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Deep soil stores a large amount of organic carbon (C) and nitrogen (N). However, little is known regarding the interactions between soil C and net N mineralization in deep soil, which complicates the prediction of ecosystem C and N dynamics. In this study, a 150-day laboratory soil incubation experiment was performed under 20 °C and 25 °C to investigate the influence of soil depth and warming on C and net N mineralization and their relationship. Soils were collected from a Hapludalfs profile in a subtropical forest with three depth intervals: 0–10 (topsoil), 10–30 (midsoil), and 30–60 cm (subsoil). Soil microbial community-level physiological profiling (CLPP) was conducted to investigate the role of the microbial community in C and N mineralization. The results demonstrated that both C and net N mineralization rates in subsoil were significantly lower than in topsoil. Compared to topsoil, subsoil had lower temperature sensitivity of C mineralization and relatively higher temperature sensitivity of net N mineralization. Cumulative soil C and net N mineralized were positively correlated in topsoil with the mineralized N per mineralized C showed as 0.19 and 0.31 at 20 °C and 25 °C, respectively. However, there was no significant correlation between cumulative soil C and net N mineralized in subsoil due to the low amount of net N mineralization. The lack of labile C source and degradable organic N were believed to limit the net N mineralization in subsoil. The microbial community in topsoil used relatively more easily decomposable carbohydrates and carboxylic acids, which favored C mineralization. In contrast, the microbial community in subsoil had relatively high utilization of amino acids (N-containing substrates), which indicated there was N limitation. This distinguished substrate utilization patterns of microbial communities could explain the observed C and N mineralization rates among the soil depths, and suggests that the microbial community played an important role in soil C and N mineralization. The decoupled relationships between soil C and net N mineralization in deep soil and their differentiated responses to warmer temperatures among soil depths indicated that deep soil should be considered separately from topsoil for ecosystem C and N cycling, especially for ecosystem C dynamic models. [ABSTRACT FROM AUTHOR]

Over 50 % of soil carbon (C) is stored in subsoil (below 30 cm). Inputs of labile C and nutrients can stimulate soil organic carbon (SOC) mineralization priming effect (PE) and subsequently affect subsoil C dynamics. However, little is known about the magnitude and mechanism of the PE occurring in subsoil, which complicates the prediction of subsoil C dynamics. Using a lab incubation experiment, the effects of glucose and nitrogen (N) addition on SOC mineralization were studied for three soil layers (0-10, 10-30, and 30-60 cm) from a subtropical forest. Five glucose (5.16 atom % C) levels were applied according to soil microbial biomass in each soil layer. Meanwhile, community-level physiological profiling was conducted to reflect microbial functional diversity and activity. We found positive PE for all soil layers. The PE magnitude in subsoil was about two times higher than that in topsoil with stronger increase in the microbial activity in mining components of SOC. N addition led to a reduction of 45 % in the PE magnitude in topsoil with relatively lower microbial activity in mining N-containing substrates (amino acids and amines) but caused an increase of 18 % in subsoil PE. Soil C and N availability were associated with microbial functional activity, the shifts of which then mediated the SOC mineralization in the presence of labile C and nutrient. These results suggested that mineralization of subsoil C was more sensitive to labile C and N addition. Any future changes in environmental conditions that affect the input and distribution of labile C and N in soil profiles could affect C dynamics in deep soil. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) and total nitrogen (TN) play major roles in soil quality and the global carbon budget. They can be measured rapidly and cost-effectively via visible and near-infrared reflectance (VNIR) spectroscopy. However, the reliability of this method is questionable because of the effects of heterogeneity. At present, only a few publications have addressed the effect of soil layers on model applicability, especially for highly heterogeneous soils in forest ecosystems. In the current work, we evaluated the performance of VNIR spectroscopy in estimating SOC and TN contents of soils collected from a mixed mountain forest in Central China. We also investigated the applicability of spectroscopic models between soil layers. We then further explored the possibility of using spiking with extra-weighting to improve model applicability. To achieve such objectives, we evaluated the applicability accuracy of the initial models (global and layered models) and modified models (spiked models with and without extra-weighting). Results showed that all the initial models successfully predicted SOC and TN. That is, for SOC, R 2 P ranged from 0.79 to 0.90, ratio of performance to inter-quartile range ( RPIQ ) ranged from 3.07 to 3.97, and the root mean square error ( RMSE P ) ranged from 0.54% to 0.88%; for TN, R 2 P ranged from 0.66 to 0.86, RPIQ ranged from 2.12 to 3.78, and RMSE P ranged from 0.05% to 0.08%. However, the prediction accuracies were seriously reduced when the model constructed from the top soil layer was used to predict the sub-surface soil properties, and vice versa. In terms of model applicability, our results demonstrated that spiking improved the applicability of the initial calibrations ( RMSE P and absolute prediction bias were obviously reduced) and that the accuracy was further improved when the spiking subset was extra-weighted. When the extra-weighting reached a certain level, the accuracies remained stable or slightly reduced. Our results illustrated that spiking alone and spiking with extra-weighing are effective approaches to improve model applicability in the VNIR estimation of SOC and TN between different soil layers in a highly heterogeneous forest. This approach is potentially useful in rapidly quantifying and monitoring soil carbon and nitrogen pools in heterogeneous landscapes. [ABSTRACT FROM AUTHOR]