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Moso bamboo forests have greater net carbon uptake benefits with increasing nitrogen deposition in the coming decades. , Atmospheric nitrogen (N) deposition affects the greenhouse gas (GHG) balance of ecosystems through the net atmospheric CO 2 exchange and the emission of non-CO 2 GHGs (CH 4 and N 2 O). We quantified the effects of N deposition on biomass increment, soil organic carbon (SOC), and N 2 O and CH 4 fluxes and, ultimately, the net GHG budget at ecosystem level of a Moso bamboo forest in China. Nitrogen addition significantly increased woody biomass increment and SOC decomposition, increased N 2 O emission, and reduced soil CH 4 uptake. Despite higher N 2 O and CH 4 fluxes, the ecosystem remained a net GHG sink of 26.8 to 29.4 megagrams of CO 2 equivalent hectare −1 year −1 after 4 years of N addition against 22.7 hectare −1 year −1 without N addition. The total net carbon benefits induced by atmospheric N deposition at current rates of 30 kilograms of N hectare −1 year −1 over Moso bamboo forests across China were estimated to be of 23.8 teragrams of CO 2 equivalent year −1 .

Soil erosion by water affects soil organic carbon (SOC) migration and distribution, which are important processes for defining ecosystem carbon sources and sinks. Little has been done to quantify soil carbon erosion in the three major basins in China, the Yangtze River, Yellow River and Pearl River Basins, which contain the most eroded areas. This research attempts to quantify the lateral movement of SOC based on spatial and temporal patterns of water erosion rates derived from an empirical Unit Stream Power Erosion Deposition Model (USPED) model. The water erosion rates simulated by the USPED model agreed reasonably with observations (R2 = 0.43, P < 0.01). We showed that regional water erosion ranged within 23.3–50 Mg ha–1 year–1 during 1992–2013, inducing the lateral redistribution of SOC caused by erosion in the range of 0.027–0.049 Mg C ha–1 year–1, and that caused by deposition of 0.0079–0.015 Mg C ha–1 year–1, in the three basins. The total eroded SOC was 0.006, 0.002 and 0.001 Pg year–1 in the Yangtze River, Yellow River and Pearl River Basins respectively. The net eroded SOC in the three basins was ~0.0075 Pg C year–1. Overall, the annual average redistributed SOC rate caused by erosion was greater than that caused by deposition, and the SOC loss in the Yangtze River Basin was greatest among the three basins. Our study suggests that considering both processes of erosion and deposition – as well as effects of topography, rainfall, land use types and their interactions – on these processes are important to understand SOC redistribution caused by water erosion.

The transport of eroded soil to rivers changes the nutrient cycles of river ecosystems and has significant impacts on the regional eco-environment and human health. The Loess Plateau, a leading vegetation restoration region in China and the world, has experienced severe soil erosion and nutrient loss, however, the extent to which vegetation restoration prevents soil erosion export (to rivers) and it caused nutrient loss is unknown. To evaluate the effects of the first stage of the Grain for Green Project (GFGP) on the Loess Plateau (started in 1999 and ended in 2013), we analyzed the vegetation change trends and quantified the effects of GFGP on soil erosion export (to rivers) and it caused nutrient loss by considering soil erosion processes. The results were as follows: (1) in the first half of study period (from 1982 to 1998), the vegetation cover changed little, but after the implementation of the first stage of the GFGP (from 1999 to 2013), the vegetation cover of 75.0% of the study area showed a significant increase; (2) The proportion of eroded areas decreased from 41.8 to 26.7% as a result of the GFGP, and the erosion intensity lessened in most regions; the implementation significantly reduce the soil nutrient loss; (3) at the county level, soil erosion export could be avoided significantly by the increasing of vegetation greenness in the study area ( R = −0.49). These results illustrate the relationships among changes in vegetation cover, soil erosion and nutrient export, which could provide a reference for local government for making ecology-relative policies.

Summary Plant functional ecology requires the quantification of trait variation and its controls. Field measurements on 483 species at 48 sites across China were used to analyse variation in leaf traits, and assess their predictability. Principal components analysis ( PCA ) was used to characterize trait variation, redundancy analysis ( RDA ) to reveal climate effects, and RDA with variance partitioning to estimate separate and overlapping effects of site, climate, life‐form and family membership. Four orthogonal dimensions of total trait variation were identified: leaf area ( LA ), internal‐to‐ambient CO 2 ratio (χ), leaf economics spectrum traits (specific leaf area ( SLA ) versus leaf dry matter content ( LDMC ) and nitrogen per area ( N area )), and photosynthetic capacities ( V cmax , J max at 25°C). LA and χ covaried with moisture index. Site, climate, life form and family together explained 70% of trait variance. Families accounted for 17%, and climate and families together 29%. LDMC and SLA showed the largest family effects. Independent life‐form effects were small. Climate influences trait variation in part by selection for different life forms and families. Trait values derived from climate data via RDA showed substantial predictive power for trait values in the available global data sets. Systematic trait data collection across all climates and biomes is still necessary.

Summary For decades, researchers have thought it was difficult to remove the uncertainty from the estimates of forest carbon storage and its changes on national sales. This is not only because of stochasticity in the data but also the bias to overcome in the computations. Most studies of the estimation, however, ignore quantitative analyses for the latter uncertainty. This bias primarily results from the widely used volume‐biomass method via scaling up forest biomass from limited sample plots to large areas. This paper addresses (i) the mechanism of scaling‐up error occurrence, and (ii) the quantitative effects of the statistical factors on the error. The error compensators were derived, and expressed by ternary functions with three variables: expectation, variance and the power in the volume‐biomass equation. This is based on analysing the effect of power‐law function convexity on scaling‐up error by solving the difference of both sides of the weighted Jensen inequality. The simulated data and the national forest inventory of China were used for algorithm testing and application, respectively. Scaling‐up error occurrence stems primarily from an effect of the distribution heterogeneity of volume density on the total biomass amount, and secondarily from the extent of function nonlinearities. In our experiments, on average 94·2% of scaling‐up error can be reduced for the statistical populations of forest stands in a region. China's forest biomass carbon was estimated as approximately 6·0 PgC or less at the beginning of the 2010s after on average 1·1% error compensation. The results of both the simulated data experiment and national‐scale estimation suggest that the biomass is overestimated for young forests more than others. It implies a necessity to compensate scaling‐up error, especially for the areas going through extensive afforestation and reforestation in past decades. This study highlights the importance of understanding how both the function nonlinearity and the statistics of the variables quantitatively affect the scaling‐up error. Generally, the presented methods will help to translate fine‐scale ecological relationships to estimate coarser scale ecosystem properties by correcting aggregation errors.