Soil respiration (R_s), after gross primary productivity, is the second largest carbon flux between terrestrial ecosystems and the atmosphere. As a key factor influencing soil carbon reserves and soil CO₂ flux, the temperature sensitivity of R_s has been given considerable attention in the research of the global carbon cycle.

In the study, the temperature response of R_s in 15 terrestrial ecosystems in China was examined by using 2-3 years of R_s data continuously measured at 10 sites of ChinaFLUX (Chinese Terrestrial Ecosystem Flux Research Network) and the spatial patterns of Q₁₀ were analyzed by Prof. YU GuiRui and his research group of Institute of Geographic Sciences and Natural Resources Research, Chinese Academy Sciences.

The results show that ecosystems in colder regions and with higher SOC content had relatively higher Q₁₀ values and ecosystems of different types showed different Q₁₀ values. The spatial variations in Q₁₀ are primarily determined by soil temperature during measurement periods, SOC content, and ecosystem type across China. The negative correlation between Q₁₀ and temperature resulted from the varying dependence of R_s processes on temperatures across ecosystems within different climatic zones. Ecosystem type also influenced the spatial variation of Q₁₀ partly due to different phenological patterns of biologic activities belowground and partly due to different microbial communities and SOC components among different ecosystem types. And partly due to ecosystems of artificial and natural types under different soil managements, the correlation between soil temperature and R_s of cropland ecosystems was lower than forest and grassland ecosystems. A temperature- and SOC dependent function for Q₁₀ is suggested for the improvement of R_s and ecosystem carbon cycle modelling in China. The spatial variation of Q₁₀ implies that regional-scale R_s derived from carbon cycle models with fixed Q₁₀ value should be used with due caution. It also implies that further studies on the spatial pattern of Q₁₀ and its controlling factors, as well as the function for describing such spatial pattern, are required in order to improve the precision of carbon budget estimations on regional scales.