Temperature sensitivity increases with decreasing soil carbon quality in forest ecosystems across northeast China
Soil respiration universally exhibits exponential temperature dependence (Respiration = R0 eβT & Q10 = e10β), and temperature sensitivity (Q10) and soil organic carbon quality (as expressed by basal respiration rate at 0 °C, R0) are the key parameters. Despite their importance for predicting the responses of forest ecosystems to climate change and quantifying the magnitude of soil CO2 efflux, the controlling factors of temperature sensitivity and soil carbon quality and their relationships among various forest types at a regional scale are as yet unknown. Here, we present a comprehensive analysis of Q10, R0, and their related variables by assembling 154 independent temperature–respiration functions under a common standard in forest ecosystems across northeast China (41°51′–51°24′ N, 118°37′–129°48′ E). The R0 values ranged from 0.1700 to 2.1194 μmol m−2 s−1 (mean = 0.8357 μmol m−2 s−1), and the Q10 values from 1.29 to 5.42 (mean = 2.72). The relationships between Q10 and R0 could be best expressed with exponential decay equations (R2 = 0.460–0.611, P < 0.01). They indicated that the temperature sensitivity decreased with increasing the soil carbon quality, and then tended to level off when the R0 values were larger than ~1 μmol m−2 s−1. Soil carbon quality (R0) was closely related with the minimum soil temperature and its corresponding soil respiration rate during the growing season (R2 = 0.696–0.857, P < 0.01). Such a synthesis is necessary to fully understand the spatial heterogeneity in the temperature sensitivity of soil respiration and to increase our ability to make robust predictions about the future carbon budget.
KeywordsSoil respiration Q10 Basal respiration Carbon quality-temperature hypothesis Forest ecosystem China
We are grateful to the scientists who contributed their work to our database. The two anonymous reviewers are appreciated for the helpful comments and suggestions.
This study was funded by the National Key Research and Development Program of China (2017YFC0503906; 2018YFA0606103).
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