Search Results (99)

Climate change threatens the stability of temperate grassland ecosystems in Inner Mongolia, a core part of the Eurasian Steppe, by driving widespread shifts in plant species distributions. Agropyron cristatum (L.) Gaertn., a dominant native perennial herb in Inner Mongolian steppes, is ecologically vital for degraded grassland restoration and forage supply, but its response to future climate change is unclear. Here, we used an optimized MaxEnt model to assess its potential distribution under current and future climate scenarios. We processed 228 initial occurrence records into 112 valid points, selected 11 non-collinear environmental variables, optimized model parameters with the R package ENMeval, and projected distributions for the 2050s and 2100s under CMIP6 SSP2-4.5 and SSP5-8.5 scenarios, while quantifying habitat fragmentation with landscape metrics. We found that annual mean temperature and annual precipitation dominate A. cristatum distribution (total contribution ~87%), with current highly suitable habitats concentrated in central-eastern Inner Mongolia. Future scenarios show stable core suitable habitats with northward and westward shifts, habitat fragmentation will slightly increase. Our findings clarify the climate response of A. cristatum and support its conservation and adaptive grassland management. Full article

This study was conducted in the Xinjiang region, China, along the Sino-Kazakh border, an area recognized as high-risk for locust outbreaks and characterized by ongoing shifts in dominant pest species. This study systematically examined the structural characteristics of locust communities across different grassland types and identified the underlying environmental driving mechanisms. Field surveys were conducted to assess the diversity characteristics, density variations, and niche width of the locust communities across the different grassland types. The locust community in the mountain meadows had a significantly lower Shannon diversity index compared with the other grassland types (p < 0.05). Although the Simpson dominance index and Pielou evenness index were also the lowest in the mountain meadows, these differences were not statistically significant (p > 0.05). Permutational multivariate analysis of variance (PermANOVA) revealed highly significant differences in locust density among the grassland types (p = 0.001). Ecological niche analysis revealed stronger interspecific competition in the lowland meadow, and weaker competition in the temperate steppe-enriched deserts and temperate desert grasslands. Structural equation modeling and random forest analysis identified soil organic, plant total potassium, and soil pH as the key factors driving locust community structure across grassland types. This study clarifies the diversity patterns of locust communities in the Xinjiang region along the Sino-Kazakh border and provides empirical data to better understand locust community structure and distribution. It also offers a scientific basis for developing sustainable locust management strategies. Full article

Grassland aboveground biomass (AGB) is a key indicator of grassland ecosystem structure and function, and its accurate monitoring is of great importance for assessing grassland ecological conditions and supporting sustainable grassland management. Traditional biomass estimation methods based on vegetation indices (VIs) often suffer from saturation due to canopy shading. However, comparative studies on VI saturation and the saturation height of AGB detectable by different indices remain limited. In this study, we evaluated 12 commonly used VIs based on field-measured AGB and hyperspectral data in the Hulunbuir meadow steppe. Relationships between vertically accumulated biomass and VIs were analyzed to identify optimal AGB fitting models and to determine the saturation height of each index. Results showed that vertical distribution of AGB followed a unimodal pattern, with biomass peaking at approximately 36 cm in this region. This study employed four models (namely the Linear model, the Logarithmic model, the Power Function model and the Gompertz model) to fit the relationship between the vegetation index and AGB. Among them, Gompertz models consistently outperformed other models, indicating saturation across all indices. Based on saturation height, the 12 VIs were classified into two groups: ARVI, GNDVI, NDRE, OSAVI, and SAVI saturated at 40 cm, whereas DVI, EVI, MSAVI, NDPI, NDVI, RVI, and VARI maintained sensitivity up to 50 cm, demonstrating a stronger anti-saturation capacity. NDVI and NDPI exhibited the highest fitting accuracy and resistance to saturation. These findings validate the saturation limitations of VIs and provide guidance for selecting appropriate indices to improve the accuracy of grassland biomass retrieval. Full article

Grassland aboveground biomass (AGB) is a crucial component of the global carbon budget in climate change studies. Precise estimation of the AGB of grassland ecosystems is essential to better understand the carbon cycle and to improve grassland conservation as well as to achieve optimal growth. Traditional vegetation indices (VIs) derived from remote sensing often saturate at medium-high biomass levels, limiting estimation accuracy. In this study, we introduced a novel AGB estimation framework by explicitly integrating near-infrared radiance (L_nir) with UAV-based hyperspectral vegetation indices (VIs×Lnir), which effectively alleviated saturation effects commonly observed in conventional VI-based models. Field measurements and hyperspectral imagery were collected in a temperate meadow steppe, and model performance was evaluated using leave-one-out cross-validation (LOOCV). The proposed VIs×Lnir model achieved the highest accuracy (R² = 0.72, RMSE = 7.52 g/m²), outperforming conventional VIs-based (R² < 0.39, RMSE > 11.13 g/m²) estimations. The study further investigated the results of fAPAR_green-related VIs×Lnir model, which yielded higher AGB estimation accuracy than that using NDVI×Lnir. Furthermore, we examined the influence of plant diversity using Menhinick’s index (DMn) and found that AGB estimation uncertainty was lowest when DMn ranged from 0.2 to 0.4, likely due to reduced spectral mixing and optimal canopy structural homogeneity. Under both lower (DMn < 0.2) and higher diversity conditions (DMn > 0.4), AGB could still be estimated, but with increased uncertainty likely caused by insufficient spectral variability at low diversity and stronger spectral mixing at high diversity. This study demonstrates the potential of incorporating L_nir into UAV hyperspectral analysis to enhance grassland AGB estimation and provides insights into the role of biodiversity in remote sensing-based biomass monitoring. Full article

Grasslands, as dominant terrestrial ecosystems, significantly influence soil microbial communities through alterations in soil properties. However, their effects on spatial patterns of soil microbial communities are still under-investigated. To address this, we quantified taxa–area (TAR) and node–area (NAR) relationships for prokaryotic and fungal communities across temperate steppe (TS), alpine steppe (AS), and alpine meadow (AM). Our findings indicated that the spatial turnover of both prokaryotic and fungal communities were higher in alpine steppe and alpine meadow than in temperate steppe, mirroring the gradient of soil environmental heterogeneity. Notably, overall species richness increased logarithmically with sampling area in all grasslands; in striking contrast, co-occurring richness exhibited an increasing and then decreasing trend in AS and AM, but declined monotonically in TS, indicating that microbial interaction networks collapse once a critical spatial threshold is exceeded regulated by ecosystem type and environmental heterogeneity. In growing season, the stochastic dominance in prokaryotic assembly (Normalized stochasticity ratio = 0.71–0.89) and deterministic dominance in fungal assembly (Normalized stochasticity ratio = 0.23–0.37) can be explained by their differences in niche breadth and migration rate. These scale-dependent biogeographic patterns demonstrate that grassland type impacts distinct interactions and spatial patterns of microbial communities. These findings provide novel insights into a comprehensive understanding of how grassland type mediates soil microbial community. Full article

Examining the long-term spatiotemporal distribution of grassland types and their transitions is crucial for better understanding regional and global changes. Most research in this field has examined the spatial distribution, temporal dynamics of grasslands, and their causes as a unified entity. This study predicted the distribution of nine major grassland types in Xinjiang under three climate change scenarios from 2041 to 2100 based on 1980s grassland maps, field data in 2023, and 28 factors. The total area of the nine grassland types showed a decreasing trend from 2041 to 2100. The lowland meadow (LM), temperate meadow steppe (TMS), temperate steppe desert (TSD), temperate desert steppe (TDS), and mountain meadow (MM) expanded, while significant declines occurred in alpine meadow (AM), alpine steppe (AS), temperate desert (TD), and temperate steppe (TS). Among cumulative contribution rate of the 28 factors examined in this study, NDVI, vegetation type, slope, elevation, soil_symbol, soil_ph, Bio1, Bio5, Bio8, Bio9, Bio10, Bio12, Bio13, Bio15, and Bio18 played important roles in most grassland types. LM, TD, and AS grassland were found to be more sensitive to E (environment), while AM, TDS, and TSD were more influenced by T (temperature). The distributions of MM and TMS are significantly influenced by the combined effects of all three categories of factors. For TS, the impacts of both temperature and environmental factors are substantial. These findings provided a robust foundation for conservation planning and the sustainable management of grassland ecosystems in temperate and alpine regions. Full article

Under the combined effects of climate change, overexploitation, and intense grazing, temperate steppe in northern China is experiencing increasing deterioration, which is typified by a shift from structural degradation to functional disruption. Accurately tracking steppe degradation using remote sensing technology has emerged as a crucial scientific concern. Prior research failed to integrate ecosystem structure and function and lacked reference baselines, relying only on individual indicators to quantify degradation. To resolve these gaps, this study established a novel degradation evaluation index system integrating ecosystem structure and function, incorporating vegetation community distribution and proportions of degradation-indicator species to define reference states and quantify degradation severity. Analyzed spatiotemporal evolution and drivers across the temperate typical steppe (2013–2022). Key findings reveal (1) non-degraded and slightly degraded areas dominated (75.57% mean coverage), showing an overall fluctuating improvement trend; (2) minimal transitions between degradation levels, with stable conditions prevailing (59.52% unchanged area), indicating progressive degradation reversal; and (3) natural factors predominated as degradation drivers. The integrated structural–functional framework enables more sensitive detection of early degradation signals, thereby informing more effective steppe restoration management. Full article

The composition, architecture, and plant traits of temperate steppe communities are intricately associated with environmental factors. However, most studies primarily focus on aboveground observations, often overlooking the critical role of belowground root systems. Here we conducted a field survey at a large-regional scale to investigate the composition of temperate steppe communities and plant root traits. Cluster analysis, correspondence analysis and Pearson correlation coefficient matrix method were employed to classify vegetation associations based on plant community composition and root traits. The principal driving and limiting factors shaping plant root communities were systematically investigated. The results showed that the temperate steppe was categorized into three community subtypes: meadow steppe, typical steppe, and desert steppe, comprising five plant groups and thirteen plant associations. The RLFS analysis, based on belowground architectural and functional traits, demonstrated a spatial gradient differentiation with three ecological adaptations: tufted herbs, rhizome herbs, and non-tufted or rhizome herbs. Key environmental driving factors for meadow steppe included precipitation, soil carbon, nitrogen, and phosphorus content, while the average growing-season temperature as a limiting factor. The environmental driving factors for the typical steppe were not apparent, and the limiting factor was water. For the desert steppe, the environmental driving factors were altitude and average growing-season temperature. These findings reveal notable spatial heterogeneity and a distinct distribution pattern in community composition and vegetation classification based on belowground root traits in the Inner Mongolia steppes. Full article

Extreme drought events may become more frequent with climate change. Understanding the impact of extreme drought on grassland ecosystems is therefore crucial for the long-term sustainability of ecosystems. Here, we identified extreme drought events in the Inner Mongolia grasslands of China using long-term standardized precipitation evapotranspiration index (SPEI) data and evaluated drought resistance of the vegetation under extreme drought based on net primary production (NPP). The impact of consecutive extreme drought events and multiple discontinuous one-year extreme drought events on grasslands were further analyzed to investigate the response strategies of different grassland types to different drought conditions. We found that the frequency and area of extreme drought in 2000–2011 were significantly higher than those in 2012–2020, and the Xilingol League region showed the highest frequency of extreme drought events. Under extreme drought, vegetation resistance was positively correlated, where annual precipitation > 300 mm. The mean resistance of different grassland types followed the order: upland meadow (UM) > lowland meadow (LM) > temperate meadow steppe (TMS) > temperate desert (TD) > temperate steppe (TS) > temperate steppe desert (TSD) > temperate desert steppe (TDS). In the analysis of two cases of consecutive two-year extreme drought, all grassland types except TSD and TD showed obvious decreased resistance in the final drought year, with the highest reduction (0.16) in LM during 2010–2011, implying the widespread and significant inhibition of grassland growth by continuous drought. However, under the multiple discontinuous extreme drought events, the resistance of all grassland types showed a fluctuating but an overall increasing trend, suggesting the adaptability of grassland to drought. The results emphasize that management departments should pay more attention to regions with low resistance and enhance the stability of grassland production by increasing the proportion of drought-resistant plants in reaction to future extreme drought scenarios. Full article

Grassland degradation poses a serious threat to ecosystem stability and the sustainable development of human societies. In this study, we propose a framework for grassland degradation risk assessments and early warning based on key ecological factors (KEFs) in Xilingol. The NDVI, NPP, and grass yield were selected as KEFs to represent vegetation coverage, ecosystem productivity, and actual biomass, respectively. By constructing a grassland degradation index (GDI) and integrating K-means clustering, the average curvature, and a gravity center shift analysis, we quantified the degradation risk levels and identified the threshold values for different grassland types. The results showed the following: (1) the grass yield was the most sensitive indicator of grassland degradation in Xilingol, with high-risk thresholds decreasing from 115.67 g·m⁻² in the temperate meadow steppes (TMSs) to 73.27 g·m⁻² in the temperate typical steppes (TTSs), and further to 32.30 g·m⁻² in the temperate desert steppes (TDSs); (2) the TDSs exhibited the highest curvature value (2.81 × 10⁻⁴) in the initial stage, indicating a higher likelihood of rapid early-stage degradation, whereas the TMSs and TTSs reached peak curvature in the latest stages; and (3) the TTSs had the largest proportion of high-risk areas (33.02%), with a northeast–southwest distribution and a probable westward expansion trend. This study provides a practical framework for grassland degradation risk assessments and early warning, offering valuable guidance for ecosystem management and sustainable land use. Full article

Understanding how prey and predator species partition activity patterns across time and space is essential for elucidating behavioral adaptation and ecological coexistence. In this study, we examined the diel and seasonal activity rhythms of two sympatric rodent species—Rhombomys opimus (Great gerbil) and Meriones meridianus (Midday gerbil)—and their primary predators, Otocolobus manul (Pallas’s cat) and Vulpes vulpes (Red fox), in a desert-steppe ecosystem on the northern slopes of the Qilian Mountains, China. Using over 8000 camera trap days and kernel density estimation, we quantified their activity intensity and spatiotemporal overlap. The two rodent species showed clear temporal niche differentiation but differed in their synchrony with predators. R. opimus exhibited a unimodal diurnal rhythm with spring activity peaks, while M. meridianus showed stable nocturnal activity with a distinct autumn peak. Notably, O. manul adjusted its activity pattern to partially align with that of R. opimus, whereas V. vulpes maintained a crepuscular–nocturnal rhythm overlapping more closely with that of M. meridianus. Despite distinct temporal rhythms, both rodent species shared high spatial overlap with their predators (overlap index OI = 0.64–0.83). These findings suggest that temporal partitioning may reduce predation risk for R. opimus, while M. meridianus co-occurs more extensively with its predators. Our results highlight the ecological role of native carnivores in rodent population dynamics and support their potential use in biodiversity-friendly rodent management strategies under arid grassland conditions. Full article

Grazing affects soil organic carbon (SOC) through plant removal, livestock trampling, and manure deposition. However, the impact of grazing on SOC is also influenced by multiple factors such as climate, soil properties, and management approaches. Despite extensive research, the mechanisms by which grazing intensity influences SOC density in grasslands remain incompletely understood. This study examines the effects of varying grazing intensities on SOC density (0–30 cm) dynamics in temperate grasslands of northern China using field surveys and experimental analyses in a typical steppe ecosystem of Inner Mongolia. Results show that moderate grazing (3.8 sheep units/ha/yr) led to substantial consumption of aboveground plant biomass. Relative to the ungrazed control (0 sheep units/ha/yr), aboveground plant biomass was reduced by 40.5%, 36.2%, and 50.6% in the years 2016, 2019, and 2020, respectively. Compensatory growth failed to fully offset biomass loss, and there were significant reductions in vegetation carbon storage and cover (p < 0.05). Reduced vegetation cover increased bare soil exposure and accelerated topsoil drying and erosion. This degradation promoted the downward migration of SOC from surface layers. Quantitative analysis revealed that moderate grazing significantly reduced surface soil (0–10 cm) organic carbon density by 13.4% compared to the ungrazed control while significantly increasing SOC density in the subsurface layer (10–30 cm). Increased precipitation could mitigate the SOC transfer and enhance overall SOC accumulation. However, it might negatively affect certain labile SOC fractions. Elucidating the mechanisms of SOC variation under different grazing intensities and precipitation regimes in semi-arid grasslands could improve our understanding of carbon dynamics in response to environmental stressors. These insights will aid in predicting how grazing systems influence grassland carbon cycling under global climate change. Full article

In the era of global climate change, existing evidence indicates that dominant species play a crucial role in regulating grassland structure and function. However, what remains overlooked are the factors that regulate the growth of dominant species under climate change. Some studies have indicated that the future climate of the Inner Mongolia grasslands will specifically show a trend of warming and humidification. Hence, in 2013, we conducted a controlled warming and precipitation addition experiment in a temperate steppe in Inner Mongolia, China. Open-top chambers (OTCs) were used to simulate warming (by 1.5 °C) and rainfall (twice a month, 10% of the average precipitation between 1960 and 2011 of the same month each time) during the growing season. In 2023, the resource utilization efficiency, morphological characteristics, leaf anatomical structure, and population quantity characteristics of the dominant species (Leymus chinensis), and community species diversity were monitored under control (CK), warming (T), precipitation addition (P), and warming plus precipitation addition (TP) conditions. We found that the plant height of L. chinensis significantly increased under warming; its height was 41.97 cm under TP, 41.84 cm under T, 29.48 cm under P, and 28.88 cm under CK. We observed that L. chinensis primarily obtains more light by increasing leaf area and height under warming conditions. Environmental changes also alter the tissue structure of L. chinensis leaves, leading to a decrease in lignification after increasing the water content. In this study, warming significantly increased the L. chinensis leaf area but decreased the leaf carbon content. Warming and precipitation addition regulated the height of L. chinensis by affecting the leaf area, leaf–stem ratio, and distance of the bottom leaf from the ground. Our results provide reasonable predictions regarding the succession direction of the L. chinensis steppe under global climate change in the future. Full article

As a vital part of the Eurasian temperate grassland, the Chinese temperate grassland is primarily distributed in the Inner Mongolia Plateau. This paper focuses on mapping temperate grassland dynamics from the 1980s to the 2010s in Inner Mongolia, which was divided into temperate meadow steppe (TMS), temperate typical steppe (TTS), temperate desert steppe (TDS), temperate steppe desert (TSD) and temperate desert (TD). Multi-source features, including multispectral reflectance, vegetation growth, topography, water bodies, meteorological data, and soil characteristics, were selected based on their distinct physical properties and remote sensing variations. Then, we applied deep neural network (DNN) models to classify them, achieving an accuracy of 79.4% in the 1980s and 81.1% in the 2000s. Additionally, validation in the 2010s through field reconnaissance demonstrated an accuracy of 72.7%, which was acceptable, confirming that DNN is an effective method for classifying temperate grasslands. The results revealed that TTS had the highest proportion in the study area (39%), while TMS and TSD had the lowest (8.2% and 8.1%, respectively). Grassland types have the distribution law of aggregation; according to statistics, 61.1% of the grassland area remained unchanged, and the transition zone between adjacent grassland classes was highly easy to change. The area variation mainly came from TTS, TDS, and TSD, but not TD. The mutual transformation of different grassland types occurred mainly in adjacent areas between them. This study demonstrates the potential of DNN for long-term grassland mapping and provides the most comprehensive classification maps of Inner Mongolia grasslands to date, which are invaluable for grassland research and conservation efforts in the area. Full article

The genus Dracocephalum (family Lamiaceae) comprises approximately 70 species, many of which have been traditionally used in various ethnomedical systems. The plants exhibit a broad distribution across steppe, semi-deserts, deserts, and alpine zones of temperate Eurasia, with isolated endemic species occurring in North America and North Africa. The traditional medicinal uses of the Dracocephalum species encompass the treatment of respiratory diseases, colds and fever, gastrointestinal disorders, liver and gallbladder ailments, musculoskeletal conditions, cardiovascular diseases, diabetes, gynecological and urological disorders, as well as ailments of the ears, throat, mouth, and eyes, as well as various dermatological conditions. The plants are rich sources of polyphenolic compounds, including flavonoids and phenolic acids, which contribute to their diverse pharmacological activities. The flavonoid profile of the Dracocephalum species is dominated by luteolin and apigenin derivatives, supplemented by mono-, di-, tri-, tetra-, and pentamethoxylated flavones. The predominant phenolic acids are chlorogenic acid, coumaric acid, rosmarinic acid, and their derivatives. Other phenolic compounds have also been identified in the genus: anthocyanins, lignans, phenylethanoids, phenylacetamide glycosides, flavonoid alkaloids, gingerols, coumarins, furanocoumarins, and cyanogenic glucosides. Despite growing scientific interest in this genus, a comprehensive review of its polyphenolic constituents, their structures, and associated biological activities remains lacking. To bridge this gap, this review presents an analysis of the polyphenolic profile of the Dracocephalum species, their ethnomedicinal uses, and the latest findings on their biological potential. Full article