干旱
干旱指数
表土
环境科学
底土
农学
生物量(生态学)
土壤水分
土壤科学
生态学
生物
作者
Lei Song,Jinsong Wang,Ruiyang Zhang,Jiaowen Pan,Yang Li,Song Wang,Shuli Niu
摘要
The responses of soil nitrogen (N) transformations to climate change are crucial for biome productivity prediction under global change. However, little is known about the responses of soil gross N transformation rates to drought gradient. Along an aridity gradient across the 2700 km transect of drylands on the Qinghai-Tibetan Plateau, this study measured three main soil gross N transformation rates in both topsoil (0-10 cm) and subsoil (20-30 cm) using the laboratorial 15 N labeling. The relevant soil abiotic and biotic variables were also determined. The results showed that gross N mineralization and nitrification rates steeply decreased with increasing aridity when aridity was less than 0.5 but just slightly decreased with increasing aridity when aridity was larger than 0.5 at both soil layers. In topsoil, the decreases of the two gross rates were accompanied by the similar decreased patterns of soil total N content and microbial biomass carbon with increasing aridity (p < .05). In subsoil, although the decreased pattern of soil total N with increasing aridity was still similar to the decreases of the two gross rates (p < .05), microbial biomass carbon did not change (p > .05). Instead, bacteria and ammonia oxidizing archaea abundances decreased with increasing aridity when aridity was larger than 0.5 (p < .05). With an aridity threshold of 0.6, gross N immobilization rate increased with increasing aridity in wetter region (aridity < 0.6) accompanied with an increased bacteria/fungi ratio, but decreased with increasing aridity in drier region (aridity > 0.6) where mineral N and microbial biomass N also decreased at both soil layers (p < .05). This study provided new insight to understand the differential responses of soil N transformation to drought gradient. The threshold responses of the gross N transformation rates to aridity gradient should be noted in biogeochemical models to better predict N cycling and manage land in the context of global change.
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