variable, and no general effect of warming was detected. The only generalizable trend across sites was a dependence between the warming response ratios and C:N ratio in controls, highlighting a legacy of the vegetation on the microbial response to warming. We detected a positive effect of warming on the litter mass and δ 15 N, which was linked to bacterial abundance under warmed conditions. This effect was stronger in experimental sites dominated by deciduous shrubs, suggesting an altered bacterial N-cycling with increased temperatures, mediated by the vegetation, and with possible consequences on ecosystem feedbacks to climate change.
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Soil enzymes as indicators of soil function: A step toward greater realism in microbial ecological modeling
Abstract Soil carbon (C) and nitrogen (N) cycles and their complex responses to environmental changes have received increasing attention. However, large uncertainties in model predictions remain, partially due to the lack of explicit representation and parameterization of microbial processes. One great challenge is to effectively integrate rich microbial functional traits into ecosystem modeling for better predictions. Here, using soil enzymes as indicators of soil function, we developed a competitive dynamic enzyme allocation scheme and detailed enzyme‐mediated soil inorganic N processes in the Microbial‐ENzyme Decomposition (MEND) model. We conducted a rigorous calibration and validation of MEND with diverse soil C‐N fluxes, microbial C:N ratios, and functional gene abundances from a 12‐year CO2 × N grassland experiment (BioCON) in Minnesota, USA. In addition to accurately simulating soil CO2fluxes and multiple N variables, the model correctly predicted microbial C:N ratios and their negative response to enriched N supply. Model validation further showed that, compared to the changes in simulated enzyme concentrations and decomposition rates, the changes in simulated activities of eight C‐N‐associated enzymes were better explained by the measured gene abundances in responses to elevated atmospheric CO2concentration. Our results demonstrated that using enzymes as indicators of soil function and validating model predictions with functional gene abundances in ecosystem modeling can provide a basis for testing hypotheses about microbially mediated biogeochemical processes in response to environmental changes. Further development and applications of the modeling framework presented here will enable microbial ecologists to address ecosystem‐level questions beyond empirical observations, toward more predictive understanding, an ultimate goal of microbial ecology.
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- PAR ID:
- 10363062
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Global Change Biology
- Volume:
- 28
- Issue:
- 5
- ISSN:
- 1354-1013
- Page Range / eLocation ID:
- p. 1935-1950
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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