Search Results (1,469)

Although Morocco is a biodiversity hotspot for wild bees, much of its bee fauna remains underexplored. This study aims to enhance knowledge on the regional diversity of Moroccan bees by monitoring wild bee species across nine regions during the 2022–2024 period. A total of 245 bee species—representing 34 genera and 6 families—are documented, accounting for ~25% of the country’s bee species, including 74 species newly recorded at the regional level. We recorded 42 species as new for the Marrakech-Safi region, bringing the total to 597 species and confirming this region as a biodiversity hotspot. New records were also observed in the southern and desert regions, such as Laayoune-Boujdour-Sakia El Hamra and Guelmim-Oued Noun (12 species for each), as well as Dakhla-Oued Ed-Dahab (7 species), highlighting the need for further exploration in these under-sampled areas. The genera Andrena, Lasioglossum, and Eucera were the most diverse, collectively representing 61% of the newly recorded species documented in this study. This research provides valuable insights into the diversity and distribution of Morocco’s bee fauna and calls for standardized monitoring, conservation efforts, and expanded research—particularly in underexplored areas such as the southern regions—to preserve Morocco’s vital bee populations and ensure the sustainability of its agroecosystems. Full article

Ecological restoration in arid coal-mining regions faces extreme challenges due to soil infertility, salinization, and water scarcity. This study addresses these limitations in the Santanghu Shitoumei No. 1 open-pit mine (Xinjiang), where gypsum gray-brown desert soil, minimal rainfall (199 mm/yr), high evaporation (1716 mm/yr), and persistent gale-force winds exacerbate revegetation efforts. To overcome the high cost, short lifespan, and poor practicality of commercial water-retaining agents, we developed a novel humic acid (HA) and sodium carboxymethyl cellulose (CMC) composite water-absorbing resin (HA-CMC). Optimal synthesis parameters—identified as acrylic acid (AA)–carboxymethyl cellulose (CMC)–humic acid (HA)–Acrylamide (AM)–N,N’-methylene diacrylamide (MBA)–Ammonium persulphate (APS) = 100%:15%:4.5%:25%:0.6%:0.8%—yielded effective crosslinking, confirmed via FTIR and SEM. Performance benchmarking against existing agents demonstrated superior attributes. Field application in the mine’s demonstration area significantly enhanced surface vegetation and soil fertility, confirming the resin’s potential for large-scale soil remediation and ecological restoration in arid mining environments. Full article

The stability of rock slope–anchoring systems is one of the core concerns in protecting the ecological environment and ensuring the safe operation of hydropower, transportation, and construction projects. The stability evaluation index system is a critical factor influencing the accuracy of such assessments. This study establishes a stability evaluation index system for rock slope–anchoring systems by incorporating multi-factor influence mechanisms. The approach involves indicator screening, development of a hierarchical analytical structure, definition of classification criteria, and comparative analysis. The results indicate the following: (1) The proposed index system fully considers the deformation and failure modes of rock slopes, the factors influencing stability, and the safety-related parameters of anchoring structures. (2) It comprehensively captures the multi-factor influence patterns affecting the stability of the rock slope–anchoring system. (3) Compared with traditional empirical and equal-interval grading methods, the grading standards defined by this system are more accurate, better reflect the intrinsic data characteristics, and yield higher classification precision. Full article

Sandy desertification is a major ecological and environmental challenge worldwide, posing a severe threat to ecological security in arid regions. A systematic understanding of the spatial–temporal dynamics of sandy desertification and their driving forces enables effective support for ecological engineering in China. We visually interpreted five Landsat imaging periods (1986–2023) to map sandy desert areas (SDA), which were confirmed by 176 field samples. Driving forces were measured using the Geographical Detector model, and changes in the extent and intensity of SDA were evaluated using intensity analysis and center of gravity migration. The results indicate the following: (1) On the temporal scale, sandy desertification land in Dengkou County experienced a significant reversal over the past 40 years, with a total reduction of 1204.72 km². On the spatial scale, the main areas of reduction were located in the central and southwestern regions. (2) Sandy desertification in Dengkou County underwent a process of initial reversal, followed by expansion, and then another reversal. The periods 1986–1995 and 2004–2023 were reversal phases, while 1995–2004 was a development phase. (3) Livestock density showed the strongest influence among anthropogenic factors (q = 0.224), suggesting a strong correlation with sandy desertification patterns. Among natural factors, geological conditions exert the most significant influence (q = 0.182). Every pair of driving factors, with the exception of slope aspect and soil moisture, showed either additive or synergistic effects, increasing their combined influence on desertification. The results provide a scientific basis for local ecological restoration and desertification control. Full article

Accurately assessing the spatiotemporal dynamics and influencing factors of ecosystem stability in arid desert regions (ADR) is crucial for ecological conservation and the achievement of high-quality regional development. However, existing assessment frameworks generally fail to adapt to the extremely fragile ecological conditions of ADR. Therefore, the Alxa Desert, a typical region, was selected as the research region, and an ecosystem stability assessment framework tailored to regional characteristics (perturbation–resilience–function) was constructed. Perturbation represents external pressure, resilience reflects the capacity for recovery and adaptation, and function serves as the supporting foundation. The three dimensions are dynamically coupled and jointly determine the stability status of the ecosystem in the Alxa Desert. Methodologically, this study innovatively introduces the Cloud Model–Analytic Hierarchy Process (CM-AHP) to calculate indicator weights, which more effectively addressed the widespread fuzziness and uncertainty inherent in ecosystem assessments compared to traditional methods. In addition, spatial autocorrelation methods was applied to reveal the spatial and temporal evolution characteristics of ecosystem stability from 2000 to 2020. Furthermore, the optimal parameters geographical detector model (OPGDM) was applied to analyze the effects of natural and human factors on the spatial differentiation of ecosystem stability in Alxa Desert. In addition, the Markov–FLUS model was employed to simulate the future trends of ecosystem stability over the next two decades. The results indicate that ecosystem stability in Alxa Desert from 2000 to 2020 was primarily characterized by vulnerable and moderate levels, with the area classified as extremely vulnerable decreasing significantly by 10% relative to its extent in 2000. Spatially, higher stability was observed in oasis regions and southeastern mountainous regions, while lower stability was concentrated in the desert hinterlands. Overall, ecosystem stability shifted from vulnerable toward moderate levels, reflecting a trend of gradual improvement. From 2000 to 2020, the Moran’s I varied between 0.78 and 0.81, showing strong spatial clustering. Surfce Soil moisture content (SSMC), Soil organic carbon (SOC), and enhanced vegetation index (EVI) were the primary factors influencing the spatial differentiation of ecosystem stability in Alxa Desert. The interaction between these factors further enhanced their explanatory power. Future forecasting results indicate that ecosystem stability will further improve by 2030 and 2040, particularly in the northern and southern areas of Alxa Left Banner and Alxa Right Banner. The findings can offer a theoretical foundation for future ecological conservation and environmental management in ADR. Full article

In order to determine the impact of urbanization on raptors in the semi-desert conditions of southwestern Kazakhstan, an analysis of the spatio-temporal distribution of raptors is presented for the first time based on the results of surveys of the avifauna of Kyzylorda. Eight species of raptors were recorded: field Hen Harrier (Circus cyaneus), Marsh Harrier (C. aeroginosus), Eurasian Sparrowhawk (Accipiter nisus), Long-Legged Buzzard (Buteo rufinus), Eurasian Buzzard (B. buteo), Steppe Eagle (Aquila nipalensis), Eurasian Hobby (Falco subbuteo), and Common Kestrel (F. tinnunculus). The probability of raptors being present was negatively associated with dense urban low-rise buildings with limited greenery in the bay and the new part of the city. At the same time, the dense urban development with little or no greenery in the old central part of the city provides adequate habitat (including a foraging base and nesting sites) for the Common Kestrel. Raptor presence was positively associated with the Syrdarya River floodplain and wasteland with small groups of trees and/or shrubs. The landfill site located on the north-eastern edge of the city serves as a feeding ground for the Long-Legged and Eurasian Buzzards, while the airport area is inhabited by the Eurasian Buzzard, Steppe Eagle, and Common Kestrel. Based on this study, we would recommend that enterprises (e.g., grain storage facilities, airports) and local executive bodies who are interested in the conservation of raptors and regulating the population of the pigeons around their territories should retain or plant more native vegetation and shrubs and preserve areas with green spaces. Full article

The complex trade-offs and synergies among ecosystem services (ESs) in arid regions influence the stability and sustainable development of regional ecosystems. As a representative oasis–desert transition zone, the Kashgar region requires quantifying the key drivers and thresholds influencing ecosystem services, which is crucial for regional management. This study examines the spatio-temporal changes and interactions of five types of ES (grain production, water yield, soil retention, carbon storage, and habitat quality) and employs Restricted Cubic Splines to quantify the nonlinear changes and threshold effects of natural and social drivers. The results indicate the following: (1) During the period from 2000 to 2020, supply services (grain production) and regulatory services (water yield and soil retention) showed growth, while support services (carbon storage and habitat quality) declined slightly; (2) the synergistic effects of ecological services improved across the entire region, but trade-off effects emerged in certain local areas; and (3) the NDVI is the core natural factor driving the spatio-temporal differentiation of ESs. In 2020, when the NDVI exceeded 0.35, it had an adverse impact on habitat quality and carbon storage. Among social factors, water yield and habitat quality exhibit the highest threshold points with land use development intensity. An increase in land development intensity significantly impacts the trade-off and synergistic relationships among ESs, leading to local imbalances in ES resource supply and demand. These findings enhance our understanding of the nonlinear characteristics and potential mechanisms of ecosystems in arid regions, providing a scientific basis for ecosystem management in these areas. Full article

The rapid desiccation of the Aral Sea has transformed the region into one of the world’s most severe soil wind-erosion hotspots. Despite growing concern, long-term, high-resolution assessments and driver attribution remain insufficient. This study integrates the Revised Wind Erosion Equation (RWEQ) with multi-source remote sensing data on the Google Earth Engine (GEE) platform to simulate wind erosion dynamics from 1990 to 2020. The residual trend method was used to disentangle the contributions of climate change and human activities, while erosion risk was assessed using the Information Quantity model and Analytic Hierarchy Process (AHP). This study reveals five key findings: (1) wind erosion increased significantly after 2011, peaking in 2015 with an annual growth rate of 2.418 kg/m². (2) The Aral Sea Basin’s relative contribution to regional erosion declined sharply, indicating a shift in dominant erosion zones to peripheral deserts. (3) Climate change emerged as the primary driver, contributing 70.19% overall, and up to 92.13% in recent years, while human activities showed a peak influence (55.53%) in 2005. (4) Spatial attribution showed climate dominance in desert areas and localized human impact in exposed lakebeds. (5) High-risk erosion zones expanded rapidly into the Kyzylkum Desert after 2010, due to rising wind speeds and vegetation loss. This study provides a robust remote sensing–based framework for wind erosion monitoring and attribution, offering critical insights for erosion mitigation and ecological restoration in arid, climate-sensitive regions. Full article

Isotopic investigations focused on determining the mobility and provenance of ancient human civilizations and sourcing of archeological artifacts continue to gain prominence in archeology. Most studies focus on the premise that the geographic variation in isotope systems of interest (e.g., Sr, Pb, Nd, O) in the natural environment is recorded in both human hard tissues of local individuals and raw materials sourced for artifacts within the same region. The introduction of multi-collection–inductively coupled plasma mass spectrometry (MC-ICP-MS) and laser ablation systems are techniques that consume smaller sample sizes compared to previous mass spectrometric approaches due to their higher ionization efficiency and increased sensitivity. This development has facilitated the isotopic measurement of trace elements present at low abundances (e.g., Pb, Nd, <1-to-low ppm range) particularly in human tooth enamel. Accurate interpretation of any isotope ratio measurement for the proveniencing of such low-abundance samples requires the adequate evaluation of post-mortem diagenetic alteration. A synopsis of practices currently in use for identifying post-mortem alteration in human archeological samples is discussed here. Post-mortem shifts in radiogenic isotope signatures resulting from secondary alteration are distinct from those potentially related to the impact of climate change on the bioavailable budgets for these elements. This topic is of interest to the archeological community and discussed here in the context of Holocene-aged samples from burial sites within the Nile River Valley System, and preferred dust source areas from the neighboring Sahara Desert. Full article

Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, this study integrates multi-source remote sensing data, energy balance modeling, and ground-based validation that significantly enhances spatiotemporal ET accuracy in the vulnerable desert steppe ecosystems. The study utilized meteorological data from several national stations and Landsat-8 imagery to process monthly remote sensing images in 2019. The Surface Energy Balance System (SEBS) model, chosen for its ability to estimate ET over large areas, was applied to derive modeled daily ET values, which were validated by a large-weighted lysimeter. It was shown that ET varied seasonally, peaking in July at 6.40 mm/day, and reaching a minimum value in winter with 1.83 mm/day in December. ET was significantly higher in southern regions compared to central and northern areas. SEBS-derived ET showed strong agreement with lysimeter measurements, with a mean relative error of 4.30%, which also consistently outperformed MOD16A2 ET products in accuracy. This spatial heterogeneity was driven by greater vegetation coverage and enhanced precipitation in the southeast. The steppe ET showed a strong positive correlation with surface temperatures and vegetation density. Moreover, the precipitation gradients and land use were primary controllers of spatial ET patterns. The process-based SEBS frameworks demonstrate dual functionality as resource-optimized computational platforms while enabling multi-scale quantification of ET spatiotemporal heterogeneity; it was therefore a reliable tool for ecohydrological assessments in an arid steppe, providing critical insights for water resource management and drought monitoring. Full article

To investigate the spatiotemporal evolution characteristics and primary driving factors of Gross Primary Productivity (GPP) on the Qinghai-Tibet Plateau, we employed an enhanced MODIS-PSN model. Utilizing the fifth-generation global climate reanalysis dataset (ECMWF ERA5), we generated GPP remote sensing products by integrating six natural factors. Through correlation analysis and geographical detector modeling, we quantitatively analyzed the spatiotemporal dynamics and key drivers of vegetation GPP across the Qinghai-Tibet Plateau from 2001 to 2022. The results demonstrate that GPP changes across the Qinghai-Tibet Plateau display pronounced spatial heterogeneity. The humid northeastern and southeastern regions exhibit significantly positive change rates, primarily distributed across wetland and forest ecosystems, with a maximum mean annual change rate of 12.40 gC/m²/year. In contrast, the central and southern regions display a decreasing trend, with the minimum change rate reaching −1.61 gC/m²/year, predominantly concentrated in alpine grasslands and desert areas. Vegetation GPP on the Qinghai-Tibet Plateau shows significant correlations with temperature, vapor pressure deficit (VPD), evapotranspiration (ET), leaf area index (LAI), precipitation, and radiation. Among the factors analyzed, LAI demonstrates the strongest explanatory power for spatial variations in vegetation GPP across the Qinghai-Tibet Plateau. The dominant factors influencing vegetation GPP on the Qinghai-Tibet Plateau are LAI, ET, and precipitation. The pairwise interactions between these factors exhibit linear enhancement effects, demonstrating synergistic multifactor interactions. This study systematically analyzed the response mechanisms and variations of vegetation GPP to multiple driving factors across the Qinghai-Tibet Plateau from a spatial heterogeneity perspective. The findings provide both a critical theoretical framework and practical insights for better understanding ecosystem response dynamics and drought conditions on the plateau. Full article

This study analyzes spatiotemporal aridity dynamics in Central Kazakhstan (1960–2022) using a monthly Aridity Index (AI = P/PET), where P is precipitation and PET is potential evapotranspiration, Mann–Kendall trend analysis, and climate zone classification. Results reveal a northeast–southwest aridity gradient, with Aridity Index ranging from 0.11 to 0.14 in southern deserts to 0.43 in the Kazakh Uplands. Between 1960–1990 and 1991–2022, southern regions experienced intensified aridity, with Aridity Index declining from 0.12–0.15 to 0.10–0.14, while northern mountainous areas became more humid, where Aridity Index increased from 0.40–0.44 to 0.41–0.46. Seasonal analysis reveals divergent patterns, with winter showing improved moisture conditions (52.4% reduction in arid lands), contrasting sharply with aridification in spring and summer. Summer emerges as the most extreme season, with hyper-arid zones (8%) along with expanding arid territories (69%), while autumn shows intermediate conditions with notable dry sub-humid areas (5%) in northwestern regions. Statistical analysis confirms these observations, with northern areas showing positive Aridity Index trends (+0.007/10 years) against southwestern declines (−0.003/10 years). Key drivers include rising temperatures (with recent degradation) and variable precipitation (long-term drying followed by winter and spring), and PET fluctuations linked to temperature. Since 1991, arid zones have expanded from 40% to 47% of the region, with semi-arid lands transitioning to arid, with a northward shift of the boundary. These changes are strongly seasonal, highlighting the vulnerability of Central Kazakhstan to climate-driven aridification. Full article

This study investigates the seasonal dynamics of energy balance, evapotranspiration (ET), and Net Ecosystem Production (NEP) in the Dengkou desert ecosystem of Inner Mongolia, China. Using eddy covariance and meteorological data from 2019 to 2022, the research focuses on understanding how these processes interact in one of the world’s most water-limited environments. This arid research area received an average of 109.35 mm per annum precipitation over the studied period, classifying the region as a typical arid ecosystem. Seasonal patterns were observed in daily air temperature, with extremes ranging from −20.6 °C to 29.6 °C. Temporal variations in sensible heat flux (H), latent heat flux (LE), and net radiation (Rn) peaked during summer season. The average ground heat flux (G) was mostly positive throughout the observation period, indicating heat transmission from atmosphere to soil, but showed negative values during the winter season. The energy balance ratio for the studied period was in the range of 0.61 to 0.80, indicating challenges in achieving energy closure and ecological shifts. ET exhibited two annual peaks influenced by vegetation growth and climate change, with annual ET exceeding annual precipitation, except in 2021. Net ecosystem production (NEP) from 2019 to 2020 revealed that the Dengkou desert were a net source of carbon, indicating the carbon loss from the ecosystem. In 2021, the Dengkou ecosystem shifted to become a net carbon sink, effectively sequestrating carbon. However, this was sharply reversed in 2022, resulting in a significant net release of carbon. The study findings highlight the complex interactions between energy balance components, ET, and NEP in desert ecosystems, providing insights into sustainable water management and carbon neutrality strategies in arid regions under climate change effect. Full article

This study investigates the effectiveness of passive design in low-rise residential buildings located in arid desert climates, using the Dubai Solar Decathlon Middle East (SDME) competition as a case study. This full-scale experiment offers a unique opportunity to evaluate design solutions under controlled, realistic conditions; prescriptive, modeled performance; and monitored performance assessments. The prescriptive assessment reviews geometry, orientation, envelope thermal properties, and shading. Most houses adopt compact forms, with envelope-to-volume and envelope-to-floor area ratios averaging 1 and 3.7, respectively, and window-to-wall ratios of approximately 17%, favoring north-facing openings to optimize daylight while reducing heat gain. Shading is strategically applied, horizontal on south façades and vertical on east and west. The thermal properties significantly exceed the local code requirements, with wall performance up to 80% better than that mandated. The modeled assessment uses Building Energy Models (BEMs) to simulate the impact of prescriptive measures on energy performance. Three variations are applied: assigning minimum local code requirements to all the houses to isolate the geometry (baseline); removing shading; and applying actual envelope properties. Geometry alone accounts for up to 60% of the variation in cooling intensity; shading reduces loads by 6.5%, and enhanced envelopes lower demand by 14%. The monitored assessment uses contest-period data. Indoor temperatures remain stable (22–25 °C) despite outdoor fluctuations. Energy use confirms that houses with good designs and airtightness have lower cooling loads. Airtightness varies widely (avg. 14.5 m³/h/m²), with some well-designed houses underperforming due to construction flaws. These findings highlight the critical role of passive design as the first layer for improving the energy performance of the built environment and advancing toward net-zero targets, specifically in arid desert climates. Full article

The unique characteristics of desert vegetation, such as different leaf morphology, discrete canopy structures, sparse and uneven distribution, etc., pose significant challenges for remote sensing-based estimation of fractional vegetation cover (FVC). The Unmanned Aerial Vehicle (UAV) system can accurately distinguish vegetation patches, extract weak vegetation signals, and navigate through complex terrain, making it suitable for applications in small-scale FVC extraction. In this study, we selected the floodplain fan with Caragana korshinskii Kom as the constructive species in Hatengtaohai National Nature Reserve, Bayannur, Inner Mongolia, China, as our study area. We investigated the remote sensing extraction method of desert sparse vegetation cover by placing samples across three gradients: the top, middle, and edge of the fan. We then acquired UAV multispectral images; evaluated the applicability of various vegetation indices (VIs) using methods such as supervised classification, linear regression models, and machine learning; and explored the feasibility and stability of multiple machine learning models in this region. Our results indicate the following: (1) We discovered that the multispectral vegetation index is superior to the visible vegetation index and more suitable for FVC extraction in vegetation-sparse desert regions. (2) By comparing five machine learning regression models, it was found that the XGBoost and KNN models exhibited relatively lower estimation performance in the study area. The spatial distribution of plots appeared to influence the stability of the SVM model when estimating fractional vegetation cover (FVC). In contrast, the RF and LASSO models demonstrated robust stability across both training and testing datasets. Notably, the RF model achieved the best inversion performance (R² = 0.876, RMSE = 0.020, MAE = 0.016), indicating that RF is one of the most suitable models for retrieving FVC in naturally sparse desert vegetation. This study provides a valuable contribution to the limited existing research on remote sensing-based estimation of FVC and characterization of spatial heterogeneity in small-scale desert sparse vegetation ecosystems dominated by a single species. Full article