Search Results (7,399)

Endangered montane endemic species face dual threats from unresolved taxonomic controversies and climate change. The genus Abies, a keystone component of alpine and subalpine ecosystems in the Northern Hemisphere, encompasses numerous species with controversial taxonomy and inadequately understood climatic response patterns. In this study, we integrated morphological and phylogenetic evidence and ecological niche modeling approaches to fill existing knowledge gaps regarding Abies ernestii, an endemic species found in southwest China. Key results are summarized below: (1) Morphological comparisons strongly support A. ernestii as a distinct species, with significant morphological differentiation from its congeneric species; phylogenetic analyses based on plastid sequences further corroborate its close phylogenetic relationship with A. kawakamii and A. beshanzuensis, rather than A. chensiensis. (2) The natural distribution range of A. ernestii is narrower than previously documented in the literature, and a newly discovered population in northern Yunnan extends its documented southern distribution boundary southward. (3) Current suitable habitats of this species are concentrated in the eastern Hengduan Mountains, where temperature seasonality-related variables (BIO11, BIO3, BIO4) exert dominant control over its distribution. (4) Future climate projections indicate a dynamic habitat shift characterized by initial expansion followed by contraction, accompanied by severe habitat fragmentation and inadequate protected area coverage. Collectively, these lines of evidence demonstrate that A. ernestii represents an endemic Fir with underestimated vulnerability, warranting immediate conservation prioritization. Full article

Ormosia microphylla is a national first-class protected wild plant in China that faces conservation challenges, including weak natural regeneration and limited environmental adaptability. Plant-associated bacterial communities are important components of host-associated microecosystems, but bacterial community patterns across the whole-plant continuum of O. microphylla remain poorly understood. To provide a descriptive micro-ecological baseline, we characterized bacterial communities across the rhizosphere–root–stem–leaf continuum of O. microphylla in three geographic habitats in Southwest China: karst mountainous area, a plateau-to-plain transitional slope zone, and a hilly area. High-throughput amplicon sequencing was used to analyze bacterial diversity and composition, and co-occurrence network analysis was used to describe statistical associations among bacterial taxa. Three main patterns were observed. First, bacterial alpha diversity generally declined from the rhizosphere to internal tissues (rhizosphere > root > stem > leaf). Second, bacterial composition varied by plant compartment and habitat. Dominant rhizosphere taxa differed among habitats, whereas internal tissues were generally dominated by Proteobacteria. Delftia showed relatively high abundance in several endophytic compartments, suggesting that this genus may be considered a candidate endophytic taxon for future validation. Third, co-occurrence network analysis showed habitat- and compartment-associated differences in network size, complexity, and positive/negative co-occurrence patterns. Overall, these results describe compartment- and habitat-associated bacterial community patterns in O. microphylla and provide a micro-ecological baseline for future culture-dependent and functional studies. Full article

Elucidating the mechanisms by which cropland fragmentation impacts production and ecological functions is critical for ensuring food security and ecological sustainability. Using Jilin Province as a case study, this research develops a cropland fragmentation evaluation framework based on landscape pattern indices. A restricted cubic spline model is employed to quantify nonlinear relationships and identify critical thresholds between fragmentation and both production and ecological functions. Furthermore, the PLUS model is utilized to simulate land-use patterns for 2030 under three scenarios: natural development, cropland protection, and ecological protection. The primary findings are as follows: (1) From 2000 to 2023, cropland fragmentation displayed pronounced spatial heterogeneity. Fragmentation was consistently high in the eastern mountainous areas and showed significant spatial clustering; the central region maintained relatively contiguous cropland, while the western region exhibited marked spatial variability. (2) Cropland fragmentation exhibits a nonlinear negative correlation with production functions, wherein the marginal negative impact attenuates beyond a threshold of 0.340. Conversely, its association with ecological functions follows a U-shaped trajectory, with a critical inflection point at 0.363 marking a directional shift in the fragmentation–ecology nexus. (3) Based on these nonlinear thresholds, the study area was delineated into production-ecology synergy zones, dysfunctional sensitive zones, and ecosystem landscape trade-off zones. Specifically, the central agricultural core is characterized by functional synergy; the ecologically fragile western zone resides near the nadir of the U-shaped curve, rendering its balance between production and ecological functions highly vulnerable to shifts in development intensity; and the eastern ecological barrier zone manifests a distinct trade-off prioritizing ecological functions. (4) Multi-scenario simulations reveal that the natural development scenario exacerbates the expansion risk of dysfunctional sensitive zones. While the cropland protection scenario enhances production capacity, it concurrently introduces risks of ecological instability. Conversely, the ecological protection scenario effectively steers sensitive zones toward ecological recovery. Consequently, we propose a differentiated spatial regulation strategy: prioritizing land consolidation in the central region, integrating ecological restoration with capacity enhancement in the west, and sustaining ecological barriers in the east, thereby fostering sustainable regional development. Full article

Numerical simulations quantify the transient impacts of implementing green infrastructure (GI) grass swales on unconfined aquifer storage and groundwater-surface water interactions around the Red Butte Creek (RBC) of Utah, USA. The Red Butte Creek Watershed (RBCW) transitions from undeveloped mountainous National Forest land to downstream urbanized areas within Salt Lake Valley (SLV). This reconnaissance-level study demonstrates that increasing stormwater infiltration in urbanized areas during the rainy months (April-June) can, until at least the subsequent March, (a) enhance aquifer recharge and support sustainable groundwater yields; and (b) improve surface water availability. Simulations predict hydrologic impacts of aquifer recharge resulting from hypothetical grass-swale implementation within a 704-acre area located around RBC. The employed model, HyperRBC, is an adaptation of a United States Geological Survey (USGS) transient numerical flow, MODFLOW, model implementation for SLV. Adaptations involved (a) uniformly refined horizontal discretization of seven aquifer layers within a sub-area encompassing parts of RBCW and an adjacent watershed; (b) updated input data; and (c) MODFLOW’s Streamflow-Routing (SFR) package to simulate RBC flow and aquifer-stream seepage. Model predictions indicated that by the end of next March: (a) about 3% of the GI-induced recharge would remain within the unconfined aquifer in the HyperRBC area; (b) 66.6% of the recharge would flow northward into the downgradient continuation of the unconfined aquifer; and (c) 30.3% would discharge to nearby stream and river. In summary, predicted hydrologic changes due to the short-term GI-induced recharge highlight increased groundwater availability within and outside the study area for at least the subsequent 12 months, including high-water-demand summer. These findings show the importance of GI in interim environmental management and in enhancing the effective use of water resources. Full article

Scientifically assessing ecosystem health and optimizing ecological source areas (ESAs) are essential for effective environmental management, particularly in ecologically strategic mountain barrier regions. However, existing studies face challenges in identifying and optimizing ESAs. To address these limitations, this study integrated the contribution–vigor–organization–resilience (CVOR)-based ecosystem health framework, a genetic algorithm (GA), and circuit theory to assess ecosystem health, optimize ESAs, and identify ecological corridors (EC) and restoration priorities in the Daiyun Mountain Rim. The results demonstrate the following: (1) a significant ecosystem health decline from 2012 to 2022, evidenced by a 38.97% to 21.09% reduction in high-priority ecological zones accompanied by increased landscape fragmentation; (2) delineation of 90 GA-optimized ESA and 248 EC (2164.71 km), forming an interconnected ecological network; (3) enhanced connectivity metrics through GA optimization, showing α-index improvements of 0.15–0.23 and β-index gains of 0.05–0.08 compared to the traditional large-patch and morphological spatial pattern analysis (MSPA)-based ESA selection methods; (4) development of a tiered spatial strategy featuring primary/secondary restoration clusters and a “three-belt–one area–multiple clusters” framework for adaptive landscape governance. Although uncertainties remain due to the selected study period, parameter settings, and lack of field-based validation, this framework provides a useful reference for ecological planning, restoration prioritization, and ecosystem management in similar mountainous ecological barrier regions. Full article

Synthetic aperture radar (SAR) data from Sentinel-1 enable land cover classification independent of cloud cover and illumination; however, classification performance is affected by inherent speckle noise. This study evaluates the influence of eight speckle filtering algorithms on classification accuracy using Sentinel-1 Ground Range Detected (GRD) data across five contrasting terrain types in eastern Slovakia (mountain, forest, urban, cropland, and water). Speckle suppression was assessed using Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Equivalent Number of Looks (ENL). Classification performance was quantified using Support Vector Machine (SVM), Random Forest (RF), and Histogram-based Gradient Boosting (HistGB) under VV, VH, and dual-polarization (VV + VH) configurations with repeated balanced sampling. Classification accuracy varies across terrain types. In croplands, Lee Sigma combined with SVM in VV + VH mode achieved Overall Accuracy (OA) = 0.746 ± 0.010, whereas in mountainous areas, OA = 0.838 ± 0.005 was achieved with Intensity-Driven Adaptive Neighborhood (IDAN) filtering. Urban areas achieved OA = 0.890 ± 0.006, whereas forest classification remained limited (best OA = 0.582 ± 0.011). Water surfaces approached saturation accuracy (OA ≈ 0.9998). Dual polarization improved performance in heterogeneous environments but had a limited effect in homogeneous classes. The results show that terrain structure influences the interaction between speckle filtering and classification performance. Full article

Snow cover plays an important role in ecological stability and seasonal water regulation in the western Greater Khingan Mountains of Inner Mongolia, a cold-region transitional zone where climate warming may intensify environmental vulnerability and sustainability challenges. Using long-term remote sensing, meteorological, and topographic datasets, this study examined the spatiotemporal changes in snow cover and assessed the relative influences of climatic and geographic factors. The results showed pronounced spatial heterogeneity, with greater snow depth and longer snow cover duration occurring in the northeastern, high-altitude, gentle-slope, and north-facing areas. Snow depth showed a slight but marginally significant declining trend during 1982–2024 at a rate of 0.026 cm a⁻¹, while snow cover days decreased by 0.39 d a⁻¹ during 1982–2020. Snow cover onset exhibited a slight but significant delay, whereas snowmelt timing showed strong interannual variability. Compared with precipitation, temperature showed stronger and more persistent associations with snow cover variations, and climatic factors explained a larger proportion of snow-depth variability than geographic factors. Overall, the results suggest that regional warming has played a leading role in recent snow cover decline. These findings improve understanding of climate-sensitive snow dynamics and provide useful evidence for ecological conservation, seasonal water-resource adaptation, and sustainable regional management in cold-region landscapes of northern China. Full article

Climate change and intensifying extreme weather events challenge plant adaptability, making the evaluation of adaptive potential imperative. This study aims to identify climatically stable habitats for Rhododendron dauricum, a nationally protected (Class II) shrub species in China. Species occurrence records were integrated with multiple environmental datasets, and habitat suitability was inferred using a maximum entropy model under current and future climate scenarios. The model outputs indicate that habitat suitability is primarily driven by temperature and moisture, vegetation plays a secondary role, and topographic and soil factors are less influential. Projections show a consistent contraction of suitable habitats, particularly in highly suitable areas, with stronger declines under higher emission scenarios and longer time horizons. Spatial patterns shift from continuous to fragmented distributions, with suitable habitats increasingly concentrated in the northeastern regions and northern mountain ranges. Core areas that remain suitable across scenarios are identified through multi-scenario consistency analysis, representing climatically stable regions. These areas should be prioritized for in situ conservation, while populations maintaining high suitability across scenarios may serve as candidate provenances for ex situ conservation and future landscape deployment. This study elucidates the adaptive potential of R. dauricum under future climate scenarios and identifies key environmental drivers, informing conservation, breeding, and climate-adaptive management. Full article

Revealing the spatial differentiation patterns of cultivated land multifunctionality contributes to the improvement of cultivated land protection policies. This study investigated the spatiotemporal differentiation characteristics and functional zoning of cultivated land multifunctionality in Alpine Valley Area from a topographic gradient perspective. An evaluation index system for cultivated land multifunctionality in Alpine Valley Area was constructed across four dimensions: production (PF), social (SF), ecological (EF), and landscape (LF) functions. Using Yulong County, Yunnan Province, as a case study, methods including kernel density analysis, standard deviation ellipse theory, topographic gradient analysis, and hierarchical clustering were employed to quantify the horizontal and topographic gradient characteristics of the multifunctionality of cultivated land from 2010 to 2020, thereby delineating functional zones. Results indicated: (1) Cultivated land multifunctionality shows clear topographically-dependent spatial differentiation: PF concentrates in central basins and northwest specialty agricultural zones, SF overlaps with production but with more dispersed high/low values, EF follows a “high in the center, low on the lateral areas” pattern, and LF remains relatively stable; (2) Significant hierarchical differences in cultivated land functions were observed along the elevation, slope, and terrain niche index (TNI) gradients. PF, EF, and LF generally decreased with increasing elevation, slope, and TNI, whereas the dominance of SF exhibited an inverted-V-shaped distribution along the gradient. (3) The study area was divided into five zones: Flat-Basin Agritourism Zone (FAZ), River-Valley Eco-Agriculture Zone (REZ), Sub-Alpine Specialty Agricultural Production Zone (SSAPZ), Sub-Alpine Steep Slope Integrated Management Zone (SSIMZ), and Mid-Mountain Steep Slope Ecological Conservation Zone (MSECZ), with differentiated strategies proposed for each. This study innovatively integrates a topographic gradient perspective, TNI, and hierarchical clustering to systematically evaluate the cultivated land multifunctionality in Alpine Valley Area, providing a new methodological framework for similar mountainous regions. Full article

Addressing the challenges of topological design and the limitations of global optimization for large-scale photovoltaic (PV) plants in complex terrains, this paper proposes a topology optimization method based on mixed-integer linear programming (MILP). The innovation of the proposed method lies in its use of a MILP framework to integrate complex terrain modeling, quantification of construction difficulty, and coordinated configuration of conductor cross-sections into a single equivalent annual cost optimization model. First, equivalent mathematical models tailored to diverse environmental features—including flat, mountainous, and hilly terrains—are developed to enable accurate spatial identification. Second, aimed at minimizing the total equivalent annual cost (EAC), a MILP model is formulated. This model comprehensively incorporates physical construction difficulties and strict electrical constraints, such as active power flow balance, cable current-carrying capacity, and node voltage deviations. A high-performance solver is then utilized to achieve global optimization for radial topologies. Furthermore, the cross-sectional areas of the conductors are dynamically configured to compensate for power quality losses caused by path detours. Case studies demonstrate that the proposed method significantly reduces the EAC and enhances the overall economic benefits of PV plants while ensuring strict electrical safety across various complex environments. Full article

Understanding the environmental drivers underlying the spatial heterogeneity of soil organic carbon (SOC) in mountainous regions remains a major challenge in digital soil mapping. This study investigated the spatial distribution and driving mechanisms of SOC contents in a typical subtropical mountainous area using an integrated modeling and interpretation framework based on 132 soil samples. The SOC content in Yangshan County ranged from 3.33 to 50.00 g kg⁻¹, with a coefficient of variation of 48.64%, indicating a moderate level of variability across the study area. Six mainstream modeling approaches were compared, including multiple linear regression (MLR), geographically weighted regression (GWR), Cubist, eXtreme Gradient Boosting (XGBoost), random forest (RF), and a hybrid RF-GWR model. The results showed that RF outperformed traditional linear methods and other machine learning approaches, achieving an R² of 0.45 and RMSE of 7.78 g kg⁻¹, while the hybrid model further improved prediction accuracy (R² = 0.48). Then, spatial mapping revealed a clear elevational gradient, with higher SOC values concentrated in forested mountainous areas in the north and lower values distributed across low-elevation cultivated and disturbed zones. SHAP analysis identified intrinsic soil properties, particularly total nitrogen (TN) and cation-exchange capacity (CEC), as dominant controls on SOC contents. When extended to prediction datasets, relative humidity (RH) and mean annual precipitation (MAP) showed greater importance on SOC, suggesting an amplification of climatic factors at the broader scale. Subsequently, hotspot analysis of GeoShapley components further revealed the spatial differentiations in group indicators, with overall contributions ranked as soil physicochemical properties (36.4%) > geographic conditions (21.1%) > climate (17.4%) > organisms (12.9%) > parent material (12.1%). Soil properties formed clustered hotspots overlaid on carbonate-dominated areas, while geographic conditions and climate primarily acted as spatial modulators, generating localized zones of intensified or weakened influence across the landscape. The integrated framework proposed in this study has potential applicability across broader regions. These findings provided a scientific basis for the localized interpretation of environmental drivers of SOC and offered valuable support for region-specific land management and sustainable decision-making. Full article

In poplar, the protracted stable genetic transformation procedure constrains rapid gene functional analyses. To address this limitation, we optimized a protocol for the high-yield isolation and efficient transient transformation of protoplasts from leaves of tissue-cultured poplar 84K (Populus alba × Populus glandulosa). Through systematic refinement, we determined that an enzymatic digestion solution containing 3% cellulase R-10, 0.3% macerozyme R-10, 0.8% pectolyase R-10, and 0.4 M mannitol was optimal. This formulation, applied over a 3 h digestion period, yielded 12.9 × 10⁶ protoplasts per gram fresh weight, with 93.45% viability. Furthermore, we optimized the parameters for polyethylene glycol -mediated transformation. Using 60 µg of plasmid DNA, 40% polyethylene glycol 4000, and a 20 min incubation, we achieved a high transfection efficiency of 68.67%. The established transient expression system thus provides a reliable, rapid, and effective platform for functional characterization-related studies, such as subcellular localization, protein–protein interactions, and gene expression analyses in poplar, thereby supporting molecular breeding applications. Full article

Baguio City, a highly populated city in the mountainous portion of the Cordillera, is vulnerable to earthquake hazards due to its proximity to earthquake generators. For this reason, identifying its threats by generating seismic hazard assessments such as peak ground acceleration (PGA) is one of the important necessities to be considered in order to mitigate damages and reduce casualties. Further, the effects of topography, aside from the site conditions, play an important role in the amplification of ground motions. In this study, a peak ground acceleration (PGA) is generated with the influence of topographic effects. Data gathered from geophysical surveys were utilized as inputs in generating the site amplification for Baguio City. The amplification values are then incorporated into the composite peak ground acceleration (PGA) generated by simulating each individual fault source surrounding Baguio City, thereby generating the final PGA for Baguio City. Results revealed that 39% of Baguio City may experience a ground acceleration value of 0.71 g to 0.8 g. Specific places, such as the Pinsao Proper area, may experience higher acceleration. Full article

Cropland abandonment is increasing in the upper and middle Yangtze River Basin due to complex terrain, urbanization, and labor migration. This threatens regional food security. To address the challenge of monitoring abandonment in fragmented hilly areas, we developed a framework. We integrated machine learning with time-series analysis. We mapped cropland probability using multi-source remote sensing data, random forest, and kernel density estimation, then applied LandTrendr to detect land-use changes and track the spatiotemporal evolution of abandonment from 2000 to 2022. Next, we combined Geodetector and linear regression to identify driving factors. The results show that abandoned cropland exhibited an increasing trend from 2000 to 2010, with an average annual growth rate of 20.4%. From 2010 to 2013, the area of abandoned cropland declined rapidly, decreasing by 44.6%. Between 2013 and 2022, abandoned cropland decreased steadily, with an average annual reduction rate of 24.7%. Spatially, abandonment was clustered in the central mountains and southern hills. Key drivers included distance to towns (DtT), total grain output (GTO), and GDP. Our approach supports cropland management and rural revitalization in regions with complex terrain. Full article

Remote sensing technologies provide valuable geospatial data for analyzing environmental conditions and for supporting spatial ecological modeling across large, heterogeneous landscapes. In this study, multi-source remote sensing–derived environmental variables were integrated with ensemble machine learning techniques to model the habitat suitability of the common leopard (Panthera pardus) in Azad Jammu and Kashmir (AJ&K), Pakistan. Environmental predictors derived from satellite observations included land cover, vegetation condition, terrain attributes, and climate-related indicators. To ensure model reliability, multicollinearity among predictors was evaluated, and spatial clustering patterns of leopard occurrence records were examined using global spatial autocorrelation analysis. Two complementary machine learning algorithms, Maximum Entropy (MaxEnt) and Random Forest (RF), were implemented and integrated through a weighted ensemble approach to improve predictive accuracy and robustness. The ensemble model achieved high predictive performance with an area under the curve (AUC) value of 0.942, outperforming individual algorithms. The resulting habitat suitability map indicates that approximately 30% of the study region is highly suitable habitat, primarily in the northern and central districts, including Muzaffarabad, Neelum, Hattian, Poonch, and Sudhnutti. Variable importance analysis identified remotely sensed land cover, elevation, vegetation cover, slope, and temperature seasonality as the dominant predictors of habitat suitability, whereas anthropogenic indicators such as proximity to roads and population density had secondary effects in fragmented areas. The results demonstrate the potential of integrating remote sensing data and ensemble machine learning for spatial habitat modeling and wildlife conservation planning in mountainous ecosystems. Full article