Abstract

AbstractOrganic carbon decomposition in lake sediments contributes substantially to the global carbon cycle and is strongly affected by temperature. However, the magnitude of temperature sensitivity (Q10) of decomposition and the underlying factors remain unclear at the continental scale. Carbon quality temperature (CQT) hypothesis asserts that less reactive and more recalcitrant molecules tend to have higher temperature sensitivities, but its support is challenged by complex composition of organic matter and environmental constraints. Here, we quantified Q10 of the sediments across 50 freshwater ecosystems along a 3500 km north–south transect, and characterized the quality of sediment dissolved organic carbon with chemodiversity reflected in molecular richness, functional traits (i.e., molecular weight, bioavailability, etc.) and composition. We further included classic environmental variables, such as climatic, physicochemical and microbial factors, to explore how Q10 is constrained by these factors or carbon quality. We found that Q10 varied greatly across lakes, with the mean value of 1.78 ± 0.62, but showed nonsignificant latitudinal pattern. Q10 was primarily predicted by chemodiversity and showed an increasing trend with the biochemical recalcitrance indicated by traits such as aromaticity and standard Gibb's Free Energy at both molecular and compositional levels. This suggests that carbon quality is the crucial determinant of Q10 in lakes, supporting the CQT hypothesis. Moreover, Q10 decreased linearly with the increase of molecular richness, implying that the resistance of decomposition to warming is associated with higher molecular diversity. Compared with the structural equation model containing only environmental variables, inclusion of chemodiversity increased 32.8% of the explained variation in Q10, and chemodiversity was the only driver showing direct effects. Collectively, this study illustrates the importance of chemodiversity in shaping the pattern of Q10, and has significant implications for accurately predicting the carbon turnover in lake ecosystems in the context of global warming.

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