Search Results (805)

Leptospirosis is a globally significant zoonosis affecting animal health, productivity, and the environment. While typically associated with tropical climates, its persistence in semi-arid regions such as La Laguna, Mexico—characterized by low humidity, high temperatures, and limited water sources—remains poorly understood. Although these adverse environmental conditions theoretically limit the survival of Leptospira, high livestock density and synanthropic reservoirs (e.g., rodents) may compensate, facilitating transmission. In this cross-sectional study, blood sera from 445 dairy cows (28 herds: 12 intensive [MI], 16 semi-intensive [MSI] systems) were analyzed via microscopic agglutination testing (MAT) against 10 pathogenic serovars. Urine samples were cultured for active Leptospira detection. Risk factors were assessed through epidemiological surveys and multivariable analysis. This study revealed an overall apparent seroprevalence of 27.0% (95% CI: 22.8–31.1), with significantly higher rates in MSI (54.1%) versus MI (12.2%) herds (p < 0.001) and an estimated true seroprevalence of 56.3% (95% CI: 50.2–62.1) in MSI and 13.1% (95% CI: 8.5–18.7) in MI herds (p < 0.001). The Sejroe serogroup was isolated from urine in both systems, confirming active circulation. In MI herds, rodent presence (OR: 3.6; 95% CI: 1.6–7.9) was identified as a risk factor for Leptospira seropositivity, while first-trimester abortions (OR:10.1; 95% CI: 4.2–24.2) were significantly associated with infection. In MSI herds, risk factors associated with Leptospira seropositivity included co-occurrence with hens (OR: 2.8; 95% CI: 1.5–5.3) and natural breeding (OR: 2.0; 95% CI: 1.1–3.9), whereas mastitis/agalactiae (OR: 2.8; 95% CI: 1.5–5.2) represented a clinical outcome associated with seropositivity. Despite semi-arid conditions, Leptospira maintains transmission in La Laguna, particularly in semi-intensive systems. The coexistence of adapted (Sejroe) and incidental serogroups underscores the need for targeted interventions, such as rodent control in MI systems and poultry management in MSI systems, to mitigate both zoonotic and economic impacts. Full article

Common bean (Phaseolus vulgaris L.) is a critical protein-rich legume supporting food and nutritional security globally. However, Fusarium wilt, caused by Fusarium solani, remains a major constraint to production, with yield losses reaching up to 84%. While biocontrol strategies have been explored, most microbial agents are sourced from mesophilic environments and show limited effectiveness under abiotic stress. Here, we report the isolation and characterization of extremophilic Bacillus spp. from the hypersaline Lake Bogoria, Kenya, and their biocontrol potential against F. solani. From 30 isolates obtained via serial dilution, 9 exhibited antagonistic activity in vitro, with mycelial inhibition ranging from 1.07-1.93 cm 16S rRNA sequencing revealed taxonomic diversity within the Bacillus genus, including unique extremotolerant strains. Molecular screening identified genes associated with the biosynthesis of antifungal metabolites such as 2,4-diacetylphloroglucinol, pyrrolnitrin, and hydrogen cyanide. Enzyme assays confirmed substantial production of chitinase (1.33–3160 U/mL) and chitosanase (10.62–28.33 mm), supporting a cell wall-targeted antagonism mechanism. In planta assays with the lead isolate (B7) significantly reduced disease incidence (8–35%) and wilt severity (1–5 affected plants), while enhancing root colonization under pathogen pressure. These findings demonstrate that extremophile-derived Bacillus spp. possess robust antifungal traits and highlight their potential as climate-resilient biocontrol agents for sustainable bean production in arid and semi-arid agroecosystems. Full article

Optimizing irrigation and the targeted use of plant growth regulators are key strategies to improve productivity in citrus systems under water-limited conditions. This study evaluated the effects of three irrigation levels (4.44, 5.18, and 7.77 mm day⁻¹) combined with variable doses of naphthaleneacetic acid (NAA) and gibberellic acid (GA₃) on physiological and productive responses in Citrus aurantiifolia. The treatment with 7.77 mm irrigation and moderate doses of NAA (100 mg L⁻¹) and GA₃ (80 mg L⁻¹) increased yield by 38% (6.2 kg/plant), and it enhanced photosystem II photochemical efficiency (F_v/F_m = 0.82), chlorophyll index (SPAD = 62), and fruit weight by 15%. In contrast, high hormone doses under water deficit reduced leaf water potential and impaired physiological performance, leading to lower productivity. These findings support the combined use of regulated deficit irrigation and hormonal biostimulation as a sustainable strategy to enhance key lime yield and resource efficiency in semi-arid environments. Full article

Understanding groundwater quality in karst environments is essential, particularly in semi-arid regions where water resources are highly vulnerable to both climatic variability and anthropogenic pressures. The Ghar Boumaaza karst aquifer, located in the semi-arid Tlemcen Mountains of Algeria, represents a critical yet understudied water resource increasingly threatened by climate change and human activity. This study integrates hydrochemical analysis, multivariate statistical techniques, and predictive modeling to assess groundwater quality and characterize the relationship between total dissolved solids (TDSs) and discharge (Q). An analysis of 66 water samples revealed that 96.97% belonged to a Ca²⁺–HCO₃⁻ facies, reflecting carbonate rock dissolution, while 3% exhibited a Cl⁻–HCO₃⁻ facies associated with agricultural contamination. A principal component analysis identified carbonate weathering (40.35%) and agricultural leaching (18.67%) as the dominant drivers of mineralization. A third-degree polynomial regression model (R² = 0.953) effectively captured the nonlinear relationship between TDSs and flow, demonstrating strong predictive capacity. Independent validation (R² = 0.954) confirmed the model’s robustness and reliability. This study provides the first integrated hydrogeochemical assessment of the Ghar Boumaaza system in decades and offers a transferable methodological framework for managing vulnerable karst aquifers under similar climatic and anthropogenic conditions. Full article

Ziziphus lotus (L.) Lam. (Rhamnaceae), a key shrub species native to North Africa, is commonly found in arid and semi-arid regions. Renowned for its resilience under harsh conditions, it forms vegetation clusters that influence the surrounding environment. These clusters create microhabitats that promote biodiversity, reduce soil erosion, and improve soil fertility. However, in agricultural fields, Z. lotus is often regarded as an undesirable species. This study investigated the bacterial diversity and community composition along spatial gradients around Z. lotus patches in barley-planted and non-planted fields. Using 16S rRNA gene sequencing, 84 soil samples were analyzed from distances of 0, 3, and 6 m from Z. lotus patches. MiSeq sequencing generated 143,424 reads, representing 505 bacterial ASVs across 22 phyla. Alpha-diversity was highest at intermediate distances (3 m), while beta-diversity analyses revealed significant differences in community composition across distances (p = 0.035). Pseudomonadota dominated close to the shrub (44% at 0 m) but decreased at greater distances, whereas Bacillota and Actinobacteriota displayed distinct spatial patterns. A core microbiome comprising 44 ASVs (8.7%) was shared across all distances, with the greatest number of unique ASVs identified at 3 m. Random forest analysis highlighted Skermanella and Rubrobacter as key discriminatory taxa. These findings emphasize the spatial structuring of bacterial communities around Z. lotus patches, demonstrating the shrub’s substantial influence on bacterial dynamics in arid ecosystems. Full article

Global interest in nematode communities and their ecological relationships as unique and complex soil ecosystems has remarkably increased in recent years. As they have a representative role in the soil biota, nematodes present great potential to help understand soil health through analyzing their food chains in different environments. The objective of this study was to analyze the spatial and dynamic distributions of nematode communities and soil properties in two riparian areas of the Caatinga biome: one with native vegetation and the other with a history of agricultural use (modified). The study was carried out in a semi-arid region of Brazil in Parnamirim, PE. In both areas, sampling grids of 60 m × 40 m were established to obtain data on soil moisture, organic matter, particle size, electrical conductivity, and pH, as well as metabolic activity and ecological indices of nematode communities. There was a greater abundance and diversity of nematodes in riparian soils with native vegetation compared to in the modified area due to agricultural use and the dominance of exotic and invasive species. In both areas, bacterivores and plant-parasitic nematodes were dominant, with the genus Acrobeles and Tylenchorhynchus as the main contributors to the community. In the modified area, soil variables (fine sand, clay, and pH) positively influenced Fu4 and PP4 guilds, while in the area with native vegetation, moisture and organic matter exerted a greater influence on Om4, PP5, and Ba3 guilds. Kriging maps showed the soil variables were more concentrated in the center in the areas with native vegetation, in contrast to the area with modified vegetation, where they concentrated more on the margins. The functional guilds in the native vegetation did not exhibit a gradual increase towards the regions close to the riverbank, unlike in the modified area. The presence of plant-parasitic nematodes, especially of the genus Tylenchorhynchus, indicates the need for greater attention in the management of these ecosystems. The study contributes to understanding the interactions between nematode communities and soil in riparian areas of the Caatinga biome, emphasizing the importance of preserving native vegetation to maintain the diversity and balance of this ecosystem, in addition to highlighting the need for appropriate management practices in areas with a history of agricultural use, aiming to conserve soil biodiversity. Full article

Image analysis is a valuable approach in a wide array of environmental applications. Mapping land cover categories depicted from satellite images enables the monitoring of landscape dynamics. Such a technique plays a key role for land management and predictive ecosystem modelling. Satellite-based mapping of environmental dynamics enables us to define factors that trigger these processes and are crucial for our understanding of Earth system processes. In this study, a reclassification scheme of image analysis was developed for mapping the adjusted categorisation of land cover types using multispectral remote sensing datasets and Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The data included four Landsat 8–9 satellite images on 2015, 2019, 2021 and 2023. The sequence of time series was used to determine land cover dynamics. The classification scheme consisting of 17 initial land cover classes was employed by logical workflow to extract 10 key land cover types of the coastal areas of Bab-el-Mandeb Strait, southern Red Sea. Special attention is placed to identify changes in the land categories regarding the thermal saline lake, Lake Assal, with fluctuating salinity and water levels. The methodology included the use of machine learning (ML) image analysis GRASS GIS modules ‘r.reclass’ for the reclassification of a raster map based on category values. Other modules included ‘r.random’, ‘r.learn.train’ and ‘r.learn.predict’ for gradient boosting ML classifier and ‘i.cluster’ and ‘i.maxlik’ for clustering and maximum-likelihood discriminant analysis. To reveal changes in the land cover categories around the Lake of Assal, this study uses ML and reclassification methods for image analysis. Auxiliary modules included ‘i.group’, ‘r.import’ and other GRASS GIS scripting techniques applied to Landsat image processing and for the identification of land cover variables. The results of image processing demonstrated annual fluctuations in the landscapes around the saline lake and changes in semi-arid and desert land cover types over Djibouti. The increase in the extent of semi-desert areas and the decrease in natural vegetation proved the processes of desertification of the arid environment in Djibouti caused by climate effects. The developed land cover maps provided information for assessing spatial–temporal changes in Djibouti. The proposed ML-based methodology using GRASS GIS can be employed for integrating techniques of image analysis for land management in other arid regions of Africa. Full article

The objective of this study was to evaluate the physiological, biochemical, and productive effects of the foliar application of bioregulators, based on auxin, cytokinin, and gibberellic acid, on yellow melon, cultivar DALI^®, plants subjected to different salinity levels in a protected environment to simulate Brazil’s semi-arid conditions. The experiment was conducted using a completely randomized block design, in a 4 × 3 factorial scheme, with four salinity levels (0, 2, 4, and 6 dS m⁻¹) and three doses of the bioregulator, Stimulate^® (0%, 100%, and 150% of the recommended dose), with six weekly applications. The physiological variables (chlorophyll a fluorescence and gas exchange) and biochemical parameters (antioxidant enzyme activity and lipid peroxidation) were evaluated at 28 and 42 days after transplanting, and the agronomic traits (fresh fruit mass, physical attributes, and post-harvest quality) were evaluated at the end of the experiment. The results indicated that salinity impaired the physiological and productive performance of the plants, especially at higher levels (4 and 6 dS m⁻¹), causing oxidative stress, reduced photosynthesis, and decreased yield. However, the application of the bioregulator at the 100% dose mitigated the effects of salt stress under moderate salinity (2 dS m⁻¹), promoting higher CO₂ assimilation rates of up to 31.5%, better water-use efficiency, and reduced lipid peroxidation. In addition, the fruits showed a greater mass of up to 66%, thicker pulp, and higher soluble solids (> 10 °Brix) content, making them suitable for sale in the market. The 150% dose did not provide additional benefits and, in some cases, resulted in inhibitory effects. It is concluded that the application of Stimulate^® at the recommended dose is effective in mitigating the effects of moderate salinity, up to ~3 dS m⁻¹, in yellow melon crops; however, its effectiveness is limited under high salinity conditions, requiring the use of complementary strategies. Full article

Rapid urbanization and climate change pose significant challenges to air quality in arid metropolitan areas, with critical implications for public health and sustainable development. This study projects the evolution of air pollution in Riyadh, Saudi Arabia, through 2070 using an integrated modeling approach that combines CMIP6 climate projections with localized air quality data. We analyzed daily concentrations of major pollutants (SO₂, NO₂) across 15 strategically selected monitoring stations representing diverse urban environments, including traffic corridors, residential areas, healthcare facilities, and semi-natural zones. Climate data from two Earth System Models (CNRM-ESM2-1 and MPI-ESM1.2) were bias-corrected and integrated with historical pollution measurements (2000–2015) using hierarchical Bayesian statistical modeling under SSP2-4.5 and SSP5-8.5 emission scenarios. Our results revealed substantial deterioration in air quality, with projected increases of 80–130% for SO₂ and 45–55% for NO₂ concentrations by 2070 under high-emission scenarios. Spatial analysis demonstrated pronounced pollution gradients, with traffic corridors (Eastern Ring Road, Northern Ring Road, Southern Ring Road) and densely urbanized areas (King Fahad Road, Makkah Road) experiencing the most severe increases, exceeding WHO guidelines by factors of 2–3. Even semi-natural areas showed significant increases in pollution due to regional transport effects. The hierarchical Bayesian framework effectively quantified uncertainties while revealing consistent degradation trends across both climate models, with the MPI-ESM1.2 model showing a greater sensitivity to anthropogenic forcing. Future concentrations are projected to reach up to 70 μg m⁻³ for SO₂ and exceed 100 μg m⁻³ for NO₂ in heavily trafficked areas by 2070, representing 2–3 times the Traffic corridors showed concentration increases of 21–24% compared to historical baselines, with some stations (R5, R13, and R14) recording projected levels above 4.0 ppb for SO₂ under the SSP5-8.5 scenario. These findings highlight the urgent need for comprehensive emission reduction strategies, accelerated renewable energy transition, and reformed urban planning approaches in rapidly developing arid cities. Full article

In water-scarce regions, natural ecosystems and agriculture increasingly compete for limited water resources, intensifying stress during periods of drought. To assess these competing demands, we applied a modified PT-JPL model that incorporates the thermal inertial approach as a substitute for relative humidity (RH) in estimating soil evaporation—a method that significantly outperforms the original PT-JPL formulation in Mediterranean semi-arid irrigated areas. This remote sensing framework enabled us to quantify spatial and temporal variations in water use across both natural and agricultural systems within the UNESCO World Heritage site of Doñana. Our analysis revealed an increasing evapotranspiration (ET) trend in intensified agricultural areas and rice fields surrounding the National Park (R = 0.3), contrasted by a strong negative ET trend in wetlands (R < −0.5). These opposing patterns suggest a growing diversion of water toward irrigation at the expense of natural ecosystems. The impact was especially marked during droughts, such as the 2011–2016 period, when precipitation declined by 16%. In wetlands, ET was significantly correlated with precipitation (R > 0.4), highlighting their vulnerability to reduced water inputs. These findings offer crucial insights to support sustainable water management strategies that balance agricultural productivity with the preservation of ecologically valuable systems under mounting climatic and anthropogenic pressures typical of semi-arid Mediterranean environments. Full article

The assessment of the hydrological functions of landscapes and the landscape–hydrological background is an important instrument for minimizing damage from rivers and preventing water conflicts under conditions of data scarcity for hydrological modeling. To assess the climate–hydrological background of the East Kazakhstan region, the Selyaninov Hydro-thermal Coefficient and the Vysotsky–Ivanov Moisture Coefficient were used. The East Kazakhstan region is a typical continental arid and semi-arid region. The presence of mountain ranges, such as the Altai, makes the climate and environment in the region highly varied. A dataset from 30 weather stations for the period 1961–2023 was used for calculations. Three interpolation methods and landscape extrapolation were used to construct maps of the coefficients. Over the observation period, the values of the moisture indices at the weather stations in the region fluctuated within a wide range. Both coefficients are in the range from extra arid to extra humid climates. Full article

The accurate delineation of shrub crown boundaries is critical for ecological monitoring, land management, and understanding vegetation dynamics in fragile ecosystems such as semi-arid shrublands. While traditional image processing techniques often struggle with overlapping canopies, deep learning methods, such as convolutional neural networks (CNNs), offer promising solutions for precise segmentation. This study employed high-resolution imagery captured by unmanned aircraft systems (UASs) throughout the shrub growing season and explored the effectiveness of transfer learning for both semantic segmentation (Attention U-Net) and instance segmentation (Mask R-CNN). It utilized pre-trained model weights from two previous studies that originally focused on tree crown delineation to improve shrub crown segmentation in non-forested areas. Results showed that transfer learning alone did not achieve satisfactory performance due to differences in object characteristics and environmental conditions. However, fine-tuning the pre-trained models by unfreezing additional layers improved segmentation accuracy by around 30%. Fine-tuned pre-trained models show limited sensitivity to shrubs in the early growing season (April to June) and improved performance when shrub crowns become more spectrally unique in late summer (July to September). These findings highlight the value of combining pre-trained models with targeted fine-tuning to enhance model adaptability in complex remote sensing environments. The proposed framework demonstrates a scalable solution for ecological monitoring in data-scarce regions, supporting informed land management decisions and advancing the use of deep learning for long-term environmental monitoring. Full article

Wildfires, whether natural or human-caused, significantly alter soil properties and increase soil erosion susceptibility, particularly through changes in rill detachment capacity (Dc). This study aimed to evaluate the influence of fire intensity on key soil properties and to recognize their relationships with Dc under controlled laboratory conditions. The research was conducted in the Darestan Forest, Guilan Province, northern Iran, a region characterized by a Mediterranean semi-arid climate. Soil samples were collected from three fire-affected conditions: unburned (NF), low-intensity fire (LF), and high-intensity fire (HF) zones. A total of 225 soil samples were analyzed using flume experiments at five slope gradients and five flow discharges, simulating rill erosion. Soil physical and chemical characteristics were measured, including hydraulic conductivity, organic carbon, sodium content, bulk density, and water repellency. The results showed that HF soils significantly exhibited higher rill detachment capacity (1.43 and 2.26 times the values compared to the LF and NF soils, respectively) and sodium content and lower organic carbon, hydraulic conductivity, and aggregate stability (p < 0.01). Strong correlations were found between Dc and various soil properties, particularly a negative relationship with organic carbon. The multiple linear equation had good accuracy (R² > 0.78) in predicting rill detachment capacity. The findings of the current study show the significant impact of fire on soil degradation and rill erosion potential. The study advocates an urgent need for effective post-fire land management, erosion control, and the development of sustainable soil restoration strategies. Full article

Reconstructing historical grazing intensity is essential for understanding long-term human–environment interactions in arid and semi-arid regions. However, historical documents often lack continuous, site-specific information on land use and grazing pressure. We present a high-resolution reconstruction of pastoral activity and hydrological evolution since 1560 AD using fungal spore assemblages from a 92 cm lacustrine-peat sequence from the Sichanghu (SCH) peatland on the northern slope of the Tianshan Mountains, Central Asia. Quantitative analysis of coprophilous fungal spores and principal component analysis (PCA) of spore influxes identify three distinct phases of pastoral intensity: gradual intensification from 1560 to 1730 AD, a sharp decline from 1730 to 1770 AD, and rapid intensification from 1770 AD to the present. These transitions are consistent with historical records of land use and human migration in Xinjiang. Additionally, fungal assemblages reveal a long-term drying trend at Sichanghu, broadly consistent with regional aridification in northwestern China. However, centennial-scale discrepancies in humidity between local and regional records—particularly during the late Little Ice Age—indicate that local hydrological responses were strongly influenced by anthropogenic disturbances. This study highlights the value of fungal spores, particularly influx-based interpretations, as robust indicators of both human activities and hydroclimatic variability. It also underscores the importance of integrating local and regional signals when reconstructing past environmental changes in sensitive dryland ecosystems. Full article

Aerosols play a critical role in modulating the land–atmosphere energy balance, influencing regional climate dynamics, and affecting air quality. Xinjiang, a typical arid and semi-arid region in China, frequently experiences dust events and complex aerosol transport processes. This study provides a comprehensive analysis of the spatiotemporal evolution and potential source regions of aerosols in Xinjiang from 2005 to 2023, based on Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products (MCD19A2), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical profiles, ground-based PM_2.5 and PM₁₀ concentrations, MERRA-2 and ERA5 reanalysis datasets, and HYSPLIT backward trajectory simulations. The results reveal pronounced spatial and temporal heterogeneity in aerosol optical depth (AOD). In Northern Xinjiang (NXJ), AOD exhibits relatively small seasonal variation with a wintertime peak, while Southern Xinjiang (SXJ) shows significant seasonal and interannual variability, characterized by high AOD in spring and a minimum in winter, without a clear long-term trend. Dust is the dominant aerosol type, accounting for 96.74% of total aerosol content, and AOD levels are consistently higher in SXJ than in NXJ. During winter, aerosols are primarily deposited in the near-surface layer as a result of local and short-range transport processes, whereas in spring, long-range transport at higher altitudes becomes more prominent. In NXJ, air masses are primarily sourced from local regions and Central Asia, with stronger pollution levels observed in winter. In contrast, springtime pollution in Kashgar is mainly influenced by dust emissions from the Taklamakan Desert, exceeding winter levels. These findings provide important scientific insights for atmospheric environment management and the development of targeted dust mitigation strategies in arid regions. Full article