Search Results (62)

Aridification and hotter droughts are triggering forest die-off events characterized by high mortality rates and declines in forest productivity. The western Mediterranean Basin is a climate change hotspot where many of these die-off events have affected several tree and shrub species in recent decades. Yet, the responses of canopy greenness and cover, radial growth, and gross primary productivity (GPP) to climate in these die-off sites remain poorly understood across species and biomes. Here, we examined 44 sites across Spain, covering humid, dry sub-humid, and semi-arid biomes, and including nine tree and one shrub species. We obtained and correlated monthly climate data, satellite-derived vegetation indices (Normalized Difference Vegetation Index, Enhanced Vegetation Index), tree-ring metrics (basal area increment, ring-width indices), and GPP. We assessed climate trends and relationships between climate, vegetation indices, growth, GPP, and resilience after five extreme drought years in the period 1984–2023. Climate warming impacted all sites, increasing vapor pressure deficit and reducing soil moisture availability, with semi-arid sites warming the most. Vegetation indices and growth showed the largest declines during extreme droughts in dry sub-humid and semi-arid sites. Correlations with climate variables highlighted strong sensitivity to drought stress, particularly regarding growth metrics. During die-off events, GPP significantly declined in the growing season, but no legacy effects were observed afterwards. Vegetation indices and growth partially recovered one year after drought, with resilience peaking for GPP in semi-arid sites. Hotter droughts constrain GPP and growth, especially in dry sub-humid and semi-arid forests. Forests and shrublands experiencing die-off are diagnostic monitors of drought-induced thresholds in ecosystem productivity. Full article

This study quantified total biomass stocks in Carrasco (CAR, n = 12), a dense tropical deciduous vegetation type from the Brazilian semi-arid region for which biomass information remains scarce. We also evaluated differences in floristic composition, diversity, structure, and biomass allocation patterns relative to Cerrado sensu stricto (CSS, n = 40). Forest inventories were conducted in southeastern Brazil. Woody biomass was estimated using a regional allometric equation. Roots were sampled in a position adjacent to the plots, and litter was collected at the center of each plot using a frame. Necromass was assessed along a linear transect corresponding to the length of each plot using the line-intersect method. Biomass differences between vegetation types were assessed using generalized linear and mixed-effects models (GLMs and GLMMs). Total biomass reached 45.24 Mg ha⁻¹ in CSS and 59.01 Mg ha⁻¹ in CAR. In CSS, woody biomass predominated (20.47 Mg ha⁻¹; 45%), followed by roots (18.47 Mg ha⁻¹; 41%), litter (5.49 Mg ha⁻¹; 12%), and necromass (0.81 Mg ha⁻¹; 2%). In CAR, roots were the dominant component (32.37 Mg ha⁻¹; 55%), followed by woody biomass (16.57 Mg ha⁻¹; 28%), litter (8.39 Mg ha⁻¹; 14%), and necromass (1.68 Mg ha⁻¹; 3%). CSS and CAR shared only 10% of their species and showed significant differences in total biomass (TB) and belowground biomass (BGB), while aboveground biomass (AGB), aboveground woody biomass (AGWB), litter, and necromass did not differ significantly (α = 0.05). The BGB/AGWB ratio was <1 in CSS and >1 in CAR, resembling global patterns of savanna/shrubland and grassland formations, respectively. Considering the sampling design adopted, despite the higher stem density in CAR, larger individuals in CSS compensated for structural differences, resulting in similar aboveground biomass stocks. Our findings reinforce the floristic and structural distinctiveness of Carrasco and reveal contrasting biomass allocation strategies, with a strong dominance of belowground biomass in CAR. These results demonstrate that aboveground-based assessments can substantially underestimate total biomass in semi-arid transitional vegetation and highlight the need to incorporate non-forest ecosystems into biomass inventories, conservation planning, and climate change mitigation strategies. Full article

Restoration of degraded shrublands is a major challenge for combating desertification in arid and semi-arid regions. Caragana korshinskii Kom., a dominant sand-fixing shrub widely planted in northern China, often shows growth decline and structural degradation as stand age increases. Stubble management is widely used to rejuvenate degraded shrublands; however, its influence on nutrient cycling and carbon-nitrogen-phosphorus (C-N-P) stoichiometric coupling within the leaf-soil system remains unclear. Here, we conducted a two-factor field experiment in a 30-year-old degraded C. korshinskii plantation in the Kubuqi Desert, northern China, manipulating stubble height and stubble density. Moderate stubble height (10 cm) significantly increased leaf N concentration (27.37 g kg⁻¹) and improved soil C and N availability, whereas higher stubble height (20 cm) led to elevated leaf N:P ratios (24.2), indicating stronger phosphorus limitation. In addition, all stubble density treatments significantly reduced leaf C:N, C:P, and N:P ratios. Among them, the two stubbled after one retained exhibited the most pronounced effect, with C:N and C:P decreasing to 14 and 273, respectively, and N:P to 20, suggesting an improved nutrient balance and allocation efficiency. Multivariate analyses showed that lower stubble heights combined with alternate-plant stubble patterns (H2D1 and H2D2) enhanced leaf-soil nutrient coupling and promoted coordinated recovery of C-N-P stoichiometry during regeneration. Overall, stubble management regulates shrub rejuvenation mainly by modifying leaf-soil nutrient coupling rather than single-element responses. It is recommended that, in the management of degraded C. korshinskii shrublands, a stubble height of approximately 10 cm combined with staggered cutting (alternate-plant or every two plants) be prioritized as an optimized management regime. Full article

Aridity is a key climatic factor influencing ecosystem dynamics and land degradation in arid and semi-arid regions. This study analyzes the spatial relationship between aridity and land cover degradation in northwestern Mexico during 2005–2020 using a Geographically Weighted Regression (GWR) model, complemented by spatial autocorrelation techniques including Moran’s I and Local Indicators of Spatial Association (LISA). Aridity was derived from climatic data, and land cover transitions were used as proxies for degradation. The results indicate that the study area is predominantly characterized by arid and semi-arid conditions, where degradation-related transitions are strongly concentrated. In particular, transitions from shrubland to grassland (59.53%) and from shrubland to bare soil (93.60%) occur primarily under arid conditions, highlighting the high vulnerability of these ecosystems to water deficit. The GWR model explains approximately 49.5% of the spatial variability in degradation. However, residual analysis shows strong spatial autocorrelation (Moran’s I = 0.72, p < 0.001), indicating spatially structured patterns not fully captured by the model. These findings demonstrate that, although aridity is a key driver, additional factors influence degradation patterns. Full article

Soil phosphorus (P) deficiency is an important factor limiting plant growth in the semi-arid Loess Plateau region in China. The topsoils in this area undergo repeated drying–rewetting (DRW) cycles, which can influence soil P availability, a process that may become more pronounced due to climate change. However, little is known about how soil P availability responds to DRW cycles under different land-use types. To investigate this issue, we conducted three 120-day soil culture experiments to investigate the direction and magnitude of soil available P and the responses of its influencing factors to repeated DRW cycles and their frequency and intensity under three typical land-use types (cropland, grassland, and shrubland) in Gansu Province, North-western China. The results showed that the available P concentration of cropland, grassland, and shrubland soils after repeated DRW cycles significantly decreased by 8.9%, 11.5%, and 14.2%, respectively, compared with a constant humidity control. With increasing intensity of the DRW cycles, the available P concentration of grassland and shrubland soils significantly increased by 14.3% and 15.5%, respectively, while in cropland soil P significantly decreased by 10.4%. Compared with low-frequency DRW cycles, high-frequency DRW cycles significantly reduced the available P concentration by 6.4% in grassland soil and increased it by 9.8% in shrubland soil but had no significant effect in cropland soil. Overall, the responses of soil P availability to repeated DRW cycles vary among different land-use types, and the magnitude of the soil P availability response to repeated DRW cycles depended strongly on soil microorganism biomass, phosphatase activity, and the initial soil properties, being more pronounced in grassland and shrubland soils than in cropland soils. It is therefore essential to consider land-use type when studying the effects of DRW on soil P cycling in semi-arid regions, especially in the context of climate change. Full article

The sustainability of resources and ecological integrity are significantly influenced by land use and land cover change (LULCC) dynamics, particularly in ecotonal semi-arid regions where biome transitions are highly sensitive to anthropogenic disturbance and climatic variability. This study aims to assess historical LULCC dynamics and spatial reconfiguration across nine classes (grassland, shrubland, wetlands, forestland, waterbodies, farmed land, built-up land, bare land, and mines/quarries) in the C5 Secondary Drainage Region of South Africa over the three periods 1990–2014, 2014–2022, and 1990–2022. Using the South African National Land Cover datasets and the TerrSet liberaGIS v20.03 Land Change Modeller, this research applied post-classification comparison, transition matrices, asymmetric gain–loss metrics, and patch-based landscape analysis to quantify the magnitude, direction, source–sink dynamics, and spatial reconfiguration of LULCC. Results showed that between 1990 and 2014, Shrubland expanded markedly (+49.1%), primarily at the expense of Grassland, Wetlands, and Bare land, indicating bush encroachment and hydrological stress. From 2014 to 2022, the trend reversed as Grassland increased substantially (+261.2%) while Shrubland declined sharply (−99.3%). Forestland also regenerated extensively (+186%) along riparian corridors, and Waterbodies expanded more than fivefold (+384.6 km²). Over the long period between 1990 and 2022, Built-up land (+30.6%), Cultivated land (+16%), Forestland (+140%), Grassland (+94.4%), and Waterbodies (+25.6%) increased, while Bare land (−58.1%), Mines and Quarries (−56.1%), Shrubland (−98.9%), and Wetlands (−82.5%) decreased. Asymmetric analysis revealed strongly directional transitions, with early Grassland-to-Shrubland conversion likely driven by grazing pressure, fire suppression, and climate variability, followed by a later Shrubland-to-Grassland reversal consistent with fire, herbivory, and ecotonal climate sensitivity. LULC dynamics in the C5 catchment show class-specific spatial reconfiguration, declining landscape diversity (SHDI 1.3 → 0.9; SIDI 0.7 → 0.43), and patch metrics indicating urban and cultivated fragmentation, shrubland loss, and grassland consolidation. Based on these quantified trajectories, we recommend targeted catchment-scale land management, shrubland restoration, and monitoring of anthropogenic hotspots to support ecosystem services, hydrological stability, and sustainable land use in ecotonal regions. Full article

The precise identification of photovoltaic power stations is essential for advancing the assessment of energy infrastructure and for the efficient management of land resources. To address the need for spatially explicit data on photovoltaic (PV) development in arid and semi-arid regions amid green energy transitions, particularly in the context of identification challenges induced by the widespread distribution of bare ground, this study optimized a remote sensing-based identification method integrating Principal Component Analysis (PCA), automated sampling via Google Earth Engine (GEE), and deep learning models, and applied it to Qinghai Province, one of China’s largest PV regions. The results showed that HRNetv2 (validation Dice = 0.9463) outperformed UNet (0.9328), Attention UNet (0.9399), and HRNet + OCR (0.9184) in small-sample (1871 training samples) PV segmentation; the PV installed area during 2020–2024 accounted for 63.5% of the total pre-2024 area (~607 km²), exceeding the cumulative area before 2019, with projects predominantly distributed in areas with elevation less than 2500 m and slope less than 2°; bare land dominated PV land use (88.7%), followed by grassland (6.9%) and shrubland (3.9%), and PV construction contributed to desert greening by modifying microclimates. The study concludes that its optimized method effectively supports PV spatial identification, and the revealed PV distribution and land use patterns provide scientific guidance for synergistic PV development and ecological conservation in arid regions, while acknowledging limitations in generalizability to other regions due to Qinghai-specific data, suggesting future algorithm refinement and expanded research scales. 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

Shrub communities play an irreplaceable role in maintaining ecological security in the stressed habitat areas of Northwest China. In these areas, multiple types of shrublands coexist simultaneously. Their diversity levels and community assembly processes may perform different patterns along different stress gradients. This study using linear model fitting, principal component analysis, analyzed the species and phylogenetic diversity of desert, alpine, and secondary shrublands along the gradients of environmental stress factors such as topography, soil, and climate, which reflect low temperature, human disturbance, and drought stress habitats. The changing trend of the phylogenetic structure of different types of shrublands was also studied with using variance decomposition, and phylogenetic structure analysis, which reveals their diversity maintenance mechanisms along environmental stress gradients. The research shows that (1) the mean annual temperature is the main environmental factor shaping the diversity patterns and maintenance processes of shrub communities because low temperatures may lead to habitat filtering; (2) in the western Loess Plateau, the community assembly of different types of shrublands is dominated by deterministic processes, but the diversity and assembly patterns of different shrublands are inconsistent across different environmental stress gradients. Systematic research on the diversity characteristics and assembly patterns of different shrub communities is of great significance for clarifying the restoration, succession, and stability of stressed habitat areas. Full article

This study investigates how landcover change between 1935 and 2020 have shaped hydrological responses in the semi-arid highlands of Tigray, Ethiopia. Focusing on the Tsili catchment (27.5 km²), it examines links between landcover change, drainage network evolution, and river channel width under conditions of population growth and climate variability. Landcover and drainage maps were derived from historical aerial photographs and satellite imagery for four time steps, and surface runoff was simulated using the SWAT model with uniform meteorological forcing to isolate landcover effects. Results show a 37.6% increase in cropland and substantial declines in shrubland (−29.3%) and forest (−10.1%). River channel width at the outlet widened from 7.5 to 10.5 m, while drainage density increased 1.5-fold. These physical changes aligned with modelled increases in surface runoff. Strong correlations were found between runoff, channel width, drainage density, and landcover types. The findings highlight that cropland expansion—at the expense of natural vegetated land—has intensified runoff and erosion risks. As climate change is expected to bring more intense rainfall to East Africa, this underscores the need for land management strategies that reduce hydrological connectivity and support sustainable agriculture in data-scarce regions. Full article

Wildfire and drought are key drivers of shrubland expansion in southwestern US landscapes. Stand-replacing fires in conifer forests induce shrub-dominated stages, and changing climatic patterns may cause a long-term shift to deciduous shrubland. We assessed change in deciduous fractional shrub cover (DFSC) in the eastern Jemez Mountains from 2019 to 2023 using topographic and Sentinel-2 satellite data and evaluated the impact of spatial scale on model performance. First, we built a 10 m and a 20 m random forest model. The 20 m model outperformed the 10 m model, achieving an R-squared value of 0.82 and an RMSE of 7.85, compared to the 10 m model (0.76 and 9.99, respectively). We projected the 20 m model to the other years of the study using imagery from the respective years, yielding yearly DFSC predictions. DFSC decreased from 2019 to 2022, coinciding with severe drought and a 2022 fire, followed by an increase in 2023, particularly within the 2022 fire footprint. Overall, DFSC trends showed an increase, with elevation being a key variable influencing these trends. This framework revealed vegetation dynamics in a semi-arid system and provided a close look at post-fire regeneration in deciduous resprouting shrubs and could be applied to similar systems. 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

The extant Patagonian mara Dolichotis patagonum (Zimmermann, 1780) is a cursorial herbivorous rodent that is hare-like in appearance. Nowadays, it occurs in some ecoregions of Argentina (28 °S–50 °S) in lowland habitats, in semi-arid thorn-scrub, in open grasslands and in shrub–land steppe. In this research, we have studied a partially preserved skull (FCDPV-2758), referred to D. patagonum, from the Late Pleistocene (Sopas Formation) in northern Uruguay (Arapey Grande River, Salto Department). Body mass estimates and morphological analyses were performed including contemporary specimens of D. patagonum, the Chaco mara Dolichotis salinicola, and extinct dolichotine species. The body mass estimate using the regression method and geometric similarity suggested a 6–8 kg range for the studied specimen, which is consistent with D. patagonum (7–8 kg) and notably greater than D. salinicola (1–2.3 kg). A comparative analysis, including the extinct D. platycephala and material previously referred to D. major from southwestern Uruguay, suggests that the studied specimen falls within the variation of D. patagonum, differing in part from D. chapalmalense and more clearly from D. salinicola, the extinct D. minor and Prodolichotis prisca. The implications of the wider geographic distributions of the living Patagonian mara at these latitudes in the Late Pleistocene in South America, and the paleoenvironmental significance are discussed. Full article

The semi-arid region of North China has undergone extensive afforestation to prevent land degradation. Although afforestation was considered an effective way to improve soil water retention, the mechanism by which it affects soil hydraulic properties remained uncertain. In this study, soil water retention curve (SWRC), soil water-stable aggregates, and other soil physicochemical properties were determined in short-term abandoned cropland (AC), shrubland (SL), and woodland (WL) that had been converted from cropland for 1, 8, and 24 years, respectively. Pearson correlation analysis and partial least-squares structural equation modeling methods were used to identify the main factors affecting soil hydraulic properties. Results showed that the SWRCs of all three land uses were well-fitted by a double-exponential model. The WL and SL land uses exhibited higher soil field capacity (0.33–0.37 cm³ cm⁻³), wilting point (0.20–0.23 cm³ cm⁻³), and available water content (0.13–0.15 cm³ cm⁻³). Surface soil exhibits a more pronounced trend in water retention capacity changes compared to subsoil under vegetation restoration. The WL and SL land uses showed more soil macroaggregates and intra-aggregate pores at surface layers, which mainly explained the variations in hydraulic properties. The main factors influencing soil hydraulic properties were soil aggregates, matrix and structural porosity, soil organic carbon (SOC), and soil bulk density (BD). Overall, afforestation can improve soil hydraulic properties and could be an effective practice for soil and water conservation in the semi-arid region of North China. Full article

Post-processing optimization refers to the refinement of land cover products by applying specific rules or algorithms to minimize erroneous changes in land cover types caused by classification uncertainty or interannual phenological variations. Global land cover (GLC) mapping has gained significant attention over the past decade, but current GLC time-series products suffer from considerable inconsistencies in mapping results between different epochs, leading to severe erroneous changes. Here, we aimed to design a novel post-processing approach by combining multi-source data to optimize the GLC_FCS30D product, which represents a groundbreaking improvement in GLC dynamic mapping at a resolution of 30 m. First, spatiotemporal filtering with a window size of 3 × 3 × 3 was applied to reduce the “salt-and-pepper” effect. Second, a temporal consistency optimization algorithm based on LandTrendr was used to identify land cover changes across the entire time series and eliminate excessively frequent erroneous changes. Third, certain land cover transitions between easily misclassified types were optimized using logical rules and multi-source data. Specifically, the illogical wetland-related transitions (wetland–water and wetland–forest) were corrected using a simple replacement rule. To address the noticeable erroneous changes in arid and semi-arid regions, the erroneous land cover transitions involving bare areas, sparse vegetation, grassland, and shrubland were corrected by combining NDVI and precipitation data. Finally, the performance of our post-processing optimization approach was evaluated and quantified. The proposed approach successfully reduced the cumulative change area from 7537.00 million hectares (Mha) in the GLC_FCS30D product without optimization to 1981.00 Mha in the GLC_FCS30D product with optimization, eliminating 5556.00 Mha of erroneous changes across 26 epochs. Furthermore, the overall accuracy of the mapping was also improved from 73.04% to 74.24% for the Land Cover Classification System (LCCS) level-1 validation system. Erroneous changes in GLC_FCS30D were considerably mitigated with the post-processing optimization method, providing more reliable insights into GLC changes from 1985 to 2022 at a 30 m resolution. Full article