Soil microbes comprise a large portion of the genetic diversity on Earth and influence a large number of important ecosystem processes. Increasing atmospheric nitrogen (N) deposition represents a major global change driver; however, it is still debated whether the impacts of N deposition on soil microbial biomass and respiration are ecosystem-type dependent. Moreover, the extent of N deposition impacts on microbial composition remains unclear. Here we conduct a global meta-analysis using 1408 paired observations from 151 studies to evaluate the responses of soil microbial biomass, composition, and function to N addition. We show that nitrogen addition reduced total microbial biomass, bacterial biomass, fungal biomass, biomass carbon, and microbial respiration. Importantly, these negative effects increased with N application rate and experimental duration. Nitrogen addition reduced the fungi to bacteria ratio and the relative abundances of arbuscular mycorrhizal fungi and gram-negative bacteria and increased gram-positive bacteria. Our structural equation modeling showed that the negative effects of N application on soil microbial abundance and composition led to reduced microbial respiration. The effects of N addition were consistent across global terrestrial ecosystems. Our results suggest that atmospheric N deposition negatively affects soil microbial growth, composition, and function across all terrestrial ecosystems, with more pronounced effects with increasing N deposition rate and duration.

Estimating forest structural attributes of planted forests plays a key role in managing forest resources, monitoring carbon stocks, and mitigating climate change. High-resolution and low-cost remote-sensing data are increasingly available to measure three-dimensional (3D) canopy structure and model forest structural attributes. In this study, we compared two suites of point cloud metrics and the accuracies of predictive models of forest structural attributes using unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) and digital aerial photogrammetry (DAP) data, in a subtropical coastal planted forest of East China. A comparison between UAV-LiDAR and UAV-DAP metrics was performed across plots among different tree species, heights, and stem densities. The results showed that a higher similarity between the UAV-LiDAR and UAV-DAP metrics appeared in the dawn redwood plots with greater height and lower stem density. The comparison between the UAV-LiDAR and DAP metrics showed that the metrics of the upper percentiles (r for dawn redwood = 0.95–0.96, poplar = 0.94–0.95) showed a stronger correlation than the lower percentiles (r = 0.92–0.93, 0.90–0.92), whereas the metrics of upper canopy return density (r = 0.21–0.24, 0.14–0.15) showed a weaker correlation than those of lower canopy return density (r = 0.32–0.68, 0.31–0.52). The Weibull α parameter indicated a higher correlation (r = 0.70–0.72) than that of the Weibull β parameter (r = 0.07–0.60) for both dawn redwood and poplar plots. The accuracies of UAV-LiDAR (adjusted (Adj)R2 = 0.58–0.91, relative root-mean-square error (rRMSE) = 9.03%–24.29%) predicted forest structural attributes were higher than UAV-DAP (Adj-R2 = 0.52–0.83, rRMSE = 12.20%–25.84%). In addition, by comparing the forest structural attributes between UAV-LiDAR and UAV-DAP predictive models, the greatest difference was found for volume (△Adj-R2 = 0.09, △rRMSE = 4.20%), whereas the lowest difference was for basal area (△Adj-R2 = 0.03, △rRMSE = 0.86%). This study proved that the UAV-DAP data are useful and comparable to LiDAR for forest inventory and sustainable forest management in planted forests, by providing accurate estimations of forest structural attributes.

Soil properties, such as clay content, are hypothesized to control decomposition of soil organic carbon (SOC). However, these hypotheses of soil property-C decomposition relationships have not been explicitly tested at large spatial scales. Here, we used a data-assimilation approach to evaluate the roles of soil properties and environmental factors in regulating decomposition of SOC. A three-pool (active, slow, and passive) C-cycling model was optimally fitted with 376 published laboratory incubation data from soils acquired from 73 sites with mean annual temperature ranging from-15 to 26 o C. Our results showed that soil physical and chemical properties regulated decomposition rates of the active and the slow C pools. Decomposition rates were lower for soils with high clay content, high field water holding capacity (WHC), and high C:N ratio. Multifactor regression and structural equation modeling (SEM) analyses showed that clay content was the most important variable in regulating decomposition of SOC. In contrast to the active and slow C pools, soil properties or environmental factors had little effect on the decomposition of the passive C pool. Our results show inverse soil property-C decomposition relationships and quantitatively evaluate the essential roles of soil texture (clay content) in controlling decomposition of SOC at a large spatial scale. The results may help model development and projection of changes in terrestrial C sequestration in future.

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