Varying interactive effects of climate, soil properties, and gross nitrogen dynamics on biomass production between the topsoil and the subsoil in natural grassland ecosystems

Soil nitrogen (N) availability, which is primarily controlled by climate and soil properties, may play a key role in regulating biomass production in natural grasslands. However, how climate, soil properties, and gross N dynamics interact in their regulation of biomass production in natural grassland ecosystems in climate-sensitive biomes remains largely unclear. We quantified gross N transformation rates using the 15N dilution technique and the abundance of bacteria, fungi, ammonia-oxidizing archaea, and bacteria in the topsoil (0-10 cm) and subsoil (30-40 cm) in four natural grassland ecosystems along an altitudinal gradient from the Inner Mongolian (IM) to the Qinghai-Tibet (QT) plateaus. The results showed that gross N mineralization rates were generally greater in the topsoil (1.28-4.79 mg N kg-1 soil d-1) than in the subsoil (0.19-0.93 mg N kg-1 soil d-1) in all four grasslands. Gross nitrification rates were also consistently greater in the topsoil (2.42-6.48 mg N kg- 1 soil d-1) than in the subsoil (<1.43 mg N kg- 1 soil d-1) in all four grasslands. Gross nitrification rates were positively correlated with gross N mineralization rates, suggesting that soil nitrification was probably substrate limited. We present evidence of different interactive effects of climate, soil properties, and gross N dynamics on biomass production between the topsoil and the subsoil. In the topsoil, mean annual precipitation influenced gross N mineralization by altering total microbial activity and, in turn, influencing aboveground biomass. However, mean annual temperature influenced underground biomass through altering gross N mineralization in the subsoil. Climate influences biomass production by directly and indirectly regulating soil gross N mineralization in natural grasslands from the IM to the QT plateaus. The findings of the present study could facilitate better prediction of soil N availability for plants in natural grasslands under projected global climate change scenarios.