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Understanding the driving mechanisms of soil organic carbon (SOC) persistence is crucial to project future carbon-climate feedback (Schmidt et al. 2011). Chen et al. (2021) (C21hereafter) hypothesized that plant carbon input, as a proxy of priming effect, governs the topsoil carbon turnover across alpine grasslands in Tibetan Plateau. A structure equation model was built to assess the relationships between environmental variables and Δ14C, an indicator of soil carbon turnover (Shiet al. 2020; Wu et al. 2021). However, only plant carbon input was considered as a direct effect on Δ14C in their final optimized model while the direct climate effect was reported to be non-significant (Fig. 3 in C21 ). From the model, plant carbon input exerts a stronger direct effect on topsoil carbon turnover, while precipitation only has a weaker indirect effect. The resultant lack of direct climate (mainly precipitation) effect on soil carbon turnover is surprising and contradictive to previous reports that soil carbon turnover is directly regulated by water availability especially in arid and semi-arid regions (Carvalhais et al. 2014; Wu et al. 2018).
We built and compared three possible structure equation models (Fig. 1 ) using the datasets provided in C21 . Model-A only considered the direct effect of plant input on topsoil Δ14C. By contrast, model-B included direct effects on topsoil Δ14C from both plant input and precipitation. Model-C further included an additional direct effect from mineral protection. Model-A generated similar statistical results withC21 , showing stronger total effect from plant input than from precipitation. In model-B, the two path coefficients related to Δ14C from both plant input and precipitation are still significant (P < 0.05), and overall model performance (AIC = 19.945) is better than model-A (AIC = 21.989). Moreover, the strength of precipitation direct effect is comparable to that of plant input. As a result, the total standardized effect of precipitation is much larger than plant carbon input. In model-C, all three path coefficients related with Δ14C fail to reject the null hypothesis based on significance level of 0.05 because of severe multicollinearity between the variables. Overall, the dataset inC21 best supported model-B that includes direct climate effect. We could not include the chemical composition of soil organic matter, which nevertheless had no significant effects for topsoil in C21 , in all the three models because the data was not made available.
We also conducted partial correlation analysis to detect the relative importance of direct effects from climate, plant input, and soil mineral composition on Δ14C. Partial correlation analysis assesses the correlation between Δ14C and one specific environmental factor after removing the effects of other environmental factors. The results showed that precipitation is still the most importance factor in regulating the topsoil carbon turnover than other factors (Fig. 2 ). Furthermore, we find precipitation governs the topsoil carbon turnover in alpine steppe, while temperature and mineral protection play major roles across alpine meadow. The difference likely implies that SOC decomposition is primarily limited by water in the drier alpine steppe, which is partially relieved in the wetter alpine meadow. In all cases, our results indicate that plant input does not play the dominant role in regulating the topsoil carbon turnover.
The overlook of direct climatic effects on SOC turnover can also bias the interpretation of the significant relationship between vegetation indices and topsoil Δ14C on a global scale (Fig. 4 in C21 ). C21 interpreted these relationships as a universal law of the plant input effects on topsoil carbon turnover. However, the correlation can arise because preferable climatic conditions (e.g., warm and moist) increase both plant input and SOC decomposition rate (Davidson & Janssens 2006). Direct climatic effects on SOC turnover should be carefully removed before interpreting the plant input effects from these global data.
Aside from overestimating the direct effects of plant input on SOC turnover, C21 treated the amount of plant input as a proxy of priming effects, which is however questionable. According to incubation experiments using soils from the same ecosystems by Chen et al.(2019), the amount of plant input does not show significant direct effect on soil priming intensity. Furthermore, components of plant input (shoot, root and mycorrhizal) usually have quite different effects in regulating priming effects (Huang et al. 2021). Therefore, it is unreasonable to simply use the amount of plant input as the indicator of priming effect.
Despite C21 likely overestimated the contribution of plant input on SOC turnover, the study raised an important scientific question on the relation between SOC turnover and priming effect. Observed earth greening increased the fresh carbon input in soil system, which may promote the priming effects and accelerate the SOC turnover (Terrer et al. 2021). This crucial process is still missing in most existing ecosystem models (Wu et al. 2020), which may lead to an overestimation of the terrestrial soil carbon storage potential.