Search Results (309)

The cessation of traditional mountain grazing has emerged as a principal driver of habitat degradation and the local extinction of Parnassius apollo (Linnaeus, 1758) in Serbia. While previous studies have cited multiple contributing factors, our research provides evidence that the abandonment of extensive livestock grazing has triggered vegetation succession, the disappearance of the larval host plant (Sedum album), and a reduction in microhabitat heterogeneity—conditions essential for the persistence of this stenophagous butterfly species. Through satellite-based analysis of vegetation dynamics (2015–2024), we identified clear structural differences between habitats that currently support populations and those where the species is no longer present. Occupied sites were characterized by low levels of exposed soil, moderate grass coverage, and consistently high shrub and tree density, whereas unoccupied sites exhibited dense encroachment of grasses and woody vegetation, leading to structural instability. Furthermore, MODIS-derived indices (2010–2024) revealed a consistent decline in vegetation productivity (GPP, FPAR, LAI) in succession-affected areas, alongside significant correlations between elevated land surface temperatures (LST), thermal stress (TCI), and reduced photosynthetic capacity. A wildfire event on Mount Stol in 2024 further exacerbated habitat degradation, as confirmed by remote sensing indices (BAI, NBR, NBR2), which documented extensive burn scars and post-fire vegetation loss. Collectively, these findings indicate that the decline of P. apollo is driven not only by ecological succession and climatic stressors, but also by the abandonment of land-use practices that historically maintained suitable habitat conditions. Our results underscore the necessity of restoring traditional grazing regimes and integrating ecological, climatic, and landscape management approaches to prevent further biodiversity loss in montane environments. Full article

Information on the effects of differences in root and soil properties on Saturated hydraulic conductivity (K_s) is crucial for estimating rainfall infiltration and evaluating sustainable ecological development. This study selected typical grass shrub composite plots widely distributed in hilly and gully areas of the Loess Plateau: Caragana korshinskii, Caragana korshinskii and Agropyron cristatum (fibrous root), and Caragana korshinskii and Artemisia gmelinii (taproot). Samples were collected at different distances from the base of the shrub (0 cm, 50 cm), with a sampling depth of 0–30 cm. The constant head method is used to measure the K_s. The K_s decreased with increasing soil depth. Due to the influence of shrub growth, there was significant spatial heterogeneity in the distribution of K_s at different positions from the base of the shrub. Compared to the sample location situated 50 cm from the base of the shrub, it was observed that in a single shrub plot, the K_s at the base were higher, while in a grass shrub composite plot, the K_s at the base were lower. Root length density, >0.25 mm aggregates, and organic matter were the main driving factors affecting K_s. The empirical equation established by using principal component analysis to reduce the dimensions of these three factors and calculate the comprehensive score was more accurate than the empirical equation established by previous researchers, who considered only root or soil properties. Root length density and organic matter had significant indirect effects on K_s, reaching 52.87% and 78.19% of the direct effects, respectively. Overall, the composite plot of taproot herbaceous and shrub (Caragana korshinskii and Artemisia gmelinii) had the highest K_s, which was 82.98 cm·d⁻¹. The ability of taproot herbaceous plants to improve K_s was higher than that of fibrous root herbaceous plants. The research results have certain significance in revealing the influence mechanism of the grass shrub composite on K_s. Full article

Leaf phenology is key to ecosystem functioning by regulating carbon, water, and energy fluxes and influencing vegetation productivity. Yet, detecting land surface phenology (LSP) in drylands using remote sensing remains particularly challenging due to sparse and heterogeneous vegetation cover, high spatiotemporal variability, and complex spectral signals. Unlike the Northern Hemisphere, these challenges are further compounded in the Southern Hemisphere (SH), where several regions experience year-round moderate temperatures. When combined with irregular rainfall, this leads to highly variable vegetation activity throughout the year. However, LSP dynamics in the SH remain poorly understood. This study presents a review of remote sensing-based phenology research in drylands, integrating (i) a synthesis of global methodological advances and (ii) a systematic analysis of peer-reviewed studies published from 2015 through April 2025 focused on SH drylands. This review reveals a research landscape still dominated by conventional vegetation indices (e.g., NDVI) and moderate-spatial-resolution sensors (e.g., MODIS), though a gradual shift toward higher-resolution sensors such as PlanetScope and Sentinel-2 has emerged since 2020. Despite the widespread use of start- and end-of-season metrics, their accuracy varies greatly, especially in heterogeneous landscapes. Yet, advanced products such as solar-induced chlorophyll fluorescence or the fraction of absorbed photosynthetically active radiation were rarely employed. Gaps remain in the representation of hyperarid zones, grass- and shrub-dominated landscapes, and large regions of Africa and South America. Our findings highlight the need for multi-sensor approaches and expanded field validation to improve phenological assessments in dryland environments. The accurate differentiation of vegetation responses in LSP is essential not only for refining phenological metrics but also for enabling more realistic assessments of ecosystem functioning in the context of climate change and its impact on vegetation dynamics. Full article

Understanding the interplay between plant leaf functional traits and plant and soil factors under different soil thicknesses is significant for quantifying the interaction between plant growth and the environment. However, in the context of ecological restoration of vegetation in mining areas, there has been a lot of research on trees, shrubs, and grasses, but the characteristics and correlations of leaf functional traits of vines have not been fully studied to a large extent. Here, we report the differences in leaf functional traits of six vine plants (Parthenocissus quinquefolia, Pueraria lobata, Hedera nepalensis, Campsis grandiflora, Mucuna sempervirens, and Parthenocissus tricuspidata) with distinct growth forms in different soil cover thicknesses (20 cm, 40 cm, and 60 cm). In addition, soil factor indicators under different soil cover thicknesses were measured to elucidate the linkages between leaf functional traits of vine plants and soil factors. We found that P. lobata showed a resource acquisition strategy, while H. nepalensis demonstrated a resource conservation strategy. C. grandiflora and P. tricuspidata shifted toward more conservative resource allocation strategies as the soil cover thickness increased, whereas M. sempervirens showed the opposite trend. In the plant trait–trait relationships, there were synergistic associations between specific leaf area (SLA) and leaf nitrogen content (LNC); leaf moisture content (LMC) and leaf nitrogen-to-phosphorus ratio (LN/P); and leaf specific dry weight (LSW), leaf succulence degree (LSD), and leaf dry matter content (LDMC). Trade-offs were observed between SLA and LSW, LSD, and LDMC; between leaf phosphorus content (LPC) and LN/P; and between LMC, LSW, and LDMC. In the plant trait–environment relationships, soil nutrients (pH, soil total phosphorus content (STP), and soil ammonium nitrogen content (SAN)) and soil enzyme activities (cellulase (CB), leucine aminopeptidase (LAP), enzyme C/N activity ratio, and enzyme N/P activity ratio) were identified as the primary drivers of variation in leaf functional traits. Interestingly, nitrogen deficiency constrained the growth of vine plants in the mining area. Our study revealed that the responses of leaf functional traits of different vines under different soil thicknesses have significant species specificity, and each vine shows different resource acquisition and conservation strategies. Furthermore, soil cover thickness primarily influences plant functional traits by directly affecting soil enzyme activities and nutrients. However, the pathways through which soil thickness impacts these traits differ among various functional traits. Our findings provide a theoretical basis and practical reference for selecting vine plants and optimizing soil cover techniques for ecological restoration in mining areas. Full article

Woody plant encroachment (WPE) is transforming grasslands globally, yet accurately mapping this process remains challenging. State-funded, publicly available high-resolution aerial imagery offers a potential solution, including the USDA’s National Agriculture Imagery Program (NAIP) and NSF’s National Ecological Observatory Network (NEON) Aerial Observation Platform (AOP). We evaluated the accuracy of land cover classification using NAIP, NEON, and both sources combined. We compared two machine learning models—support vector machines and random forests—implemented in R using large training and evaluation data sets. Our study site, Konza Prairie Biological Station, is a long-term experiment in which variable fire and grazing have created mosaics of herbaceous plants, shrubs, deciduous trees, and evergreen trees (Juniperus virginiana). All models achieved high overall accuracy (>90%), with NEON slightly outperforming NAIP. NAIP underperformed in detecting evergreen trees (52–78% vs. 83–86% accuracy with NEON). NEON models relied on LiDAR-based canopy height data, whereas NAIP relied on multispectral bands. Combining data from both platforms yielded the best results, with 97.7% overall accuracy. Vegetation indices contributed little to model accuracy, including NDVI (normalized digital vegetation index) and EVI (enhanced vegetation index). Both machine learning methods achieved similar accuracy. Our results demonstrate that free, high-resolution imagery and open-source tools can enable accurate, high-resolution, landscape-scale WPE monitoring. Broader adoption of such approaches could substantially improve the monitoring and management of grassland biodiversity, ecosystem function, ecosystem services, and environmental resilience. Full article

This review presents information obtained over the past 10 years on the methods to control the widespread worldwide phytopathogen Pectobacterium carotovorum subsp. carotovorum (Pcc). This bacterium is among the ten most dangerous phytopathogens; it affects a wide range of cultivated plants: vegetables, ornamental and medicinal crops, both during vegetation and during the storage of fruits. Symptoms of Pcc damage include the wilting of plants, blackening of vessels on leaves, stems and petioles. At the flowering stage, the stem core gradually wilts and, starting from the root, the stem breaks and the plant dies. Pcc is a rod-shaped, non-capsule and endospore-forming facultative anaerobic Gram-negative bacterium with peritrichous flagellation. Pcc synthesizes bacteriocins—carocins. The main virulence factors of Pcc are the synthesis of N-acyl-homoserine lactone (AHL) and plant cell wall-degrading enzymes (PCWDEs) (pectinases, polygalacturonases, cellulases, and proteases). Diagnostic methods for this phytopathogen include polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP), multilocus genotyping of strain-specific genes and detection of unique volatile organic compounds (VOCs). The main methods to control this microorganism include the use of various chemicals (acids, phenols, esters, salts, gases), plant extracts (from grasses, shrubs, trees, and algae), antagonistic bacteria (Bacillus, Pseudomonas, Streptomyces, and lactic acid bacteria), viruses (including a mixture of bacteriophages), and nanomaterials based on metals and chitosan. Full article

Vegetation restoration is critical for improving soil quality and microbial community dynamics in degraded mining areas. This study explored the effects of different vegetation types (grassland, shrubland, and mixed grass–shrub areas) on soil physicochemical properties, organic carbon fractions, and abundant versus scarce microbial taxa assemblies in a Loess Plateau coal mining area. Soil samples from four depths (0–100 cm) were analyzed using high-throughput sequencing for nutrient content; carbon components, soil organic carbon (SOC), particulate organic carbon (POC), mineral-associated organic carbon (MAOC), dissolved organic carbon (DOC), microbial biomass organic carbon (MBC), and readily oxidizable organic carbon (ROC); microbial diversity. Shrubland soils exhibited significantly higher total nitrogen (TN), total phosphorus (TP), and organic carbon components (SOC, MAOC, and POC) than other vegetation types (p < 0.05), with the greatest carbon accumulation noted in the surface layer depths (0–20 cm). Microbial communities displayed vegetation-specific patterns: abundant taxa (e.g., Actinobacteria, Proteobacteria) dominated nutrient cycling and exhibited resilience to environmental gradients, while rare taxa (e.g., Methylomirabilota, Olpidiomycota) correlated strongly with labile carbon fractions (DOC and POC) and demonstrated metabolic flexibility. Mantel tests identified soil pH, TN, and organic carbon components as key drivers of microbial community divergence (p < 0.01). Shrubland vegetation enhanced soil nutrient retention and carbon stabilization, whereas the mixed grass–shrub systems promoted niche partitioning among rare taxa. These findings highlight the roles of vegetation-mediated carbon inputs and environmental filtering in shaping microbial assembly, providing a scientific framework for optimizing restoration strategies in mining ecosystems. Full article

As a typical desert oasis ecosystem in the arid region of Northwest China, the Ejina Delta plays a crucial role in regional ecological security through its vegetation dynamics and landscape pattern changes. Based on Landsat remote sensing images (1990–2020), runoff data, and vegetation landscape surveys, this study investigated the evolutionary patterns and driving mechanisms of vegetation degradation and restoration processes using Normalized Difference Vegetation Index (NDVI), landscape metrics, and Land Use Transition Matrix (LUTM) methods. The following key findings were obtained: (1) Since the implementation of the Ecological Water Diversion Project (EWDP) in the Heihe River Basin (HRB) in 2000, a significant recovery in vegetation coverage has been observed, with an NDVI growth rate of 0.0187/10 yr, which is five times faster than that in the pre-diversion period. The areas of arbor vegetation, shrubland, and grassland increased to 356.8, 689.5, and 2192.6 km², respectively. However, there is a lag of about five years for the recovery of arbor and shrub compared to grass. (2) The implementation of EWDP has effectively reversed the trend of vegetation degradation, transforming the previously herb-dominated fragmented landscape into a more integrated pattern comprising multiple vegetation types. During the degradation period (1990–2005), the landscape exhibited a high degree of fragmentation, with an average number of patches (NP) reaching 45,875. In the subsequent recovery phase (2005–2010), fragmentation was significantly reduced, with the average NP dropping to 30,628. (3) Stronger vegetation growth and higher NDVI values were observed along the riparian zone, with the West River demonstrating greater restoration effectiveness compared to the East River. This study revealed that EWDP serves as the key factor driving vegetation recovery. To enhance oasis stability, future ecological management strategies should optimize spatiotemporal water allocation while considering differential vegetation responses. Full article

The cattle sector plays a critical role in Peru’s agricultural economy, yet it faces challenges related to low productivity and environmental degradation. Sustainable alternatives like silvo-pastoral systems (SPSs) offer promising solutions to enhance both economic returns and ecological outcomes in cattle farming. This study examines the economic viability of an intensive SPS (SPSi) compared to traditional monoculture grass systems in San Martín, Peru. The SPSi under study is in the evaluation phase, integrates grasses, legumes, shrubs, and trees, and has the potential to enhance cattle farming profitability while simultaneously offering environmental benefits such as improved soil health and reduced greenhouse gas emissions. Through a discounted cash flow model over an eight-year period, key profitability indicators—Net Present Value (NPV), Internal Rate of Return (IRR), Benefit–Cost Ratio (BC), and payback period—were estimated for four dual-purpose cattle production scenarios: a traditional system and three SPSi scenarios (pessimistic, moderate, and optimistic). Monte Carlo simulations were conducted to assess risk, ensuring robust results. The results show that the NPV for the traditional system was a modest USD 61, while SPSi scenarios ranged from USD 9564 to USD 20,465. The IRR improved from 8.17% in the traditional system to between 26.63% and 30.33% in SPSi scenarios, with a shorter payback period of 4.5 to 5.8 years, compared to 7.98 years in the traditional system. Additionally, the SPSi demonstrated a 30% increase in milk production and a 50% to 250% rise in stocking rates per hectare. The study recommends, subject to pending validations through field trials, promoting SPSi adoption through improved access to credit, technical assistance, and policy frameworks that compensate farmers for ecosystem services. Policymakers should also implement monitoring mechanisms to mitigate unintended consequences, such as deforestation, ensuring that SPSi expansion aligns with sustainable land management practices. Overall, the SPSi presents a viable solution for achieving economic resilience and environmental sustainability in Peru’s cattle sector. Full article

Livestock production systems can negatively affect soil structure, resulting in negative changes in physical–hydraulic properties, compromising soil functioning and productivity. This research aimed to evaluate the effects of rotational silvopastoral systems on soil physical–hydraulic functioning in their second year of implementation. The study was performed under Oxisol soil with a loamy sand texture in Southeast Brazil. We considered four grazing systems: an intensive silvopastoral system with Panicum maximum in consortium with Leucaena leucocephala (ISPS + L), an intensive silvopastoral system with Panicum maximum in consortium with Tithonia diversifolia (ISPS + T), an silvopastoral system with Panicum maximum (SPS) with tree row (TRs), and open pasture under a rotational grazing system with Panicum maximum (OP). The treatments ISPS + L, ISPS + T, and SPS had tree rows (TRs) every 20 m composed of Khaya ivorenses, Leucaena leucocephala, Eucalyptus urograndis, Acacia mangium, and Gliricidia sepium. Nine physical–hydraulic indicators were evaluated in the first 0.40 m of depth: bulk density (Bd), total porosity (TP), macroporosity (MaP), microporosity (MiP), field capacity (FC), permanent wilting point (PWP), available water content (AWC), total soil aeration capacity (ACt), and S-index. The soil physical–hydraulic properties were sensitive to the effects of the livestock systems. The use of silvopastoral systems in consortium with grass (ISPS + L and ISPS + T) allowed for better soil water retention, resulting in higher FC and AWC than the OP, SPS, and TR. The indicators Bd, ACt, MaP, FC, MiP, and S-index presented the greatest variance; however, FC, ACt, MaP, and MiP enabled the greatest differentiation among systems. Therefore, these properties are important in studies on soil physical quality since they provide information about the soil porous status and its ability to retain water and exchange soil air and gases. Therefore, enhancing the physical–hydraulic attributes of the soil in silvopastoral systems with shrub species is crucial for ensuring long-term productive sustainability and strengthening environmental resilience against future climate challenges. Full article

Fire is important in boreal forest ecosystems, but comprehensive recovery analysis is lacking for soil nutrients and plant traits in China boreal forests, where the strict “extinguish at sight” fire prevention policy has been implemented. Based on over 50 years of forest fire recordings in the Daxing’anling Mts, 48 pairs of burnt and unburnt controls (1066 plots) were selected for 0–20 cm soil sampling and plant surveys. We recorded 18 plant parameters of the abundance of each tree, shrub, grass, and plant size (height, diameter, and coverage), 7 geo-topographic data parameters, and 2 fire traits (recovery year and burnt area). We measured eight soil properties (soil organic carbon, SOC; total nitrogen, TN; total phosphorus, TP; alkali-hydrolyzed P, AP; organic P, Po; inorganic P, Pi; total glomalin-related soil protein, T-GRSP; easily-extracted GRSP, EE-GRSP). Paired T-tests revealed that the most significant impact of the fire was a 25%–48% reduction in tree sizes, followed by decline in the plant diversity of arbors and shrubs but increasing plant diversity in herbs. GRSP showed an >18% increase and P_o decreased by 17% (p < 0.05). Redundancy ordination showed that the post-fire recovery years and burnt area were the most potent explainer for the variations (p < 0.05), strongly interacting with latitudes and longitudes. Plant richness and tree size were directly affected by fire traits, while the burnt area and recovery times indirectly increased the GRSP via plant richness. A fire/control ratio chronosequence found that forest community traits (tree size and diversity) and soil nutrients could be recovered to the control level after ca. 30 years. This was relatively shorter than in reports on other boreal forests. The possible reasons are the low forest quality from overharvesting in history and the low fire severity from China’s fire prevention policy. This policy reduced the human mistake-related fire incidence to <10% in the 2010s in the studied region. Chinese forest fire incidences were 3% that of the USA. The burnt area/fire averaged 5 hm² (while the USA averaged 46 hm², Russia averaged 380 hm², and Canada averaged 527 hm²). Overharvesting resulted in the forest height declining at a rate of >10 cm/year. Our finding supports forest management and the evaluation of forest succession after wildfires from a holistic view of plant–soil interactions. Full article

Background: Exposure to household air pollution (HAP) is one of the primary risk factors for acute lower respiratory infection (ARI) morbidity and mortality among children in low-income settings. This study aimed to examine the relative contribution of residing in deprived neighbourhoods and exposure to HAP on the occurrence of ARI among children using data from the 2014–2015 Chad Demographic and Health Survey (DHS). Methods: We applied multilevel modelling techniques to survey data of 2882 children from 372 communities to compute the odds ratio (OR) for the occurrence of ARI between children of respondents exposed to clean fuels (e.g., electricity, liquid petroleum gas, natural gas, and biogas) and respondents exposed to polluting fuel (e.g., kerosene, coal/lignite, charcoal, wood, straw/shrubs/grass, and animal dung). Results: The results showed that children exposed to household polluting fuels in Chad were 215% more likely to develop ARI than those not exposed to household air pollution (OR = 3.15; 95% CI 2.41 to 4.13). Further analysis revealed that the odds of ARI were 185% higher (OR = 2.85; 95% CI 1.73 to 4.75) among children living in rural residents and those born to teenage mothers (OR = 2.75; 95% CI 1.48 to 5.15) who were exposed to household polluting fuels compared to their counterparts who were not exposed. In summary, the results of the study show that the risk of ARI is more common among children who live in homes where household air-polluting cooking fuel is widely used, those living in rural areas, those living in socioeconomically deprived neighbourhoods and from the least wealthy households, and those born to teenage mothers in Chad. Conclusions: In this study, an independent relative contribution of variables, such as HAP from cooking fuel, neighbourhood deprivation, living in rural areas, being from a low-income household, having a mother who is a manual labourer worker, and being given birth to by a teenage mother, to the risk of ARI among children is established. Full article

In order to clarify the change characteristics of soil organic carbon and its components in sandy land after restoration of different artificial vegetation measures, this study took 4 common artificial planting measures in Hulunbuir Sandy Land as the research object and took mobile sandy land as the control (CK). The results show the following: (1) The soil organic carbon content of the treatment measures was as follows: arbor-irrigation-grass (22.96 g·kg⁻¹) > single arbor (12.68 g·kg⁻¹) > single shrub (11.17 g·kg⁻¹) > single herb (8.89 g·kg⁻¹) > CK (1.14 g·kg⁻¹). The soil organic carbon showed an increasing trend, and the change was significant. (2) The contents of POC (Particulate Organic Carbon), MBC (Microbial Organic Carbon), DOC (Soluble Organic Carbon) and EOC () in soil were significantly increased by planting treatment measures, and the contents of each component in the combination mode of tree, shrub and grassland were the highest, which were increased by 1541.32%, 302.44%, 340% and 204.88% compared with CK. (3) There Please check that intended meaning has been retained. was a significant positive correlation between SOC and its components, and the correlation coefficients were 0.93, 0.91, 0.93, and 0.93, respectively. TC, TN and TP are all important influencing factors of POC. The correlation coefficient between DOC and MBC, MBC and EOC reached 0.96. (4) The carbon sequestration effect of the combination of trees, shrubs, and grassland is good, and the vegetation growth is good, which is conducive to the accumulation of organic carbon in the surface soil. It is suggested that the combination of treess, shrubs, and grassland should be adopted in the process of sandy land management. Full article

Land surface reflectance is a basic physical parameter in many quantitative remote sensing models. However, the existing reflectance conversion techniques for drone-based (or UAV-based) remote sensing need further improvement and optimization due to either cumbersome operational procedures or inaccurate results. To tackle this problem, this study proposes a novel method to mathematically implement the separation of direct and scattering radiation using a self-developed multi-angle light intensity device. The verification results from practical experiments demonstrate that the proposed method has strong adaptability, as it can obtain accurate surface reflectance even under complicated conditions where both illumination intensity and component change simultaneously. Among the six selected typical land cover types (i.e., lake water, slab stone, shrub, green grass, red grass, and dry grass), green grass has the highest error among the five multispectral bands with a mean absolute error (MAE) of 1.59%. For all land cover types, the highest MAE of 1.01% is found in the red band. The above validation results indicate that the proposed land surface reflectance conversion method has considerably high accuracy. Therefore, the study results may provide valuable references for quantitative remote sensing applications of drone-based multispectral data, as well as the design of future multispectral drones. Full article

Water scarcity is a growing global issue, especially in arid and semi-arid rangelands, primarily due to climate change and population growth. Groundwater is a crucial resource for vegetation in these ecosystems, yet its role in supporting plant life is often not fully understood. This review explores the interactions between groundwater and vegetation dynamics in various rangeland types. Groundwater serves as a critical water source that helps sustain plants, but changes in its availability, depth, and quality can significantly impact plant health, biodiversity, and ecosystem stability. Research indicates that groundwater depth affects vegetation types and their distribution, with specific plants thriving at certain groundwater levels. For instance, in grasslands, shallow groundwater can support diverse herbaceous species, while deeper conditions may favor drought-tolerant shrubs and trees. Similarly, in forest ecosystems, extensive root systems access both groundwater and soil moisture, playing a vital role in water regulation. Savanna environments showcase complex interactions, where trees and grasses compete for water, with groundwater potentially benefiting trees during dry seasons. Climate change poses additional challenges by altering rainfall patterns and temperatures, affecting groundwater recharge and availability. As a result, it is crucial to develop effective management strategies that integrate groundwater conservation with vegetation health. Innovative monitoring techniques, including remote sensing, can provide valuable information about groundwater levels and their impact on vegetation, enhancing water resource management. This review emphasizes the importance of understanding groundwater–vegetation interactions to guide sustainable land and water management practices. By enhancing our knowledge of these connections and utilizing advanced technologies, we can promote ecosystem resilience, secure water resources, and support biodiversity in rangeland systems. Collaborative efforts among local communities, scientists, and policymakers are essential to address the pressing issues of water scarcity and to ensure the sustainability of vital ecosystems for future generations. Full article