Chin J Plant Ecol ›› 2020, Vol. 44 ›› Issue (6): 687-698.DOI: 10.17521/cjpe.2019.0300

• Research Articles • Previous Articles    

Spatial variation and controlling factors of temperature sensitivity of soil respiration in forest ecosystems across China

ZHENG Jia-Jia1,2, HUANG Song-Yu1,2, JIA Xin1,2,3,*(), TIAN Yun1,3, MU Yu1,2, LIU Peng1,2, ZHA Tian-Shan1,2,3   

  1. 1School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
    2Yanchi Ecology Research Station of Mau Us Desert, Beijing 100083, China
    3Key Laboratory of State Forestry and Grassland Administration on Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
  • Received:2019-11-04 Accepted:2020-02-02 Online:2020-06-20 Published:2020-03-26
  • Contact: JIA Xin
  • Supported by:
    National Natural Science Foundation of China(31670708);National Natural Science Foundation of China(31670710);National Natural Science Foundation of China(31901366);Fundamental Research Funds for the Central Universities(2015ZCQ-SB-02)

Abstract:

Aims Our objective was to determine the spatial variation of the temperature sensitivity of soil respiration (Q10) and it’s controlling factors in forest ecosystems across China.
Methods Based on published papers, the field measurement data of soil respiration were collected to build the dataset of annual Q10 in forest ecosystems across China. Further, the spatial variation and the drivers of Q10 in different forest types were analyzed.
Important findings The results showed that 1) Q10 ranges from 1.09 to 6.24, with a mean value (± standard error) of 2.37 (± 0.04) and no significant difference among different forest types; 2) When all forest types were considered, Q10 increased with increasing latitude, altitude, soil organic carbon content (SOC) and soil total nitrogen content (TN), but decreased with increasing longitude, mean annual temperature (MAT) and mean annual precipitation (MAP). Climate (MAT, MAP) and soil (SOC, TN) factors together explained 32.8% variations in Q10. MAT and SOC were considered as the primary factors driving the spatial variation of Q10. 3) Q10 of different forest types responded differently to climate and soil factors. Q10 decreased with the increase of MAP in the deciduous needleleaf forest (DNF), while Q10 showed no significant correlation with MAP in other forest types. Q10 increased with the increase of TN in evergreen broadleaved forest (EBF), deciduous broadleaved forest (DBF), evergreen needleleaf forest (ENF), and the sensitivity of Q10 to TN was the highest in EBF and the lowest in ENF. Although Q10 showed concentrated distribution trend, more attention should be paid to the large range of variation in future C budget studies. The primary driving factors and the response to environmental factors of Q10 varied among forest types. Under the scenario of future climate change, Q10 may vary divergently among different forest types. Therefore, the divergent responses of key parameters of carbon cycle in different forest types to climate change should also be considered in future carbon-climate models.

Key words: soil respiration, temperature sensitivity, carbon cycle, CO2 flux, soil carbon flux