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Atmospheric nitrogen (N) deposition has been found to significantly affect plant growth and physiological performance in terrestrial ecosystems. Many individual studies have investigated how N addition influences plant functional traits, however these investigations have usually been limited to a single species, and thereby do not allow derivation of general patterns or underlying mechanisms. We synthesized data from 56 papers and conducted a meta-analysis to assess the general responses of 15 variables related to leaf economics, gas exchange, and hydraulic traits to N addition among 61 woody plant species, primarily from temperate and subtropical regions. Results showed that under N addition, leaf area index (+10.3%), foliar N content (+7.3%), intrinsic water-use efficiency (+3.1%) and net photosynthetic rate (+16.1%) significantly increased, while specific leaf area, stomatal conductance, and transpiration rate did not change. For plant hydraulics, N addition significantly increased vessel diameter (+7.0%), hydraulic conductance in stems/shoots (+6.7%), and water potential corresponding to 50% loss of hydraulic conductivity ( , +21.5%; i.e., became less negative), while water potential in leaves (-6.7%) decreased (became more negative). N addition had little effect on vessel density, hydraulic conductance in leaves and roots, or water potential in stems/shoots. N addition had greater effects on gymnosperms than angiosperms and ammonium nitrate fertilization had larger effects than fertilization with urea, and high levels of N addition affected more traits than low levels. Our results demonstrate that N addition has coupled effects on both carbon and water dynamics of woody plants. Increased leaf N, likely fixed in photosynthetic enzymes and pigments leads to higher photosynthesis and water use efficiency, which may increase leaf growth, as reflected in LAI results. These changes appear to have downstream effects on hydraulic function through increases in vessel diameter, which leads to higher hydraulic conductance, but lower water potential and increased vulnerability to embolism. Overall, our results suggest that N addition will shift plant function along a tradeoff between C and hydraulic economies by enhancing C uptake while simultaneously increasing the risk of hydraulic dysfunction.

The response of plant CO 2 diffusion conductances (mesophyll and stomatal conductances, g m and g sc ) to soil drought has been widely studied, but few studies have investigated the effects of soil nitrogen addition levels on g m and g sc . In this study, we investigated the responses of g m and g sc of Manchurian ash and Mongolian oak to four soil nitrogen addition levels (control, low nitrogen, medium nitrogen and high nitrogen) and the changes in leaf anatomy and associated enzyme activities (aquaporin (AQP) and carbonic anhydrase (CA)). Both g m and g sc increased with the soil nitrogen addition levels for both species, but then decreased under the high nitrogen addition level, which primarily resulted from the enlargements in leaf and mesophyll cell thicknesses, mesophyll surface area exposed to intercellular space per unit leaf area and stomatal opening status with soil nitrogen addition. Additionally, the improvements in leaf N content and AQP and CA activities also significantly promoted g m and g sc increases. The addition of moderate levels of soil nitrogen had notably positive effects on CO 2 diffusion conductance in leaf anatomy and physiology in Manchurian ash and Mongolian oak, but these positive effects were weakened with the addition of high levels of soil nitrogen.

Recent studies have reported worldwide vegetation suppression in response to increasing atmospheric vapor pressure deficit (VPD). Here, we integrate multisource datasets to show that increasing VPD caused by warming alone does not suppress vegetation growth in northern peatlands. A site-level manipulation experiment and a multiple-site synthesis find a neutral impact of rising VPD on vegetation growth; regional analysis manifests a strong declining gradient of VPD suppression impacts from sparsely distributed peatland to densely distributed peatland. The major mechanism adopted by plants in response to rising VPD is the “open” water-use strategy, where stomatal regulation is relaxed to maximize carbon uptake. These unique surface characteristics evolve in the wet soil‒air environment in the northern peatlands. The neutral VPD impacts observed in northern peatlands contrast with the vegetation suppression reported in global nonpeatland areas under rising VPD caused by concurrent warming and decreasing relative humidity, suggesting model improvement for representing VPD impacts in northern peatlands remains necessary. Growing vapor pressure deficit inhibits vegetation growth. Here, Chen et al. combine satellite and eddy covariance data with field experiments showing that plant growth in northern peatlands is not constrained by water even in the presence of a warming-induced water pressure deficit.

Flux tower is a link between ground measurements and large-scale remote sensing data. A large number of remote sensing model methods are used to estimate the regional scale Gross Primary Productivity (GPP) based on this principle. In this study, Vegetation Photosynthesis Model (VPM) and Vegetation Indexes (VIs) were used to estimate the GPP based on Earth Observing 1 (EO-1) Hyperion hyperspectral data in Changbai Mountain temperate forest. Result shows that the two different types of remote sensing input data of the VPM has similar result at the same level. For different time scope, 3-day flux data can better match remote sensing data. For different footprint, the effect of 500, 1000, 1500 m almost no difference in our area. Among the comparison of the four types of VIs, Bands Ratio (BR), Bands Subtraction (BS) and Bands Difference (BD) have a higher correlation significant than Single Band (SB). 457 nm is the optimum band for SB. The best bands combination of BR, BS, and BD mainly focus on near infrared region. Our research shows that for VPM, and other Light Use Efficiency (LUE) remote sensing model, the difference is not significant between multispectral data and hyperspectral data. At the comparison of VPM and VIs, although the estimation of former is more accurate, the latter is more convenient for that the establishment of VIs just need several bands of remote sensing data. Our findings will help to improve future research on GPP estimation based on hyperspectral observations, which is being more important with increasing availability of hyperspectral satellite data products.

Climate change profoundly impacts the physiological processes and adaptation strategies of plants. However, the physiological mechanisms of coniferous species responding and adapting to combined drought and nitrogen (N) addition remain unclear. Here, based on 2-year multi-level N addition and drought experiments, we investigated the responses of carbon assimilation (net photosynthetic rate A n , stomatal conductance g s and intrinsic water use efficiency WUE i ) and carbon reserves (non-structural carbohydrates, NSC) of 7-year-old Korean pine ( Pinus koraiensis ) saplings. Our results showed that: (1) Drought decreased A n and g s , while N addition increased A n and decreased g s . N addition decreased A n and WUE i but increased g s in plants under drought conditions, indicating that N addition under drought stress will maintain gas exchange by increasing stomatal opening, but failed to mitigate the reduction of A n . (2) Both drought (moderate and severe) and N addition reduced leaf NSC concentrations. Under moderate drought stress, however, N addition led to an increase in leaf NSC concentrations. (3) The interconversion between leaf starch and soluble sugars slowed the decrease in carbon assimilation caused by drought. P. koraiensis saplings adopted a conservative strategy of increasing leaf mass per area (LMA) to adapt to reduced water use efficiency. The study highlights the coordinated relationship between carbon assimilation and carbon reserves of Korean pine saplings under combined drought and N addition, which improves our understanding of the diverse carbon dynamics of different species under climate change.

The net ecosystem CO2 exchange (NEE) is a critical parameter for quantifying terrestrial ecosystems and their contributions to the ongoing climate change. The accumulation of ecological data is calling for more advanced quantitative approaches for assisting NEE prediction. In this study, we applied two widely used machine learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), to build models for simulating NEE in major biomes based on the FLUXNET dataset. Both models accurately predicted NEE in all biomes, while XGBoost had higher computational efficiency (6~62 times faster than RF). Among environmental variables, net solar radiation, soil water content, and soil temperature are the most important variables, while precipitation and wind speed are less important variables in simulating temporal variations of site-level NEE as shown by both models. Both models perform consistently well for extreme climate conditions. Extreme heat and dryness led to much worse model performance in grassland (extreme heat: R2 = 0.66~0.71, normal: R2 = 0.78~0.81; extreme dryness: R2 = 0.14~0.30, normal: R2 = 0.54~0.55), but the impact on forest is less (extreme heat: R2 = 0.50~0.78, normal: R2 = 0.59~0.87; extreme dryness: R2 = 0.86~0.90, normal: R2 = 0.81~0.85). Extreme wet condition did not change model performance in forest ecosystems (with R2 changing −0.03~0.03 compared with normal) but led to substantial reduction in model performance in cropland (with R2 decreasing 0.20~0.27 compared with normal). Extreme cold condition did not lead to much changes in model performance in forest and woody savannas (with R2 decreasing 0.01~0.08 and 0.09 compared with normal, respectively). Our study showed that both models need training samples at daily timesteps of >2.5 years to reach a good model performance and >5.4 years of daily samples to reach an optimal model performance. In summary, both RF and XGBoost are applicable machine learning algorithms for predicting ecosystem NEE, and XGBoost algorithm is more feasible than RF in terms of accuracy and efficiency.

A shelterbelt is an important measure to protect farmland and increase crop yield. However, how a shelterbelt structure affects crop yield is still unclear due to the difficulties accessing sufficient data from traditional field observations. To address this problem, we developed an innovative framework to estimate the shelterbelt structure and crop yield profile at a regional scale based on Google Earth and Sentinel-2 data. Using this method, we quantified the impact of the shelterbelt structure on the corn yield at 302 shelterbelts in the Northeast Plain of China. Generally, the corn yield increased (by 2.41% on average) within a distance of 1.2–15 times the tree height from the shelterbelt. Such an effect was particularly prominent within a distance of two to five times the tree height, where the corn yield was significantly increased by up to 4.63%. The structure of the shelterbelt has a significant effect on the magnitude of increase in yield of the surrounding corn. The increment of corn yields with high-, medium-high-, medium- and low-width-gap grade shelterbelt were 2.01%, 2.21%, 1.99%, and 0.91%, respectively. The medium-high grade shelterbelt achieved the largest yield increase effect. The location of the farmland relative to the shelterbelt also affected the yield, with a yield increase of 2.39% on the leeward side and 1.89% on the windward side, but it did not change the relationship between the yield increase effect and the shelterbelt structure. Our findings highlight the optimal shelterbelt structure for increasing corn yield, providing practical guidance on the design and management of farmland shelterbelts for maximizing yield.

Satellite-based precipitation data have contributed greatly to quantitatively forecasting precipitation, and provides a potential alternative source for precipitation data allowing researchers to better understand patterns of precipitation over ungauged basins. However, the absence of calibration satellite data creates considerable uncertainties for The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product over high latitude areas beyond the TRMM satellites latitude band (38°NS). This study attempts to statistically assess TMPA V7 data over the region beyond 40°NS using data obtained from numerous weather stations in 1998-2012. Comparative analysis at three timescales (daily, monthly and annual scale) indicates that adoption of a monthly adjustment significantly improved correlation at a larger timescale increasing from 0.63 to 0.95; TMPA data always exhibits a slight overestimation that is most serious at a daily scale (the absolute bias is 103.54%). Moreover, the performance of TMPA data varies across all seasons. Generally, TMPA data performs best in summer, but worst in winter, which is likely to be associated with the effects of snow/ice-covered surfaces and shortcomings of precipitation retrieval algorithms. Temporal and spatial analysis of accuracy indices suggest that the performance of TMPA data has gradually improved and has benefited from upgrades; the data are more reliable in humid areas than in arid regions. Special attention should be paid to its application in arid areas and in winter with poor scores of accuracy indices. Also, it is clear that the calibration can significantly improve precipitation estimates, the overestimation by TMPA in TRMM-covered area is about a third as much as that in no-TRMM area for monthly and annual precipitation. The systematic evaluation of TMPA over mid-high latitudes provides a broader understanding of satellite-based precipitation estimates, and these data are important for the rational application of TMPA methods in climatic and hydrological research.

Carbon (C) isotope discrimination during photosynthetic CO2 assimilation has been extensively studied, but the whole process of fractionation from leaf to soil has been less well investigated. In the present study, we investigated the δ13C signature along the C transfer pathway from air to soil in a coniferous and broad-leaved mixed forest in northeast China and examined the relationship between δ13C of respiratory fluxes (leaf, trunk, soil, and the entire ecosystem) and environmental factors over a full growing season. This study found that the δ13C signal of CO2 from canopy air was strongly imprinted in the organic and respiratory pools throughout C transfer due to the effects of discrimination and isotopic mixing on C assimilation, allocation, and respiration processes. A significant difference in isotopic patterns was found between conifer and broadleaf species in terms of seasonal variations in leaf organic matter. This study also found that δ13C in trunk respiration, compared with that in leaf and soil respiration, was more sensitive to seasonal variations of environmental factors, especially soil temperature and soil moisture. Variation in the δ13C of ecosystem respiration was correlated with air temperature with no time lag and correlated with soil temperature and vapor pressure deficit with a lag time of 10 days, but this correlation was relatively weak, indicating a delayed linkage between above- and belowground processes. The isotopic linkage might be confounded by variations in atmospheric aerodynamic and soil diffusion conditions. These results will help with understanding species differences in isotopic patterns and promoting the incorporation of more influencing factors related to isotopic variation into process-based ecosystem models.

Permafrost soils represent an enormous carbon (C) pool that is highly vulnerable to climate warming. We used the model output ensemble of the Coupled Model Intercomparison Project Phase 6 to estimate the C storage in soil, litter, and vegetation in the current extent of northern Asian permafrost during 1900–2100. The contemporary (1995–2014) C storage was estimated to be 368.1 ± 82.5 Pg C for the full column depth of the soil, 13.3 ± 4.6 Pg C in litter, and 22.2 ± 3.2 Pg C in vegetation biomass, while these C storage levels are projected to decline by 3.9 Pg C (1.1%) in soils, increase of 0.03 Pg C (0.2%) in litter, and increase by 21.1 Pg C (95.3%) in vegetation biomass by the end of the 21st century under SSP585. The total C storage was dominated by warming-induced vegetation growth. Partial correlation analysis showed that surface air temperature (TAS), soil liquid water, and soil mineral nitrogen (SMN) dominated the soil and vegetation C pools, while SMN controlled litter C during the historical period. Under future scenarios, TAS and SMN dominated the changes of soil and litter C, while TAS determined the vegetation C increase. The growing soil C loss with warming indicates positive C-climate feedback in soils; this warming-induced acceleration of soil C loss was canceled out by the enhanced vegetation C accumulation, leading to a strong C sink in the 21st century.