15N recovery under ambient and deepened snow treatments

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.