Spatial variations and controls of carbon use efficiency in China's terrestrial ecosystems

Spatio-temporal patterns and control mechanism of the ecosystem carbon use efficiency across the Mongolian Plateau

Carbon use efficiency (CUE) is a crucial parameter that reflects the carbon storage within ecosystems, providing insight into the potential for carbon sequestration at the ecosystem scale and its feedback on climate change. The Mongolian Plateau exemplifies an arid and semi-arid region with a delicate ecological environment that displays heightened sensitivity to global climate change. Understanding the variation and control of CUE is critical for assessing regional carbon. However, few studies have focused on the interaction of factors influencing CUE; furthermore, how CUE responds to climate change and anthropogenic activities remains unclear. Here, we aimed to investigate spatiotemporal patterns and their control mechanisms by generating CUE data based on multi-source remote sensing data. CUE demonstrated a slow downward trend from 2000 to 2018, with higher values in relatively dry-cool regions and lower values in relatively humid-warm regions. Furthermore, CUE values were ranked by biome as follows: grassland > sandy vegetation > cropland > shrubs > forest, driven by climate characteristics, vegetation coverage, water stress, stand age, and management practices. Additionally, climatic factors affected CUE more than the soil variables, except for alpine meadows. The climate factors of precipitation (PPT), index of water availability (IWA) (QPPT = 0.487, QIWA = 0.444), and soil factors, e.g., pH and soil organic content (SOC) (QPH = 0.397, QSOC = 0.372), had the greatest influence on CUE. Finally, most two explanatory factors interacted to effectively enhance the explanation of CUE; the synergy of the IWA and PPT contributed the most to CUE (QIWA∩PPT = 0.604). Moreover, the joint effect of climate change and anthropogenic activities was identified as the major contributor (68 %) to the decline in CUE within this region. This study presents compelling evidence highlighting the importance of considering climate change and anthropogenic disturbances in ecosystem management and conservation efforts in arid and semi-arid regions.

Assessment of water and carbon use efficiency in the SAARC region for ecological resilience under changing climate

According to the United Nations International Children's Emergency Fund (UNICEF) assessment report released in 2021, South Asian countries were among the most vulnerable in the world to the effects of climate change on future generations. Hence it is become crucial to assess how resilient the ecosystems are to these changes. The current study incorporated a novel approach, the Combined Ecological Resiliency Indices Approach (CERIA), to assess ecological resiliency status at various scales during hydroclimatic disturbances. Water and carbon use efficiency (WUE and CUE, respectively) were used as indicators for the examination of ecological resilience. The standardized Precipitation Index (SPI) was adopted to assess the initial stage of hydroclimatic disturbances (meteorological drought). A resiliency analysis based on combined R_d and R_d' indices (derived from WUE and CUE, respectively) revealed that just 1.87% land cover area of the entire SAARC (South Asian Association for Regional Cooperation) region's total 17 land cover classes was resilient to meteorological drought. At the river basin scale, only 16.58% of the total 62 river basins were found resilient. Only 11 (27.46%) of the 21 climate classes on the Koppen climate classification scale were resilient to the hydro-climatic disturbance period. To achieve the United Nations sustainable development goals (SDGs goal-2 and goal-13) of 'No Hunger' and 'Protect the Planet', the Joint Ecosystem Resiliency Enhancement Programme (JEREP) should be adopted in land cover, river basins, or climatic classes of the SAARC region that were highly affected.

Ecosystem carbon use efficiency in ecologically vulnerable areas in China: Variation and influencing factors

Ecologically vulnerable areas (EVAs) are regions with ecosystems that are fragile and vulnerable to degradation under external disturbances, e.g., environmental changes and human activities. A comprehensive understanding of the climate change characteristics of EVAs in China is of great guiding significance for ecological protection and economic development. The ecosystem carbon use efficiency (CUEe) can be defined as the ratio of the net ecosystem productivity (NEP) to gross primary productivity (GPP), one of the most important ecological indicators of ecosystems, representing the capacity for carbon transfer from the atmosphere to a potential ecosystem carbon sink. Understanding the variation in the CUEe and its controlling factors is paramount for regional carbon budget evaluation. Although many CUEe studies have been performed, the spatial variation characteristics and influencing factors of the CUEe are still unclear, especially in EVAs in China. In this study, we synthesized 55 field measurements (3 forestland sites, 37 grassland sites, 6 cropland sites, 9 wetland sites) of the CUEe to examine its variation and influencing factors in EVAs in China. The results showed that the CUEe in EVAs in China ranged from -0.39 to 0.67 with a mean value of 0.20. There were no significant differences in the CUEe among different vegetation types, but there were significant differences in CUEe among the different EVAs (agro-pastoral ecotones < Tibetan Plateau < arid and semiarid areas < Loess Plateau). The CUEe first decreased and then increased with increasing mean annual temperature (MAT), soil pH and soil organic carbon (SOC) and decreased with increasing mean annual precipitation (MAP). The most important factors affecting the CUEe were biotic factors (NEP, GPP, and leaf area index (LAI)). Biotic factors directly affected the CUEe, while climate (MAT and MAP) and soil factors (soil pH and SOC) exerted indirect effects. The results illustrated the comprehensive effect of environmental factors and ecosystem attributes on CUEe variation, which is of great value for the evaluation of regional ecosystem functions.

Urbanization Impact on Regional Sustainable Development: Through the Lens of Urban-Rural Resilience

The urban-rural system is an economically, socially, and environmentally interlinked space, which requires the integration of industry, space, and population. To achieve sustainable and coordinated development between urban and rural systems, dynamic land use change within the urban-rural system and the ecological and social consequences need to be clarified. This study uses system resilience to evaluate such an impact and explores the impact of land use change, especially land conversion induced by urbanization on regional development through the lens of urban-rural resilience. The empirical case is based on the Beijing-Tianjin-Hebei Urban Agglomeration (BTHUA) in China from 2000 to 2020 when there was rapid urbanization in this region. The results show that along with urbanization in the BTHUA, urban-rural resilience is high in urban core areas and low in peripheral areas. From the urban core to the rural outskirts, there is a general trend that comprehensive resilience decreases with decreased social resilience and increased ecological resilience in this region. Specifically, at the city level, comprehensive resilience decreases sharply from the urban center to its 3-5 km buffer zone and then remains relatively stable in the rural regions. A similar trend goes for social resilience at the city level, while ecological resilience increases sharply from the urban center to its 1-3 km buffer zone, and then remains relatively stable in the rural regions in this region, except for cities in the west and south of Hebei. This study contributes to the conceptualization and measurement of urban-rural resilience in the urban-rural system with empirical findings revealing the impact of rapid urbanization on urban-rural resilience over the last twenty years in the BTHUA in China. In addition, the spatial heterogeneity results could be used for policy reference to make targeted resilience strategies in the study region.

Review of Remote Sensing Applications in Grassland Monitoring

The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite.

Effects of Nitrogen Addition on Microbial Carbon Use Efficiency of Soil Aggregates in Abandoned Grassland on the Loess Plateau of China

Soil microbial carbon use efficiency (CUE) plays a crucial role in terrestrial C cycling. However, how microbial CUE responds to nitrogen addition and its mechanisms in soil aggregates from abandoned grassland systems remains poorly understood. In this study, we designed a nitrogen (N) addition experiment (0 (N0), 10 (N1), 20 (N2), 40 (N3), 80 (N4) kg N ha−1yr−1) from abandoned grassland on the Loess Plateau of China. Subsequently, the enzymatic stoichiometry in soil aggregates was determined and modeled to investigate microbial carbon composition and carbon utilization. The vegetation and soil aggregate properties were also investigated. Our research indicated that soil microbial CUE changed from 0.35 to 0.53 with a mean value of 0.46 after N addition in all aggregates, and it significantly varied in differently sized aggregates. Specifically, the microbial CUE was higher and more sensitive in macro-aggregates after N addition than in medium and micro-aggregates. The increasing microbial CUE in macro-aggregates was accompanied by an increase in soil organic carbon and microbial biomass carbon, indicating that N addition promoted the growth of microorganisms in macro-aggregates. N addition significantly improved the relative availability of nitrogen in all aggregates and alleviated nutrient limitation in microorganisms, thus promoting microbial CUE. In conclusion, our study indicates that soil microbial CUE and its influencing factors differ among soil aggregates after N addition, which should be emphasized in future nutrient cycle assessment in the context of N deposition.

Climate change impacts plant carbon balance, increasing mean future carbon use efficiency but decreasing total forest extent at dry range edges

Carbon use efficiency (CUE) represents how efficient a plant is at translating carbon gains through gross primary productivity (GPP) into net primary productivity (NPP) after respiratory costs (R_a ). CUE varies across space with climate and species composition, but how CUE will respond to climate change is largely unknown due to uncertainty in R_a at novel high temperatures. We use a plant physiological model validated against global CUE observations and LIDAR vegetation canopy height data and find that model-predicted decreases in CUE are diagnostic of transitions from forests to shrubland at dry range edges. Under future climate scenarios, we show mean growing season CUE increases in core forested areas, but forest extent decreases at dry range edges, with substantial uncertainty in absolute CUE due to uncertainty in R_a . Our results highlight that future forest resilience is nuanced and controlled by multiple competing mechanisms.

Linking Remotely Sensed Carbon and Water Use Efficiencies with In Situ Soil Properties

The capacity of terrestrial ecosystems to sequester carbon dioxide (CO2) from the atmosphere is expected to be altered by climate change and CO2 fertilization, but this projection is limited by our understanding of how the soil system interacts with plants. Understanding the soil–vegetation interactions is essential to assess the magnitude and response of terrestrial ecosystems to the changing climate. Here, we used soil profile and satellite data to explore the role that soil properties play in regulating water and carbon use by plants. Data obtained for 19 terrestrial ecosystem sites in a warm temperate and humid climate were used to investigate the relationship between remotely sensed data and soil physical and chemical properties. Classification and regression tree results showed that in situ soil carbon isotope (δ13C), and soil order were significant predictors (r2 = 0.39, mean absolute error (MAE) = 0 of 0.175 gC/KgH2O) of remotely sensed water use efficiency (WUE) based on the Moderate Resolution Imaging Spectroradiometer (MODIS). Soil extractable calcium (Ca), and land cover type were significant predictors of remotely sensed carbon use efficiency (CUE) based on MODIS and Landsat data-(r2 = 0.64–0.78, MAE = 0.04–0.06). We used gross primary productivity (GPP) derived from solar-induced fluorescence (SIF) data, based on the Orbiting Carbon Observatory-2 (OCO-2), to calculate WUE and CUE (referred to as WUESIF and CUESIF, respectively) for our study sites. The regression tree analysis revealed that soil organic matter and soil extractable magnesium (Mg), δ13C, and soil silt content were the important predictors of both WUESIF (r2 = 0.19, MAE = 0.64 gC/KgH2O) and CUESIF (r2 = 0.45, MAE = 0.1), respectively. Our results revealed the importance of soil extractable Ca, soil carbon (S13C is a facet of soil carbon content), and soil organic matter predicting CUE and WUE. Insights gained from this study highlighted the importance of biotic and abiotic factors regulating plant and soil interactions. These types of data are timely and critical for accurate predictions of how terrestrial ecosystems respond to climate change.

Climate Change Will Reduce the Carbon Use Efficiency of Terrestrial Ecosystems on the Qinghai-Tibet Plateau: An Analysis Based on Multiple Models

The carbon use efficiency (CUE) of ecosystems, expressed as the ratio of net primary production (NPP) and gross primary production (GPP), is extremely sensitive to climate change and has a great effect on the carbon cycles of terrestrial ecosystems. Climate change leads to changes in vegetation, resulting in different CUE values, especially on the Qinghai-Tibet Plateau, one of the most climate-sensitive regions in the world. However, the change trend and the intrinsic mechanism of climate effects on CUE in the future climate change scenario are not clear in this region. Based on the scheme of the coupled model intercomparison project (CMIP6), we analyze the simulation results of the five models of the scenario model intercomparison project (ScenarioMIP) under three different typical future climate scenarios, including SSP1-2.6, SSP3-7.0 and SSP5-8.5, on the Qinghai-Tibet Plateau in 2015–2100 with methods of model-averaging to average the long-term forecast of the five several well-known forecast models for three alternative climate scenarios with three radiative forcing levels to discuss the CUE changes and a structural equations modeling (SEM) approach to examine how the trends in GPP, NPP, and CUE related to different climate factors. The results show that (1) GPP and NPP demonstrated an upward trend in a long time series of 86 years, and the upward trend became increasingly substantial with the increase in radiation forcing; (2) the ecosystem CUE of the Qinghai-Tibet Plateau will decrease in the long time series in the future, and it shows a substantial decreasing trend with the increase in radiation forcing; and (3) the dominant climate factor affecting CUE is temperature of the factors included in these models, which affects CUE mainly through GPP and NPP to produce indirect effects. Temperature has a higher comprehensive effect on CUE than precipitation and CO2, which are negative effects on CUE on an annual scale. Our finding that the CUE decreases in the future suggests that we must pay more attention to the vegetation and CUE changes, which will produce great effects on the regional carbon dynamics and balance.

Thermal acclimation of leaf dark respiration of Larix gmelinii: A latitudinal transplant experiment

The response of tree leaf dark respiration (R_d) to temperature change is important in modeling and predicting forest carbon (C) cycling under climate change, but it has rarely been investigated in nature. We conducted a field experiment by transplanting the trees of Larix gmelinii - the dominant tree species in Chinese boreal forests from four latitudinal sites to a common garden near the warm border of its range. Our objective was to explore thermal acclimation of R_d and the underlying mechanisms by comparing the temperature-response curves of R_d and related leaf traits both in the common garden and at the original sites. We found that warming significantly decreased R_d and its temperature sensitivity (Q₁₀), which changed across the growing season and were correlated with the mean annual temperature of the original sites, reflecting a combination of both short- and long-term respiratory acclimation to warming. The trees from the southern sites tended to have higher thermal acclimation of R_d and lower Q₁₀ than that from the northern sites. R_d and Q₁₀ were highly correlated with the concentrations of leaf nitrogen and soluble sugars, which may be used as proxies for assessing thermal acclimation of respiration. Considering both short- and long-term thermal acclimation of R_d likely improves the prediction of forest C cycling in response to climate change.

Quantitative analysis of the contributions of land use change and CO2 fertilization to carbon use efficiency on the Tibetan Plateau

Carbon use efficiency (CUE) is a key element in the vegetation carbon cycle, and determines how vegetation allocates carbon. Here, our research provides the spatio-temporal variations of CUE on the Tibetan Plateau (TP) based on ensemble simulations from 12 terrestrial ecosystem models. Moreover, the experimental design of simulations adds one time-varying driver at a time, thus quantitative analysis of the response of CUE to climate factors (i.e., temperature, precipitation and radiation), land use and land cover change (LULCC), and CO₂ fertilization can be investigated. Results show that average CUE value of the multi-model simulations (0.583 ± 0.064) on the TP is slightly lower than that derived from the satellite-based product, the Moderate Resolution Imaging Spectroradiometer (0.646). However, CUE varies greatly among models due to differences in simulating plant photosynthetic productivity and respiratory rate, with range of 0.489-0.661. LULCC and CO₂ fertilization contribute 4.24% and 0.79% of the annual mean CUE, respectively. Among the climatic factors, temperature and precipitation have positive correlations with CUE over most areas of the TP while solar radiation shows a negative impact.