Search Results (4,152)

Dynamic Habitat Indices (DHIs) are crucial for understanding species richness patterns and provide a powerful tool for large-scale biodiversity conservation research. DHIs summarize three key aspects of vegetation productivity: (a) cumulative annual productivity, (b) minimum productivity, and (c) intra-annual variability. While DHIs have demonstrated potential for predicting richness patterns globally and in tropical regions, their predictive ability across climate zones and using multiple sources of species richness data remains untested. We assess the feasibility of using DHIs to predict the richness of resident birds in China and explore approaches to improve model performance. We used (a) IUCN range maps of terrestrial resident birds in China, and (b) species distribution models (SDMs) to delineate bird richness patterns, classifying species into six habitat guilds: forest, shrubland, grassland, cropland, wetland, and all resident birds. We linked DHIs to richness in each guild and quantified their predictive power using three modeling approaches: linear (GLM) and non-linear models (GAM, Random Forest). We also recorded the relative importance of each DHI component in the models. Our results show that DHIs best predicted cropland bird richness (SDM-based richness, adjusted R² = 0.73), while for IUCN-based guilds, adjusted R² ranged from 0.68 to 0.71. The Random Forest model achieved the highest performance and interpretability. Among DHI components, cumulative DHI consistently played the most dominant role in predicting richness from both SDM and IUCN sources. DHIs effectively capture the link between energy availability and resident bird richness in China, demonstrating considerable potential for biodiversity assessment and conservation planning. Full article

Grasslands are ecosystems of global importance; in Peru, they represent more than half of the country’s territory. However, few studies have been conducted on high Andean grasslands. The objective was to study morphological, productive, resource allocation, and nutritional characteristics in five populations of Festuca dolichophylla grown under similar conditions. Populations that originated from Huancavelica Community and University, Junín, Pasco, and Puno were grown in Huancavelica Community in a randomized block design. After twelve months, a uniformization cut was performed, and five months later they were evaluated. Morphological characteristics, productivity, and resource allocation were analyzed with ANCOVA, the nutritional characteristics were analyzed with one-way ANOVA (considering population as a factor). Significant differences (p < 0.05) were found for morphological characteristics such as height, number and length of stems, and number of inflorescences. The resource allocation was 13.8% root, 18.4% crown, 29.2% culms + sheaths, 34.8% blades, and 3.8% inflorescence, with no differences between populations (p > 0.05). The Puno population stood out for its greater biomass, linked to more stems and inflorescences. Nutritional characteristics varied among populations in terms of crude fiber, neutral detergent fiber, acid detergent fiber, and in vitro dry matter digestibility. These findings are useful for selecting populations in revegetation or genetic breeding programs. Full article

As a pivotal component of the global carbon cycle, the spatial variation in soil respiration (Rs) is crucial for forecasting climate change trajectories. Despite extensive research on the spatial patterns of total Rs, the distinct drivers of its two components, heterotrophic respiration (Rh) and autotrophic respiration (Ra), are still not well defined. We compiled a global dataset from studies published between 2007 and 2023 to investigate the drivers of spatial variations in Rs, Ra, and Rh. This dataset comprises 308 annual flux measurements from 172 sites. The results showed that Rh contributed 63% and 60% to Rs in forest and grassland ecosystems, respectively. Further analyses using structural equation modelling (SEM) showed that the spatial variation in Rh and Ra exhibited divergent responses to climatic factors and plant community structure (mostly driven by gross primary production, GPP). Rh was more affected by mean annual temperature (MAT) than by mean annual precipitation (MAP), with standardized total effects of 0.17 (forests) and 0.57 (grasslands) for MAT versus 0.10 and 0.07 for MAP, respectively. In contrast, Ra exhibited greater sensitivity to MAP (0.08 and 0.18) than to MAT (−0.01 and 0.04). GPP exerted biome-specific effects: in forests, high GPP enhanced Rh (0.18) more substantially than Ra (0.08), while in grasslands, elevated GPP significantly increased Ra (0.34) but suppressed Rh (−0.30). Moreover, these variables incorporated into the SEMs accounted for a greater proportion of the variation in Rh and Ra in grasslands (R² = 0.73 for Rh, 0.48 for Ra) as compared to forests (R² = 0.21 for Rh, 0.22 for Ra), suggesting the greater complexity in forest soil C dynamics. By using the whole yearly measured soil respiration data around the world, this study highlights the differential environmental regulation of Rh and Ra, providing critical insights into the mechanisms governing Rs variations under climate change. Full article

Grassland aboveground biomass (AGB) is a crucial component of the global carbon budget in climate change studies. Precise estimation of the AGB of grassland ecosystems is essential to better understand the carbon cycle and to improve grassland conservation as well as to achieve optimal growth. Traditional vegetation indices (VIs) derived from remote sensing often saturate at medium-high biomass levels, limiting estimation accuracy. In this study, we introduced a novel AGB estimation framework by explicitly integrating near-infrared radiance (L_nir) with UAV-based hyperspectral vegetation indices (VIs×Lnir), which effectively alleviated saturation effects commonly observed in conventional VI-based models. Field measurements and hyperspectral imagery were collected in a temperate meadow steppe, and model performance was evaluated using leave-one-out cross-validation (LOOCV). The proposed VIs×Lnir model achieved the highest accuracy (R² = 0.72, RMSE = 7.52 g/m²), outperforming conventional VIs-based (R² < 0.39, RMSE > 11.13 g/m²) estimations. The study further investigated the results of fAPAR_green-related VIs×Lnir model, which yielded higher AGB estimation accuracy than that using NDVI×Lnir. Furthermore, we examined the influence of plant diversity using Menhinick’s index (DMn) and found that AGB estimation uncertainty was lowest when DMn ranged from 0.2 to 0.4, likely due to reduced spectral mixing and optimal canopy structural homogeneity. Under both lower (DMn < 0.2) and higher diversity conditions (DMn > 0.4), AGB could still be estimated, but with increased uncertainty likely caused by insufficient spectral variability at low diversity and stronger spectral mixing at high diversity. This study demonstrates the potential of incorporating L_nir into UAV hyperspectral analysis to enhance grassland AGB estimation and provides insights into the role of biodiversity in remote sensing-based biomass monitoring. Full article

This study investigates the spatial and economic differentiation of organic farming across the European Union by analyzing regional specialization patterns using Location Quotients (LQ). The results reveal a highly heterogeneous landscape shaped by the interaction of agro-ecological conditions, production traditions, market development, and structural characteristics of national agricultural systems. Six distinct regional models of organic farming are identified: the Nordic–Baltic cereal–forage model, the Alpine–Central European grassland model, the Mediterranean permanent-crop model, the Central–Eastern European raw-material model, the Western European intensive horticultural model, and the island-based niche-specialization model. Regression analyses show that overall organic specialization is strongly associated with market development, whereas the structure of organic crop production is primarily determined by agro-ecological and structural factors rather than consumer demand or purchasing power. These findings highlight the strong embeddedness of organic farming within long-term regional development pathways and underscore the need for regionally differentiated policy instruments within the Common Agricultural Policy. Effective support measures should be tailored to dominant crop types, production systems, and comparative advantages across Member States. Full article

Outside their native habitat, goldenrods (Solidago spp.) threaten ecosystem biodiversity through aggressive vegetative reproduction and by establishing dense stands. Climate-driven fire risks and illegal grassland burning increase exposure to smoke-derived compounds such as karrikins (KARs), which are known to regulate germination and development in many species but have never been studied in goldenrods. Understanding KARs’ effects on seeds and rhizomes is essential for predicting invasion dynamics and designing effective management strategies. This study aimed to determine whether karrikin 1 (KAR1) influences seed germination and rhizome bud development in Solidago gigantea, thereby affecting its invasiveness and offering a potential method of control. Two geographically isolated populations were analyzed using seeds, soil, above-ground plant biomass and rhizomes. Germination tests evaluated whether KAR mimics light and gibberellic acid (GA), a known germination stimulant. Greenhouse trials assessed rhizome response, while field experiments monitored whole-plant performance over two years. KAR stimulated seed germination comparably to light and GA and promoted seedling emergence from the seed bank, but it inhibited rhizome sprouting by about 15%. It also enhanced the emergence of other species, suggesting broad physiological activity and the potential to influence early-season plant community dynamics. These findings highlight KAR’s potential as a management tool for invasive goldenrod and provide new insights into smoke-derived compounds as ecological regulators. Full article

Grassland productivity faces heightened uncertainty under nonlinear climatic forcing. This study characterizes the spatial heterogeneity of nonlinear variations and nonstationary climate sensitivities across the Eurasian Steppe Region (EASR) to provide a scientific basis for its adaptive management. Using the aboveground net primary productivity (ANPP) and climate datasets (1982–2015), we employed piecewise linear regression, LOWESS, and sliding window partial correlation analysis to identify temporal turning points and dynamic climate–productivity relationships. We identified distinct turning points in 1994 and 2008, revealing a phased “Increasing–Decreasing–Increasing” trajectory. A key novelty is the mapping of eight phased trajectory patterns, illustrating significant spatial heterogeneity in productivity trends. Furthermore, we demonstrate temporally reversed climate sensitivities. Notably, the sensitivity of ANPP to temperature shifted from positive to negative as warming-induced water stress intensified. While precipitation remains the dominant driver (68% of the region), its influence is nonstationary and state-dependent. In the Qinghai–Tibet Plateau, the limiting factor transitioned from thermal to water availability. Overall, productivity in the EASR appears to undergo phased reorganization under shifting climatic baselines. Our findings suggest that future ecosystem models should incorporate time-varying sensitivity parameters to account for nonlinear dynamics and potential trend reversals in grassland ecosystems. Full article

Timely access to soil moisture conditions in farmland crops is the foundation and key to achieving precise irrigation. Due to their high spatiotemporal resolution, unmanned aerial vehicle (UAV) remote sensing has become an important method for monitoring soil moisture. This study addresses soil moisture retrieval in alfalfa fields across different growth stages. Based on UAV multispectral images, a multi-source feature set was constructed by integrating spectral and texture features. The performance of three machine learning models—random forest regression (RFR), K-nearest neighbors regression (KNN), and XG-Boost—as well as two ensemble learning models, Voting and Stacking, was systematically compared. The results indicate the following: (1) The integrated learning models generally outperform individual machine learning models, with the Voting model performing best across all growth stages, achieving a maximum R² of 0.874 and an RMSE of 0.005; among the machine learning models, the optimal model varies with growth stage, with XG-Boost being the best during the branching and early flowering stages (maximum R² of 0.836), while RFR performs better during the budding stage (R² of 0.790). (2) The fusion of multi-source features significantly improved inversion accuracy. Taking the Voting model as an example, the accuracy of the fused features (R² = 0.874) increased by 0.065 compared to using single-texture features (R² = 0.809), and the RMSE decreased from 0.012 to 0.005. (3) In terms of inversion depth, the optimal inversion depth for the branching stage and budding stage is 40–60 cm, while the optimal depth for the early flowering stage is 20–40 cm. In summary, the method that integrates multi-source feature fusion and ensemble learning significantly improves the accuracy and stability of alfalfa soil moisture inversion, providing an effective technical approach for precise water management of artificial grasslands in arid regions. Full article

Nest-site selection in birds is a hierarchical process shaped by environmental filters operating across multiple spatial scales. In species that depend on burrows excavated by ecosystem engineers, understanding how these filters interact is essential for effective conservation. We evaluated nest-site selection by the Burrowing owl (Athene cunicularia) within colonies of the Mexican prairie dog (Cynomys mexicanus) in the southern Chihuahuan Desert using a multiscale analytical framework spanning burrow, site, colony, and landscape levels. During the 2010 and 2011 breeding seasons, we located 56 successful nests and paired each with an inactive non-nest burrow within the same colony. Eighteen structural and environmental variables were measured and analyzed using binary logistic regression models, with model selection based on an information-theoretic approach (AICc) and prior screening for predictor collinearity. Nest-site selection was associated with greater internal burrow development and reduced external exposure at the burrow scale, proximity to satellite burrows and low-to-moderate vegetation structure at the site scale, higher densities of active prairie dog burrows at the colony scale, and reduced predation risk and agricultural disturbance at the landscape scale. The integrated multiscale model showed substantially greater support and discriminatory power than single-scale models, indicating that nest-site selection emerges from interactions among spatial scales rather than from isolated factors. These findings support hierarchical habitat-selection theory and underscore the importance of conserving functional Mexican prairie dog colonies and low-disturbance grassland landscapes to maintain suitable breeding habitats for Burrowing owls in the southern Chihuahuan Desert. Full article

Globally, grasslands, savannas, and wetlands are degrading rapidly and increasingly being replaced by woody vegetation. Woody Plant Encroachment (WPE) disrupts natural landscapes and has significant consequences for biodiversity, ecosystem functioning, and key ecosystem services. This review synthesizes findings from 159 peer-reviewed studies identified through a PRISMA-guided systematic literature review to evaluate the drivers of WPE, its ecological impacts, and the remote sensing (RS) approaches used to monitor it. The drivers of WPE are multifaceted, involving interactions among climate variability, topographic and edaphic conditions, hydrological change, land use transitions, and altered fire and grazing regimes, while its impacts are similarly diverse, influencing land cover structure, water and nutrient cycles, carbon and nitrogen dynamics, and broader implications for ecosystem resilience. Over the past two decades, RS has become central to WPE monitoring, with studies employing classification techniques, spectral mixture analysis, object-based image analysis, change detection, thresholding, landscape pattern and fragmentation metrics, and increasingly, machine learning and deep learning methods. Looking forward, emerging advances such as multi-sensor fusion (optical– synthetic aperture radar (SAR), Light Detection and Ranging (LiDAR)–hyperspectral), cloud-based platforms including Google Earth Engine, Microsoft Planetary Computer, and Digital Earth, and geospatial foundation models offer new opportunities for scalable, automated, and long-term monitoring. Despite these innovations, challenges remain in detecting early-stage encroachment, subcanopy woody growth, and species-specific patterns across heterogeneous landscapes. Key knowledge gaps highlighted in this review include the need for long-term monitoring frameworks, improved socio-ecological integration, species- and ecosystem-specific RS approaches, better utilization of SAR, and broader adoption of analysis-ready data and open-source platforms. Addressing these gaps will enable more effective, context-specific strategies to monitor, manage, and mitigate WPE in rapidly changing environments. 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

Grasslands represent an essential resource for rural economies and for the provision of ecosystem services, yet they are increasingly affected by anthropogenic pressures, functional land-use changes, and institutional constraints. This study develops a geospatial decision-support framework for assessing grassland suitability in Hunedoara County, Romania, by integrating the Analytic Hierarchy Process (AHP) and Weighted Overlay Analysis (WOA) within a GIS environment. The assessment is based on nine criteria thematically grouped into three dimensions: (A) physical-geographical, including topographic suitability, climatic pressure, and hydrological risk exposure; (B) ecological and conservation-related, reflected by ecological conservation value, ecological carrying capacity, and the anthropic pressure index; and (C) socio-economic and functional, represented by spatial accessibility, recreational value, and policy support mechanisms. Suitability is defined as the integrated capacity of grasslands to sustain productive and multifunctional uses compatible with ecological conservation and the existing policy framework. Results indicate that 0.43% of the grassland area exhibits very high suitability (Class 1), 44.51% high suitability (Class 2), and 54.75% moderate suitability (Class 3), while unfavorable areas account for only 0.31% of the total (Class 4). The proposed methodology is reproducible and transferable, providing support for prioritizing management interventions, agri-environmental payments, and rural planning in mountainous and hilly regions. Full article

As a vital ecological security barrier and climate regulator in northwestern China, the spatial patterns and evolving formation mechanisms of ecosystem services within the Qinghai Lake basin hold significant strategic value for ecological conservation and national park development in the region. This study selected land use data during 2000–2020, integrating the equivalent factor method, spatial correlation analysis, and the geodetector approach to systematically investigate the spatial heterogeneity characteristics of ESV in the Qinghai Lake basin and its corresponding driving mechanisms. The results indicate the following: (1) During the period 2000–2020, grassland consistently constituted the primary land cover category within the Qinghai Lake Basin, accounting for over 60% of the total area; water bodies (16.67%) and unused land (16.56%) represented the secondary land use categories. Over this twenty-year period, the total ESV exhibited a slight increasing trend, rising from USD 30.30 × 10⁸ to USD 30.75 × 10⁸, representing a growth of 0.31%. Regulating services constituted the primary component of ESV. The highest contribution to ESV originated from water bodies, with grassland ranking second. (2) ESV displayed a spatial arrangement marked by “high values in the lake center and low values in the surrounding areas” and “higher values in the southeast and lower values in the northwest.” Its spatial correlation exhibits a pronounced positive relationship. The number of units classified as high-high clusters (primarily water bodies at low elevations) and low-low clusters (mainly grasslands and unused land at high elevations) both increased over the study period, indicating a continuous intensification of ESV spatial agglomeration. (3) Results from the geographical detector reveal that both natural and anthropogenic factors collectively drive the spatial variation in ESV, with natural factors exhibiting stronger explanatory capacity. Among these, elevation and temperature are identified as the dominant drivers of ESV spatiotemporal differentiation. The combined effect of two interacting factors surpasses the influence exerted by any single factor in isolation. This research clarifies that the spatial distribution of ESV in the Qinghai Lake Basin, which features “high values in the lake center and low values in the surrounding areas” as well as “higher values in the southeast and lower values in the northwest,” is jointly shaped by the combined control of vertical zonality governed by topographic and climatic factors and the spatial differentiation of human activities. In low-altitude lakeshore zones, ESV rose as a consequence of water body expansion and the enforcement of ecological conservation measures, leading to the emergence of high-value clusters. In contrast, ESV improvement in high-elevation regions remained limited, constrained by fragile natural conditions and minimal human intervention. The insights derived from this research offer a scientific foundation for refining the “one core, four zones, one ring, multiple points” functional zoning framework of the Qinghai Lake National Park, as well as for developing tailored management approaches suited to distinct elevation-based regions. Full article

This study is essential for medium- and long-term land-use management, as land-use patterns directly influence local economic and social development. Geographic Information System (GIS) techniques are fundamental tools for analyzing a wide range of geomorphological processes, including relief fragmentation density, relief energy, soil texture, slope gradient, and slope orientation. The present research focuses on the Pesceana river basin in the Southern Carpathians, Romania. It addresses three main objectives: (1) to analyze land-use dynamics derived from CORINE Land Cover (CLC) data between 1990 and 2018, along with the long-term distribution of the Normalized Difference Vegetation Index (NDVI) for the period 2000–2025; (2) to evaluate the basin’s natural potential byintegrating topographic data (contour lines and profiles) with relief fragmentation density, relief energy, vegetation cover, soil texture, slope gradient, aspect, the Stream Power Index (SPI), and the Topographic Wetness Index (TWI); and (3) to assess the spatial distribution of habitat types, characteristic plant associations, and soil properties obtained through field investigations. For the first two research objectives, ArcGIS v. 10.7.2 served as the main tool for geospatial processing. For the third, field data were essential for geolocating soil samples and defining vegetation types across the entire 247 km² area. The spatiotemporal analysis from 1990 to 2018 reveals a landscape in which deciduous forests clearly dominate; they expanded from an initial area of 80 km² in 1990 to over 90 km² in 2012–2018. This increase, together with agricultural expansion, is reflected in the NDVI values after 2000, which show a sharp increase in vegetation density. Interestingly, other categories—such as water bodies, natural grasslands, and industrial areas—barely changed, each consistently representing less than 1 km² throughout the study period. These findings emphasize the importance of land-use/land-cover (LULC) data within the applied GIS model, which enhances the spatial characterization of geomorphological processes—such as vegetation distribution, soil texture, slope morphology, and relief fragmentation density. This integration allows a realistic assessment of the physical–geographic, landscape, and pedological conditions of the river basin. Full article

The West Liaohe River Basin, a core arid region in Northeast China, faces a significant evaporation–precipitation imbalance and exhibits fragmented land systems, epitomized by the Horqin Sandy Land. Integrating three decades of land use/land cover (LULC) data with meteorological, ecological, and socioeconomic variables, we employed obstacle diagnosis and structural equation modeling (SEM) to elucidate the spatiotemporal dynamics and drivers of LULC transformations. The results demonstrate the following: (1) Land use exhibited a spatially heterogeneous pattern, with forests, shrubs, and grasslands predominantly concentrated in the northwest and southwest. (2) Vegetation coverage significantly increased from 53.15% in 1990 to 61.32% in 2020, whereas cropland and sandy land areas declined. While the overall basin landscape underwent a marked increase in fragmentation. (3) Human activities were the dominant contributor of LULC changes, particularly for cropland conversion, with key determinants such as population and GDP showing negative path coefficients of −0.59 and −0.77, respectively. Climate change was a secondary contributor, with precipitation exerting a strong positive path coefficient (0.63) that was particularly pronounced during the conversion of grassland to forest. These findings offer a scientific basis for land management, ecological restoration strategies, and water resource utilization in the basin. Full article