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  • Title: Carbon quality and soil microbial property control the latitudinal pattern in temperature sensitivity of soil microbial respiration across Chinese forest ecosystems.
    Author: Wang Q, Liu S, Tian P.
    Journal: Glob Chang Biol; 2018 Jul; 24(7):2841-2849. PubMed ID: 29476638.
    Abstract:
    Understanding the temperature sensitivity (Q10 ) of soil organic C (SOC) decomposition is critical to quantifying the climate-carbon cycle feedback and predicting the response of ecosystems to climate change. However, the driving factors of the spatial variation in Q10 at a continental scale are fully unidentified. In this study, we conducted a novel incubation experiment with periodically varying temperature based on the mean annual temperature of the soil origin sites. A total of 140 soil samples were collected from 22 sites along a 3,800 km long north-south transect of forests in China, and the Q10 of soil microbial respiration and corresponding environmental variables were measured. Results showed that changes in the Q10 values were nonlinear with latitude, particularly showing low Q10 values in subtropical forests and high Q10 values in temperate forests. The soil C:N ratio was positively related to the Q10 values, and coniferous forest soils with low SOC quality had higher Q10 values than broadleaved forest soils with high SOC quality, which supported the "C quality temperature" hypothesis. Out of the spatial variations in Q10 across all ecosystems, gram-negative bacteria exhibited the most importance in regulating the variation in Q10 and contributed 25.1%, followed by the C:N ratio (C quality), fungi, and the fungi:bacteria ratio. However, the dominant factors that regulate the regional variations in Q10 differed among the tropical, subtropical, and temperate forest ecosystems. Overall, our findings highlight the importance of C quality and microbial controls over Q10 value in China's forest ecosystems. Meanwhile, C dynamics in temperate forests under a global warming scenario can be robustly predicted through the incorporation of substrate quality and microbial property into models.
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