Search Results (5,633)

Under the global goals of carbon peaking and carbon neutrality, China’s northern agro-pastoral ecotone—an ecologically fragile transition zone with drastic land use/cover change (LUCC)—is characterized by a lack of in-depth understanding of its “land use conflict–carbon sink response” mechanism, which is essential for regional land optimization and carbon neutrality. This study quantified the spatiotemporal dynamics of carbon storage in the zone from 2000 to 2020 using the InVEST model and identified key driving factors by combining the XGBoost model (R² = 0.73–0.88) with the SHAP framework. The results showed that regional total carbon storage increased by 30.11 × 10⁶ tons (a net growth of 0.57%), mainly driven by forest carbon sinks (+65.74 × 10⁶ tons, accounting for 218.3% of the total increase), while cropland and grassland underwent continuous carbon loss (−53.87 × 10⁶ tons and −35.80 × 10⁶ tons, respectively). Spatially, this presents a pattern of “high-value agglomeration in the central–southern region and low-value fragmentation at urban–rural edges”. The Normalized Difference Vegetation Index (NDVI) was the primary driver (average SHAP value: 426.15–718.91), with its interacting temperature factor evolving from air temperature (2000) to nighttime surface temperature (2020). This study reveals the coupling mechanism of “vegetation restoration–microenvironment regulation–carbon sink gain” driven by the Grain for Green Program, providing empirical support for land use optimization and carbon neutrality in agro-pastoral areas. Full article

Land use change is closely related to land surface temperature (LST). Based on remote sensing data from 2001 to 2020, this study analyzed the spatiotemporal variations and driving mechanisms of daytime and nighttime LST in the Urumqi Metropolitan Area (UMA) by combining traditional methods with the eXtreme Gradient Boosting (XGBoost)–SHAP coupled model. Although the average LST trend in the region was one of warming, the pixel-level significance analysis indicated that statistically significant warming (p < 0.05) is concentrated mainly in the urban core (2.65% of the area), while the majority of the region (70%) showed a non-significant warming trend. LST displayed significant spatial clustering, with Moran’s I remaining above 0.990, indicating a positive spatial autocorrelation in spatial distribution. With the advancement of urbanization, the proportion of impervious surfaces increased from 0.87% to 1.14%, while wastelands consistently accounted for approximately 50% of the total area. Different land use types showed distinct effects on the urban heat island (UHI) phenomenon: water bodies, grasslands, and forests played cooling roles, whereas barren land and impervious areas were the main heat contributors. The XGBoost-SHAP analysis further revealed that the importance ranking of driving factors has evolved over time. Among these factors, Elevation dominates, while the influence of population-related factors increased significantly in 2020. This study provides a scientific basis for regulating the thermal environment of cities in arid regions from the perspective of land use. This study provides a scientific basis for regulating the thermal environment of arid-region cities from the perspective of land use. Full article

Epichloë is a genus of endophytic fungi that forms systemic, vertically transmitted, and asymptomatic mutualistic associations with grasses in the subfamily Pooideae. These symbioses are non-pathogenic and are of considerable importance in agronomic and livestock systems due to their roles in enhancing host fitness under biotic and abiotic stress. Several studies have reported associations between Epichloë endophytes and species of the genus Bromus, a taxonomically complex group characterized by varying ploidy levels and frequent hybridization. Among its sections, Bromopsis includes the highest number of species naturally colonized by Epichloë fungi, while sections Bromus and Ceratochloa show lower infection rates. In South America, endophytes such as E. pampeana, E. tembladerae, E. typhina, and morphotypes of Neotyphodium spp. have been documented in species including B. auleticus, B. brachyanthera, and B. setifolius, where they appear to contribute to stress resilience. Although most findings originate from Argentina, significant knowledge gaps remain regarding the diversity and distribution of these endophytes in native Bromus species across the continent. This review synthesizes the current understanding of Epichloë–Bromus interactions, emphasizing their ecological and agronomic relevance, particularly in South America. Key factors influencing the establishment of these symbioses are examined, and future research directions are proposed to advance the study of these associations. Full article

The Tibetan Plateau is a globally critical climate-sensitive and ecologically fragile region. Vegetation phenology serves as a key indicator of ecosystem responses to climate change and simultaneously influences regional carbon cycling, water regulation, and ecological security. However, systematic quantitative assessments of phenological responses under the combined effects of multiple climate factors remain limited. This study integrates multi-source remote sensing data (MODIS MCD12Q2) and ERA5-Land meteorological data from 2001 to 2023, leveraging the Google Earth Engine (GEE) cloud platform to extract key phenological metrics, including the start (SOS) and end (EOS) of the growing season, and growing season length (GSL). Sen’s slope estimation, Mann–Kendall trend tests, and partial correlation analyses were applied to quantify the independent effects and spatial heterogeneity of temperature, precipitation, solar radiation, and evapotranspiration (ET) on GSL. Results indicate that: (1) GSL on the Tibetan Plateau has significantly increased, averaging 0.24 days per year (Sen’s slope +0.183 days/yr, Z = 3.21, p < 0.001; linear regression +0.253 days/yr, decadal trend 2.53 days, p = 0.0007), primarily driven by earlier spring onset (SOS: Sen’s slope −0.183 days/yr, Z = −3.85, p < 0.001), while autumn dormancy (EOS) showed limited delay (Sen’s slope +0.051 days/yr, Z = 0.78, p = 0.435). (2) GSL changes exhibit pronounced spatial heterogeneity and ecosystem-specific responses: southeastern warm–wet regions display the strongest responses, with temperature as the dominant driver (mean partial correlation coefficient 0.62); in high–cold arid regions, warming substantially extends GSL (Z = 3.8, p < 0.001), whereas in warm–wet regions, growth may be constrained by water stress (Z = −2.3, p < 0.05). Grasslands (Z = 3.6, p < 0.001) and urban areas (Z = 3.2, p < 0.01) show the largest GSL extension, while evergreen forests and wetlands remain relatively stable, reflecting both the “climate sentinel” role of sensitive ecosystems and the carbon sequestration value of stable ecosystems. (3) Multi-factor interactions are complex and nonlinear; temperature, precipitation, radiation, and ET interact significantly, and extreme climate events may induce lagged effects, with clear thresholds and spatial dependence. (4) The use of GEE enables large-scale, multi-year, pixel-level GSL analysis, providing high-precision evidence for phenological quantification and critical parameters for carbon cycle modeling, ecosystem service assessment, and adaptive management. Overall, this study systematically reveals the lengthening and asymmetric patterns of GSL on the Tibetan Plateau, elucidates diverse land cover and climate responses, advances understanding of high-altitude ecosystem adaptability and climate resilience, and provides scientific guidance for regional ecological protection, sustainable management, and future phenology prediction. Full article

This study aimed to assess the potential of machine learning models applied to high spatial resolution images from UAVs for estimating the aboveground biomass (AGB) of forage grass cultivated in the Brazilian semiarid region. The fresh and dry AGB were determined in Cenchrus ciliare plots with an area of 0.04 m². Spectral data were obtained using a multispectral sensor (Red, Green, and NIR) mounted on a UAV, from which 45 vegetation indices were derived, in addition to a structural variable representing plant height (H95). Among these, H95, GDVI, GSAVI2, GSAVI, GOSAVI, GRDVI, and CTVI exhibited the strongest correlations with biomass. Following multicollinearity analysis, eight variables (R, G, NIR, H95, CVI, MCARI, RGR, and Norm G) were selected to train Random Forest (RF), Support Vector Machine (SVM), and XGBoost models. RF and XGBoost yielded the highest predictive performance, both achieving an R² of 0.80 for AGB—Fresh. Their superiority was maintained for AGB—Dry estimation, with R² values of 0.69 for XGBoost and 0.67 for RF. Although SVM produced higher estimation errors, it showed a satisfactory ability to capture variability, including extreme values. In modeling, the incorporation of plant height, combined with spectral data obtained from high spatial resolution imagery, makes AGB estimation models more reliable. The findings highlight the feasibility of integrating UAV-based remote sensing and machine learning algorithms for non-destructive biomass estimation in forage systems, with promising applications in pasture monitoring and agricultural land management in semi-arid environments. Full article

Montane shrubby grasslands, as one of the world’s important ecosystems, are highly sensitive to climate change and human activities, especially in the subtropical regions experiencing rapid economic development. However, little is known about how anthropogenic activities, such as firebreak construction, interact with elevation to influence plant diversity in these ecosystems. Shrub and herbaceous communities were surveyed in subtropical montane shrubby grassland within Baishanzu National Park, eastern China. Nine transects were established along firebreaks, each with two edge plots near firebreak and two interior plots away firebreak, and twelve additional control plots in adjacent undisturbed areas. Species diversity was assessed using the Hill index. Our results revealed distinct responses of shrubs and herbs to firebreak disturbance and elevation. Firebreaks reduced shrub diversity but enhanced herb diversity, and both groups exhibited contrasting elevational patterns. In control areas, shrub diversity decreased while herb diversity increased with elevation, whereas in firebreak zones, these relationships were altered, with edge plots showing a hump-shaped diversity pattern. Differences in shrub diversity but not herbs between interior and edge plots decreased with elevation. Species composition also differed significantly between firebreak and control areas, driven mainly by elevation in control areas and by soil properties near firebreaks. These findings demonstrate that firebreak construction reshapes the elevation–diversity relationships of both herbs and shrubs, highlighting the sensitivity of high-elevation montane shrubby grasslands to small-scale disturbances. Effective firebreak management should therefore account for both elevational context and disturbance intensity to maintain ecosystem biodiversity and stability. Full article

The Jingbaoer Grassland Jade Mine situated approximately 20 km northwest of Mazongshan Town in Gansu Province, China, represents an important source of nephrite dating back to the pre-Qin period. In this study, 58 representative nephrite samples were analyzed to investigate their mineralogical and geochemical characteristics using polarized light microscopy, scanning electron microscopy (SEM), electron probe microanalysis (EPMA), and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The mine is situated near the contact zone between the Silurian Gongpoquan Group and Devonian granite, with surrounding rocks primarily consisting of Precambrian dolomitic marble. The nephrite displays diverse colors—white, bluish-white, sugar-white, and cyan—with darker tones and abundant manganese-stained dendritic and flocculent inclusions. It shows a relative density of 2.82–2.99, a refractive index of 1.60–1.62, and a vitreous to greasy luster. Texturally, the jade is predominantly composed of micro-fibrous interwoven tremolite, occasionally exhibiting oriented recrystallization textures. Minor minerals include diopside, apatite, titanite, chlorite, epidote, allanite, rutile, and graphite. Chemically, the samples are rich in SiO₂, MgO, and CaO, with trace amounts of FeO, MnO, Al₂O₃, and Na₂O. Notably, Sr and Sm are enriched, Nb is slightly depleted, and Eu shows a distinct negative anomaly. The average total rare earth content is 4.25 µg/g. The study suggests that the deposits in the research area are typical of the contact-metasomatic type, formed through multi-stage hydrothermal metasomatism between acidic granitic intrusions and dolomitic marble, creating favorable conditions for the formation of high-quality tremolite jade. Comparative analysis with jade artifacts excavated from the Tomb of Marquis Yi of Zeng suggests a possible provenance link to the Jingbaoer deposit, providing valuable evidence for the historical mining and distribution of nephrite during the Warring States period. Full article

Analysis of historical and future land use/land cover (LULC) dynamics using spatiotemporal data is crucial for better management of natural resources and environmental monitoring. This study investigated LULC transformations over a span of 60 years (1984–2044) for the Giba basin in northern Ethiopia. ArcGIS and the Cellular Automata and Artificial Neural Network (CA-ANN) model were used to develop the historical (1984, 2004, 2014, and 2024) and projected future (2034 and 2044) LULC maps of the basin, respectively. The results show that LULC categories experienced shifts from one class to another by 35%, 33%, and 40% in 2004–2014, 2014–2024, and 2004–2024, respectively. During 1984–2024, the largest and smallest percentage of positive changes were observed in settlement (7700%) and shrubs and bushes (25%), which increased from negligible to 78 km² and from 1668 km² to 2082 km², respectively. Furthermore, barren land and forestland showed the largest (−80%) and smallest (−37%) declines, which decreased from 956 km² to 187 km² and from 164 km² to 103 km² during the same period, respectively. Overall, the last 40 years witnessed considerable changes to LULC dynamics in the Giba basin. Cropland, water bodies, and settlements showed a continuously increasing trend throughout the historical study period, while grassland exhibited a continuous decreasing trend. Results of the CA-ANN model showed that the majority of the LULC categories (including water body, forest, bushes and shrubs, grassland, and barren land) will decrease, except for a slight increase of cropland (+6%) and settlements (+16%), which is projected to increase from 2570 km² to 2733 km² and from 78 km² to 91 km², respectively, in the next two decades, from 2024 to 2044. In general, high population increase, changes in government policies, and armed conflicts were found to be the most influential driving factors of LULC changes in the basin. Full article

Pastoralist sedentarization has accelerated globally since the late 20th century, driven by climate change, government policies, and economic transitions. In Inner Mongolia, China, this process advanced under 1950s socialist initiatives and the 1980s Grassland Household Contract Policy (GHCP), which allocated land use rights to individual households. This study examines the 1960–2020 transition from seasonal nomadism to settled pastoralism in a Gacha, emphasizing changes in grazing strategies and water access. Migration distances declined from about 55 km in the 1960s to 4 km in the 1980s, with sedentarization becoming permanent after the GHCP. Grazing practices shifted toward fixed facilities and supplementary feed, while water use moved to deep wells and storage tanks, increasing both costs and groundwater risks. These transformations modestly improved productivity but heightened social vulnerability. Full article

The grasslands and shrublands of northern and central Mexico cover nearly 25% of the country and harbor high biodiversity. However, they are increasingly degraded by agriculture, urbanization, infrastructure development, and water overexploitation. To assess the status of medium- and large-sized mammals in these threatened ecosystems, we quantified species richness, relative abundance, and naïve occupancy across vegetation types and seasons. From April 2023 to February 2024, monthly track surveys and camera trapping were performed, and the data were analyzed in R. We documented 16 species representing four orders and nine families, with Carnivora being the most diverse (eight species). The species richness varied by habitat, ranging from 11 in montane forest to 13 in semi-desert grassland, the latter habitat having the highest Shannon and Simpson indices, particularly in the dry season. Odocoileus virginianus and Sylvilagus audubonii were consistently the most abundant species in montane forest and desert scrub, whereas Cynomys mexicanus predominated in semi-desert grasslands, accounting for >90% of detections during the rainy season. Rare species included Lynx rufus, Taxidea taxus, and Ursus americanus, each with isolated detections. Rarefaction and sample coverage curves approached asymptotes (>99%), indicating sufficient sampling effort. Naïve occupancy and encounter rates were highest for O. virginianus (0.82) and S. audubonii (0.68), with a strong positive correlation between the two metrics (r² = 0.92). These findings provide robust baseline information on mammalian diversity, abundance, and habitat associations in semi-arid anthropogenic landscapes, supporting future monitoring and conservation strategies. Full article

Identifying the key drivers of plant community turnover under disturbance is essential for understanding ecological processes and informing conservation efforts. We investigated the Kangxi Grassland in the Yeyahu Wetland Nature Reserve, Beijing, using Generalized Dissimilarity Modeling (GDM) across two spatial scales and three areas, integrating soil properties, remote sensing data, and geographic distance. The models explained 25–49% of the deviance with low cross-validation error, showing a clear nonlinear turnover pattern. Pronounced species replacement occurred at short ecological distances, followed by slower change at greater distances. Although the overall patterns were similar, driver importance varied among areas: available nitrogen (AN) dominated in the Southeast Area, while soil water content (SWC) was the primary driver in the Northwest Area and across the entire Study Area; in all cases, geographic distance consistently ranked second. Texture indices, although weaker than geographic distance, still outperformed most vegetation indices and spectral bands. These results indicate that soil properties, geographic distance, and texture indices jointly shape spatial patterns of species turnover, with their relative importance varying by scale or area. Disturbances, such as drought, grazing, tourism, and fluctuations in inundated areas caused by variations in water levels in a nearby reservoir, influenced species turnover by directly or indirectly altering key drivers. In combination with a comparative analysis of species importance values (IVs) and ecological types, this study further demonstrates that the factors driving species turnover are influenced not only by scale but also by the complex and diverse ecological processes operating at their respective scales. It also shows the applicability of GDM in analyzing fine-scale turnover patterns and the factors driving them in disturbed grasslands. Full article

Global ecosystems have undergone significant degradation and deterioration, making the identification of ecosystem changes essential for promoting sustainable development and enhancing quality of life. Hami City, a representative region characterized by the complex “desert–oasis–mountain” ecosystem in Xinjiang, China, provides a critical context for examining ecosystem changes in extremely arid environments. This study utilizes remote sensing data alongside the Revised Wind Erosion Equation and Revised Universal Soil Loss Equation models to analyze the transformations within the desert–oasis ecosystems of Hami City and their driving forces. The findings reveal that (1) over the past 24 years, there have been substantial alterations in the ecosystem patterns of Hami City, primarily marked by an expansion of cropland and grassland ecosystems and a reduction in desert ecosystems. (2) Between 2000 and 2023, there has been an upward trend in Fractional Vegetation Cover, Net Primary Productivity, and windbreak and sand fixation amount in Hami City, whereas soil retention has shown a declining trend. (3) The overall ecosystem change in Hami City is moderate, encompassing 61.85% of the area, with regions exhibiting positive change comprising 16.79% and those with negative change comprising 21.33%. (4) Temperature, precipitation, and evapotranspiration are the primary drivers of ecosystem change in Hami City. Although the overall changes in ecosystems in Hami City have shown an improving trend, significant spatial heterogeneity still exists. The natural climatic conditions of Hami City constrain the potential for further ecological improvement. This study enhances the understanding of ecosystem change processes in extremely arid regions and demonstrates that strategies for mitigating or adapting to climate change need to be implemented as soon as possible to ensure the sustainable development of ecosystems in arid areas. Full article

Native bee communities in Arkansas remain poorly documented, particularly within fire-managed prairie ecosystems that provide critical habitat for pollinators. This study surveyed bee assemblages at two native prairie remnants in the Arkansas River Valley, one large (Cherokee Prairie Natural Area, CPNA) and one small urban fragment (Jewel Moore Nature Reserve, JMNR), both managed using prescribed fire. Using pan trapping, we recorded 599 individuals representing 96 species across 25 genera, including 49% singletons. Despite differences in size and landscape context, both prairies supported similarly rich bee communities per sample day, with JMNR and CPNA averaging 16.1 and 13.75 species, respectively. However, species composition diverged notably, with only 34.5% similarity, suggesting distinct community structure driven by site-specific habitat conditions and management histories. CPNA was dominated by large-bodied ground-nesting and cavity-nesting solitary bees, while JMNR supported smaller eusocial halictids and cavity nesters. Results highlight the value of prescribed fire in maintaining nesting substrates and floral resources. Even small, urban prairie remnants like JMNR can support high pollinator richness, emphasizing their role as conservation assets. Our findings contribute to a foundational baseline for native bee diversity in Arkansas and highlight the importance of both large and small fire-managed prairies in regional pollinator conservation planning. Full article

Despite the growing demand for agricultural products, land abandonment is increasing in developed countries, leading to the recolonization of natural vegetation and affecting ecosystem services, biodiversity, and the economy. Understanding the drivers of land abandonment is crucial for the protection of historic rural landscapes. This study assessed land use in the Latium region during the mid-twentieth century, analyzing the transitions of agro-forestry landscapes starting from areas that are now classified as natural and semi-natural formations. The analysis revealed that much of today’s wilderness derives from agricultural land, mostly arable land, and complex cultivation patterns. Extensive grasslands, once widespread, have largely transitioned into woodland or shrubland, with significant impacts. The resulting simplification of the landscape contributes to agro-biodiversity loss and a decline in ecosystem services, presenting major challenges for meeting future habitat restoration targets set by environmental policies. Full article

Rice, as one of the world’s three major staple crops, provides a food source for nearly half of the global population. Timely and accurate acquisition of rice cultivation information is crucial for optimizing spatial distribution, guiding production practices, and safeguarding food security. Taking Bishan District of Chongqing as the study area, NDVI values were derived from Sentinel-2 satellite imagery to construct standard NDVI time-series curves for typical land-cover types, including paddy fields, dryland, water bodies, construction land, and forest and grassland. These curves were then used in the NDVI time-series characteristics method to identify paddy fields. First, the Euclidean distance between the standard NDVI time series of paddy fields and those of other land-cover types was calculated. The sum of these element-wise differences was used to determine the upper threshold for paddy field extraction. Second, the mean absolute deviation between elements of the rice sample dataset and the standard NDVI time series was calculated for each time step. The sum of these average deviations was used as the lower threshold to extract the initial paddy field data. On this basis, an extreme-value constraint was introduced to reduce the interference of mixed pixels from forest and grassland and construction land, effectively eliminating anomalous pixels and improving the accuracy of paddy field identification. Finally, the results were validated and compared with those from other extraction methods. The results indicate that: (1) Paddy fields exhibit distinct NDVI time-series characteristics throughout the entire growing season, which can serve as a reference standard. By calculating the Euclidean distance between the NDVI curves of other land-cover types and those of paddy fields, similarity can be quantified, enabling rice identification. (2) The extraction method based on NDVI time-series characteristics successfully identified paddy fields through the appropriate setting of thresholds. The overall accuracy and Kappa coefficient remained high, while the F1-score consistently exceeded 0.8, indicating a good balance between precision and recall. Furthermore, the bootstrap uncertainty analysis revealed narrow 95% confidence intervals across all metrics, confirming the robustness and statistical reliability of the results. Overall, the proposed method demonstrated excellent performance in paddy field classification and significantly outperformed traditional machine learning methods implemented on the GEE platform. (3) Mixed pixels considerably affected the accuracy of rice classification; however, the introduction of the extreme-value constraint effectively mitigated this influence and further improved classification results. Full article