Grassland Soil Carbon Responses to Nitrogen Additions

Using a long-term continental scale experiment, we tested if increases in nitrogen (N) inputs augment the accumulation of plant and microbial residues onto mineral soil surfaces. This research investigates N effects on molecular biogeochemistry across six sites from the Nutrient Network (NutNet) experiment. The coupling between concurrently changing carbon (C) and N cycles remains a key uncertainty in understanding feedbacks between the terrestrial C cycle and climate change. Existing models do not consider the full suite of linked C-N processes, particularly belowground, that could drive future C-climate feedbacks. Soil harbors a wealth of diverse organic molecules, most of which have not been measured in hypothesis driven field research. For the first time we systematically assess the chemical composition of soil organic matter (SOM) and functional characteristics of the soil microbiome, to enhance our understanding of the molecular underpinnings of ecosystem C and N cycling. We have acquired soils from 5 ecosystem experiments across the US that have been subjected to 8 years of N addition treatments. These soils have been analyzed for chemical composition to identify how the soil fertility and stability is altered by N fertilization. We found distinct SOM signatures from our field experiments and shifts in soil chemistry in response to 8 years of N fertilization. Across all sites, we found the molecular composition of SOM varied with clay content, supporting the importance of soil mineralogy in the accumulation of specific chemical classes of SOM. While many molecules were common across sites, we discovered a suite of molecules that were site specific. N fertilization had a significant effect on SOM composition. Differences between control and N amended plots were greater in sites rich in lipids and more complex molecules, compared to sites with SOM rich in amino-sugar and protein like substances. Our results have important implications for how SOM is represented in models and the possibility for incorporating molecular understanding to advance ecosystem predictions.