ABSTRACT
Microbially driven nitrification and denitrification play important roles in regulating soil N availability and N2O emissions. However, how the composition of nitrifying and denitrifying prokaryotic communities respond to long-term N additions and regulate soil N2O emissions in subtropical forests remains unclear. Seven years of field experiment which included three N treatments (+0, +50, +150 kg N ha-1 yr-1; CK, LN, HN) was conducted in a subtropical forest. Soil available nutrients, N2O emissions, net N mineralization, denitrification potential and enzyme activities, and the composition and diversity of nitrifying and denitrifying communities were measured. Soil N2O emissions from the LN and HN treatments increased by 42.37% and 243.32%, respectively, as compared to the CK. Nitrogen addition significantly inhibited nitrification (N mineralization) and significantly increased denitrification potentials and enzymes. Nitrification and denitrification abundances (except nirK) were significantly lower in the HN, than CK treatment and were not significantly correlated with N2O emissions. Nitrogen addition significantly increased nirK abundance while maintaining the positive effects of denitrification and N2O emissions to N deposition, challenging the conventional wisdom that long-term N addition reduces N2O emissions by inhibiting microbial growth. Structural equation modeling showed that the composition, diversity, and abundance of nirS- and nirK-type denitrifying prokaryotic communities had direct effects on N2O emissions. Mechanistic investigations have revealed that denitrifier keystone taxa transitioned from N2O-reducing (complete denitrification) to N2O-producing (incomplete denitrification) with increasing N addition, increasing structural complexity and diversity of the denitrifier co-occurrence network. These results significantly advance current understanding of the relationship between denitrifying community composition and N2O emissions, and highlight the importance of incorporating denitrifying community dynamics and soil environmental factors together in models to accurately predict key ecosystem processes under global change.
