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15N recovery under ambient and deepened snow treatments

Citation

Liu, Lingli (2022), 15N recovery under ambient and deepened snow treatments, Dryad, Dataset, https://doi.org/10.5061/dryad.9kd51c5kp

Abstract

Seasonal differences in plant and microbial nitrogen (N) acquisition are believed to be a major mechanism that maximizes ecosystem N retention. There is also a concern that climate change may interrupt the delicate balance in N allocation between plants and microbes. Yet, convincing experimental evidence is still lacking. Using a 15N tracer, we assessed how deepened snow affects the temporal coupling between plant and microbial N utilization in a temperate Mongolian grassland. We found that microbial 15N recovery peaked in winter, accounting for 22% of the total ecosystem 15N recovery, and then rapidly declined during the spring thaw. By stimulating N loss via N2O emission and leaching, deepened snow reduced the total ecosystem 15N recovery by 42% during the spring thaw. As the growing season progresses, the 15N released from microbial biomass was taken up by plants, and the competitive advantage for N shifted from microbes to plants. Plant 15N recovery reached its peak in August, accounting for 17% of the total ecosystem 15N recovery. The Granger causality test showed that the temporal dynamics of plant 15N recovery can be predicted by microbial 15N recovery under ambient snow but not under deepened snow. In addition, plant 15N recovery in August was positively correlated with and best explained by microbial 15N recovery in March. The lower microbial 15N recovery under deepened snow in March reduced plant 15N recovery by 73% in August. Together, our results provide direct evidence of seasonal differences in plant and microbial N utilization that are conducive to ecosystem N retention, however, deepened snow disrupted the temporal coupling between plant-microbial N use and turnover. These findings suggest that changes in snowfall patterns may significantly alter ecosystem N cycling and N-based greenhouse gas emissions under future climate change. We highlight the importance of better representing winter processes and their response to winter climate change in biogeochemical models when assessing N cycling under global change.

Funding

National Natural Science Foundation of China, Award: 32125025

National Natural Science Foundation of China, Award: 32101307

National Natural Science Foundation of China, Award: 31988102

Strategic Priority Research Program of the Chinese Academy of Science, Award: XDA23080301

Strategic Priority Research Program of the Chinese Academy of Science, Award: XDA26010303