Search

Agglomeration economies can bring about positive externalities, while excessive agglomeration backfires. However, existing literature on place‐based policies mainly focuses on policies that enhance agglomeration economies. Few studies discuss dispersion policies coping with the negative externality from excessive agglomeration. Owing to the unclear effects of dispersion policies, we conduct empirical research on the noncapital function relief (NCFR) policy, which is implemented to decrease the agglomeration in Beijing, the capital of China, and promote the development of surrounding areas. Exploiting the difference‐in‐difference model, we find that the NCFR policy implementation significantly facilitates the economic growth of the surrounding areas of Beijing. Changes in the distribution of new firm entry and population are two main channels, while no significant effect on the productivity is found. Furthermore, areas that are less‐developed could benefit more from the NCFR policy. Our findings may enlighten other developing countries in urban economic management. [ABSTRACT FROM AUTHOR]

Tropospheric ozone significantly damages vegetation and reduces net primary productivity (NPP). We developed a stable linear NPP response model based on accumulated ozone exposure over a threshold of 40 parts per billion (ppb) (AOT40). We then estimated the effects of regional ozone damage on NPP for different vegetation types. The study suggests an average decrease in NPP of 24.7% due to ozone pollution in the North China Plain, similar to previous estimates ranging from 10.1% to 24.7%, with a maximum reduction exceeding 200 [g C m‐2 yr‐1] and more than 50%. Vegetation types such as broadleaf forests, needleleaf forest, crops, and grasses showed significant NPP decreases of 47.1%, 37.8%, 36.7%, and 44.6%, respectively. Declining NPP also had negative impacts on several Chinese crop species. Our work highlights the need for urgent and effective action to mitigate ozone pollution's substantial detrimental effects on ecosystem health and productivity. Plain Language Summary: Ozone pollution led to an average 25% decrease in net primary productivity in the North China Plain. This decline is consistent with previous estimates that ranged from 10% to 25%. Productivity of forest trees, crops, and grasses declined significantly, ranging from 36.7% to 47.1%. These results have implications for Chinese crops, as lower crop productivity can negatively affect crop yields. Our study highlights the urgent need for action to mitigate the detrimental effects of ozone pollution on ecosystem health and productivity. Key Points: We developed a stable model to estimate ozone impact on the productivity of plants'Ozone pollution resulted in an average decline in productivity of 25% per year in all ecosystem types in the North China Plain regionOzone pollution led to net primary productivity declines in forest, crops, and grasses, with reductions ranging from 36.7% to 47.1% [ABSTRACT FROM AUTHOR]

As a phytotoxic pollutant, surface ozone (O3) not only affects plant physiology but also influences meteorological fields and air quality by altering leaf stomatal functions. Previous studies revealed strong feedbacks of O3-vegetation coupling in China but with large uncertainties due to the applications of varied O3 damage schemes and chemistry-vegetation models. In this study, we quantify the O3 vegetation damage and the consequent feedbacks to surface meteorology and air quality in China by coupling two O3 damage schemes (S2007 vs. L2013) into a fully coupled regional meteorology-chemistry model. With different schemes and damaging sensitivities, surface O3 is predicted to decrease summertime gross primary productivity by 5.5 %–21.4 % and transpiration by 5.4 %–23.2 % in China, in which the L2013 scheme yields 2.5–4 times of losses relative to the S2007 scheme. The damages to photosynthesis of sunlit leaves are ~2.6 times that of shaded leaves in the S2007 scheme but show limited differences in the L2013 scheme. Though with large discrepancies in offline responses, the two schemes yield similar magnitude of feedback to surface meteorology and O3 air quality. The O3-induced damage to transpiration increases national sensible heat by 3.2–6.0 W m-2 (8.9 % to 16.2 %) while reduces latent heat by 3.3–6.4 W m-2 (-5.6 % to -17.4 %), leading to a 0.2–0.51 °C increase in surface air temperature and a 2.2–3.9 % reduction in relative humidity. Meanwhile, surface O3 concentrations on average increase by 1.3–3.3 μg m-3 due to the inhibitions of stomatal uptake and the anomalous enhancement in isoprene emissions, the latter of which is attributed to the surface warming by O3-vegetaion coupling. Our results highlight the importance of O3 control in China due to its adverse effects on ecosystem functions, deterioration of global warming, and exacerbation of O3 pollution through the O3-vegetation coupling. [ABSTRACT FROM AUTHOR]

The Silk Road Economic Belt and the 21st Century Maritime Silk Road Initiative (BRI) proposed in 2013 by China has greatly accelerated the social and economic development of the countries along the Belt and Road (B&R) region. However, the international community has questioned its impact on the ecological environment and a comprehensive assessment of ecosystem quality changes is lacking. Therefore, this study proposes an objective and automatic method to assess ecosystem quality and analyzes the spatiotemporal changes in the B&R region. First, an ecosystem quality index (EQI) is established by integrating the vegetation status derived from three remote sensing ecological parameters including the leaf area index, fractional vegetation cover and gross primary productivity. Then, the EQI values are automatically categorized into five ecosystem quality levels including excellent, good, moderate, low and poor to illustrate their spatiotemporal changes from the years 2016 to 2020. The results indicate that the spatial distributions of the EQIs across the B&R region exhibited similar patterns in the years 2016 and 2020. The regions with excellent levels accounted for the lowest proportion of less than 12%, while regions with moderate, low and poor levels accounted for more than 68% of the study area. Moreover, based on the EQI pattern analysis between the years 2016 and 2020, the regions with no significant EQI change accounted for up to 99.33% and approximately 0.45% experienced a significantly decreased EQI. Therefore, this study indicates that the ecosystem quality of the B&R region was relatively poor and experienced no significant change in the five years after the implementation of the "Vision and Action to Promote the Joint Construction of the Silk Road Economic Belt and the 21st Century Maritime Silk Road". This study can provide useful information for decision support on the future ecological environment management and sustainable development of the B&R region. [ABSTRACT FROM AUTHOR]

To enable grasslands to serve a larger ecological and service role against the backdrop of climate change and human activity, management is essential following long-term fencing. Using desert grassland that had been fenced for 20 years, we conducted experiments on different mowing frequencies (once, twice, and three times a year) and different amounts of vegetation coverage (mowing removal, mowing, and in situ mulch; and mowing, in situ mulch, and additional coverage) and control (no mowing, no removal, and no cover). In 2021 and mid-August 2022, the aboveground biomass, species diversity, and vegetation cover of each species were assessed, and the impacts of species complementarity and competition on the productivity of the fenced grassland were examined. Our findings revealed several significant outcomes: (1) Two and three times of mowing per year promoted growth and development of annual or biennial plants and increased species diversity of the community. (2) The interaction impact of mowing and covering two and three times a year was the most important, as it encouraged compensatory development of plants, particularly the dominant species, and increased aboveground biomass of the fenced grassland (p < 0.05). (3) The number of complementary species groups was c. 11% lower for mowing and covering than for mowing removal, indicating that the interspecific relationship for the mowing and covering interaction was dominated by competition, which was more beneficial to the increase in community productivity. (4) Lespedeza potaninii Vass. (which dominated the semi-shrubs) and Agropyron mongolicum Keng (which dominated the tufted grasses) exhibited compensatory growth, which alleviated the change of mowing disturbance and precipitation fluctuation on the productivity of this grassland ecosystem. (5) Adding vegetation cover after mowing positively influenced community production but did not significantly impact litter decomposition. Our research provides data support for the sustainable development of long-term fenced desert grasslands. [ABSTRACT FROM AUTHOR]

Ozone is a phytotoxic pollutant that could damage the vegetation growth and lead to complicated impacts on air quality through meteorological and biogeochemical feedbacks. This study implements a semi‐empirical parameterization regarding the impacts of ozone exposure on photosynthesis rate and stomatal resistance into the Noah‐Multi‐parameterization (Noah‐MP) dynamic vegetation module of Weather Research and Forecasting with Chemistry (WRF/Chem) model. The gaseous dry deposition and biogenic emission algorithms are also coupled with Noah‐MP to enable the ozone‐vegetation coupling. This model reproduces the near‐surface meteorology, air pollutants and vegetation physiology in China, with a spatial correlation coefficient more than 0.9 and normalized mean bias from −0.19 to 0.42. The optimized model also improves the simulations of vegetation physiology (e.g., a reduction of model error by 18%–32%) and ozone dry deposition velocity. The elevated ozone damages plant photosynthesis, and decreases the national gross primary productivity (−28.85%) and leaf area index (−17.41%). The plant transpiration and surface heat flux, as well as air temperature (e.g., up to +0.16°C in summer) and other associated meteorological variables are also altered, finally contributing to 0.49 μg m−3 increase of surface ozone. Otherwise, the suppressed vegetation LAI and biogenic emissions, as well as the lower dry deposition velocity in response to the ozone‐vegetation coupling contribute to the remaining ozone changes by −1.07 μg m−3 and 1.18 μg m−3, jointly constituting the complicated ozone‐vegetation feedbacks on air quality. Our results highlight the necessity of including the ozone‐vegetation coupling in models for reliable prediction of regional climate and air quality. Plain Language Summary: Vegetation controls the land‐atmosphere carbon/water cycle and material exchange, and is thus important in global/regional climate and atmospheric environment. Chronic exposure to ozone could damage the vegetation growth and lead to complicated impacts on regional climate and air quality. This study considers the impacts of ozone exposure on plant photosynthesis and stomata using a semi‐empirical parameterization into the WRF/Chem‐Noah‐MP regional meteorology‐chemistry‐vegetation coupled model. The model well reproduces the near‐surface meteorology, air pollutants and vegetation characteristics in China, with moderate model improvements due to the optimized dynamic vegetation and dry deposition modules. The elevated ozone damages plant photosynthesis and decreases the national gross primary productivity (−28.85%) and leaf area index (−17.41%). Otherwise, the plant transpiration and surface heat flux, as well as air temperature (e.g., up to +0.16°C in summer) and other associated meteorological variables are also altered. The meteorological changes, along with the suppressed biogenic emissions from vegetation and lower dry deposition velocity due to the ozone‐vegetation damage jointly constitute the complicated ozone‐vegetation feedbacks on air quality and contribute to an average of −1.1∼1.2 μg m−3 surface ozone change. Our results highlight the necessity of including the ozone‐vegetation coupling for reliable model predictions. Key Points: The optimized model with ozone‐vegetation coupling improves the simulations of vegetation physiology and dry deposition velocity in ChinaThe O3‐damaged photosynthesis and stomatal conductance could affect regional meteorology, finally contributing to surface ozone increaseThe ozone‐vegetation coupling constitutes complex feedbacks on ozone itself by altered meteorology, biogenic emissions and dry deposition [ABSTRACT FROM AUTHOR]

Water use efficiency (WUE) is an important indicator for the characterization of the regional carbon–water interaction. Understanding the influence of climate change and human activities on it is essential for terrestrial ecosystems and water management. In this study, we selected the Tao river basin in northwestern China as an example to quantify the impacts of climate change and human activities on the variation of regional WUE from 1982 to 2015. Results revealed that: (1) WUE values ranged from 0.54 to 3.14 g C kg−1 H2O across the basin, fluctuated with an overall increasing trend during the statistic time period, more remarkably in the area of downstream. (2) The influence of the actual evapotranspiration on ∆WUE was more notable in the up- and midstream, while the gross primary productivity dominated ∆WUE downstream, the contributions of both across the basin were quantified at 88.19% and 11.81%. (3) ∆WUE due to climate change and human activities were estimated at multi-yearly averaged values of 0.05 and − 0.03 g C kg−1 H2O, respectively. The positive effect of climate change imposed on WUE occurred more in the downstream, while the negative effect of human activities occurred more in the midstream. Overall, the relative contribution rates of climate change and human activities to ∆WUE were 0.62 and − 0.38 across the whole basin, respectively, during the time period from 1982 to 2015. The study provided a case example to understand the WUE variation in diverse geo-ecological regions across large river basins. [ABSTRACT FROM AUTHOR]

Net primary productivity (NPP) can be one of the most important indicators to evaluate the ecosystem structure and function in grassland protection. Therefore, it is necessary to rapidly and accurately evaluate the NPP under the carbon cycle of the ecosystem. The land surface process model can be used to realize the time series simulation of NPP using the physical mechanism. However, the default parameters of the models cannot be suitable for the target area, leading to the simulation deviation. There is also limited cognition of the physiological and biochemical characteristics of vegetation. Therefore, it is a high demand to optimize the parameters, when applied to the target area. The CESM framework has recently released the land surface model CLM5.0, indicating the most promising land surface process model for the evaluation of grassland productivity. In this study, the spatiotemporal simulation of grassland productivity was carried out in the Hulunbuir grassland, Inner Mongolia, China. Firstly, the local sensitivity analysis was used to choose the ten sensitive parameters of the CLM5.0_BGC module. Taking the global daily NPP data products as the reference, the Differential Evolution Adaptive Metropolis (DREAM) was then utilized to optimize ten sensitive parameters after sensitivity analysis. The accuracy of optimization was verified using global daily NPP data and GLASS annual net primary productivity products. Finally, the parameter setting was established for the CLM5.0 model for the grassland productivity simulation. The results were as follows: 1) Local sensitivity analysis was conducted to determine 74 parameters of the CLM5.0_BGC module. The most sensitive parameters were obtained to simulate the grassland productivity, including respiration, followed by carbon transfer. 2) Similar to the leaf C/N ratio, the Gaussian distribution was found in the posterior probability distribution in the slope parameter of the relationship between the stomatal conductance and photosynthesis. The optimization was observed in the benign constraint. The effectiveness of the carbon/nitrogen correlation and respiration parameters were verified to simulate the optimized model. 3) Parameter optimization effectively improved the simulation performance of CLM5.0 on the productivity of the grassland. The parameter optimization of the model shared a great improvement for the targeted pixel, where the relative error decreased from 33.82% to 10.97%, a relative improvement of 67%, and root mean square error decreased by 0.12 g/m². The optimization effect of the model in summer was also better than that in autumn. 4) The grassland productivity in the next year was evaluated on the simulation with the optimized parameter, where the relative error decreased from 8.74% to 2.51%, and the absolute difference in annual mean index decreased from 15.64 g/m² to 13.63 g/m². Spatially, the accuracy of the optimized model was improved in the different types of grassland. Among them, the relative errors of meadow grassland, lowland meadow, and typical grassland decreased from 43.43% to 37.81%, from 39.23% to 31.17%, and from 42.03% to 33.00%, respectively. The root mean square error was reduced from 0.58 g/m² to 0.52 g/m² in the meadow grassland, from 0.62 g/m² to 0.57 g/m² in the lowland meadow, and from 0.51 g/m² to 0.44 g/m² in the typical grassland. The finding can provide a strong reference to simulate the grassland productivity in the Hulunbuir region using CLM5.0. There can be offered a positive role in the reasonable assessment of the grassland ecosystem under the carbon cycle. [ABSTRACT FROM AUTHOR]

The Songliao Plain is the largest maize (Zea mays L.) cropland area in China and, thus, is most influenced by water stress. To mitigate the adverse impact of water stress on maize yield and quality, various agricultural irrigation strategies have been implemented. Based on land surface temperature and an enhanced vegetation index, this study constructed the temperature vegetation dryness index (TVDI) and combined the Hurst index and Sen trend to analyze the spatiotemporal characteristics of drought and waterlogging. From the correlation between TVDI and gross primary productivity, the weight coefficients of different growth cycles of maize were derived to determine the drought and waterlogging stresses on maize in Songliao Plain for 2000–2020. The drought hazard on the western side of Songliao Plain was high in the west and low in the east, whereas the waterlogging hazard was high in the east. Waterlogging likely persisted according to the spatiotemporal trends and patterns of drought and waterlogging. During the second growth cycle, maize was most severely affected by water stress. There was a spatial heterogeneity in the severity of the hazards and the stress degree of maize. For the reason that precipitation in the study area was concentrated between mid-late July and early August, maize was susceptible to drought stress during the first two growth stages. Irrigation concentrated in the early and middle stages of maize growth and development in the western part of the Songliao Plain reduced the drought stress-induced damage. Spatiotemporally-detected drought and waterlogging couplings and hazards for maize in the Songliao Plain for 2000–2020 provide actionable insights into the prevention and mitigation of such disasters and the implementation of water-saving irrigation practices at the regional scale. [ABSTRACT FROM AUTHOR]

Background: Mangrove forests are a significant contributor to the global carbon cycle, and the accurate estimation of their gross primary productivity (GPP) is essential for understanding the carbon budget within blue carbon ecosystems. Little attention has been given to the investigation of spatiotemporal patterns and ecological variations within mangrove ecosystems, as well as the quantitative analysis of the influence of geo-environmental factors on time-series estimations of mangrove GPP. Methods: This study explored the spatiotemporal dynamics of mangrove GPP from 2000 to 2020 in Gaoqiao Mangrove Reserve, China. A leaf area index (LAI)-based light-use efficiency (LUE) model was combined with Landsat data on Google Earth Engine (GEE) to reveal the variations in mangrove GPP using the Mann-Kendall (MK) test and Theil-Sen median trend. Moreover, the spatiotemporal patterns and ecological variations in mangrove ecosystems across regions were explored using four landscape indicators. Furthermore, the effects of six geo-environmental factors (species distribution, offshore distance, elevation, slope, planar curvature and profile curvature) on GPP were investigated using Geodetector and multi-scale geo-weighted regression (MGWR). Results: The results showed that the mangrove forest in the study area experienced an area loss from 766.26 ha in 2000 to 718.29 ha in 2020, mainly due to the conversion to farming, terrestrial forest and aquaculture zones. Landscape patterns indicated high levels of vegetation aggregation near water bodies and aquaculture zones, and low levels of aggregation but high species diversity and distribution density near building zone. The mean value of mangrove GPP continuously increased from 6.35 g C.m-2.d-1 in 2000 to 8.33 g C.m-2.d-1 in 2020, with 23.21% of areas showing a highly and significantly increasing trend (trend value > 0.50). The Geodetector and MGWR analyses showed that species distribution, offshore distance and elevation contributed most to the GPP variations. Conclusions: These results provide guidelines for selecting GPP products, and the combination of Geodetector and MGWR based on multiple geo-environmental factors could quantitatively capture the mode, direction, pathway and intensity of the influencing factors on mangrove GPP variation. The findings provide a foundation for understanding the spatiotemporal dynamics of mangrove GPP at the landscape or regional scale. [ABSTRACT FROM AUTHOR]

Plant nutrient concentration and resorption play a major role in determining primary production and development of forests. Yet how plant nutrient concentration and resorption respond to altered interannual precipitation and how interannual precipitation variability mediates the responses of plant nutrient concentration and resorption to N addition remain uncertain. Here, we examined the responses of needle nutrient (N and P) concentrations of all aged classes, nutrient resorption efficiency, and proficiency to N addition (10 g N m−2 year−1) over a 4‐year period (from 2016 to 2019) that experienced high variability in precipitation in a Mongolian pine plantation in a semiarid region of northeast China. There were significant interannual variations in N and P concentrations in all aged needles and litters, N and P resorption efficiency, as well as soil inorganic N and available P concentrations in the control plots. Increased precipitation resulted in increased needle nutrient concentrations across the four consecutive years. However, needle nutrient concentrations did not covary with soil available nutrients. The responses of needle N resorption efficiency (NRE) to N addition differed across the four years, with N addition decreasing NRE in wet years, but not in dry years. Taken together, our results showed that foliar nutrient concentrations are sensitive to altered annual precipitation and that different interaction effects exist between N addition and sampling year for different nutrient variables. Such differential responses highlight the challenge of predicting effects of N addition on nutrient cycling over temporal scales. [ABSTRACT FROM AUTHOR]

Paddy rice is an important grain production in the world. Southern China experienced substantial losses in paddy rice area over the last three decades in traditionally cropping area. As gross primary production (GPP) is a proxy of land productivity, study on its spatial–temporal dynamics is helpful to understand effect of cropping practices on variation of rice grain production. In our study, Moderate Resolution Imaging Spectroradiometer (MODIS) data and meteorological data were combined to drive Vegetation Photosynthesis Model (VPM) for estimating GPP in paddy rice fields of southern China (Hubei province) during 2000–2015 at 1 km spatial resolution. Paddy fields area was varied by the influence of industrialization and urbanization of the region as well as changes in aquaculture area during the 16 years. Annual GPP showed significant increasing trends during 2000–2004 and 2009–2015, and sharply decreasing trend from 2004 to 2009. The assessment of relationship between annual GPP and grain production of paddy rice at municipal-scale and provincial-scale during 2000–2015 demonstrated the potential of annual GPP derived from satellite-based GPP model as a tool to estimate annual rice grain production in the region. The analysis of spatial–temporal pattern of rice GPP demonstrated a high correlation between cropping frequency of paddy rice and mean annual GPP of paddy rice along the main rivers of Hubei province, which provided positive suggestions for promote the production of paddy rice cultivation in sustainable development. [ABSTRACT FROM AUTHOR]

Light use efficiency (LUE) models have been widely used to estimate terrestrial gross primary production (GPP). However, the estimation of GPP still has large uncertainties owing to an insufficient understanding of the complex relationship between water availability and photosynthesis. The plant water stress index (PWSI), which is based on canopy temperature, is very sensitive to the plant stomatal opening and has been regarded as a good indicator for monitoring plant water status at the regional scale. In this study, we selected a cork oak plantation in northern China with an obvious seasonal drought as the research object. Using the ground-observed data, we evaluated the applicability of the LUE models with typical water stress scalars (MOD17, MODTEM, EC-LUE, ECM-LUE, SM-LUE, GLO-PEM, and Wang) in a GPP simulation of the cork oak plantation and explored whether the model's accuracy can be improved by applying PWSI to modify the above models. The results showed that among the seven LUE models, the water stress scalar had a greater impact on the model's performance than the temperature stress scalar. On sunny days, the daily GPP simulated by the seven LUE models was poorly matched with the measured GPP, and all models explained only 23–52% of the GPP variation in the cork oak plantation. The modified LUE models can significantly improve the prediction accuracy of the GPP and explain 49–65% of the variation in the daily GPP. On cloudy days, the performance of the modified LUE models did not improve, and the evaporative fraction was more suitable for defining the water stress scalar in the LUE models. The ECM-LUE and the modified GLO-PEM based on PWSI had optimal model structures for simulating the GPP of the cork oak plantation under cloudy and sunny days, respectively. This study provides a reference for the accurate prediction of GPP in terrestrial ecosystems in the future. [ABSTRACT FROM AUTHOR]

Vegetation, a key intermediary linking water, the atmosphere, and the ground, performs extremely important functions in nature and for our existence. Although satellite-based remote-sensing technologies have become important for monitoring vegetation dynamics, selecting the correct remote-sensing vegetation indicator has become paramount for such investigations. This study investigated the consistencies between a photosynthetic activity index (the solar-induced chlorophyll fluorescence (SIF) indicator) and the traditional vegetation index (the Normalized Difference Vegetation Index (NDVI)) among different land-cover types and in different seasons and explored the applicability of NDVI and SIF in different cases by comparing their performances in gross primary production (GPP) and grain-yield-monitoring applications. The vegetation cover and photosynthesis showed decreasing trends, which were mainly concentrated in northern Xinjiang and part of the Qinghai–Tibet Plateau; a decreasing trend was also identified in a small part of Northeast China. The correlations between NDVI and SIF were strong for all land-cover types except evergreen needleleaf forests and evergreen broadleaf forests. Compared with NDVI, SIF had some advantages when monitoring the GPP and grain yields among different land-cover types. For example, SIF could capture the effects of drought on GPP and grain yields better than NDVI. To summarize, as the temporal extent of the available SIF data is extended, SIF will certainly perform increasingly wide applications in agricultural-management research that is closely related to GPP and grain-yield monitoring. [ABSTRACT FROM AUTHOR]

Qinghai spruce (Picea crassifolia Kom.) forests play a key role in the carbon sequestration in the Qilian Mountains, Northwestern China. Carbon sequestration is closely related to canopy photosynthesis, which greatly depends on the leaf-level photosynthesis. Quantifying the magnitude of canopy photosynthesis will improve the modeling of forest carbon cycling. Although biochemical models have been widely used to estimate primary production, the effects of canopy partition on the photosynthetic rates and its feed-backs into the carbon cycle are generally not represented. In this study, the difference and connection between the leaf gas exchange indices in the different canopy partitions and the diameter at breast height (DBH), tree height (H), crown diameter (CD), and living under branch height (BH) were determined, and a multiple linear regression model was explored. This study was performed to develop a regression model to link morphological and physiological characteristics to canopy photosynthetic rate. The classification methods improved the working efficiency, fitting performance, and prediction accuracy compared with the classical method. The classified DBH method exhibited the best fitting performance, because it had the highest determination coefficient (R2 = 0.9060). The photosynthetic model of the spruce canopy with the aforementioned factors produced a good simulation (R2 = 0.9773) and provided data support to further estimate the carbon budget and primary net productivity of the spruce canopy. The developed model was also very useful for updating and modifying forest base maps and registries of spatial distribution of biomass and carbon storage. [ABSTRACT FROM AUTHOR]

Drought largely affects terrestrial gross primary productivity (GPP) by its duration (i.e., drought time‐scales). However, the large‐scale impact of drought time‐scales on GPP is still not well understood, partly due to limited understanding of ecological processes and difficulties in quantifying drought time‐scales. Here, we examined the responses of GPP to drought in China during the last four decades, using state‐of‐the‐art GPP data estimates and a multiscalar drought metric. Most of the GPP data suggested that the time‐scales at which drought has the largest influence on annual GPP (dominant drought time‐scales) were well related to climate region and vegetation type. The GPP responded to drought more rapidly in arid regions than that in semiarid and subhumid regions, with a mean dominant drought time‐scale of 15 and 20 months, respectively; as plants in arid regions can rapidly adapt to water shortage through physiological strategies, while, in semiarid and subhumid regions plants usually can withstand a degree of water deficits. In a humid region, GPP did not rapidly respond to drought, probably because the high soil water availability buffered the effect of drought. In addition, most of the GPP estimates suggested that grasslands and croplands tended to respond to drought at shorter time‐scales than that of forests, shrublands, and sparse vegetation. The study highlights the importance of improving the effects of water availability on vegetation activity and further consideration of vegetation adaptability to drought in both remote sensing GPP algorithms and parameterizations of current terrestrial ecosystem models. Plain Language Summary: Gross primary productivity (GPP) is the entire photosynthetic production of organic compounds in an ecosystem. It plays an important role in carbon cycles, and is the basis for food, fiber, and wood production. Drought widespreadly and negatively affects GPP by its duration (i.e., drought time‐scales). Thus, it is critical to understand the dominant time‐scale at which drought has the largest influence on GPP. We investigated dominant time‐scales of GPP response to drought in China using six GPP estimates and a multiscalar drought metric. We found that the dominant time‐scales were well related to climate region and vegetation type. The GPP responded to drought more quickly in arid regions than that in semiarid and subhumid regions. This might be due to plants in arid regions can rapidly adapt them to water shortage, while, in semiarid and subhumid regions, plants can withstand a degree of water deficits. In a humid region, GPP did not rapidly respond to drought, probably because the high soil water availability buffered the effect of drought. The grasslands and croplands responded to drought at shorter time‐scales than forests, shrublands, and sparse vegetation. The study could benefit the management and mitigation of drought effects on GPP. Key Points: The magnitudes of gross primary productivity (GPP) response to drought highly vary among different GPP estimates and across time‐scalesGPP responds to drought at shorter time‐scales in arid regions than that in other climate regionsGPP of forests responds to drought at longer time‐scales than that of shrublands, grasslands, and croplands [ABSTRACT FROM AUTHOR]

Fruit type is a key reproductive trait associated with plant evolution and adaptation. However, large-scale geographical patterns in fruit type composition and the mechanisms driving these patterns remain to be established. Contemporary environment, plant functional traits and evolutionary age may all influence fruit type composition, while their relative importance remains unclear. Here, using data on fruit types, plant height and distributions of 28,222 (∼ 90.1%) angiosperm species in China, we analyzed the geographical patterns in the proportion of fleshy-fruited species for all angiosperms, trees, shrubs, and herbaceous species separately, and compared the relative effects of contemporary climate, ecosystem primary productivity, plant height, and evolutionary age on these patterns. We found that the proportion of fleshy-fruited species per grid cell for all species and different growth forms all showed significant latitudinal patterns, being the highest in southeastern China. Mean plant height per grid cell and actual evapotranspiration (AET) representing ecosystem primary productivity were the strongest drivers of geographical variations in the proportion of fleshy-fruited species, but their relative importance varied between growth forms. From herbaceous species to shrubs and trees, the relative effects of mean plant height decreased. Mean genus age had significant yet consistently weaker effects on proportion of fleshy-fruited species than mean plant height and AET, and environmental temperature and precipitation contributed to those of only trees and shrubs. These results suggest that biotic and environmental factors and evolutionary age of floras jointly shape the pattern in proportion of fleshy-fruited species, and improve our understanding of the mechanisms underlying geographical variations in fruit type composition. Our study also demonstrates the need of integrating multiple biotic and abiotic factors to fully understand the drivers of large-scale patterns of plant reproductive traits. [ABSTRACT FROM AUTHOR]

The large-scale relationship between gross primary productivity (GPP) and plant species richness (PSR) remains a fundamental topic in macro-ecology. Although there have been numerous studies of the connection between GPP and PSR, this relationship (linear or unimodal) is still debatable at geographical scales. Using geographically weighted regression and ordinary least squares, GPP and PSR data from protected areas in China were used to explore the complex relationship between GPP and PSR across different taxonomic groups (i.e. vascular plants, ferns, seed plants, gymnosperms, and angiosperms). Results showed that GPP was significantly related to PSR for all taxonomic groups at geographical scales (p < 0.001). Such relationships varied among taxonomic groups. The explanatory power of GPP was highest for PSR in seed plants (58.5%; p < 0.001). Further, the study found that the GPP–PSR relationships across different taxonomic groups at geographical scales were best described by a cubic regression, differing from the linear or unimodal relationships described thus far. Specifically, negative relationships were found between GPP and PSR at extremely low levels of GPP and GPP–PSR relationships were unimodal at relatively high GPP levels. This analysis provides new evidence regarding the relationship between GPP and PSR based on different taxonomic groups and develops the mechanisms and models of GPP linking to PSR at broad scales. [ABSTRACT FROM AUTHOR]

It is important to understand regional contributions to interannual and decadal variabilities of vegetation productivity. However, most studies of regional terrestrial carbon cycles focus on the contributions of regional ecosystems to the mean or trend of net primary production (NPP), rather than its interannual variability (IAV). In this study, the IAV of NPP is explored using the simulation of the Australian Community Atmospheric Biosphere Land Exchange model, version 2.1 (CABLE2.1), compared with an eight‐member Trends in Net Land–Atmosphere Carbon Exchanges (TRENDY) ensemble under present conditions (1951–2010). NPP IAV is also analyzed using only CABLE2.1 under future conditions (2041–2100). Under the present conditions, the Central China region (region CC) contributes the largest fraction (CABLE2.1, 28.2%; TRENDY, 33.3% ± 11.5%) of NPP IAV in China. Such an NPP IAV is strongly associated with circulation‐driven variations in precipitation in most of China (∼85%). During the future period, region CC still plays a key role in the national NPP IAV, at ∼24.6%. Although precipitation IAV is still the main driver regulating the NPP IAV for region CC under the future conditions, climate sensitivity of NPP IAV to precipitation IAV may weaken compared with those under the present conditions. Our results highlight the contribution of region CC to the NPP IAV of China as a whole. Key Points: China's interannual variability (IAV) of net primary production (NPP) is dominated by the Central China region (region CC) under present and future conditionsNPP IAV in China is strongly associated with the variability of precipitation rather than temperatureHigh precipitation IAV may be the largest contributor to the national NPP IAV for region CC [ABSTRACT FROM AUTHOR]

A key challenge in ecology is to understand the relationships between organismal traits and ecosystem processes. Here, with a novel dataset of leaf length and width for 10 480 woody dicots in China and 2374 in North America, we show that the variation in community mean leaf size is highly correlated with the variation in climate and ecosystem primary productivity, independent of plant life form. These relationships likely reflect how natural selection modifies leaf size across varying climates in conjunction with how climate influences canopy total leaf area. We find that the leaf size‒primary productivity functions based on the Chinese dataset can predict productivity in North America and vice‐versa. In addition to advancing understanding of the relationship between a climate‐driven trait and ecosystem functioning, our findings suggest that leaf size can also be a promising tool in palaeoecology for scaling from fossil leaves to palaeo‐primary productivity of woody ecosystems. [ABSTRACT FROM AUTHOR]

Aims Land cover changes can disrupt water balance and alter the partitioning of precipitation into surface runoff, evapotranspiration and groundwater recharge. The widely planted Eucalyptus trees in south-western China have the potential to bring about hydrologic impacts. Our research aims to elucidate the hydrologic balance characteristics of the introduced exotic Eucalyptus grandis × Eucalyptus urophylla plantation and to assess whether its high productivity results from high water use efficiency (WUE) or large water consumption. Methods A 400-m2 experimental plot was established in an E. grandis × E. urophylla plantation in south-western China. Water balance components, including stand transpiration (T r), evapotranspiration (E t) and runoff (R) were obtained as follows: T r was estimated based on sap flow measurements, E t was estimated as the average of surface transpiration and evaporation weighted by the fractional green vegetation cover using a modeling approach, and R was collected using the installed metal frame. Net primary productivity (NPP) was obtained from allometric equation and annual diameter at breast height (DBH) increment determination. Important Findings Annual E t and T r were 430 ± 31 and 239 ± 17 mm, respectively. Annual T r accounts for 56 ± 8% of total evapotranspiration on average. WUE (NPP/ T r) of the E. grandis × E. urophylla was estimated to be 3.3–3.9 mmol·mol−1. Based on the comparative analysis of T r and WUE, E. grandis × E. urophylla had a high productivity due to its high WUE without exhibiting prodigal water use. Meteorological factors including vapor pressure deficit and global solar radiation (R s) were key factors regulating E t and T r in our research site. Annual surface runoff, E t and canopy interception occupied 7%, 27–30% and 16% of total precipitation, while the remaining 46–50% of precipitation was used for sustaining groundwater recharge and altering soil water storage. The higher runoff coefficient (7.1%) indicated the weaker capability of E. grandis × E. urophylla to reserve water resource than natural forests and less disturbed plantations. The planting and protection of understory vegetation may decrease the surface runoff and exert beneficial effects on water conservation capacity of Eucalyptus plantation. [ABSTRACT FROM AUTHOR]

• Climate factors with higher importance are Tmax, PET and RH. • Climate influence tree growth directly, and indirectly via DBH and site quality. • Climate and site quality predominantly determine the variabilities in tree growth. • The relative effects are: climate > site quality > tree size > competition. Tree growth is driven by various factors, including site climate, stand and tree variables. A thorough understanding of the respective roles of the main drivers is imperative to project or promote forest productivity in future scenarios. However, the complex relationships among the drivers and tree growth remained insufficiently understood. Using the structural equation modeling (SEM) approach, we examined the multiple and interactive relationships between annual tree growth and climate, stand- (site quality and competition) and tree-level (tree age and tree size) variables based on data from Chinese fir (Cunninghamia lanceolata) plantations, Southeastern China. To simplify the SEM structures, randomforest (RF) was employed initially to screen for factors with greater impacts on tree growth. SEMs showed that annual tree growth largely depended on climate and site quality, while competition had a smaller relative effect of less than 8%. Climate was the primary driver. Different climate factors influenced tree growth directly, and also indirectly via tree size and site quality. Tree growth increments decreased with the increase of maximum daily temperature (especially in current-year August), while increased with relative humidity (especially in previous-year October) and potential evapotranspiration (especially in current-year May). Site index (SI) and tree size were both of higher importance in predicting the annual tree increments and had a positive impact on tree growth, contributing about 1/3 and 15 % to the total effect, respectively. In contrast, both the direct and indirect effects of stand competition were negative. Basal area of larger trees (BAL) appeared to better capture competition than stand density. This study suggested that SEM combined with RF provided a promising way to understand the complexity of tree growth. Larger trees in forests with higher site quality and lower density grow faster, however, their annual increments seem to decrease with increasing temperature. [Display omitted] [ABSTRACT FROM AUTHOR]

Anthropogenic activities and climate change are exacerbating the occurrence of extreme rainfall that normally brings large amounts of nutrient-rich freshwater from the land to the sea, resulting in acute salinity decrease and nutrient increase. To evaluate the effects of such changes in salinity and nutrients, we tracked the changes in photosynthetic efficiency and growth of phytoplankton assemblages from the northern South China Sea at 5 salinity levels and at an intermediate salinity level with 3 or 4 nitrogen concentrations. The results showed that the reduction of salinity reduced the maximum photochemical quantum yield (F V /F M) of photosystem II of phytoplankton within a short-term cultivation (i.e. 24-72 h), followed by a stimulatory effect. The reducing effect of reduced salinity lasted longer in the nearshore area than in the offshore area, so the stimulatory effect occurred later in the former area. Nitrogen enrichment mitigated the negative effect of reduced salinity in short-term cultivation and showed a positive effect on F V /F M in long-term cultivation. Moreover, both reduced salinity and enriched nitrogen stimulated phytoplankton growth after an acclimation period. Our results suggest that the reduced salinity stresses phytoplankton in the short term, which is mitigated by nitrogen enrichment, but benefits them in the long term. This sheds light on how phytoplankton thrive and even flourish in coastal or estuarine environments where salinity and nutrients typically covary strongly after extreme rainfall. • Reduced salinity suppresses phytoplankton in the short term, but benefits it in the long term. • Reducing effect of reduced salinity lasts longer in offshore than nearshore areas. • Both reduced salinity and enriched nitrogen stimulated phytoplankton photosynthesis and growth in the long term. [ABSTRACT FROM AUTHOR]

[Display omitted] • The NPP of marsh wetland vegetation increased from 2005 to 2015 in China. • Pearl River Delta, upper Yangtze River, Poyang Lake, and Zoige had the highest NPP. • Gramineous plants contributed most to the total NPP of the marsh wetland vegetation. Wetlands are one of the world's three major ecosystems. Marsh wetland vegetation, with its unique characteristics, is playing an increasingly prominent role in absorbing atmospheric carbon dioxide and mitigating climate warming. This study estimated the annual, seasonal, and monthly trends of marsh wetland vegetation net primary productivity (NPP) in China from 2005 to 2015 based on the Carnegie–Ames–Stanford approach (CASA) model. The temporal and spatial characteristics of NPP were analyzed and the driving factors were discussed. The results showed that the annual marsh wetland vegetation NPP had an increasing trend from 2005 to 2015 on national scale in China, with the lowest and highest values in 2005 (202.03 gCm−2a−1) and 2015 (222.58 gCm−2a−1), respectively. The monthly seasonal variations in marsh wetland vegetation NPP consistent with the typical growth pattern of vegetation observed in China. Monthly marsh wetland vegetation NPP showed a unimodal distribution, and May to September were the main months for NPP accumulation. The highest vegetation NPP values occurred during the summer season. Marsh wetland vegetation types did impact the NPP distribution, Gramineous marsh vegetation contributed most to the total NPP (29%). Spatially, the NPP was higher in east than in the west, and higher in the center than in the south and north. An overall positive impact of precipitation and temperature on the Chinese marsh wetland vegetation NPP was revealed. The study recommended to give greater attention the conservation of Gramineous vegetation alongside the overall protection of Chinese marsh wetlands. Our findings can offer important evaluation results and guidance for the future addressing climate change and conservation and maintenance of the carbon sink capacity of marsh wetland vegetation in China. [ABSTRACT FROM AUTHOR]

Chlorophyll a concentration [ρ(Chla)] in phytoplankton is used to understand algal biomass and the primary productivity of a water body. To investigate the seasonal dynamics of [ρ(Chla)] and their relationship with environmental factors Tiegang Reservoir at Guangdong China, continuous observation and analysis was carried out from April 2013 to June 2014. The distribution of algae had an obvious spatial heterogeneity, and the ρ(Chla) gradual increased from the southeast to the northwest of the reservoir. From April to early December 2013 and from late April to June 2014, the dominant algae was cyanobacteria. In other months, bacillariophyta were dominant. Chlorophyta were at a low level throughout the year. Multivariate analysis showed that water temperature (WT) was the primary driving factor of the spatio-temporal distribution of algae. The main environmental factors of cyanobacteria, bacillariophyta and chlorophyta were greatly different. Single environmental factors such as WT, total organic carbon (TOC), nitrate nitrogen (NO3--N), and chemical oxygen demand (COD) were significantly related to the competitiveness of cyanobacteria and bacillariophyta. And they can be used as early warning indexs for cyanobacteria and diatom blooms. The dual synergistic effects of WT and other environmental factors on the competitiveness of cyanobacteria and bacillariophyta were analyzed. A combination of environmental factors at high risk for cyanobacteria or diatoms were found. [ABSTRACT FROM AUTHOR]

To explore the effects of coastal eutrophication and warming on phytoplankton biomass and cell size, we analyzed current and historical data for size-fractionated chlorophyll a (chla) in Xiangshan Bay, China. Results showed that micro- and nanophytoplankton overwhelmingly dominated (>84%) in all seasons. The contribution of micro-chla was significantly lower in warm than in cold seasons, whereas contribution of pico-chla showed the opposite result. Overall, the micro-chla contribution increased with decreasing pico-chla contribution from the stable, clear, eutrophic upper bay to the turbulent, turbid lower bay, indicating that phytoplankton size structure on a spatial scale was largely shaped by water column stability and light rather than by nutrients. Since the 1980s, phytoplankton biomass, primary productivity, and micro-chla contribution in Xiangshan Bay have increased sharply with increasing nutrient amounts and temperature. Additionally, it seems that algal bloom seasonality has shifted forward from spring to winter since the power plant operations in 2006. Unlabelled Image • Micro- and nano-sized fractions overwhelmingly dominated phytoplankton community. • Large-celled phytoplankton tended to predominate in well-mixed water column. • Microphytoplankton dominance has increased under eutrophication and warming. • Phytoplankton biomass and primary productivity have increased sharply since the 1980s. • Algal bloom seasonality has shifted forward because of thermal discharge. [ABSTRACT FROM AUTHOR]

Highlights • We use remote sensing satellite data to fit the population distribution in China. • We combine the advantages of DMSP/OLS and NPP/VIIRS nighttime light data. • NPP and average slope index are added to improve the population fitting. • The fitting of population distributions in China (2012–2013) is clearly improved. • This method can improve population research in different regions and periods. Abstract Remote sensing satellite data from 2012 to 2013 are used to fit the Chinese cities' population distributions over the same period in order to verify the population distribution in China from a relatively objective perspective. Most scholars have used nighttime light data and vegetation indexes to fit the population distribution, but the fitting effect has not been satisfactory. In this paper, processed Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data, net primary productivity of vegetation (NPP), and average slope data were used to fit the population distribution from the three dimensions of economic growth, ecological environment, and topographic factors, respectively. The fitting effect was significantly improved compared with other studies (R2 values of 0.9244 and 0.9253 in 2012 and 2013, respectively). Therefore, this method provides a practical and effective way to fit the population distribution for remote cities or areas lacking census data. Furthermore, there is important practical significance for the government to formulate its population policies rationally, optimize the spatial distribution of population, and improve the ecological quality of the city. [ABSTRACT FROM AUTHOR]

• The correlation between tree-ring δ13C and ecosystem GPP was assessed in a forest. • Early-wood and late-wood δ13C were analyzed separately. • Tree-ring δ13C positively correlated with forest GPP of the current-year. • Early- and late-wood δ13C traced GPP seasonally and annually. Tree-ring carbon (C) isotope composition (δ13C) varies depending on photosynthetic capacity and stomatal conductance, and it is therefore intrinsically associated with vegetation productivity. Still, very little is known about the relationship between tree-ring δ13C values and forest gross primary productivity (GPP). Here, we investigated relationships between tree-ring δ13C, tree-ring width index (RWI) and ecosystem-level GPP in a Korean pine (Pinus koraiensis)-dominated old-growth temperate forest in northeastern China. Specifically, we generated chronologies of RWI and δ13C from early-wood (δ13C EW) and late-wood (δ13C LW) of Korean pines, and we built a 17-year continuous ecosystem-level GPP series using eddy covariance measurements, in which a GPP model was used for gap filling. RWI showed vague relationships with tree-ring δ13C and whole-forest GPP, and it was insensitive to interannual variations in climate at the study site. By contrast, both δ13C EW and δ13C LW showed significant relationships with climate variables and GPP. This suggests that isotopic signals of photosynthetic C uptake were imprinted on the formation of tree rings and that tree-ring δ13C of Korean pine is a better proxy for forest GPP than RWI. In addition, tree-ring δ13C of Korean pines was positively correlated with GPP, with an especially close relationship between δ13C EW and GPP of the current year. This implies that photosynthetic activity, rather than stomatal conductance, was the main driver of tree-ring δ13C signals of Korean pine and it was mainly influenced by the climate conditions of the current year, probably because of a limited use of previously fixed C during early-wood formation. Our findings demonstrate the great potential of tree-ring δ13C for reconstructing GPP timeseries at the centennial scale for forest ecosystems, and they could help to constrain parameters in terrestrial C cycle models to improve predictions of C fluxes. [ABSTRACT FROM AUTHOR]

Large-scale estimation of regional terrestrial gross primacy production (GPP) can improve our understanding of carbon cycle. However, model based estimates are subject to uncertainty. In this study, eight satellite-based models (i.e. VPM, TG, GR, VI, CFIX, ECLUE, VPRM and MODIS-GPP) were compared for GPP simulation in northern China grassland based on 17 site-year eddy covariance measurements, meteorological data and satellite data. Also, the regional spatial–temporal GPP patterns during 2001–2013 in northern China grassland were simulated and their uncertainties were quantified. The results showed that the model simulations exhibited significant correlations with observed GPP across these eight models and R 2 or pseudo R 2 ranged between 0.64 and 0.89 ( p  < .001), ECLUE model performed best. The annual grassland GPP had been growing in fluctuations from 2001 to 2013, with the averaged value of 241.8 g C m −2  a −1 . Substantial spatial heterogeneity existed in grassland GPP, increasing from the west to the east. The disparities of satellite-based model structures resulted in the overall 49% relative uncertainty in regional simulation of GPP, which was high in area with arid dry climate. Our study highlighted the uncertainty traced back to model approaches under different environmental stresses (photosynthetically active radiation, soil water content and air temperature). For the accurate simulation of grassland GPP, uncertainty in alpine grassland and arid cold area on regional grassland GPP should be focused. [ABSTRACT FROM AUTHOR]

The objective of this study was to map and discriminate variations of vegetation net primary production (NPP) from the effects of precipitation variability and thereby identify the effects of grazing on NPP variations in the Xilingol Grassland. Satellite-based simulations of NPP collected over 14-year period and residuals method were used to normalize the effects of interannual precipitation on NPP. A fixed-size moving-window method was developed in this paper to quantify the annual grazing pressure (defined as grazing pressure index, GPI) on the regional scale, using vegetation cover images from 2001 to 2014. Temporal and spatial analysis methods were conducted to show patterns and trends of GPI and NPP residuals (NPP res ), and further to examine the effects of grazing pressure changes on variations of NPP res . Our results showed 80.7% of the study area experienced increasing NPP res and 18.5% with significantly increasing trends ( p  < 0.05). 76.7% of the study area showed decreasing grazing pressure, and 11.8% of areas experienced significantly decreasing trends ( p  < 0.05), with a small patches scattered distribution pattern. The negative correlations between NPP res and GPI were found in 94.6% of the study area, and 41.9% with significant correlation ( p  < 0.05); and long-term decreasing grazing pressure had positive impacts on vegetation NPP. They indicate grazing is a main driving factor of variations in vegetation NPP other than precipitation in Xilingol Grassland. The spatial patterns and temporal trends of NPP res , grazing pressure and responses of NPP res to grazing pressure changes varied among three main grassland types. [ABSTRACT FROM AUTHOR]

Understanding the spatial interactions among multiple ecosystem services is crucial for ecosystem services management. Ecosystem services, including crop production, freshwater supply, aquatic production, net primary production, soil conservation, water conservation, flood regulation, forest recreation, were measured at 1-km grid scale covering the Taihu Lake Basin (TLB) of China. Our objective is to get a comprehensive understanding of the spatial distributions, trade-offs, synergies of multiple ecosystem services across the TLB. Our results found that: 1) majority of ecosystem services were clustered in space and had a similar spatial distribution pattern with the geographical resource endowment. Most of the landscape contributed a high supply of no services, one or two, and a low supply of three to seven services. 2) There were high correlation between forest recreation and freshwater supply and regulating services. Aquatic production had low correlation with other services. 3) The changes of provisioning services led to trade-offs between regulating services and cultural services in the TLB, while synergies mainly occurred among the provisioning service. 4) The spatial relationships of multiple services are consistent at 1-km spatial scale, counties and provinces. This research could help integrate multiple ecosystem services across scales and serve as a reference for decision making. [ABSTRACT FROM AUTHOR]

Spatiotemporal variations in net primary productivity (NPP) reflect the dynamics of water and carbon in the biosphere, and are often closely related to temperature and precipitation. We used the ecosystem model known as the Carnegie-Ames-Stanford Approach (CASA) to estimate NPP of semiarid grassland in northern China counties between 2001 and 2013. Model estimates were strongly linearly correlated with observed values from different counties (slope = 0.76 (p < 0.001), intercept = 34.7 (p < 0.01), R2 = 0.67, RMSE = 35 g C·m-2·year-1, bias = -0.11 g C·m-2·year-1). We also quantified inter-annual changes in NPP over the 13-year study period. NPP varied between 141 and 313 g C·m-2·year-1, with a mean of 240 g C·m-2·year-1. NPP increased from west to east each year, and mean precipitation in each county was significantly positively correlated with NPP—annually, and in summer and autumn. Mean precipitation was positively related to NPP in spring, but not significantly so. Annual and summer temperatures were mostly negatively correlated with NPP, but temperature was positively correlated with spring and autumn NPP. Spatial correlation and partial correlation analyses at the pixel scale confirmed precipitation is a major driver of NPP. Temperature was negatively correlated with NPP in 99% of the regions at the annual scale, but after removing the effect of precipitation, temperature was positively correlated with the NPP in 77% of the regions. Our data show that temperature effects on production depend heavily on recent precipitation. Results reported here have significant and far-reaching implications for natural resource management, given the enormous size of these grasslands and the numbers of people dependent on them. [ABSTRACT FROM AUTHOR]

The interactive relationships between ecological and hydrological processes drive plant performance, community structure, and community succession in arid areas. Yet the nature of potential hydrologic thresholds for responses of vegetation remains poorly understood. In this paper, we report on hydrologic thresholds associated with aboveground net primary production (ANPP) of Haloxylon ammodendron (HA) and sand-fixation region (SFR) between 1987 and 2012 in the ecotone of desert and oasis in the northwest China. In particular, we focused on precipitation and soil moisture dynamics. Our results showed that 1) ANPP and soil moisture of both HA and SFR decreased from 1987 to 2005, and then reached a stable state; 2) nonlinear models provided a much better fit to the data than linear models, highlighting the presence of a discontinuity in vegetation ANPP changes along precipitation and soil moisture gradients; 3) precipitation, accumulated between preceding-year June to current-year August, of <160 mm, or soil moisture at < 1.4–1.5% may decrease ANPP. Our results provide insights into the thresholds of precipitation and soil moisture in a long-term sand-fixing ecosystem, and highlight the importance of legacy precipitation for the recovery of the sand-fixing ecosystem. Consequently, the findings provide useful references for further understanding of the mechanisms of ANPP changes in a sand-fixing ecosystem with changes in precipitation and soil moisture. [ABSTRACT FROM AUTHOR]

The process-based Biome-BGC (BioGeochemical Cycles) model is widely used to simulate the storage and flux of water, carbon, and nitrogen within an ecosystem. However, the Biome-BGC model neglects the existence of underground persistent portion of perennial grasses as well as functional differences between the fast-cycling and persistent roots, which are assumed to negatively impact the accurate estimation of net primary productivity (NPP) of alpine meadow on the Qinghai-Tibet Plateau. This study presents the Perennial Deciduous Grasses (PDG) NPP model developed under the framework of Biome-BGC version 4.2. In the PDG NPP model, underground roots of perennial grasses were partitioned into two portions, the fast-cycling portion and the persistent portion. These two portions were represented by different turnover fractions, carbon to nitrogen ratios, and allocation ratios in the PDG NPP model. Processes primarily involving allocation, maintenance respiration, growth respiration, litterfall and mortality, and transfer growth were modified to account for the differences between fast-cycling and persistent roots. Measured NPP including both aboveground NPP (ANPP) and belowground NPP (BNPP) at Zhenqin Station in Qinghai Province, China, was used to validate estimates of the PDG NPP model. The results showed that the PDG NPP model has a higher estimation accuracy of NPP than the Biome-BGC model. The root mean square error (RMSE) and mean absolute percent error (MAPE) of total NPP estimated from the PDG NPP model were 15.1 gC/m 2 /yr and 12.1%, respectively, whereas those from the Biome-BGC model were 49.8 gC/m 2 /yr and 39.9%, respectively. Both RMSE and MAPE of ANPP and BNPP estimates based on the Biome-BGC model were approximately 3 times those based on the PDG NPP model. Improvements of NPP estimation were mainly attributed to the decreasing estimates of gross primary productivity which was caused by the increase of water stress and decrease of soil N availability simulated during growing seasons. Therefore, the PDG NPP model is more reflective of the limitations of water and nutrients to vegetation growth and productivity when applied to the Qinghai-Tibet Plateau. [ABSTRACT FROM AUTHOR]

We investigated the long-term variations in primary production in Lake Taihu using Moderate Resolution Imaging Spectroradiometer (MODIS) data, based on the Vertically Generalized Production Model (VGPM). We firstly test the applicability of VGPM in Lake Taihu by comparing the results between the model-derived and the in situ results, and the results showed that a strong significant correlation (R2 = 0.753, p < 0.001, n = 63). Then, VGPM was used to map temporal-spatial distributions of primary production in Lake Taihu. The annual mean daily primary production of Lake Taihu from 2003 to 2013 was 1094.06 ± 720.74 mg·C·m−2·d−1. Long-term primary production maps estimated from the MODIS data demonstrated marked temporal and spatial variations. Spatially, the primary production in bays, especially in Zhushan Bay and Meiliang Bay, was consistently higher than that in the open area of Lake Taihu, which was caused by chlorophyll-a concentrations resulting from high nutrient concentrations. Temporally, the seasonal variation of primary production from 2003 to 2013 was: summer > autumn > spring > winter, with significantly higher primary production found in summer and autumn than in winter (p < 0.005, t-test), primarily caused by seasonal variations in water temperature. On a monthly scale, the primary production exerts a clear character of bimodality, increasing from January to May, decreasing in June or July, and finally reaching its highest value during August or September. Wind is another important factor that could affect the spatial variations of primary production in the large, eutrophic and shallow Lake Taihu. [ABSTRACT FROM AUTHOR]

Keeping high productivity and low soil carbon emission (CE) while reducing chemical fertilizer is a significant challenge for crop production in developing modern agriculture. A field experiment was conducted to evaluate the effect of intercropping green manure with reduced N on maize productivity and carbon mitigation in northwestern China. The field experiment used a split-plot design with three replicates. Three cropping patterns were maize-common vetch intercropping (M/V), maize-rape intercropping (M/R), and sole maize (M). Two N fertilizer rates formed the subplots: the conventional N rate was reduced by 25% (N 1 : 270 kg ha−1) and the conventional N rate (N 2 : 360 kg ha−1). The compensation effect (TCE, %) was used to quantify the degree of recovery of intercropping green manure on maize grain yield (GY) under N reduction. It was shown by the results that the negative effect of N reduction on maize GY was compensated by intercropping green manure. Maize-common vetch intercropping integrated with N reduction (M/VN 1) had a more substantial compensation effect (TCE) with the planting years lengthening, and TCE was 1.0%, 0.9%, 4.1%, and 4.9% in 2018, 2019, 2020, and 2021, respectively. The CE of the cropping system was decreased by intercropping green manure compared to sole maize. The CE in the intercropping system was further decreased by N reduction, and the CE of M/RN 1 and M/VN 1 was decreased by 27.7% and 32.1%, respectively, compared to MN 2 (i.e., the control). Finally, the CE of the intercropping system was decreased by M/VN 1 by 6.5% compared to M/RN 1 in 2019–2021. We used the ratio of net primary productivity to carbon emission (NPP/CE) to determine soil C sequestration (CS) potential. Intercropping green manure with N reduction in enhancing soil CS mainly occurred in the late growth period of maize. Over the four years of study, the ratios of NPP/CE for M/RN 1 and M/VN 1 were 23.3% and 32.9%, respectively, greater than that of MN 2 ; also, it of M/VN 1 was increased by 8.9% compared to M/RN 1 in 2019–2021. M/VN 1 had great potential for enhancing soil CS. Thus, adding green manure as an intercropping component in the maize cropping system while reducing chemical N fertilizer is a promising system to enhance maize productivity and carbon mitigation in the farmlands of arid areas. • Green manure intercropping minimized negative N reduction on maize yield. • Compensation effect was enhanced with the extension of time. • N reduction reduced C emission and boosted net ecosystem production of intercrop. • Intercrop with reduced N enhanced C sequestration in the late period of maize. • Intercrop with reduced N is a promising strategy for maize production. [ABSTRACT FROM AUTHOR]

• The applicability of traditional physical models and ML models was compared. • GPP simulated by the ML models were closer to the observed value. • The SVR achieved good prediction accuracy and high stability. • The performance of ANN and LSTM was more sensitive to parameter selection than SVR. As an important part of the energy-matter cycle, gross primary productivity (GPP) reflects the ability of plants to absorb carbon dioxide from the atmosphere. The accurate estimation of GPP is critical to understanding the regional carbon cycle. With the development of machine learning (ML) theory, machine learning models are increasingly used to study complex phenomena with high variability in time and space. In this study, three machine learning models (Support vector regression (SVR), artificial neural network (ANN), and long short-term memory networks (LSTM)) were investigated for predicting GPP in northwest China and compared them with the traditional physical models. Carbon flux, various environmental factors, and maize growth indices were measured in the maize field over five years in northwest China. The rigorous analysis which included statistical comparison and cross-validation for the prediction of GPP confirmed that the machine learning models performed better than traditional physical models. And the SVR model performed the best among the considered ML models with the highest nash-efficiency coefficient and the lowest root mean squared error. The machine learning model also outperformed the traditional physical models on cloudy days and after irrigation. The SVR achieved good prediction accuracy and high stability. With different training data sets, the ANN and LSTM were relatively more sensitive to the training data set. When the training data was sufficient, SVR, ANN and LSTM could achieve similar prediction accuracy, but SVR was slightly higher. When the training data was small, the simulation accuracy of SVR was better than ANN and LSTM. The performance of ANN and LSTM was more sensitive to parameter selection, and the relationship between model performance and parameter selection had no obvious regularity. Based on this comprehensive comparison study, it was elicited that the SVR model can be successfully applied to GPP simulation of maize fields, which provided a new perspective for the application of machine learning modeling in GPP simulation. [ABSTRACT FROM AUTHOR]

Quantifying the driving force is significant to understand the impact of climate variation and human activities on grassland degradation. In this study, we selected net primary productivity (NPP) as an indicator to quantitatively assess the relative roles of climate variation and human activities in China, Mongolia, Pakistan and Uzbekistan from 2000 to 2013. The results showed that 1.9% of grassland areas experienced degradation in Uzbekistan. By contrast, 29.6%, 16%, and 32.5% of grassland areas underwent restoration in China, Mongolia and Pakistan, respectively. Furthermore, 83.9%, 85.1%, 6.7% of restored grassland areas were influenced by climate variation and 65%, 79.1%, 11.6% of degraded areas were affected by human activities in Mongolia, Pakistan and Uzbekistan, respectively. The NPP variation also could be calculated to evaluate the impacts of these factors and results were consistent with the findings based on area. Therefore, climate variation dominated grassland restoration, human activities dominated degradation in Mongolia and Pakistan, and Uzbekistan was just the opposite. In China, 38.5% of the grassland restoration areas was caused by climate variations compared with 38% induced by human activities. On the contrary, 37.4% of grassland degradation was caused by climate variation and 30% resulted from human activities. In addition, the results based on NPP variation revealed that 39.2% of restored grassland areas were influenced by human activities and 38.2% of degraded areas were affected by climate variation. Therefore, climate variation dominated grassland degradation and the driving force of restoration was determined by the effectiveness of environmental protection programs. [ABSTRACT FROM AUTHOR]

Heavy grazing and unsuitable farming practices have led to grassland degradation in northern China. This study examined soil CO 2 efflux (Fc) from five land-use types during the non-growing season on the southeastern edge of the Mongolian Plateau in China. The land-use types included three native vegetation steppes subjected to differing stocking rates [ungrazed (UG), moderately grazed (MG) and heavily grazed (HG)], a fertilized annual cropland (CL) and a perennial pasture (PP) used for haying and winter grazing. Values of Fc were measured at 3-day to 2-week intervals during the non-growing season in two contrasting hydrological years (2012-13 and 2013-14) using closed chambers. The Fc during 1 Oct. 2013 to 30 April 2014 averaged 475 mg C m −2 for all sites compared to a significantly (P < 0.05) lower Fc (102 mg C m −2 ) during 1 Oct. 2012 to 30 April 2013. The seasonal Fc patterns followed the same trend during the two non-growing seasons with greater Fc observed in the autumn and spring freeze-thaw periods compared to the winter permanently frozen period, which accounted for 4.8% of accumulated total non-growing season Fc. The heavily grazed site showed less soil CO 2 efflux compared to UG, MG, PP and CL land-use types due to a larger reduction in gross primary productivity (GPP) compared to ecosystem respiration. Grazing reduced Fc by 23% for MG and 32% for HG compared to UG. Soil CO 2 efflux from the PP land-use type, which was grazed during the non-growing season, was 23% greater than that from the UG and CL land-use types. Air temperature during the non-growing season was the main factor controlling soil CO 2 efflux (R 2 = 0.40, P < 0.001), although soil water content also played a role. Precipitation received during the growing season had a large legacy effect on Fc. Annual weather variation overshadowed the influence of land-use types on Fc. [ABSTRACT FROM AUTHOR]

Temperate wetlands in the Northern Hemisphere have high long-term carbon sequestration rates, and play critical roles in mitigating regional and global atmospheric CO 2 increases at the century timescale. We measured soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) from 11 typical freshwater wetlands (Heilongjiang Province) and one saline wetland (Jilin Province) in Northeast China, and estimated carbon sequestration rates using 210 Pb and 137 Cs dating technology. Effects of climate, net primary productivity, and nutrient availability on carbon sequestration rates (R carbon ) were also evaluated. Chronological results showed that surface soil within the 0–40 cm depth formed during the past 70–205 years. Soil accretion rates ranged from 2.20 to 5.83 mm yr −1 , with an average of 3.84 ± 1.25 mm yr −1 (mean ± SD). R carbon ranged from 61.60 to 318.5 gC m −2  yr −1 and was significantly different among wetland types. Average R carbon was 202.7 gC m −2  yr −1 in the freshwater wetlands and 61.6 gC m −2  yr −1 in the saline marsh. About 1.04 × 10 8 tons of carbon was estimated to be captured by temperate wetland soils annually in Heilongjiang Province (in the scope of 45.381–51.085°N, 125.132–132.324°E). Correlation analysis showed little impact of net primary productivity (NPP) and soil nutrient contents on R carbon , whereas climate, specifically the combined dynamics of temperature and precipitation, was the predominant factor affecting R carbon . The negative relationship observed between R carbon and annual mean temperature (T) indicates that warming in Northeast China could reduce R carbon . Significant positive relationships were observed between annual precipitation (P), the hydrothermal coefficient (defined as P/AT, where AT was accumulative temperature ≥10 °C), and R carbon , indicating that a cold, humid climate would enhance R carbon . Current climate change in Northeast China, characterized by warming and drought, may form positive feedbacks with R carbon in temperate wetlands and accelerate carbon loss from wetland soils. [ABSTRACT FROM AUTHOR]

Bivalve-macroalgae polyculture (BMP) is one of the most important aquacultural means and can maintain a nutrient balance and prevent eutrophication in aquaculture waters. Sansha Bay is a typical mariculture bay on the coasts of southeastern China. The quantity of BMP has expanded in recent decades, while the ecological functions and risks of intensive BMP remain unknown. This study compared the food web structure and ecosystem characteristics between 2010 and 2019 using Ecopath and simulated individual and combined effects of BMP on the ecosystem to detect the ecological functions of polyculture using Ecosim. The results showed that with the expansion of aquaculture in Sansha Bay during the last decade (2010–2019), the total biomass (492.82 g/m2/a) and total flow (19,553.75 g/m2/a) of the ecosystem in 2019 were higher, while the contribution of phytoplankton to primary production accounted for only 13.5%. In Sansha Bay, the total transfer efficiencies were lower than in other aquaculture ecosystems, 4.16% and 4.27% in 2010 and 2019, respectively. The maturity declined slightly, and the ecosystem's primary productivity was still redundant in Sansha Bay based on the analysis of ecological characteristics. The biomass of phytoplankton increased more than two times to remove BMP, which indicated the positive control of eutrophication and harmful algae blooms. However, each culture species should be scientifically evaluated to reduce the redundancy of the ecosystem, improve the system's maturity, and make the aquaculture ecosystem more resilient and stable. • Expansive bivalve-macroalgae polyculture is a double-edged sword. • BMP pattern effectively controls eutrophication and HABs in the aquaculture ecosystem. • Expansive BMP increases the redundancy and ecological risks of aquaculture ecosystem. • The potential risks of expansive macroalgal culture should be paying more attention. • EwE is a useful tool for diagnosing the ecological risks of the aquaculture ecosystem. [ABSTRACT FROM AUTHOR]

The Sanjiang Plain has been experienced significant wetland loss due to expanded agricultural activities, and will be potentially restored by the China National Wetland Conservation Action Plan (NWCP) in future. The objective of this study is to evaluate the impact of future climate warming and wetland restoration on wetland CH4 emissions in northeast China. We used an atmosphere-vegetation interaction model (AVIM2) to drive a modified biogeophysical model (CH4MODwetland), and projected CH4 flux variations from the Sanjiang Plain wetlands under different Representative Concentration Pathway scenarios throughout the 21st century. Model validation showed that the regressions between the observed and simulated CH4 fluxes by the modified model produced an R2 of 0.49 with a slope of 0.87 (p<0.001, n = 237). According to the AVIM2 simulation, the net primary productivity of the Sanjiang Plain wetlands will increase by 38.2 g m-2 yr-1, 116.6 g m-2 yr-1 and 250.4 g m-2 yr-1 under RCP 2.6, RCP 4.5 and RCP 8.5, respectively, by the end of this century. For RCP 2.6, 4.5 and 8.5 scenarios, the CH4 fluxes will increase by 5.7 g m-2 yr-1, 57.5 g m-2 yr-1 and 112.2 g m-2 yr-1. Combined with the wetland restoration, the regional emissions will increase by 0.18‒1.52 Tg. The CH4 emissions will be stimulated by climate change and wetland restoration. Regional wetland restoration planning should be directed against different climate scenarios in order to suppress methane emissions. [ABSTRACT FROM AUTHOR]

Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m-2 d-1 and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m-2 d-1 and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution. [ABSTRACT FROM AUTHOR]

Terrestrial gross primary production (GPP) is a major flux affecting land–atmosphere CO 2 exchange and is important for regulating atmospheric CO 2 concentrations, thereby affecting climate change. Dynamic global vegetation models (DGVMs) are important tools for simulation of vegetation productivity and can be coupled with other components of Earth system models. This study simulated GPP of terrestrial ecosystems in China from 1982 to 2010 utilizing five state-of-the-art DGVMs, which considered increasing atmospheric CO 2 concentrations and climate change. Our models consistently showed an ascending GPP gradient from northwest to southeast China. The annual total GPP in China estimated by the DGVMs (mean = 7.97 PgC yr −1 ; range = 6.14–9.76 PgC yr −1 ) were generally higher than estimations from previous studies. The greatest overestimation of GPP occurred in south China in warm, wet climates. All DGVMs and JU11 indicated that annual GPP in China increased from 1982 to 2010. There was a statistically significant correlation between simulated GPP and temperature in the Tibetan Plateau, which was supported by flux tower measurements. Additionally, there was a significant correlation between simulated GPP and precipitation in east China, though this should be interpreted cautiously. Further research is needed to improve simulations to better account for spatial and temporal variations in GPP at regional scales by improving representations of existing processes and incorporating currently unconsidered processes. [ABSTRACT FROM AUTHOR]

Analyzing the approaches that climatic factors affect the spatial variation of annual gross primary productivity (GPP yr ) would improve our understanding on its spatial pattern. Based on network eddy covariance measurements and published data in literature, we separated GPP yr into radiation use efficiency (RUE) and annual absorbed photosynthesis active radiation (APAR yr ), where APAR yr can be regarded as the product of the fraction of absorbed annual photosynthesis active radiation (FPAR yr ) and annual PAR (PAR yr ). Given that PAR yr affects the spatial variation of GPP yr directly through itself, we investigated factors affecting the spatial variations of RUE and FPAR yr , to reveal how climatic factors affect the spatial variation of GPP yr . Results suggest that the spatial variation of RUE was directly affected by annual mean air temperature (MAT) and annual mean CO 2 mass concentration ( ρ cyr ). The increasing MAT and ρ cyr directly enhanced RUE. The increasing annual precipitation (MAP) directly prompted FPAR yr . Therefore, MAT and ρ cyr affected the spatial variation of GPP yr through altering RUE while the effect of MAP was achieved through altering FPAR yr . Our study could also provide an alternative way for regional GPP yr assessment. [ABSTRACT FROM AUTHOR]

[Display omitted] • A third-order sequence is identified in Eocene shales in the Dongying Depression. • The abundance, type and preservation of organic matter (OM) are intimately related to the sequence development. • Sequence development affects OM accumulation, resulting in differential OM distribution within the Eocene sequence stratigraphic framework. Eocene organic-rich shales in the Dongying Depression, Eastern China, are well preserved and ideal for examining stratigraphic controls on the variations of organic matter (OM). This study concerns the Eocene sedimentary succession in the Dongying Depression and recognizes a third-order sequence based on the stacking patterns of lithofacies, mineralogical changes, and geochemical proxies. The total organic carbon content, Rock-Eval parameters S1 and S2 values, and the chloroform bitumen "A" content are low in the lowstand systems tracts, reaching their maxima within the transgressive systems tracts and gradually decreasing within the highstand systems tracts. The paleoclimate index and ratios of quartz/feldspar, Sr/Ca, TiO 2 /Al 2 O 3 , Cr/Al, Ni/V, Th/U, Zn/Al, and Ni/Al indicate that OM accumulation was influenced by primary productivity, sedimentation rate (SR), and redox conditions, which are intimately related to the sequence development under the influence of tectonics, climate, sediment supply, and relative lake-level fluctuations. The OM distribution is related to the sequence stratigraphy in three ways: (1) climate and relative lake-level fluctuations affected the primary productivity and OM production via algae blooms and terrestrial OM input; (2) relative lake-level fluctuations and sediment supply were influenced by tectonics and climate, causing a change in SR and OM deposition; and (3) relative lake-level fluctuations under different climatic conditions affected the development of anoxic bottom-water favoring OM preservation. [ABSTRACT FROM AUTHOR]

The Late Ordovician−Early Silurian is a convergence period of multiple events in geological history, including dramatic variations of biology, environment, and geology. Extensive research has demonstrated remarkable fluctuations in ocean chemistry, microbial ecosystems, and biogeochemical elemental cycles experienced. However, the spatial-temporal evaluation of marine primary productivity and its links to organic matter accumulation during this key period remain elusive. Here, we present high-resolution marine primary productivity proxy (organic carbon accumulation rates: OCAR) and geochemical data from the Fengtonggang and Qiliao sections deposited in South China and adopted numerous other coeval sections of geochemical data globally, to improve the understanding of these fundamental scientific questions. The results show that all statistical profiles presented broadly high marine primary productivity before and after the Hirnantian glaciation maximum and low values during the glaciation, and a maximum marine primary productivity typically recorded at the uppermost part of the P.pacificus Zone. The C org /P total ratios indicate that oceanic anoxia was ubiquitous during the Late Katian to Early Rhuddanian, but strong spatial heterogeneous during the Hirnantian glaciation maximum. The volcanism and nitrogen availability may have played an important role in regulating the variations of marine primary productivity globally. Besides, redox-controlled phosphorus cycling may have also exerted a remarkable influence on marine primary productivity. The high C org /P total ratios (exceed the Redfield ratio of 106/1) observed in the Wufeng and Longmaxi formations indicate that phosphorus was recycled effectively back to the water column, promoting a positive productivity feedback. By contrast, the low C org /P total ratios found in the Guanyinqiao Bed reflect phosphorus retention in the sediment, limiting phosphorus recycling back to the water column, consequently restricting the primary production. The enhanced marine primary productivity resulted from nutrient regeneration, ocean anoxia, and high sea level may have led to high organic carbon export and preservation. The consistency between the highest marine primary productivity and the first pulse of mass extinction suggests that eutrophication-induced marine anoxia may have played an essential killing mechanism during the first Late Ordovician mass extinction. • A model of the global high-resolution spatial-temporal evolution of marine primary productivity was established. • Marine primary productivity was jointly affected by volcanism, nitrogen availability, and redox－controlled phosphorus cycle. • The enhanced primary productivity, ocean anoxia, and high sea level were likely to control the organic matter accumulation. • Eutrophication－induced marine anoxia may have played a key kill mechanism during the first Late Ordovician mass extinction. [ABSTRACT FROM AUTHOR]

Profound and long-lasting effects on ecosystems have occurred during the past three decades’ development in China, which have threatened the country's sustainable development. The extent to which the biosphere's annual primary production has been altered by human intervention in China, however, is still unclear. Human appropriation of net primary production (HANPP) has gained great attention and been extensively used as an indicator of sustainability in recent decades. Through the combined use of statistical methods and remote-sensing data, the temporal and spatial features of HANPP in China have been investigated. From 2001 to 2010, HANPP in China increased remarkably, rising from 1.85 Pg C/yr to 2.44 Pg C/yr, an average annual increase of 3.15% per year. Population pressure and rapid economic development may explain this trend to a large degree. To achieve the goal of sustainable development and a “beautiful China,” in the coming years more attention should be paid to increasing the efficiency of land use and biomass extraction, and reducing environmental pollution. [ABSTRACT FROM AUTHOR]

Qualification of gross primary production (GPP) of terrestrial ecosystem over large areas is important in understanding the response of terrestrial ecosystem to global climate change. While light use efficiency (LUE) models were widely used in regional carbon budget estimates, few studies consider the effect of diffuse radiation on LUE caused by clouds using a big leaf model. Here we developed a cloudiness index light use efficiency (CI-LUE) model based on the MOD17 model algorithm to estimate the terrestrial ecosystem GPP, in which the base light use efficiency encompassed the cloudiness index, maximum LUE and clear sky LUE. GPP measured at seven sites from 2003 to 2007 in China were used to calibrate and validate the CI-LUE model. The results showed that at forest sites and cropland site the CI-LUE model outperformed the Vegetation Photosynthesis Model (VPM), Terrestrial Ecosystem Carbon flux model (TEC), MOD17 model algorithm driven by in situ meteorological measurements and MODIS GPP products, especially the R 2 of simulated GPP against flux measurements at Dinghushan forest site increased from 0.17 (MODIS GPP products) to 0.61 (CI-LUE). Instead, VPM model had the best agreement with GPP measurements followed by CI-LUE model and lastly TEC model at two grassland sites. Meanwhile, GPP calculated by CI-LUE model has less underestimation under cloudy skies in comparison with MOD17 model. This study demonstrated the potential of the CI-LUE model in improving GPP simulations resulting from the inclusion of diffuse radiation in regulating the base light use efficiency and maximum light use efficiency. [ABSTRACT FROM AUTHOR]

Accurate estimates and partition of evapotranspiration (ET) are difficult but critical for understanding terrestrial ecosystem processes and primary productivity. A multi-year, multi-technique study including the catchment water balance (12 years), a semi-empirical ET model (9 years), eddy covariance (9 years), canopy water balance (1 year) and sap flow (1 year) techniques was conducted to measure ET and its components within an intact forested watershed in the subtropical region of southern China. Over a 9-year period, the estimates of annual ET using the catchment water balance, eddy covariance and semi-empirical ET model techniques were in close agreement, averaging 809.9 ± 62.8, 803.8 ± 36.6 and 801.6 ± 46.9 mm per year, respectively, amounting to an average of approximately 50.2% of the mean annual precipitation. Qualitative similarities in seasonal and diurnal variation were observed between the sap flow and eddy covariance estimates of water flux. Sap flow estimates of transpiration were approximately 60.2% of the annual ET estimated with the eddy covariance technique. Interception evaporation was 31.7% of the total ET and was demonstrated to be a main contributor of total evaporation. Soil evaporation was calculated to be approximately 8.1% of the total annual ET and 4.7% of the annual precipitation. Furthermore, daytime transpiration exhibited a logarithmic increase with increases in the vapour pressure deficit (VPD) on daily scale in both the wet and dry seasons, and tended to level off when the VPD was >1 kPa because of the stomatal regulation of transpiration. Correspondingly, nighttime sap flow amounted to an average of approximately 6% of the total daily sap flow, and no significant correlations with the VPD, air temperature or relative humidity were found. Essential differences among the mechanisms of evaporation and transpiration were demonstrated, suggesting that soil evaporation is primarily a climate-driven process, with air temperature and wind speed as the predominant driving forces. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]