Atmospheric nitrogen (N) deposition has enhanced the carbon (C) sink in temperate forests. We have a limited understanding, however, of why some forests show strong gains in C uptake and others do not, an area of uncertainty that impedes our ability to predict whether these forests will continue to reduce climate warming. At the heart of this knowledge gap is the failure of models to integrate the active marketplace for C and N that roots and soil microbes sustain in the rhizosphere. Here, we use a long term N fertilization experiment at the Fernow Experimental Forest, WV as a platform to challenge a novel modeling framework that dynamically links predictions of plant C investment into the rhizosphere with the impacts of this C on microbial activity. In particular, we tested whether the model could capture empirical differences between tree species that vary in mycorrhizal association in rhizosphere processes (i.e., exudation, enzyme production) under both ambient and elevated N. We then ran the model forward for thirty years to test the interactive effects of elevated CO2 and N on soil C pools.
Results/Conclusions
Under ambient N deposition, we found that the model was able to capture differences between ectomycorrhizal (ECM) and arbuscular mycorrhizal (AM) tree species in rates of root exudation. Empirically, these rates were substantially lower than previous estimates in temperate forests likely reflecting the high ambient N load that these WV forests have received historically. In the fertilized watershed, both the model and measurements show that enzyme activity declined to a greater extent in ECM than AM stands, but the spatial location differed. The model overestimated and underestimated the importance of rhizosphere declines in enzyme activity in ECM and AM stands, respectively; highlighting the need to better parameterize transfers of C and N between the rhizosphere and surrounding bulk soil. When we ran the model forward, we found that soil C gains were greater in ECM than AM stands, due to the strong reduction in priming that elevated N induced when we increased CO2 levels. By contrast, when we turned off plant-microbial interactions, the model predicted little difference between mycorrhizal associations. Collectively, these results highlight the critical need to dynamically link plants and soil microbes in models in order to accurately predict the response of soil C to global change.