Carbon Cycling In Terrestrial Ecosystems Research Articles

Re-identifying farmland carbon neutrality gap under a new carbon counting and the framework of regional interactions in China

The farmland ecosystem, with its numerous material cycles and energy flows, is an important part of the carbon cycle in terrestrial ecosystems. Focusing on the carbon neutrality of farmland is meaningful for mitigating global warming and serving national low-carbon strategies. This study enriches the carbon accounting items of farmland and establishes a new research framework to check the carbon neutrality of farmland from the aspect of regional interactions and, subsequently, the inequality among China's provinces. The results revealed that there is still a great gap in the capability of China's farmland to reach carbon neutrality, with a gap value of up to 10,503 × 104 t C. All of the provinces presented net carbon emissions, and the per unit area carbon neutrality gaps showed spatial regularity decreasing from the coastal regions to the inland areas. Anthropogenic carbon emissions on farmland played a dominant role compared with soil organic carbon. Five provinces had reduced interior-regional carbon emissions through grain trade, and the amounts were especially high for developed regions, such as Guangdong, Zhejiang, Beijing, Shanghai and Jiangsu. Sixteen provinces gained external carbon emissions through trade; these were the less developed regions located mainly in the north, such as Inner Mongolia, Hebei, Jilin, Heilongjiang and Xinjiang. Under regional inequality, 15 provinces added to the net amount of the carbon emissions generated in external regions, with China's megacities adding the highest percentage, especially Beijing, with 389.95 % compared with its original emissions. Inequality showed that most provinces had a moderate status. Sichuan and Hunan experienced weak advantages, and six provinces had disadvantages. Therefore, constructing compensation and trade-based rights and responsibilities traceability mechanisms is important.

Carbon and nitrogen addition-derived enzyme activities in topsoil but nitrogen availability in subsoil controls the response of soil organic carbon decomposition to warming

Subsoil stores the majority of soil organic carbon (SOC), and plays a vital role in the global carbon cycle in terrestrial ecosystems and in regulating climate change. Response of SOC decomposition to temperature warming (TR) is a crucial parameter to predict SOC dynamics under global warming. However, it remains unknown how TR varies across the whole soil profile and responds to exogenous C and N inputs. To assess this, we designed a novel incubation system to measure SOC-derived CO2 efflux across the whole soil column (i.e., 60 cm length), allowing manual addition of 13C-labeled glucose and ammonium nitrate, and incubated it under ambient or warmed temperatures (+4 °C). We found that C addition significantly increased TR in 0–20 cm, 20–40 cm and 40–60 cm by 64.3 %, 68.1 % and 57.2 %, respectively. However, the combined addition of C and N decreased TR by 11.1 % - 15.3 % compared to without anything addition (CK) in the whole soil profile. The effect of N on TR ranged from −22.8 % to −40.4 % in the whole soil profile, and was significantly lower in topsoil than in subsoil. Furthermore, sole N addition significantly promoted TR compared to CK by 79.0 % and 94.7 % in 20–40 cm and 40–60 cm subsoil, only 9.8 % in 0–20 cm topsoil. These results together suggested that TR is sensitive to increasing C availability in the whole soil profile and increasing N availability in 20–60 cm subsoil. Random forest model indicated that soil enzyme activities (explained 21.3 % of the variance) and DOC (explained 11.1 % of the variance) dominantly governed TR in topsoil, but N availability displayed a predominant control of TR in subsoil. Overall, our results suggested that increased C and N availability under climate warming scenarios could further increase the risk of carbon loss especially in subsoil with substrate deficiency, but labile C (e.g., root exudation) input under climate warming and N enrichment could reduce SOC decomposition and benefit for C sequestration by decreasing TR.

Characteristics and influencing factors of carbon source/sink variations in the Zoige grassland wetland ecological function zone on the eastern slope of the Tibetan Plateau

Net ecosystem productivity (NEP) refers to the portion of net primary productivity (NPP) that is available for carbon cycling in terrestrial ecosystems after subtracting photosynthetic carbon consumed by heterotrophic respiration. The amount of the NEP reflects the size of carbon sinks/sources in terrestrial ecosystems, holding great significance for the research of climate change and global carbon cycle. In this study, the NEP of the Zoige grassland wetland ecological function zone (ZGW) on the eastern slope of the Tibetan Plateau from 2001 to 2020 is estimated by using the improved Carnegie-Ames-Stanford Approach model for NPP and a statistical model for soil heterotrophic respiration, based on the meteorological data, vegetation data and socioeconomic data. Additionally, the spatio-temporal variations of the NEP are analyzed, and the influences of natural factors and anthropogenic activities on the NEP are investigated. The results indicate that the ZGW overall plays a role as a carbon sink, and the carbon sink area accounts for approximately 99.3% of the whole ZGW. The annual average NEP in the study area is 447.9 g·m−2, showing a gradual increase at a rate of 5.0 g·m−2·a−1, although the increasing trend is not significant. The carbon sink capacity increased in 93.5% of the ZGW, remained relatively stable in 5.9% of the ZGW, and decreased and significantly decreased in 0.6% of the ZGW. Climate warming and humidifying promote the enhancement of carbon sink capacity in the ecosystem of the ZGW, and precipitation is the dominant climatic factor influencing NEP variations. Natural factors are the determinants of NEP variations, while anthropogenic activities play a secondary role. The implementation of ecological restoration and management projects in the areas along the Yellow River, around the main roads and the core area of wetlands, as well as the continuation of green and coordinated development policies of orderly developing grassland resources, is conducive to enhancing vegetation carbon sink capacity of the ZGW.

Enlarging interface reverses the dominance of fungi over bacteria in litter decomposition

Soil microorganisms are primary decomposers driving carbon and nutrient cycling in terrestrial ecosystems. One prevailing view is that fungi, rather than bacteria, play a predominant role in litter decomposition. However, the roles of bacteria and factors that restrict their activity during decomposition remain unclear. We hypothesized that the limiting activity of bacterial decomposers is associated with litter size beyond chemical quality. To address this gap, we conducted a 180-d decomposition microcosm experiment to investigate the effect of fragment size (large, 1–2 mm; middle, 0.18–0.28 mm; small, <0.07 mm) of oak and pine litters on bacterial or fungal decomposition. Bacterial and fungal decomposition were accelerated with fragment size decrease, suggesting that an interface effect existed between microbial decomposers and litter. Generally, the decomposition ability of bacteria was more sensitive to changes in fragment size compared to fungi. Fungi resulted in faster decomposition of large fragments than bacteria. For small oak litter fragments, bacteria showed faster decomposition than fungi, whereas the opposite was true for small pine litter. Therefore, the decomposition dominance of bacteria and fungi was regulated by fragment size and influenced by the chemical quality of litter, especially the lignin:N ratio. The contrasting decomposition dominances of bacteria versus fungi were likely attributed to filamentous fungi penetrating litter interiors and forming mycelial bridges between scattered litters. Bacteria resided on litter surfaces and even formed biofilms. Consistent with the findings of the microcosm experiment, the proportion of small fragments was greater in the fresh litter layer than in the decomposed layer in the field, and greater in the pine forest than in the oak forest, suggesting the fresh, large, and low-quality litter was preferentially fragmented by fungi. Consequently, the dominance of fungi and bacteria during litter decomposition in the conventional view should be revisited considering the litter size.

The response of soil carbon mineralization losses to changes in rainfall frequency is seasonally dependent in an estuarine saltmarsh

Altered rainfall distribution patterns resulting from climate change have substantial effects on soil carbon (C) cycling in terrestrial ecosystems particularly in water-limited regions. However, how rainfall redistribution affects soil C mineralization (CO2 and CH4 fluxes) in humid regions such as of the coastal saltmarshes remain unclear. We conducted mesocosm experiments in an estuarine saltmarsh in the Yellow River Delta of China, where we simulated three rainfall frequency scenarios (high-frequency, medium-frequency and low-frequency) with the same total rainfall amount in the dry and wet seasons, respectively. Soil CO2 and CH4 fluxes were measured before and after rain frequency treatment during a 40-day period for each season. The decrease in rainfall frequency significantly reduced the mean soil CO2 and CH4 fluxes during the dry season, but had no effect on either flux during the wet season. The seasonal variation in the response of soil C mineralization to rainfall frequency changes could be explained by the changes in antecedent soil water and salinity conditions, soil C substrate, microbial activities and diversity. Thus, the effects of changes in rainfall frequency on soil C mineralization are regulated by season, and should be considered when predicting the future C balance of coastal wetland ecosystems. Furthermore, the shift in precipitation frequency distribution towards increasing heavy rainfall events during the dry season in this region will have a great effect on soil C losses, potentially feeding back into the soil C budget and stability in this estuarine saltmarsh.

Enhanced plant litter impacts nematode communities and carbon use efficiency in a mixed forest

Abstract Rising temperatures and higher atmospheric CO2 levels can potentially increase plant photosynthesis and boost forest productivity, thereby spurring litter inputs to soils on a global scale. Understanding the feedback loops between increased litter inputs and soil biota activity will allow for better prediction of organic matter and carbon (C) cycling in terrestrial ecosystems. Nematodes represent the full spectrum of trophic groups within the soil biota. Accordingly, we studied the link between nematode metabolic activities and plant litter input. For this, different litter input levels were manipulated in a temperate forest, including control zones without litter, natural litter input zones and zones with double, triple and quadruple amounts of natural litter input. Soil top layers (0–10 cm) were then collected to study the responses of nematode communities. Increased litter input positively influenced the abundance and diversity of nematodes, except for plant‐parasitic species. Higher litter input levels led to an enhancement in the ratio of bacterivores to the total of bacterivores and fungivores. More litter increased the K/r strategist ratio among bacterivores and the corresponding nematode metabolic footprints. More litter also stimulated nematode production and respiration components and improved the carbon use efficiency of bacterivores. Moreover, structural equation modelling revealed the crucial role of bacterivores in influencing soil organic C under increased litter input. In conclusion, our study shows that increased plant above‐ground litter input modifies the taxonomic and functional communities of soil nematodes, ultimately regulating carbon cycling in forest soils. Read the free Plain Language Summary for this article on the Journal blog.

Carbon and water vapor exchanges coupling for different irrigated and rainfed conditions on Andean potato agroecosystems

The fundamental exchange of water for carbon lays the groundwork for understanding the interplay between carbon and water cycles in terrestrial ecosystems, providing valuable insights into global water and carbon balances and vegetation growth. Inherent water use efficiency (IWUE) was used as a study framework of the diurnal patterns and degree of coupling of carbon and water exchange to investigate the net ecosystem carbon exchange (NEE) responses of three water regime potato cropping systems [full-irrigation (FI), deficit-irrigation (DI), and rainfed (RF)] in Cundinamarca, Colombia. The eddy covariance method was used to determine CO2 and water fluxes, surface resistances, and the omega decoupling factor (Ω). Additionally, leaf area index (LAI), and specific leaf area (SLA) were assessed to determine the canopy influence on carbon and water exchange. The highest carbon sink activity (NEE = -311.96 ± 12.82 g C m−2) at FI, is primarily attributed to a larger canopy with high autotrophic activity and low internal resistance. This supported a highly coupled and synchronized exchange between evapotranspiration (ET) and gross primary production (GPP), as reflected in the highest IWUE (4.7 mg C kPa s−1 kg−1 H2O). In contrast, the lower sink capacity at DI (NEE = − 17.3 ± 4.6 g C m−2) and the net carbon source activity from RF (NEE = 187.21 ± 3.84 g C m−2) were related to a smaller leaf area available for water and carbon exchange, resulting in lower IWUE (2.3 and 1.01 mg C kPa s−1 kg−1 H2O, respectively) and a decoupled and desynchronized gas exchange caused by unbalanced restrictions on ET and GPP fluxes. These results provide new information on carbon–water interactions in potatoes and improve the understanding of carbon sequestration and drought effects on potato sink activity.

Geographical Environment and Plant Functional Group Shape the Spatial Variation Pattern of Plant Carbon Density in Subalpine-Alpine Grasslands of the Eastern Loess Plateau, China

The carbon density of subalpine-alpine grasslands (SGs) is significantly vital to sustaining the carbon cycle in global terrestrial ecosystems. However, on the Loess Plateau of China, it remains unclear how the geographical environment and plant functional groups affect the spatial variation pattern of plant carbon density in these grasslands. Here, nine typical SGs distributed in the eastern Loess Plateau with elevations ranging from 1720 to 3045 m were investigated. The biomass indices from grassland plants of different functional groups were investigated using plot surveys. The Kriging interpolation method was used to explore the spatial variation pattern of plant carbon density along geographical gradients. We found that (1) the total plant carbon density of SGs was 2676.825 g C/m2 on the eastern plateau, with 37.07%, 37.50%, and 25.43% contributed by the northern, central, and southern areas, respectively. Above- (666.338 g C/m2) and belowground (2010.488 g C/m2) carbon density accounted for 24.9% and 75.11% of the total, respectively. (2) At the horizontal scale, the plant carbon density in the northern SGs was high in the northwest and low in the southeast; in the central SGs, it was low in the northwest and high in the southeast; and in the southern SGs, it was high in the southwest and low in the northeast. At the vertical scale, plant carbon density in all SGs decreased with increasing altitude. (3) The carbon density of grasses, forbs, and sedges was 247.419 g C/m2, 26.073 g C/m2, and 23.471 g C/m2, respectively. With increased latitude, the carbon density of all functional groups (grasses, forbs, and sedges) decreased; the carbon density of forbs and grasses increased with increased longitude, while that of sedges decreased; and with increased altitude, the carbon density of all functional groups increased. In conclusion, the spatial variation pattern of plant carbon density in the SGs was not only influenced by the geographical environment but also by the plant functional groups at the horizontal and vertical scales on the eastern Loess Plateau of China.

Spatiotemporal Modeling of Carbon Fluxes over Complex Underlying Surfaces along the North Shore of Hangzhou Bay

Urban areas contribute to over 80% of carbon dioxide emissions, and considerable efforts are being undertaken to characterize spatiotemporal variations of CO2 (carbon dioxide) at a city, regional, and national level, aiming at providing pipelines for carbon mission reduction. The complex underlying surface composition of urban areas makes process-based and physiology-based models inadequate for simulating carbon flux in this context. In this study, long short-term memory (LSTM), support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were employed to develop and investigate their viability in estimating carbon flux at the ecosystem level. All the data used in our study were derived from the long-term chronosequence observations collected from the flux towers within urban complex underlying surface, along with meteorological reanalysis datasets. To assess the generalization ability of these models, the following statistical metrics were utilized: coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Our analysis revealed that the RF model performed the best in simulating carbon flux over long time series, with the highest R2 values reaching up to 0.852, and exhibiting the smallest RMSE and MAE values at 0.293 μmol·m−2·s−1 and 0.157 μmol·m−2·s−1. As a result, the RF model was chosen for simulating carbon flux at spatial scale and assessing the impact of urban impervious surfaces in the simulation. The results showed that the RF model performs well in simulating carbon flux at the spatial scale. The input of impervious surface area index can improve the performance of the RF model in simulating carbon flux, with R2 values of 84.46% (with the impervious surface area index in) and 83.74% (without the impervious surface area index in). Furthermore, the carbon flux in Fengxian District, Shanghai, exhibited significant spatial heterogeneity: the CO2 flux in the western part of Fengxian District was less than in the eastern part, and the CO2 flux gradually increased from the west to the east. In addition, we creatively introduced the diurnal impervious surface area index based on the Kljun model, and clarified the influence of impervious surface on the spatiotemporal simulation of CO2 flux over the complex urban underlying surface. Based on these findings, we conclude that the RF models can be effectively applied for estimating carbon flux on the complex underlying urban surface. The results of our study reduce the uncertainty in modeling carbon cycling in terrestrial ecosystems, and make the variety of models for the carbon cycling of terrestrial ecosystems more diverse.

Spatial distribution patterns and drivers of above- and below- biomass in Chinese terrestrial ecosystems

Unraveling the dynamics of the global carbon cycle and assessing the sustainability of terrestrial ecosystems are critically dependent on a comprehensive understanding of vegetation biomass. This exploration delves into the pivotal role of biomass within vegetation communities, emphasizing its impact on ecosystem health, productivity, and community structure development. These insights are invaluable for advancing ecological science and conservation efforts. The synthesis of aboveground (AGB) and belowground (BGB) biomass data from 4485 and 3442 locations across China, respectively, collates a wide range of published sources. Integrating this extensive dataset with environmental parameters and applying advanced machine learning techniques facilitated an in-depth analysis of AGB and BGB spatial patterns within China. Techniques such as variance decomposition analysis and piecewise structural equation modeling were employed to dissect the factors contributing to the spatial variability of vegetation biomass. Significant spatial heterogeneity in biomass distribution was uncovered, with vegetation biomass in the northwest markedly lower than in the southern and northeastern regions. It was observed that AGB consistently surpassed BGB. Climatic conditions, soil characteristics, and soil nutrients were found to significantly explain 53 % and 48 % of the total variance in AGB and BGB, respectively. Specifically, solar radiation and soil total nitrogen were identified as critical factors influencing variations in AGB and BGB. The findings offer profound contributions to the understanding of the global carbon balance and the evaluation of terrestrial ecosystems sustainability. Moreover, they provide essential insights into the ecosystems' response mechanisms to global changes, serving as a fundamental reference for future studies on terrestrial ecosystem carbon cycling and carbon sequestration potentials.

Variation in and Regulation of Carbon Use Efficiency of Grassland Ecosystem in Northern China

Ecosystem carbon use efficiency (CUE) is a key parameter in the carbon cycling of terrestrial ecosystems. The magnitude of CUE reflects the ecosystem’s potential for CO2 sequestration. China’s grasslands play an important role in the carbon cycle. Here, we aimed to investigate the comparation of CUE and its environmental regulation among different grassland in Northern China based on eddy covariance carbon fluxes measurements of 31 grassland sites. The results showed that the average CUE of grassland in Northern China was 0.05 ± 0.22, with a range from −0.42 to 0.66. It was demonstrated that there were significant differences in CUE among different grassland types, and CUE values were ranked by type as follows: alpine grassland &gt; temperate meadow steppe &gt; temperate typical steppe &gt; temperate desert steppe, driven by a combination of climatic, soil, and biological factors, with net ecosystem productivity (NEP) having the greatest impact on them. Except for meadow steppes, moisture had a greater impact on grassland CUE in Northern China than temperature. While temperate desert grassland CUE decreased with increasing soil water content (SWC), the CUE of other grassland types increased with higher precipitation and SWC. These findings will advance our ability to predict future grassland ecosystem carbon cycle scenarios.

A spectral mixture analysis based framework for estimating and charactering water use efficiency in heterogeneous drylands

Climate change and irrational land utilization have significantly disrupted the function and structure of the dryland ecosystems, leading to tremendously potential effects on the carbon and water cycles of the global terrestrial ecosystems. Ecosystem-scale water use efficiency (WUE) is a crucial metric that defines the ratio of gross primary productivity (GPP) to evapotranspiration (ET), revealing the coupling relationship between such two ecological processes and playing a significant role in water resource management decision-making. However, quantifying WUE in drylands remains a challenge because of their heterogeneous land surface challenging to characterize model parameters using traditional remote sensing techniques. Here, we developed a process-based framework to estimate GPP and ET, as well as WUE based on physical endmember fractions from spectral mixture analysis (SMA) model. And such framework is used to quantify the spatial and temporal evolution of WUE by optimizing the light use efficiency and assisting in the construction of a GV-Cws model for the separation of transpiration and evaporation processes during computation by utilizing the physically fractional vegetation and soil obtained from SMA in the Hexi Corridor of China, a typical dryland ecosystem. The results demonstrate that our framework can accurately estimate GPP and ET in the Hexi Corridor, as indicated by favorable performance in R2 (0.669, 0.705), RMSE (0.383,0.885) and NSE (0.534, 0.690) respectively. Such results surpass the accuracy of both two Moderate Resolution Imaging Spectroradiometer (MODIS) products and Penman-Monteith-Leuning Evapotranspiration V2 (PML_V2)’s ET product. These advantages highlight that our SMA-based framework can provide highly accurate and distinct characterization of surface carbon and water interaction processes. Meanwhile, the WUE of the Hexi Corridor ecosystem exhibited a positive trend from 2001 to 2017 (0.016 gC·m−2·mm−1·a-1), characterized by simultaneous increases in GPP and ET.

General reversal of N-decomposition relationship during long-term decomposition in boreal and temperate forests

Decomposition of dead organic matter is fundamental to carbon (C) and nutrient cycling in terrestrial ecosystems, influencing C fluxes from the biosphere to the atmosphere. Theory predicts and evidence strongly supports that the availability of nitrogen (N) limits litter decomposition. Positive relationships between substrate N concentrations and decomposition have been embedded into ecosystem models. This decomposition paradigm, however, relies on data mostly from short-term studies analyzing controls on early-stage decomposition. We present evidence from three independent long-term decomposition investigations demonstrating that the positive N-decomposition relationship is reversed and becomes negative during later stages of decomposition. First, in a 10-y decomposition experiment across 62 woody species in a temperate forest, leaf litter with higher N concentrations exhibited faster initial decomposition rates but ended up a larger recalcitrant fraction decomposing at a near-zero rate. Second, in a 5-y N-enrichment experiment of two tree species, leaves with experimentally enriched N concentrations had faster decomposition initial rates but ultimately accumulated large slowly decomposing fractions. Measures of amino sugars on harvested litter in two experiments indicated that greater accumulation of microbial residues in N-rich substrates likely contributed to larger slowly decomposing fractions. Finally, a database of 437 measurements from 120 species in 45 boreal and temperate forest sites confirmed that higher N concentrations were associated with a larger slowly decomposing fraction. These results challenge the current treatment of interactions between N and decomposition in many ecosystems and Earth system models and suggest that even the best-supported short-term controls of biogeochemical processes might not predict long-term controls.

Modeling the Ecosystem Water Use Efficiency of Croplands Using Machine Learning and MODIS Data

The ecosystem water use efficiency (WUE), which is the ratio of carbon gain (gross primary productivity, GPP) to water consumption (evapotranspiration, ET), is widely regarded as a crucial link in coupling the carbon and water cycles of terrestrial ecosystems on a global scale. The estimation of cropland WUE is considerably important for improving agricultural management, simulating the carbon–water cycle processes of croplands, and enhancing crop yields. Currently, the estimate of WUE primary relies on the modeled GPP and ET data. However, differences in model structures and parameter uncertainties cause remarkable differences in the accuracy of the estimated WUE among different models. The eddy covariance technology enables the continuous observation and research of carbon and water fluxes at a site scale, ensuring simulation accuracy. However, the technology is constrained by its limited spatial coverage. In this study, we integrated data from 20 global cropland sites and built 7 machine learning models based on remote sensing to directly estimate cropland WUE, thereby expanding the spatial scope of WUE simulations, omitting the simulation process for obtaining GPP and ET, and ensuring a certain degree of precision. Results show that, compared with other machine learning algorithms, the ensemble learning model has the strongest ability to reproduce cropland WUE, with a determination coefficient of 0.90 and a root mean square error of 1.54–1.75 . These results indicate that a combination of machine learning methods and remote sensing can reasonably simulate the WUE of agricultural ecosystems.

Impacts of Compound Hot–Dry Events on Vegetation Productivity over Northern East Asia

Climate extremes, such as heatwaves and droughts, significantly impact terrestrial ecosystems. This study investigates the influence of compound hot–dry (CHD) events on vegetation productivity in northern East Asia. Four of the most widespread CHD events occurring during the summer from 2003 to 2019 were selected as the focus of this research. We first verified the performance of the Community Land Model version 5 (CLM5) in the region and then conducted factor-controlled experiments using CLM5 to assess the effects of different climate factors on gross primary productivity (GPP) changes during CHD events. Our results show that vegetation productivity exhibits greater sensitivity to CHD events within the transitional climatic zone (TCZ) than in other affected areas. In grassland areas within the TCZ, precipitation deficit is the primary factor leading to the decrease in GPP (explaining 56%–90% of GPP anomalies), while high temperatures serve as a secondary detrimental factor (explaining 13%–32% of GPP anomalies). In high-latitude forests outside the TCZ, high temperature has a more significant impact on suppressing GPP, while the decrease in soil moisture has a synchronously negligible impact on GPP. There are differences in the effects of high solar radiation on grasslands and woodlands during CHD events. It was observed that high radiation benefits trees by increasing the maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax), as well as enhancing photosynthesis, but has a negligible impact on grasses. Furthermore, this study highlights the potential for compound events to impact vegetation productivity more than expected from individual events due to confounding nonlinear effects between meteorological factors. More than 10% of the negative anomalies in GPP during two CHD events in 2017 and 2010 were attributed to these nonlinear effects. These research findings are significant for understanding ecosystem responses to climate extremes and their influence on carbon cycling in terrestrial ecosystems. They can also contribute to more precisely evaluating and predicting carbon dynamics in these regions.

Global photosynthetic capacity jointly determined by enzyme kinetics and eco-evo-environmental drivers

Accurate understanding of global photosynthetic capacity (i.e. maximum RuBisCO carboxylation rate, Vc, max) variability is critical for improved simulations of terrestrial ecosystem photosynthesis metabolisms and carbon cycles with climate change, but a holistic understanding and assessment remains lacking. Here we hypothesized that Vc, max was dictated by both factors of temperature-associated enzyme kinetics (capturing instantaneous ecophysiological responses) and the amount of activated RuBisCO (indexed by Vc, max standardized at 25 ℃, Vc, max25), and compiled a comprehensive global dataset (n = 7339 observations from 428 sites) for hypothesis testing. The photosynthesis data were derived from leaf gas exchange measurements using portable gas exchange systems. We found that a semi-empirical statistical model considering both factors explained 78% of global Vc, max variability, followed by 55% explained by enzyme kinetics alone. This statistical model outperformed the current theoretical optimality model for predicting global Vc, max variability (67%), primarily due to its poor characterization on global Vc, max25 variability (3%). Further, we demonstrated that, in addition to climatic variables, belowground resource constraint on photosynthetic machinery built-up that directly structures the biogeography of Vc, max25 was a key missing mechanism for improving the theoretical modelling of global Vc, max variability. These findings improve the mechanistic understanding of global Vc, max variability and provide an important basis to benchmark process-based models of terrestrial photosynthesis and carbon cycling under climate change.

Distribution Characteristics of Carbon Density in Plant–Soil System of Temperate Steppe and Temperate Desert in the Longzhong Loess Plateau

Grassland, as a key component of the carbon cycle in terrestrial ecosystems, is vital in confronting global climate change. Characterising the carbon density of grassland ecosystems in the Longzhong Loess Plateau is important for accurately assessing the contribution of grasslands to global climate change and achieving the goal of “peak carbon” and “carbon neutral”. In this study, the Longzhong Loess Plateau was used as the research object to explore changes in the plant–soil system carbon density in two grassland types by analysing the aboveground vegetation biomass carbon density, belowground vegetation biomass carbon density, 0–100 cm soil carbon density, and ecosystem carbon density of temperate steppe and temperate desert. The results showed that the vegetation biomass (standing and living, litter, and belowground biomass), soil, and ecosystem carbon densities of the temperate steppe were significantly higher than those of the temperate desert (p &lt; 0.05). Their carbon densities were 700.51, 7612.95, and 8313.45 g·m−2, respectively. The vertical distribution of belowground biomass and soil carbon density in the temperate steppe was significantly higher than that in the temperate desert. The overall trend of belowground biomass carbon density in the temperate steppe and temperate desert showed a gradual decrease, whereas soil carbon density showed a steady increase. More than 91% and 96% of the carbon was stored in soil in the temperate steppe and temperate desert, respectively, and the belowground biomass carbon stock accounted for more than 84% of the total biomass carbon pools in both temperate steppe and temperate desert. Temperate steppe has a significant effect in improving the carbon stock of grassland ecosystems, so ecological protection and restoration of grassland should be strengthened in the future to enhance the capacity of grassland to sequester carbon and increase sinks.

Global spectra of plant litter carbon, nitrogen and phosphorus concentrations and returning amounts

Abstract Litter decomposition is a key ecological process that determines carbon (C) and nutrient cycling in terrestrial ecosystems. The initial concentrations of C and nutrients in litter play a critical role in this process, yet the global patterns of litter initial concentrations of C, nitrogen (N) and phosphorus (P) are poorly understood. We employed machine learning with a global database to quantitatively assess the global patterns and drivers of leaf litter initial C, N and P concentrations, as well as their returning amounts (i.e. amounts returned to soils). The medians of litter C, N and P concentrations were 46.7, 1.1, and 0.1%, respectively, and the medians of litter C, N and P returning amounts were 1.436, 0.038 and 0.004 Mg ha−1 year−1, respectively. Soil and climate emerged as the key predictors of leaf litter C, N and P concentrations. Predicted global maps showed that leaf litter N and P concentrations decreased with latitude, while C concentration exhibited an opposite pattern. Additionally, the returning amounts of leaf litter C, N and P all declined from the equator to the poles in both hemispheres. Synthesis: Our results provide a quantitative assessment of the global concentrations and returning amounts of leaf litter C, N and P, which showed new light on the role of leaf litter in global C and nutrients cycling.

The strengthened impact of water availability at interannual and decadal time scales on vegetation GPP

AbstractWater availability (WA) is a key factor influencing the carbon cycle of terrestrial ecosystems under climate warming, but its effects on gross primary production (EWA‐GPP) at multiple time scales are poorly understood. We used ensemble empirical mode decomposition (EEMD) and partial correlation analysis to assess the WA‐GPP relationship (RWA‐GPP) at different time scales, and geographically weighted regression (GWR) to analyze their temporal dynamics from 1982 to 2018 with multiple GPP datasets, including near‐infrared radiance of vegetation GPP, FLUXCOM GPP, and eddy covariance–light‐use efficiency GPP. We found that the 3‐ and 7‐year time scales dominated global WA variability (61.18% and 11.95%), followed by the 17‐ and 40‐year time scales (7.28% and 8.23%). The long‐term trend also influenced 10.83% of the regions, mainly in humid areas. We found consistent spatiotemporal patterns of the EWA‐GPP and RWA‐GPP with different source products: In high‐latitude regions, RWA‐GPP changed from negative to positive as the time scale increased, while the opposite occurred in mid‐low latitudes. Forests had weak RWA‐GPP at all time scales, shrublands showed negative RWA‐GPP at long time scales, and grassland (GL) showed a positive RWA‐GPP at short time scales. Globally, the EWA‐GPP, whether positive or negative, enhanced significantly at 3‐, 7‐, and 17‐year time scales. For arid and humid zones, the semi‐arid and sub‐humid zones experienced a faster increase in the positive EWA‐GPP, whereas the humid zones experienced a faster increase in the negative EWA‐GPP. At the ecosystem types, the positive EWA‐GPP at a 3‐year time scale increased faster in GL, deciduous broadleaf forest, and savanna (SA), whereas the negative EWA‐GPP at other time scales increased faster in evergreen needleleaf forest, woody savannas, and SA. Our study reveals the complex and dynamic EWA‐GPP at multiple time scales, which provides a new perspective for understanding the responses of terrestrial ecosystems to climate change.

Involvements of mycorrhizal fungi in terrestrial ecosystem carbon cycling

Involvements of mycorrhizal fungi in terrestrial ecosystem carbon cycling