期刊
GLOBAL CHANGE BIOLOGY
卷 23, 期 8, 页码 3393-3402出版社
WILEY
DOI: 10.1111/gcb.13613
关键词
decomposition; forest; grassland; regional variation; soil organic matter; temperature sensitivity
资金
- National Nature Science Foundation of China [2016YFC0500202, 2016YFC0500102]
- Natural Science Foundation of China [31290221, 41571130043, 31470506]
- Program for Kezhen Distinguished Talents in Institute of Geographic Sciences and Natural Resources Research, CAS [2013RC102]
How to assess the temperature sensitivity (Q(10)) of soil organic matter (SOM) decomposition and its regional variation with high accuracy is one of the largest uncertainties in determining the intensity and direction of the global carbon (C) cycle in response to climate change. In this study, we collected a series of soils from 22 forest sites and 30 grassland sites across China to explore regional variation in Q(10) and its underlying mechanisms. We conducted a novel incubation experiment with periodically changing temperature (5-30 C), while continuously measuring soil microbial respiration rates. The results showed that Q(10) varied significantly across different ecosystems, ranging from 1.16 to 3.19 (mean 1.63). Q(10) was ordered as follows: alpine grasslands (2.01) > temperate grasslands (1.81) > tropical forests (1.59) > temperate forests (1.55) > subtropical forests (1.52). The Q(10) of grasslands (1.90) was significantly higher than that of forests (1.54). Furthermore, Q(10) significantly increased with increasing altitude and decreased with increasing longitude. Environmental variables and substrate properties together explained 52% of total variation in Q(10) across all sites. Overall, pH and soil electrical conductivity primarily explained spatial variation in Q(10). The general negative relationships between Q(10) and substrate quality among all ecosystem types supported the C quality temperature (CQT) hypothesis at a large scale, which indicated that soils with low quality should have higher temperature sensitivity. Furthermore, alpine grasslands, which had the highest Q(10), were predicted to be more sensitive to climate change under the scenario of global warming.
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