Bingrui Jia

Protection by metal (hydr) oxides is one of the key mechanisms for the long‐term stabilization of soil organic carbon (SOC). However, the source and turnover of (metal‐) bound organic carbon (OC) in soils are poorly constrained. Here we present the first large‐scale study on the ¹³C and ¹⁴C characteristics of bound OC in 15 wetland and upland soil profiles. We find that bound OC has similar δ¹³C as SOC, suggesting no preference for plant‐ or microbe‐derived carbon. However, bound OC Δ¹⁴C is more negative than SOC in wetland but not upland mineral soils, and decreases with increasing reactive minerals. Hence, in contrast to the conventional assumption, bound OC is better preserved relative to SOC in wetlands with high contents of reactive metals. Our finding highlights the dynamic exchange of bound OC with SOC in upland soils and calls for a better recognition of reactive metals in stabilizing OC in wetlands.

Forest soil respiration (Rs) plays an important role in the carbon balance of terrestrial ecosystems. China's forest occupies a large part of the world's forest. However, due to the lack of integrated observation data and appropriate upscaling methodologies, substantial uncertainties exist in the Rs evaluation, which limits our understanding of the carbon balance. Here, we re‐evaluated the total soil carbon effluxes across China by combining field observations from 634 published annual Rs with a machine learning technique (i.e., Random Forest (RF)). Our results revealed that the combination of systematic measurements with the RF model allowed a definite estimate. The average annual Rs was 773.6 g C m⁻² yr⁻¹, ranging from 404.6 to 1,772.3 g C m⁻² yr⁻¹. Total forest soil carbon effluxes amounted to 1.17 Pg C yr⁻¹ in China. Geographically, annual Rs showed a clear spatially increasing trend from northeast to southwest. Forest type is an important factor in determining the soil respiration rate. Bamboo and Evergreen broadleaf forests were higher than other types of forests. These results provide a unique insight into the magnitudes and mechanisms of soil CO₂ emissions in China's forest ecosystems.

Accurate estimation of regional and global patterns of ecosystem respiration (ER) is crucial to improve the understanding of terrestrial carbon cycles and the predictive ability of the global carbon budget. However, large uncertainties still exist in regional and global ER estimation due to the drawbacks of modeling methods. Based on eddy covariance ER data from 132 sites in China from 2002 to 2020, we established Intelligent Random Forest (IRF) models that integrated ecological understanding with machine learning techniques to estimate ER. The results showed that the IRF models performed better than semiempirical models and machine learning algorithms. The observed data revealed that gross primary productivity (GPP), living plant biomass, and soil organic carbon (SOC) were of great importance in controlling the spatiotemporal variability of ER across China. An optimal model governed by annual GPP, living plant biomass, SOC, and air temperature (IRF‐04 model) matched 93% of the spatiotemporal variation in site‐level ER, and was adopted to evaluate the spatiotemporal pattern of ER in China. Using the optimal model, we obtained that the annual value of ER in China ranged from 5.05 to 5.84 Pg C yr⁻¹ between 2000 and 2020, with an average value of 5.53 ± 0.22 Pg C yr⁻¹. In this study, we suggest that future models should integrate process‐based and data‐driven approaches for understanding and evaluating regional and global carbon budgets.

China's forests rank fifth in the world by area, covering a broad climatic gradient from cold-temperate to tropical zones, and play a key role in the global carbon cycle. Studies of forest soil respiration (R_s) have increased rapidly in China over the last two decades, but the resulting R_s data need to be summarized. Here, we compile a comprehensive dataset of R_s in China's undisturbed forest ecosystems from the literature published up to 31 December 2018, including monthly R_s and the concurrently measured soil temperature (N=8317), mean monthly R_s (N=5003), and annual R_s (N=634). Detailed plot information was also recorded, such as geographical location, climate factors, stand characteristics, and measurement description. We examine some aspects of the dataset – R_s equations fitted with soil temperature, temperature sensitivity (Q₁₀), monthly variations, and annual effluxes in cold-temperate, temperate, subtropical, and tropical zones. We hope the dataset will be used by the science community to provide a better understanding of the carbon cycle in China's forest ecosystems and reduce uncertainty in evaluating of carbon budget at a large scale. The dataset is publicly available at https://doi.org/10.1594/PANGAEA.943617 (Sun et al., 2022).

China’s forests rank fifth in the world by area and cover a broad climatic gradient from cold-temperate to tropical zones, and play a key role in the global carbon cycle. Studies on forest soil respiration (Rs) are increasing rapidly in China over the last two decades, but the resulting Rs data need to be summarized. Here, we compile a comprehensive database of Rs in China’s undisturbed forest ecosystems from literature published up to December 31, 2018, including monthly Rs and the concurrently measured soil temperature (N = 8317), mean monthly Rs (N = 5003), and annual Rs (N = 634). Detailed plot information was also recorded, such as geographical location, climate factors, stand characteristics, and measurement description. We examine some aspects of the database – Rs equations fitted with soil temperature, temperature sensitivity (Q₁₀), monthly variations and annual effluxes in cold-temperate, temperate, subtropical and tropical zones. We hope the database will be used by the science community to provide a better understanding of carbon cycle in China's forest ecosystems and reduce uncertainty in evaluating of carbon budget at the large scale. The database is publicly available at https://www.pangaea.de/tok/788910d8d3ae0a415c7bad2e7025a3f16f042a1b (Sun et al. 2021).

Forest soil is the largest carbon pool in terrestrial ecosystem, and the soil-to-atmosphere CO ₂ flux (soil respiration, Rs ) is the main link between soil and atmosphere. However, due to the lack of integration of field observations, substantial uncertainties exist in quantifying large-scale soil carbon effluxes, which limit our understanding of the fate of forest soil in a warming world. Here, China’s forest ecosystems were divided into six forest types in six regions, and an integrated soil respiration database (N=634) was compiled to evaluate soil carbon effluxes by random sampling with replacement. Average annual Rs was 783 g C m ^-2 yr ^-1 across China, ranking from the highest to the lowest as follows: East, Southwest, South, Northwest, Northeast and North. Total soil carbon emissions were 1472.6 Tg C yr ^-1 in China’s forest ecosystems, and about 69% from three southern regions (i.e., Southwest, Southern China and Eastern China) and 31% from three northern regions (i.e., Northwest, Northern China and Northeast). Evergreen needleleaf forest (529.09 Tg C yr ^-1 , 52%) and evergreen broadleaf forest (343.01 Tg C yr ^-1 , 34%) were the main source of soil carbon emissions in three southern regions, while deciduous broadleaf forest (334.36 Tg C yr ^-1 , 74%) was the main emissions in three northern regions. Our results provide a better understanding of the distribution and magnitude of soil carbon effluxes in China’s forest ecosystems.