Root traits explain observed tundra vegetation nitrogen uptake patterns: Implications for trait-based land models

• Published in: Journal of Geophysical Research: Biogeosciences
• Main Authors: Zhu, Qing, Iversen, Colleen M., Riley, William J., Slette, Ingrid J., Vander Stel, Holly M.
• Language: unknown
• Published: 2021
• Subjects: 58 GEOSCIENCES; Arctic; permafrost; Tundra
• Online Access: http://www.osti.gov/servlets/purl/1474984; https://www.osti.gov/biblio/1474984; https://doi.org/10.1002/2016JG003554

Ongoing climate warming will likely perturb vertical distributions of nitrogen availability in tundra soils through enhancing nitrogen mineralization and releasing previously inaccessible nitrogen from frozen permafrost soil. But, arctic tundra responses to such changes are uncertain, because of a lack of vertically explicit nitrogen tracer experiments and untested hypotheses of root nitrogen uptake under the stress of microbial competition implemented in land models. We conducted a vertically explicit 15N tracer experiment for three dominant tundra species to quantify plant N uptake profiles. Then we applied a nutrient competition model (N-COM), which is being integrated into the ACME Land Model, to explain the observations. Observations using an 15 N tracer showed that plant N uptake profiles were not consistently related to root biomass density profiles, which challenges the prevailing hypothesis that root density always exerts first-order control on N uptake. By considering essential root traits (e.g., biomass distribution and nutrient uptake kinetics) with an appropriate plant-microbe nutrient competition framework, our model reasonably reproduced the observed patterns of plant N uptake. Additionally, we show that previously applied nutrient competition hypotheses in Earth System Land Models fail to explain the diverse plant N uptake profiles we observed. These results cast doubt on current climate-scale model predictions of arctic plant responses to elevated nitrogen supply under a changing climate and highlight the importance of considering essential root traits in large-scale land models. Finally, we provided suggestions and a short synthesis of data availability for future trait-based land model development.