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.