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Leaf δ 13 C is an indicator of water‐use efficiency and provides useful information on the carbon and water balance of plants over longer periods. Variation in leaf δ 13 C between or within species is determined by plant physiological characteristics and environmental factors. We hypothesized that variation in leaf δ 13 C values among dominant species reflected ecosystem patterns controlled by large‐scale environmental gradients, and that within‐species variation indicates plant adaptability to environmental conditions. To test these hypotheses, we collected leaves of dominant species from six ecosystems across a horizontal vegetation transect on the Tibetan Plateau, as well as leaves of Kobresia pygmaea (herbaceous) throughout its distribution and leaves of two coniferous tree species ( Picea crassifolia, Abies fabri ) along an elevation gradient throughout their distribution in the Qilian Mountains and Gongga Mountains, respectively. Leaf δ 13 C of dominant species in the six ecosystems differed significantly, with values for evergreen coniferous<evergreen broadleaved tree<alpine shrub<sedges∼graminoid<xeromorphs. Leaf δ 13 C values of the dominant species and of K. pygmaea were negatively correlated with annual precipitation along a water gradient, but leaf δ 13 C of A. fabri was not significantly correlated with precipitation in habitats without water‐stress. This confirms that variation of δ 13 C between or within species reflects plant responses to environmental conditions. Leaf δ 13 C of the dominant species also reflected water patterns on the Tibetan Plateau, providing evidence that precipitation plays a primary role in controlling ecosystem changes from southeast to northwest on the Tibetan Plateau.

Drought has been one of the most important limiting factors for crop production, which deleteriously affects food security worldwide. The main objective of the present study was to quantitatively assess the effect of drought on the agronomic traits (e.g., plant height, biomass, yield, and yield components) of rice and wheat in combination with several moderators (e.g., drought stress intensity, rooting environment, and growth stage) using a meta-analysis study. The database was created from 55 published studies on rice and 60 published studies on wheat. The results demonstrated that drought decreased the agronomic traits differently between rice and wheat among varying growth stages. Wheat and rice yields decreased by 27.5% and 25.4%, respectively. Wheat grown in pots showed greater decreases in agronomic traits than those grown in the field. Rice showed opposite growing patterns when compared to wheat in rooting environments. The effect of drought on rice increased with plant growth and drought had larger detrimental influences during the reproductive phase (e.g., blooming stage, filling stage, and maturity). However, an exception was found in wheat, which had similar decreased performance during the complete growth cycle. Based on these results, future droughts could produce lower yields of rice and wheat when compared to the current drought.

Abstract Wetlands play a critical role in mitigating carbon emission. However, little is known about soil carbon emission and their environmental controls from inland floodplain wetlands. This study aimed to determine the effects of hydrologic and edaphic controllers (water table depth [ WTD ], soil temperature [ Ts ], and soil water content [ SWC ]) on soil C emission in Dongting Lake wetland, China. One‐year emissions were measured in Carex meadow and mudflat using static chambers during 2013 to 2014, including nonflooded season ( NFs ) and flooded season ( Fs ). The results showed that soil C emission in the Carex meadow and mudflat was 307.8 and 264.3 g C·m −2 ·year −1 , respectively, and 50–66% of soil C were emitted during NFs. Compared with NFs, CO 2 emission was significantly decreased by 57% but CH 4 emission was significantly increased by 38 times during Fs in the Carex meadow. Stepwise regression combined with structural equation model analysis showed that CO 2 and CH 4 flux were mainly influenced by Ts during NFs, and they were controlled by water temperature ( Tw ) during Fs. During NFs, CO 2 flux increased with increasing Ts and SWC but decreased significantly when SWC was over 66% and 52% in the Carex meadow and mudflat, respectively. CH 4 flux showed an emission pulse at SWC and Ts of 65% and 17.2 °C, respectively. These results indicate that flooding significantly inhibited soil CO 2 emission but stimulated CH 4 emission. The continuous decrease of flooding days caused by anthropogenic disturbances may induce soil C loss in Dongting Lake wetlands. , Key Points Soil C emission in Dongting Lake floodplain was 264.3–307.8 g C.m ‐2 .year ‐1 Flooding significantly inhibited soil CO 2 emission but stimulated CH 4 emission The decline of flooding days in Dongting Lake wetlands can potentially increase soil C loss

How to effectively combine remote sensing data with the eddy covariance (EC) technique to accurately quantify gross primary production (GPP) in coastal wetlands has been a challenge and is also important and necessary for carbon (C) budgets assessment and climate change studies at larger scales. In this study, a satellite-based Vegetation Photosynthesis Model (VPM) combined with EC measurement and Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to evaluate the phenological characteristics and the biophysical performance of MODIS-based vegetation indices (VIs) and the feasibility of the model for simulating GPP of coastal wetland ecosystems. The results showed that greenness-related and water-related VIs can better identify the green-up and the senescence phases of coastal wetland vegetation, corresponds well with the C uptake period and the phenological patterns that were delineated by GPP from EC tower (GPPEC). Temperature can explain most of the seasonal variation in VIs and GPPEC fluxes. Both enhanced vegetation index (EVI) and water-sensitive land surface water index (LSWI) have a higher predictive power for simulating GPP in this coastal wetland. The comparisons between modeled GPP (GPPVPM) and GPPEC indicated that VPM model can commendably simulate the trajectories of the seasonal dynamics of GPPEC fluxes in terms of patterns and magnitudes, explaining about 85% of GPPEC changes over the study years (p < 0.0001). The results also demonstrate the potential of satellite-driven VPM model for modeling C uptake at large spatial and temporal scales in coastal wetlands, which can provide valuable production data for the assessment of global wetland C sink/source.

Carbon allocation is an important mechanism through which plants respond to environmental changes. To enhance our understanding of maximizing carbon uptake by controlling planting densities, the carbon allocation module of a process-based model, TRIPLEX-Management, was modified and improved by introducing light, soil water, and soil nitrogen availability factors to quantify the allocation coefficients for different plant organs. The modified TRIPLEX-Management model simulation results were verified against observations from northern Jiangsu Province, China, and then the model was used to simulate dynamic changes in forest carbon under six density scenarios (200, 400, 600, 800, 1000, and 1200 stems ha−1). The mean absolute errors between the predicted and observed variables of the mean diameter at breast height, mean height, and estimated aboveground biomass ranged from 15.0% to 26.6%, and were lower compared with the original model simulated results, which ranged from 24.4% to 60.5%. The normalized root mean square errors ranged from 0.2 to 0.3, and were lower compared with the original model simulated results, which ranged from 0.3 to 0.6. The Willmott index between the predicted and observed variables also varied from 0.5 to 0.8, indicating that the modified TRIPLEX-Management model could accurately simulate the dynamic changes in poplar (Populus spp.) plantations with different densities in northern Jiangsu Province. The density scenario results showed that the leaf and fine root allocation coefficients decreased with the increase in stand density, while the stem allocation increased. Overall, our study showed that the optimum stand density (approximately 400 stems ha−1) could reach the highest aboveground biomass for poplar stands and soil organic carbon storage, leading to higher ecological functions related to carbon sequestration without sacrificing wood production in an economical way in northern Jiangsu Province. Therefore, reasonable density control with different soil and climate conditions should be recommended to maximize carbon sequestration.

Abstract A significant increase in reactive nitrogen (N) added to terrestrial ecosystems through agricultural fertilization or atmospheric deposition is considered to be one of the most widespread drivers of global change. Modifying biomass allocation is one primary strategy for maximizing plant growth rate, survival, and adaptability to various biotic and abiotic stresses. However, there is much uncertainty as to whether and how plant biomass allocation strategies change in response to increased N inputs in terrestrial ecosystems. Here, we synthesized 3516 paired observations of plant biomass and their components related to N additions across terrestrial ecosystems worldwide. Our meta‐analysis reveals that N addition (ranging from 1.08 to 113.81 g m −2  year −1 ) increased terrestrial plant biomass by 55.6% on average. N addition has increased plant stem mass fraction, shoot mass fraction, and leaf mass fraction by 13.8%, 12.9%, and 13.4%, respectively, but with an associated decrease in plant reproductive mass (including flower and fruit biomass) fraction by 3.4%. We further documented a reduction in plant root‐shoot ratio and root mass fraction by 27% (21.8%–32.1%) and 14.7% (11.6%–17.8%), respectively, in response to N addition. Meta‐regression results showed that N addition effects on plant biomass were positively correlated with mean annual temperature, soil available phosphorus, soil total potassium, specific leaf area, and leaf area per plant. Nevertheless, they were negatively correlated with soil total N, leaf carbon/N ratio, leaf carbon and N content per leaf area, as well as the amount and duration of N addition. In summary, our meta‐analysis suggests that N addition may alter terrestrial plant biomass allocation strategies, leading to more biomass being allocated to aboveground organs than belowground organs and growth versus reproductive trade‐offs. At the global scale, leaf functional traits may dictate how plant species change their biomass allocation pattern in response to N addition.

Abstract. Leaf area index (LAI) is an important parameter related to carbon, water, and energy exchange between canopy and atmosphere and is widely applied in process models that simulate production and hydrological cycles in forest ecosystems. However, fine-scale spatial heterogeneity of LAI and its controlling factors have yet to be fully understood in Chinese subtropical forests. We used hemispherical photography to measure LAI values in three subtropical forests (Pinus massoniana–Lithocarpus glaber coniferous and evergreen broadleaved mixed forests, Choerospondias axillaris deciduous broadleaved forests, and L. glaber–Cyclobalanopsis glauca evergreen broadleaved forests) from April 2014 to January 2015. Spatial heterogeneity of LAI and its controlling factors were analysed using geostatistical methods and the generalised additive models (GAMs) respectively. Our results showed that LAI values differed greatly in the three forests and their seasonal variations were consistent with plant phenology. LAI values exhibited strong spatial autocorrelation for the three forests measured in January and for the L. glaber–C. glauca forest in April, July, and October. Obvious patch distribution pattern of LAI values occurred in three forests during the non-growing period and this pattern gradually dwindled in the growing season. Stem number, crown coverage, proportion of evergreen conifer species on basal area basis, proportion of deciduous species on basal area basis, and forest types affected the spatial variations in LAI values in January, while stem number and proportion of deciduous species on basal area basis affected the spatial variations in LAI values in July. Floristic composition, spatial heterogeneity, and seasonal variations should be considered for sampling strategy in indirect LAI measurement and application of LAI to simulate functional processes in subtropical forests.

Plants interact to the seasonality of their environments, and changes in plant phenology have long been regarded as sensitive indicators of climatic change. Plant phenology modeling has been shown to be the simplest and most useful tool to assess phenol–climate shifts. Temperature, solar radiation, and water availability are assumed to be the key factors that control plant phenology. Statistical, mechanistic, and theoretical approaches have often been used for the parameterization of plant phenology models. The statistical approaches correlate the timing of phenological events to environmental factors or heat unit accumulations. The approaches have the simplified calculation procedures, correct phenological mechanism assumptions, but limited applications and predictive abilities. The mechanistic approaches describe plant phenology with the known or assumed “cause–effect relationships” between biological processes and key driving variables. The mechanistic approaches have the improved parameter processes, realistic assumptions, broad applications, and effective predictions. The theoretical approaches assume cost–benefit tradeoff strategies in trees. These methods are capable of capturing and quantifying the potential impacts and consequences of global climate change and human activity. However, certain limitations still exist related to our understanding of phenological mechanisms in relation to (1) interactions between plants and their specific climates, (2) the integration of both field observational and remote sensing data with plant phenology models across taxa and ecosystem type, (3) amplitude clarification of scale-related sensitivity to global climate change, and (4) improvements in parameterization processes and the overall reduction of modeling uncertainties to forecast impacts of future climate change on plant phenological dynamics. To improve our capacity in the prediction of the amplitude of plant phenological responses with regard to both structural and functional sensitivity to future global climate change, it is important to refine modeling methodologies by applying long-term and large-scale observational data. It is equally important to consider other less used but critical factors (such as heredity, pests, and anthropogenic drivers), apply advanced model parameterization and data assimilation techniques, incorporate process-based plant phenology models as a dynamic component into global vegetation dynamic models, and test plant phenology models against long-term ground observations and high-resolution satellite data across different spatial and temporal scales.