Wed, Aug 04, 2021:On Demand
Background/Question/Methods
Most Earth System Models (ESMs) respond to enhanced nitrogen (N) deposition by increasing plant productivity and soil carbon (C) inputs, leading to increased soil C stocks and respiration rates. However, observations suggest that increased soil C stocks with N additions result from slower decomposition rates and lead to decreased soil respiration. One reason these models do not capture this observed response is because they do not dynamically represent plant-microbe interactions. Here, we tested the N-addition responses of a microbially explicit and a microbially implicit soil model—both driven by the same plant inputs and forcing data—to evaluate the advantages of a model with physiological representation of microbes in predicting soil biogeochemical responses to environmental change. The Microbial-Mineral Carbon Stabilization (MIMICS) model explicitly represents microbial physiology and the Carnagie-Aimes-Stanford Approach (CASA) soil model relies on a first-order representation of microbes – an approach used in most ESM soil models. We parameterized and validated these models against observations and then compared their responses to observations from a long-term, whole-watershed N fertilization experiment at the Fernow Experimental Forest, WV, USA.
Results/Conclusions In baseline simulations, both models showed increases in NPP, soil respiration, and soil C and N stocks in response to N fertilization. Most of the enhanced NPP was allocated to fine roots and leaves. However, field observations found no difference in fine root or leaf production and suggest a strong shift in C allocation that favors woody biomass production compared to belowground C allocation with N additions. Thus, we modified the models by adding a root exudate flux that diminished with N additions, and we modified the fixed plant C allocation parameterization to favor wood production with N additions. These modifications led to modeled vegetation responses more similar to observations and only slightly reduced the positive soil respiration response (~30%) of both models. To reflect the hypothesis that N additions directly inhibit decomposition of structural plant material, we also reduced the models’ decay rates of structural plant material with N additions. The modified MIMICS model produced soil responses to N additions more aligned with observations (e.g., greater soil C:N) than the CASA model. These results indicate that while a microbially explicit model has more potential for accurately predicting how N addition impacts soil biogeochemistry, additional mechanisms that drive the “vital connections” between plants and microbes (e.g. dynamic C allocation, root exudation, dynamic decomposition) further improves model predictions.
Results/Conclusions In baseline simulations, both models showed increases in NPP, soil respiration, and soil C and N stocks in response to N fertilization. Most of the enhanced NPP was allocated to fine roots and leaves. However, field observations found no difference in fine root or leaf production and suggest a strong shift in C allocation that favors woody biomass production compared to belowground C allocation with N additions. Thus, we modified the models by adding a root exudate flux that diminished with N additions, and we modified the fixed plant C allocation parameterization to favor wood production with N additions. These modifications led to modeled vegetation responses more similar to observations and only slightly reduced the positive soil respiration response (~30%) of both models. To reflect the hypothesis that N additions directly inhibit decomposition of structural plant material, we also reduced the models’ decay rates of structural plant material with N additions. The modified MIMICS model produced soil responses to N additions more aligned with observations (e.g., greater soil C:N) than the CASA model. These results indicate that while a microbially explicit model has more potential for accurately predicting how N addition impacts soil biogeochemistry, additional mechanisms that drive the “vital connections” between plants and microbes (e.g. dynamic C allocation, root exudation, dynamic decomposition) further improves model predictions.