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中国北方草地土壤呼吸的空间变异及成因

侯建峰1,2,吕晓涛1,王超1,王朋1**   

  1. (1中国科学院沈阳应用生态研究所森林与土壤生态国家重点实验室, 沈阳 110164; 2中国科学院大学, 北京 100049)
  • 出版日期:2014-10-18 发布日期:2014-10-18

Variation of soil respiration and its underlying mechanism in grasslands of northern China.

HOU Jian-feng1,2, LU Xiao-tao1, WANG Chao1, WANG Peng1   

  1. (1State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China; 2University of Chinese Academy of Sciences, Beijing 100049, China)
  • Online:2014-10-18 Published:2014-10-18

摘要:

土壤呼吸是陆地生态系统碳循环的关键指标,决定了土壤源二氧化碳(CO2)进入大气的通量,对预测全球气候变化背景下区域乃至全球碳循环变化具有重要意义.本文通过室内短期培养试验测定了中国北方草地样带土壤样品的呼吸速率,研究了北方草地土壤呼吸的区域尺度格局及其与主要调控因子的关系.结果表明:土壤呼吸速率自西向东随年均降水量(MAP)增加呈逐渐增加的趋势,变化范围为0.35~2.09 μg CO2C·g-1·h-1.其中,MAP<100 mm时,土壤呼吸速率为0.35~0.73 μg CO2C·g-1·h-1;100 mm <MAP<200 mm时,土壤呼吸速率为0.57~0.98 μg CO2C·g-1·h-1;MAP>300 mm时,土壤呼吸速率为0.83~2.10 μg CO2C·g-1·h-1.土壤呼吸速率与年均降水量、地上生物量、土壤有机碳氮含量呈显著正相关,而与年均温和pH值呈显著负相关.增强回归树分析显示,年均降水量、地上生物量、土壤有机碳含量和土壤有机氮含量分别解释了土壤呼吸总变异的25.5%、23.6%、18.3%和12.5%,而土壤pH和年均温仅解释了10.8%和9.2%.
 

Abstract:

Soil respiration is one of the most important variables in terrestrial ecosystem progresses and global carbon cycle, and determines the CO2 flux from soil to atmosphere. Soil respiration also has great implications for predicting regional and even global carbon cycle changes under the background of global climate change. We measured respiration rates of soil samples collected from northern China grassland transect by short term incubation experiment in laboratory. Results showed that soil respiration rates increased with mean annual precipitation (MAP) from west sites to east sites, ranging from 0.35 to 2.09 μg CO2C·g-1·h-1. The variation range of soil respiration rates were 0.35-0.73 μg CO2C·g-1·h-1 with MAP<100 mm, 0.57-0.98 μg CO2C·g-1·h-1 with MAP between 100 mm and 200 mm and 0.83-2.10 μg CO2C·g-1·h-1 with MAP>300 mm, respectively. Soil respiration had a significant positive relationship with MAP, aboveground biomass, soil organic carbon and nitrogen content, while had a negative relationship with mean annual temperature and soil pH. Analysis of boosted regression tree showed that the predictors accounted for the explained variation included MAP (25.5%), aboveground biomass (23.6%), soil organic carbon content (18.3%) and soil organic nitrogen content (12.5%), and soil pH and mean annual temperature only explained 10.8% and 9.2% of the total variation, respectively.