Aboveground biomass stocks of species-rich natural forests in southern China are influenced by stand structural attributes, species richness and precipitation.

Forests, the largest terrestrial carbon sinks, play an important role in carbon sequestration and climate change mitigation. Although forest attributes and environmental factors have been shown to impact aboveground biomass, their influence on biomass stocks in species-rich forests in southern China, a biodiversity hotspot, has rarely been investigated. In this study, we characterized the effects of environmental factors, forest structure, and species diversity on aboveground biomass stocks of 30 plots (1 ha each) in natural forests located within seven nature reserves distributed across subtropical and marginal tropical zones in Guangxi, China. Our results indicate that forest aboveground biomass stocks in this region are lower than those in mature tropical and subtropical forests in other regions. Furthermore, we found that aboveground biomass was positively correlated with stand age, mean annual precipitation, elevation, structural attributes and species richness, although not with species evenness. When we compared stands with the same basal area, we found that aboveground biomass stock was higher in communities with a higher coefficient of variation of diameter at breast height. These findings highlight the importance of maintaining forest structural diversity and species richness to promote aboveground biomass accumulation and reveal the potential impacts of precipitation changes resulting from climate warming on the ecosystem services of subtropical and northern tropical forests in China. Notably, many natural forests in southern China are not fully stocked. Therefore, their continued growth will increase their carbon storage over time.

Topography and life stage regulate species aboveground biomass distribution in combination in a tropical montane rainforest.

Understanding the correlation between topography, species biomass and species life stage would allow forest managers to better foster carbon storage in forests. Using census data from a 60-ha plot in south China, we first quantified aboveground biomass (AGB) and how much it varied among different topographies. Next, the specific contribution of 42 dominant species to total aboveground biomass was analyzed for each of the different topographies. We also explored whether these species-topography associations changed, in terms of species' AGB distribution, during each of the three life stages (sapling, juvenile, adult) for these 42 species. Our results showed that the average AGB was 368.79 Mg ha-1 and that it varied noticeably among the four topographies (Low valley, Slope, High valley and Ridge, which were classified by using fuzzy C-mean clustering based on elevation, convexity, and slope). AGB was significantly lower in the two valleys than in the two other topographies. Of the 42 species, 88.1% showed topographic preferences, and 78.6% appeared to exhibit topographic preferences that changed with life stage. Our work highlights the importance of topography and life stage in species biomass distribution and suggests that different combinations of species and life stages, based on species topographic preferences across life stages, may be better suited in different tropical rainforest topographies to maximize carbon storage overall.

Spatial pattern of forest aboveground biomass and its environmental influencing factors in Qinling-Daba Mountains, central China.

Accurate estimation of forest aboveground biomass (AGB) is crucial for understanding and managing forest ecosystems in the context of global environmental change, and also provides a scientific basis for national and regional ecological planning and carbon emission reduction policies. In order to investigate the regional pattern of forest AGB and its influencing factors in central China, a total of 469 sample plots were measured along four sample transects and on six mountains in field survey. The results showed that: 1) Two longitudinal distribution patterns of forest AGB were found, and one was that the AGB in the Qinling Mountains and the Daba Moutains gradually decreased from east to west, and the other was that the AGB in the areas between the two mountains gradually increased from east to west. The latitudinal distribution pattern of the forest AGB showed an "increasing-decreasing-increasing-decreasing" trend from south to north. The altitudinal distribution pattern showed a "first increasing-then decreasing" pattern with increasing altitude. 2) The influence of each factor on the spatial pattern of forest AGB was manifested as temperature > precipitation > HAI > topography, indicating that the spatial pattern of forest AGB in central China was the result of the interaction of climate, human activities and natural factors.

Assessing the impact of climate warming on tree species composition and distribution in the forest region of Northeast China.

Global climate change has markedly influenced the structure and distribution of mid-high-latitude forests. In the forest region of Northeast China, the magnitude of climate warming surpasses the global average, which presents immense challenges to the survival and habitat sustainability of dominant tree species. We predicted the potential changes in aboveground biomass, dominant tree species composition, and distribution in the forest region of Northeast China over the next century under different climatic conditions encompassing the current scenario and future scenarios (RCP2.6, RCP4.5, and RCP8.5). Forest ecosystem process model LINKAGES 3.0 was used to simulate dynamic changes in species-level aboveground biomass under four climate scenarios at the homogeneous land-type unit level. The potential spatial distribution of tree species was investigated based on three indicators: extinction, colonization, and persistence. The results showed that LINKAGES 3.0 model effectively simulated the aboveground biomass of 17 dominant tree species in the forest region of Northeast China, achieving a high accuracy with R² = 0.88. Under the current, RCP2.6, and RCP4.5 climate scenarios, the dominant tree species presented gradual increases in aboveground biomass, whereas under RCP8.5, an initial increase and subsequent decline were observed. With increasing warming magnitude, cold-temperate coniferous tree species will gradually be replaced by other temperate broad-leaved tree species. Furthermore, a large temperature increase under RCP8.5 will likely produce a significant contraction in the potential distribution range of tree species like Larch, Scotch pine, Ribbed birch, Spruce and Fir, while most temperate broad-leaved tree species and Korean pine are expected to demonstrate a northward migration. These findings provide guidance for enhancing the adaptability and resilience of forest ecosystems in middle and high latitudes and addressing the threats posed by climate warming.

Optimizing Agronomic Management Practices for Enhanced Radiation Capture and Improved Radiation Use Efficiency in Winter Wheat.

Increased aboveground biomass is contingent on enhanced photosynthetically active radiation intercepted by the canopy (IPAR), improved radiation use efficiency (RUE), or both. We investigated whether and how optimized agronomic management practices promote IPAR and RUE. Four integrated agronomic management treatments, i.e., local traditional practice (LP), improved local traditional practice (ILP), high-yield agronomic management (HY), and improved high-yield agronomic management (IHY), were compared over two wheat (Triticum aestivum L.) growing seasons. The average grain yield obtained with IHY was 96% relative to that of HY and was 7% and 23% higher than that with ILP and LP, respectively. Both HY and IHY consistently supported large values of the leaf area index and IPAR fraction, thereby increasing total IPAR. Treatment HY showed increased pre-anthesis RUE, manifested as a higher specific leaf nitrogen content and whole-plant N nutrition index at anthesis. The highest pre-anthesis aboveground biomass was obtained with HY due to the highest pre-anthesis IPAR and RUE. Along with a higher canopy apparent photosynthetic rate, IHY produced higher post-anthesis aboveground biomass due to its higher post-anthesis IPAR and RUE. Treatment IHY had a slightly lower total IPAR but a similar total RUE and harvest index, thus producing a slightly lower grain yield relative to HY. These results demonstrate that the optimized agronomic management practice used under IHY effectively enhances radiation capture and improves radiation utilization. Additionally, the net profit for IHY was higher than that for HY, ILP, and LP by 8%, 11%, and 88%, respectively. Considering the high grain yield, high RUE and high economic benefits, we recommend IHY as the agronomic management practice in the target region, although further study of improvements in pre-anthesis RUE is required.

Principle, technique and application of grassland improvement.

Global grassland degradation poses a significant threat to the sustainable socio-economic development of humanity. However, this trend can be effectively mitigated through scientifically sound and rational grassland improvement measures. Grassland improvement utilizes the theory of pratacultural science, especially the theory of the four production levels of grassland agro-ecosystems, to solve the fundamental contradiction between the seasonal imbalance of grassland supply and livestock demand through integrates a number of improvement techniques. In order to clarify the implementation subject, target and specific measures of grassland improvement and to improve the science and efficiency of management and conservation, we classify grassland improvement into four types according to the target, scale and attributes of grassland and livestock feedback mechanism. Grassland improvement is generally based on one key technology, with multiple technologies used in combination, and synergy or superposition formed between integrated technologies. Individual technologies mainly include enclosure of livestock, reclamation, ripping, overseeding, fertilization, irrigation, fire work and grazing management, while integrated technologies are a combination of two or more technologies. Compared to individual techniques, the integrated approach resulted in a significant enhancement of community aboveground biomass by 17-38% and species richness by 2-24%, with no discernible impact on soil properties in the short term. The establishment of a standardized grazing-based improvement process while adhering to the principles of improvement after utilization, comprehensiveness, standardization and scale consistence to improve the structure and function of grassland ecosystems. Strategy of grassland improvement reassesses "nuisance" species as "citizens" of the ecosystem because they supply productivity, species biodiversity and other ecosystem services, and they can be managed at an unharmful and even benefitful level through identifying the ecological and economic thresholds.

Water addition but not reduction alters plant biomass-diversity relationship.

The relationship between plant aboveground biomass and diversity typically follows a unimodal pattern, showing a positive correlation in resource-poor habitats and a negative correlation in resource-rich environments. Precipitation is a crucial resource for both plant biomass and diversity in terrestrial ecosystems. However, the impact of precipitation changes on the relationship between plant biomass and diversity remains unclear. We conduct a water addition field experiment in a semiarid grassland and identify a unimodal relationship between plant biomass and species richness under ambient conditions. Water addition delays the declining phase of this unimodal curve and shift it upward compared to ambient conditions. Our meta-analysis of water addition experiments conducted across major biomes worldwide (grassland, shrubland, desert, and forest) supports this finding, while water reduction does not alter the biomass-diversity relationship. Water addition increases biomass in all climate but only increases species richness in arid and semiarid climate. Similarly, water reduction decreases biomass in all climate but only reduces species richness in arid and semiarid climate. Species richness in dry subhumid and humid climate does not change significantly. Furthermore, our field experiment shows that water addition increases plant diversity while decreasing soil inorganic nitrogen levels. The increase in one resource, such as water, leads to the scarcity of another, such as nutrient, thus postponing the declining phase of the plant biomass-diversity relationship typically observed in resource-rich habitats. Our research contributes to predicting the plant biomass-diversity relationship under changing precipitation conditions and highlights the complex interplay between water availability, nutrient level, and plant diversity.

Effects of nitrogen addition and Bothriochloa ischaemum and Lespedeza davurica mixture on plant chlorophyll fluorescence and community production in semi-arid grassland.

Background: Grass-legume mixture can effectively improve productivity and stimulate overyielding in artificial grasslands, but may be N-limited in semi-arid regions. This study investigated the effects of N addition on chlorophyll fluorescence and production in the grass-legume mixtures community. Methods: An N addition experiment was conducted in the Bothriochloa ischaemum and Lespedeza davurica mixture community, with seven mixture ratios (B0L10, B2L8, B4L6, B5L5, B6L4, B8L2, and B10L0) according to the sowing abundance of B.ischaemum and L.davurica and four N addition levels, N0, N25, N50, and N75 (0,25,50,75kgNhm-2 a-1), respectively. We analyzed the response of chlorophyll fluorescence parameters of the two species, the rapid light-response curves of chlorophyll fluorescence, as well as aboveground biomass (AGB) and overyielding. Results: Our results showed that the two species showed different photosynthetic strategies, with L.davurica having significantly higher initial fluorescence (Fo), effective photochemical quantum yield of PSII (ΦPSII), and coefficient of photochemical fluorescence quenching (qP) than B. ischaemum, consisting with results of rapid light-response curves. N addition and mixture ratio both had significant effects on chlorophyll fluorescence and AGB (p<0.001). The ΦPSII and qP of L.davurica were significantly lowest in B5L5 and B6L4 under N addition, and the effect of N varied with mixture ratio. The photosynthetic efficiency of B. ischaemum was higher in mixture than in monoculture (B10L0), and ΦPSII was significantly higher in N50 than in N25 and N50 at mixture communities except at B5L5. The community AGB was significantly higher in mixture communities than in two monocultures and highest at B6L4. In the same mixture ratio, the AGB was highest under the N50. The overyielding effects were significantly highest under the N75 and B6L4 treatments, mainly attributed to L.davurica. The partial least squares path models demonstrated that adding N increased soil nutrient content, and complementary utilization by B.ischaemum and L.davurica increased the photosynthetic efficiency. However, as the different photosynthetic strategies of these two species, the effect on AGB was offset, and the mixture ratio's effects were larger than N. Our results proposed the B6L4 and N50 treatments were the optimal combination, with the highest AGB and overyielding, moderate grass-legume ratio, optimal community structure, and forage values.

Regional differences in the impact paths of climate on aboveground biomass in alpine grasslands across the Qinghai-Tibet Plateau.

Alpine grasslands on the Qinghai-Tibet Plateau (QTP) play an essential role in water conservation, biodiversity protection and climate feedback, with aboveground biomass (AGB) serving as a crucial indicator of grassland health and functionality. While previous studies have independently explored the phenological differences, cumulative effects, and spatial variability of climatic impacts on biomass/productivity in alpine grasslands, the cascading effects regarding climate and phenology on AGB still present knowledge gaps. Here, using peak AGB measurements, remote sensing and gridded climate data in the QTP alpine grasslands during 2002-2018, we systematically analyzed the impact paths of climatic variables (i.e., cumulative precipitation, CP; growing degree-days, GDD) and phenology-mediated paths (start and peak date of the growing season, SOS and POS) on AGB and their regional differences. During the preseason (pre60) or the growing season (sos-pos), climate primarily directly impacted variations in AGB across different climatic regions, although a phenology-mediated path by which climate indirectly affected AGB existed (i.e., GDDsos-pos â†’ POS â†’ AGB). Three general patterns were revealed: In the plateau temperate arid regions, an increase in CPpre60 significantly promoted AGB (path coefficients w = 0.61-0.71), whereas an increase in GDDpre60 inhibited AGB (w = -0.42 ~ -0.49); In the plateau sub-cold regions, increases in both CPsos-pos and GDDsos-pos significantly promoted AGB, respectively (w = 0.46-0.81 and w = 0.37-0.70); Similarly, in the plateau temperate arid or semi-arid regions, increases in CPsos-pos also significantly promoted the AGB (w = 0.56-0.73). This study highlights that the water and heat accumulation mainly exert direct impacts on alpine grassland AGB across various climatic regions and phenological stages, providing insights into the mechanism driving AGB by climate and phenology during spring and summer.

Extraction of vegetation disturbance range using aboveground biomass estimated from Sentinel-2 imagery in coal mining areas with high groundwater table.

Coal mining in regions characterized by high groundwater table markedly predisposes to surface subsidence and water accumulation, thereby engendering substantial harm to surface vegetation, soil, and hydrological resources. Developing effective methods to extract surface disturbance information aids in quantitatively assessing the comprehensive impacts of coal mining on land, ecology, and society. Due to the shortcomings of traditional indicators in reflecting mining disturbance, vegetation aboveground biomass (AGB) is introduced as the primary indicator for extracting the mining disturbance range. Taking the Huaibei Coal Base as an example, Sentinel-2 MSI imagery is firstly used to calculate spectral factors and vegetation indices. Multiple machine learning algorithms are coupled to perform remote sensing estimation and spatial inversion of vegetation AGB based on measured samples of vegetation AGB. Secondly, an Orientation Distance-AGB (OD-AGB) curve is constructed outward from the center of subsidence water areas (SWA), with the Boltzmann function used for curve fitting. According to the location of the inflection point of the curve, the boundary points of vegetation disturbance are identified, and then the disturbance range is divided. The results show that (1) the TV-SVM model, utilizing total variables and support vector machine, achieves the highest estimation accuracy, with σMAE and σRMSE values of 208.47 g/m2 and 290.19 g/m2, respectively, for the validation set. (2) Thirty-six effective disturbance areas, totaling 29.89 km2, are identified; the Boltzmann function provides a good fit for the OD-AGB curve, with an R2 exceeding 0.8 for typical disturbance areas. (3) Analysis of general statistical laws indicates that disturbance distance conforms to the general characteristics of normal distribution, exhibiting boundedness and directional heterogeneity. The research is expected to provide scientific guidance for hierarchical zoning management, land reclamation, and ecological restoration in coal mining areas with high groundwater table.

Differences of soil carbon pools and crop growth across different typical agricultural fields in China: The role of geochemistry and climate change.

Carbon storage and the aboveground biomass of farmland provide practical significance for understanding global changes and ensuring food production and quality. Based on soil carbon storage, aboveground biomass, climate, geochemistry, and other data from 19 farmland ecological stations in China, we analysed the distribution characteristics of farmland carbon storage in topsoil and aboveground biomass. We notably revealed the response direction and degree of climate and geochemical factors to farmland carbon storage in topsoil and aboveground biomass. The results indicated that the average carbon stocks of farmland in different regions ranged from 0.28 to 7.91 kg m-2, the average fresh weight of the aboveground biomass (FAB) ranged from 1370.64 to 5997.28 g m-2, and the average dry weight of the aboveground biomass (DAB) ranged from 119.95 to 852.35 g m-2. The least angle regression (LARS) and the best subsection selection regression (BSS) showed that evapotranspiration and extreme low temperatures were significant climatic factors affecting carbon sequestration and aboveground biomass on long-time scales. The linear mixed-effects model (LMM) further showed that AN and AP had significant long-term effects on carbon sequestration and aboveground biomass (p < 0.05), with AN having the highest contribution to SOC%, FAB, and DAB. The structural equation model (SEM) showed that carbon sequestration and aboveground biomass in agricultural fields were significantly positively correlated (p < 0.05). Moreover, the climate had a less direct contribution to carbon sequestration and above-ground biomass compared to geochemistry (PCc < 0.1

[Climate change affects plant aboveground biomass by regulating the growth periods in alpine grasslands of the Tibetan Plateau, China]. / 气候变化通过调控青藏高原高寒草原植物生育期影响地上生物量积累.

Climate change significantly affects plant biomass and phenological occurrence time in alpine grasslands of Tibetan Plateau. The changes in phenological periods are closely related to the length of vegetative and reproductive growth periods, which may further affect aboveground biomass accumulation. In this study, based on fixed-point observations of plant biomass and phenology as well as the corresponding climatic data from 1997 to 2020 in the alpine grasslands of Tibetan Plateau, we used statistical methods such as ordinary linear regression and piecewise structural equation model to explore the characteristics of interannual climate change in the study area, the variation trends of plant biomass and phenological periods, and the correlations between biomass and phenological and climatic factors. The results showed that mean annual temperature and annual precipitation in the study area increased significantly from 1997 to 2020, suggesting a clear "warm-wet" trend. Aboveground biomass and relative biomass of Stipa sareptana var. krylovii (the dominant species) decreased significantly. However, absolute and relative biomass of subdominant species (Kobresia humilis) increased significantly, indicating that the dominance of K. humilis increased. The warm-wet climates enhanced aboveground biomass accumulation of K. humilis by extending the period of reproductive growth. Mean annual temperature and annual precipitation decreased aboveground biomass of S. sareptana by shortening the length of vegetative growth period. In a word, the warmer and wetter climate significantly affected aboveground biomass accumulation by regulating the changes in the phenological period, and the interspecific difference in their response resulted in a larger change in community composition. This study area may show a trend from alpine grassland to alpine meadow, and thus further works are urgently needed.

Mapping and analyzing the spatiotemporal dynamics of forest aboveground biomass in the ChangZhuTan urban agglomeration using a time series of Landsat images and meteorological data from 2010 to 2020.

In the context of global warming, there is a substantial demand for accurate and cost-effective assessment and comprehensive understanding of forest above-ground biomass (AGB) dynamics. The timeliness and low cost of optical remote sensing data enable the mapping of large-scale forest AGB dynamics. However, mapping forest AGB with optical remote sensing data presents challenges primarily due to data uncertainty and the complex nature of the forest environment. Previous studies have demonstrated the potential of meteorological data in enhancing forest AGB mapping. To accurately capture the dynamics of forest AGB, we initially acquired Landsat datasets, digital elevation model (DEM), and meteorological datasets (temperature, humidity, and precipitation) from 2010 to 2020 in Changsha-Zhuzhou-Xiangtan urban agglomeration (CZT) located in Hunan Province, China. Spectral variables (SVs), including spectral bands and vegetation indices, were extracted from Landsat images, while meteorological variables (MVs) were derived from the monthly meteorological data using the Savitzky-Golay (S-G) filtering algorithm. Additionally, terrain variables (TVs) were also extracted from the DEM data. Three modelling models, multiple linear regression (MLR), K nearest neighbor (KNN) and random forest (RF), were developed for mapping the dynamics of forest AGB in CZT. The result revealed that MVs have the potential to improve forest AGB mapping. Integration of MVs into the models resulted in a significant reduction in root mean square error (RMSE) ranging from 32.85 % to 19.25 % compared to utilizing only SVs. However, minimal improvement was observed with the inclusion of TVs due to negligible topographic relief within the study area. An upward trend of forest AGB in CZT was observed during this period, which can be attributed to the effective implementation of government environmental protection policies. It is confirmed that the meteorological data has significant contribution to forest AGB mapping, thereby endorsing advancements in forest resource monitoring and management programs.

Biomass recovery of coastal young mangrove plantations in Central Thailand.

Around one-third of the world's most carbon-rich ecosystems, mangrove forests, have already been destroyed in Thailand owing to coastal development and aquaculture. Improving these degraded areas through mangrove plantations can restore various coastal ecosystem services, including CO2 absorption and protection against wave action. This study examines the biomass of three coastal mangrove plantations (Avicennia alba) of different ages in Samut Prakarn province, Central Thailand. Our aim was to understand the forest biomass recovery during the early stages of development, particularly fine root biomass expansion. In the chronosequence of the mangrove plantations, woody biomass increased by 40% over four years from 79.7 ± 11.2 Mg C ha-1 to 111.7 ± 12.3 Mg C ha-1. Fine root biomass up to a depth of 100 cm was 4.47 ± 0.33 Mg C ha-1, 4.24 ± 0.63 Mg C ha-1, and 6.92 ± 0.32 Mg C ha-1 at 10, 12, and 14 year-old sites, respectively. Remarkably, the fine root biomass of 14-year-old site was significantly higher than those of the younger sites due to increase of the biomass at 15-30 cm and 30-50 cm depths. Our findings reveal that the biomass recovery in developing mangrove plantations exhibit rapid expansion of fine roots in deeper soil layers.

Climate and soil stressed elevation patterns of plant species to determine the aboveground biomass distributions in a valley-type Savanna.

Introduction: Extreme environments such as prolonged high temperatures and droughts can cause vulnerability of vegetation ecosystems. The dry-hot valleys of Southwestern China, known for their extremely high annual temperature, lack of water, and unique non-zonal "hot island" habitat in the global temperate zone, provide exceptional sites for studying how plant adapts to the prolonged dry and hot environment. However, the specific local biotic-environment relationships in these regions remain incompletely elucidated. The study aims to evaluate how valley-type Savanna vegetation species and their communities adapt to long-term drought and high-temperature stress environments. Methods: The study investigated the changes in species diversity and communities' aboveground biomass of a valley-type Savanna vegetation along an elevation gradient of Yuanmou dry-hot valley in Jinsha River basin, southwest China. Subsequently, a general linear model was utilized to simulate the distribution pattern of species diversities and their constituent biomass along the elevation gradient. Finally, the RDA and VPH mothed were used to evaluate the impacts and contributions of environmental factors or variables on the patterns. Results and discussion: The field survey reveals an altitudinal gradient effect on the valley-type Savanna, with a dominant species of shrubs and herbs plants distribution below an elevation of 1700m, and a significant positive relationship between the SR, Shannon-Wiener, Simpson, and Pielou indices and altitudes. Relatively, the community aboveground biomass did not increase significantly with elevation, which was mainly due to a decreased biomass of herbaceous plants along the elevation. Different regulators of shrub-herbaceous plant species and their functional groups made different elevation patterns of species diversity and aboveground biomass in valley-type Savannas. Herbaceous plants are responsible for maintaining species diversity and ensuring stability in the aboveground biomass of the vegetation. However, the influence of shrubs on aboveground biomass became more pronounced as environmental conditions varied along the altitudinal gradient. Furthermore, species diversity was mainly influenced by soil and climatic environmental factors, whereas community biomass was mainly regulated by plant species or functional groups. The study demonstrates that the spatial pattern of valley-type Savanna was formed as a result of different environmental responses and the productive capacity of retained plant species or functional groups to climate-soil factors, highlighting the value of the Yuanmou dry-hot Valley as a microcosm for exploring the intricate interactions between vegetation evolution and changes in environmental factors.

Monitoring and influencing factors of grassland livestock overload in Xinjiang from 1982 to 2020.

It is crucial to estimate the theoretical carrying capacity of grasslands in Xinjiang to attain a harmonious balance between grassland and livestock, thereby fostering sustainable development in the livestock industry. However, there has been a lack of quantitative assessments that consider long-term, multi-scale grass-livestock balance and its impacts in the region. This study utilized remote sensing and empirical models to assess the theoretical livestock carrying capacity of grasslands. The multi-scale spatiotemporal variations of the theoretical carrying capacity in Xinjiang from 1982 to 2020 were analyzed using the Sen and Mann-Kendall tests, as well as the Hurst index. The study also examined the county-level grass-livestock balance and inter-annual trends. Additionally, the study employed the geographic detector method to explore the influencing factors. The results showed that: (1) The overall theoretical livestock carrying capacity showed an upward trend from 1982 to 2020; The spatial distribution gradually decreased from north to south and from east to west. In seasonal scale from large to small is: growing season > summer > spring > autumn > winter; at the monthly scale, the strongest livestock carrying capacity is in July. The different grassland types from largest to smallest are: meadow > alpine subalpine meadow > plain steppe > desert steppe > alpine subalpine steppe. In the future, the theoretical livestock carrying capacity of grassland will decrease. (2) From 1988 to 2020, the average grass-livestock balance index in Xinjiang was 2.61%, showing an overall increase. At the county level, the number of overloaded counties showed an overall increasing trend, rising from 46 in 1988 to 58 in 2020. (3) Both single and interaction factors of geographic detectors showed that annual precipitation, altitude and soil organic matter were the main drivers of spatiotemporal dynamics of grassland load in Xinjiang. The results of this study can provide scientific guidance and decision-making basis for achieving coordinated and sustainable development of grassland resources and animal husbandry in the region.

Long-term species-level observations indicate the critical role of soil moisture in regulating China's grassland productivity relative to phenological and climatic factors.

As a sensitive indicator of climate change and a key variable in ecosystem surface-atmosphere interaction, vegetation phenology, and the growing season length, as well as climatic factors (i.e., temperature, precipitation, and sunshine duration) are widely recognized as key factors influencing vegetation productivity. Recent studies have highlighted the importance of soil moisture in regulating grassland productivity. However, the relative importance of phenology, climatic factors, and soil moisture to plant species-level productivity across China's grasslands remains poorly understood. Here, we use nearly four decades (1981 to 2018) of in situ species-level observations from 17 stations distributed across grasslands in China to examine the key mechanisms that control grassland productivity. The results reveal that soil moisture is the strongest determinant of the interannual variability in grassland productivity. In contrast, the spring/autumn phenology, the length of vegetation growing season, and climate factors have relatively minor impacts. Generally, annual aboveground biomass increases by 3.9 to 25.3 g∙m2 (dry weight) with a 1 % increase in growing season mean soil moisture across the stations. Specifically, the sensitivity of productivity to moisture in wetter and colder environments (e.g., alpine meadows) is significantly higher than that in drier and warmer environments (e.g., temperate desert steppes). In contrast, the sensitivity to the precipitation of the latter is greater than the former. The effect of soil moisture is the most pronounced during summer. Dominant herb productivity is more sensitive to soil moisture than the others. Moreover, multivariate regression analyses show that the primary climatic factors and their attributions to variations in soil moisture differ among the stations, indicating the interaction between climate and soil moisture is very complex. Our study highlights the interspecific difference in the soil moisture dependence of grassland productivity and provides guidance to climate change impact assessments in grassland ecosystems.

UAV and Satellite Synergies for Mapping Grassland Aboveground Biomass in Hulunbuir Meadow Steppe.

Aboveground biomass (AGB) is an important indicator of the grassland ecosystem. It can be used to evaluate the grassland productivity and carbon stock. Satellite remote sensing technology is useful for monitoring the dynamic changes in AGB across a wide range of grasslands. However, due to the scale mismatch between satellite observations and ground surveys, significant uncertainties and biases exist in mapping grassland AGB from satellite data. This is also a common problem in low- and medium-resolution satellite remote sensing modeling that has not been effectively solved. The rapid development of uncrewed aerial vehicle (UAV) technology offers a way to solve this problem. In this study, we developed a method with UAV and satellite synergies for estimating grassland AGB that filled the gap between satellite observation and ground surveys and successfully mapped the grassland AGB in the Hulunbuir meadow steppe in the northeast of Inner Mongolia, China. First, based on the UAV hyperspectral data and ground survey data, the UAV-based AGB was estimated using a combination of typical vegetation indices (VIs) and the leaf area index (LAI), a structural parameter. Then, the UAV-based AGB was aggregated as a satellite-scale sample set and used to model satellite-based AGB estimation. At the same time, spatial information was incorporated into the LAI inversion process to minimize the scale bias between UAV and satellite data. Finally, the grassland AGB of the entire experimental area was mapped and analyzed. The results show the following: (1) random forest (RF) had the best performance compared with simple regression (SR), partial least squares regression (PLSR) and back-propagation neural network (BPNN) for UAV-based AGB estimation, with an R2 of 0.80 and an RMSE of 76.03 g/m2. (2) Grassland AGB estimation through introducing LAI achieved higher accuracy. For UAV-based AGB estimation, the R2 was improved by an average of 10% and the RMSE was reduced by an average of 9%. For satellite-based AGB estimation, the R2 was increased from 0.70 to 0.75 and the RMSE was decreased from 78.24 g/m2 to 72.36 g/m2. (3) Based on sample aggregated UAV-based AGB and an LAI map, the accuracy of satellite-based AGB estimation was significantly improved. The R2 was increased from 0.57 to 0.75, and the RMSE was decreased from 99.38 g/m2 to 72.36 g/m2. This suggests that UAVs can bridge the gap between satellite observations and field measurements by providing a sufficient training dataset for model development and AGB estimation from satellite data.

Productivity and Species Diversity of Plant Communities Are Higher inside than outside the West Ordos National Nature Reserve, Northern China.

Nature reserves play an important role in the protection of biological habitats and the maintenance of biodiversity, but the performance and mechanisms of desert steppe nature reserves in improving plant community productivity, biodiversity and soil nutrient content are still largely unknown. To investigate the conservation effects of desert steppe nature reserve management on plant productivity and biodiversity, we compared the plant biomass, diversity and soil nutrient content inside and outside the West Ordos National Nature Reserve through sample survey, biomass determination, diversity index calculation and soil nutrient content determination. We found the following: (1) The aboveground biomass and belowground biomass of plant communities in the nature reserve were significantly larger than those outside the reserve; and the aboveground biomass of plant communities in shrub-steppe was significantly larger than that of herb grassland in both the nature reserve and the outside of the reserve. (2) The Margalef richness index, Shannon-Wiener index and Simpson index were significantly greater in the reserve than outside the nature reserve. In the desert steppe, the establishment of the nature reserve increased the α-diversity of the plant community. (3) The soil organic carbon (SOC) and soil total nitrogen (STN) were greater in the nature reserve than outside the reserve, and for the 10-20 cm and 20-40 cm soil layers, the SOC and STN were significantly greater in the core protected zone of the reserve than outside the reserve. The reserve significantly increased the nutrient content of the deeper soil layers. (4) The aboveground biomass of the plant community had a significant positive linear relationship with the species richness index, the Shannon index, and the Simpson index. There was a positive correlation between the diversity of the plant community and the soil nutrients. In summary, the nature reserve improved local plant productivity, biodiversity and the soil nutrient content, and the soil nutrient content in deeper soil layers may be the driving factor for the increase in productivity and biodiversity, which deepens our understanding of the conservation effectiveness of the nature reserve and its mechanisms.

Aboveground Biomass Mapping and Analysis of Spatial Drivers in the Qinghai-Xizang Plateau Permafrost Zone: A Case Study of the Beilu River Basin.

Aboveground biomass (AGB) serves as a crucial measure of ecosystem productivity and carbon storage in alpine grasslands, playing a pivotal role in understanding the dynamics of the carbon cycle and the impacts of climate change on the Qinghai-Xizang Plateau. This study utilized Google Earth Engine to amalgamate Landsat 8 and Sentinel-2 satellite imagery and applied the Random Forest algorithm to estimate the spatial distribution of AGB in the alpine grasslands of the Beiliu River Basin in the Qinghai-Xizang Plateau permafrost zone during the 2022 growing season. Additionally, the geodetector technique was employed to identify the primary drivers of AGB distribution. The results indicated that the random forest model, which incorporated the normalized vegetation index (NDVI), the enhanced vegetation index (EVI), the soil-adjusted vegetation index (SAVI), and the normalized burn ratio index (NBR2), demonstrated robust performance in regards to AGB estimation, achieving an average coefficient of determination (R2) of 0.76 and a root mean square error (RMSE) of 70 g/m2. The average AGB for alpine meadows was determined to be 285 g/m2, while for alpine steppes, it was 204 g/m2, both surpassing the regional averages in the Qinghai-Xizang Plateau. The spatial pattern of AGB was primarily driven by grassland type and soil moisture, with q-values of 0.63 and 0.52, and the active layer thickness (ALT) also played a important role in AGB change, with a q-value of 0.38, demonstrating that the influences of ALT should not be neglected in regards to grassland change.