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

2020 ◽  
Vol 44 (6) ◽  
pp. 687-698
Author(s):  
Jia-Jia ZHENG ◽  
Song-Yu HUANG ◽  
Xin JIA ◽  
Yun TIAN ◽  
Yu MU ◽  
...  
2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Huimei Wang ◽  
Wei Liu ◽  
Wenjie Wang ◽  
Yuangang Zu

Thinning management is used to improve timber production, but only a few data are available on how it influences ecosystem C sink capacity. This study aims to clarify the effects of thinning on C sinks of larch plantations, the most widespread forests in Northeastern China. Both C influx from biomass production and C efflux from each soil respiration component and its temperature sensitivity were determined for scaling-up ecosystem C sink estimation: microbial composition is measured for clarifying mechanism for respiratory changes from thinning treatment. Thinning management induced 6.23 mol C m−2 yr−1increase in biomass C, while the decrease in heterotrophic respiration (Rh) at the thinned sites (0.9 mol C m−2 yr−1) has enhanced 14% of this biomass C increase. This decrease inRhwas a sum of the 42% decrease (4.1 mol C m−2 yr−1) in litter respiration and 3.2 mol C m−2 yr−1more CO2efflux from mineral soil in thinned sites compared with unthinned control. Increases in temperature, temperature sensitivity, alteration of litters, and microbial composition may be responsible for the contrary changes inRhfrom mineral soil and litter respiration, respectively. These findings manifested that thinning management of larch plantations could enhance biomass accumulation and decrease respiratory efflux from soil, which resulted in the effectiveness improvement in sequestrating C in forest ecosystems.


2015 ◽  
Vol 93 ◽  
pp. 105-110 ◽  
Author(s):  
Zhenfeng Xu ◽  
Shishan Tang ◽  
Li Xiong ◽  
Wanqin Yang ◽  
Huajun Yin ◽  
...  

2018 ◽  
Author(s):  
Jiguang Feng ◽  
Jingsheng Wang ◽  
Yanjun Song ◽  
Biao Zhu

Abstract. Soil respiration (Rs), a key process in the terrestrial carbon cycle, is very sensitive to climate change. In this study, we synthesized 54 measurements of annual Rs and 171 estimates of Q10 value (the temperature sensitivity of soil respiration) in grasslands across China. We quantitatively analyzed their spatial patterns and controlling factors in five grassland types, including temperate typical steppe, temperate meadow steppe, temperate desert steppe, alpine grassland, and warm-tropical grassland. Results showed that the mean (± SE) annual Rs was 582.0 ± 57.9 g C m−2 yr−1 across Chinese grasslands. Annual Rs significantly differed among grassland types, and positively correlated with mean annual temperature, mean annual precipitation, soil organic carbon content and aboveground biomass, but negatively correlated with latitude and soil pH (P < 0.05). Among these factors, mean annual precipitation was the primary factor controlling the spatial variation of annual Rs in Chinese grasslands. The mean contributions of growing season Rs and heterotrophic respiration to annual Rs were 78.7 % and 72.8 %, respectively. Moreover, the mean (± SE) of Q10 across Chinese grasslands was 2.60 ± 0.08, ranging from 1.03 to 8.13, and varied largely within and among grassland types, and among soil temperature measurement depths. Generally, the seasonal variation of soil respiration in Chinese grasslands cannot be well explained by soil temperature using the van't Hoff equation. Longitude and altitude were the dominant driving factors and accounted for 26.0 % of the variation in Q10 derived by soil temperature at the depth of 5 cm. Overall, our findings advance our understanding of the spatial variation and environmental control of soil respiration and Q10 across Chinese grasslands, and also improve our ability to predict soil carbon efflux under climate change on the regional scale.


Author(s):  
Di Tong ◽  
Zhongwu Li ◽  
Haibing Xiao ◽  
Xiaodong Nie ◽  
Chun Liu ◽  
...  

PLoS ONE ◽  
2012 ◽  
Vol 7 (8) ◽  
pp. e42354 ◽  
Author(s):  
Ji Luo ◽  
Youchao Chen ◽  
Yanhong Wu ◽  
Peili Shi ◽  
Jia She ◽  
...  

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