Search Results (440)

This study presents a novel ensemble deep-learning framework integrating Convolutional Neural Networks (CNN), self-attention mechanisms, and Long Short-Term Memory (LSTM) networks, designed to generate high-resolution drought susceptibility maps for the Oroqen Autonomous Banner of Inner Mongolia. The model was further enhanced through two metaheuristic optimization techniques—Differential Evolution (DE) and Biogeography-Based Optimization (BBO)—which tuned hyperparameters including CNN filters, LSTM units, and learning rate. Model evaluation—quantified via predictive accuracy (RMSE = 0.22 and MAE = 0.12), goodness-of-fit (R² = 0.79), and classification discrimination [Area Under the Receiver Operating Characteristic curve (AUROC) = 0.91]—revealed that the BBO-optimized ensemble achieved the best overall performance on the test set, outperforming the DE-enhanced (AUROC = 0.86) and baseline models (AUROC = 0.80). Pairwise z-statistics confirmed the statistical superiority of the BBO-enhanced ensemble with a p-value < 0.001. The final susceptibility map—classified into five levels using the Jenks natural breaks method—identified western rangelands and transitional ecotones as high-susceptibility zones, while eastern areas were marked by lower susceptibility. The resulting outputs offer decision-makers and land managers an interpretable, high-precision tool to guide drought preparedness, implement resource allocation strategies, and design early-warning systems. This research establishes a scalable, interpretable, and statistically robust approach for drought susceptibility assessment in vulnerable landscapes. Full article

Land use/cover (LUCC) change in arid oasis–desert ecotones has significant implications for spatial governance in ecologically fragile regions. To better capture the temporal and spatial complexity of land transitions, this study developed a vector tracing method by integrating time-series remote sensing data with vector-based transfer pathways. Analysis of northern Oman from 1995 to 2020 revealed the following: (1) Arable land and impervious surfaces expanded from 0.51% to 1.09% and from 0.31% to 0.98%, respectively, while sand declined from 99.03% to 97.01%. Spatially, arable land was concentrated in piedmont irrigation zones, impervious surfaces near coastal cities, and shrubland and grassland along the Al-Hajar Mountains, forming a complementary land use mosaic. (2) Human activities were the dominant driver, with typical one-way chains accounting for 69.76% of total change. Sand was mainly transformed into arable land (7_C1, 7_D1, 7_E1; where the first part denotes the original type, the letter denotes the year of change, and the last digit denotes the new type), impervious surfaces (7_C6, 7_D6, 7_E6), and shrubland (7_E4). (3) Water scarcity and an arid climate remained primary constraints, manifested in typical reciprocating chains in the oasis–desert interface (7_D1_E7, 7_A1_B7, 7_C1_D7) and in the arid vegetation zone along the Al-Hajar Mountain foothills (7_D3_E7, 7_C3_D7), together accounting for 24.50% of total change. (4) The region exhibited coordinated transitions among oasis, urban, and ecological land, avoiding the common conflict of cropland loss to urbanization. During the study period, transitions among arable land, impervious surfaces, forest, shrubland, and wetland were rare (Type 16: 3.31%, Type 82: 2.89%, Type 12: 0.04%, Type 18: 0.01%). The case of northern Oman provides a valuable reference for collaborative spatial governance in ecologically fragile arid zones. Future research should integrate socio-economic drivers, climate change projections, and higher-temporal-resolution data to enhance the applicability of the chain-spectrum method in other arid regions. Full article

The ecological interface between grasslands and farmlands forms a critical landscape component, significantly contributing to the stability and functioning of ecosystems within the agro-pastoral transition zone of northern China. Nevertheless, the variation patterns and interactions between soil physicochemical attributes and microbial community diversity at this interface remain poorly understood. In this study, we investigated nine sites located within 50 m of the grassland–farmland boundary in the Songnen Plain, northeastern China. We assessed the soil’s physicochemical properties and the composition of bacterial and fungal communities across these sites. Results indicated a declining gradient in soil physicochemical characteristics from grassland to farmland, except for pH and total phosphorus (TP). The composition of bacterial and fungal communities differed notably in response to contrasting land-use types across the ecological interface. Soil environmental variables were closely aligned with shifts observed in bacterial and fungal assemblages. Concentrations of total nitrogen (TN), available phosphorus (AP), alkali-hydrolyzable nitrogen (AN), and available potassium (AK) exhibited inverse correlations with both bacterial and fungal populations. Alterations in microbial community composition were significantly linked to TN, TP, total potassium (TK), AN, AP, AK, and soil pH levels. Variability in soil properties, as well as microbial biomass and diversity, was evident across the grassland–cropland boundary. Long-term utilization and conversion of grassland into cultivated land altered the soil’s physicochemical environment, thereby indirectly shaping the structure of microbial communities, including both bacteria and fungi. These findings provide a valuable basis for understanding the ecological implications of land-use transitions and inform microbial-based indicators for assessing soil health in agro-pastoral ecotones. Full article

Burned area (BA) mapping and fire severity assessment are essential for understanding fire occurrence patterns, formulating post-fire restoration strategies and evaluating vegetation recovery processes. However, existing BA datasets are primarily derived from coarse-resolution satellite imagery and often lack sufficient consideration of fire severity. To address these limitations, this study utilized dense time-series Landsat imagery available on the Google Earth Engine, applying the qualityMosaic method to generate annual composites of minimum normalized burn ratio values. These composites imagery enabled the rapid identification of fire sample points, which were subsequently used to train a random forest classifier for estimating per-pixel burn probability. Pixels with a burned probability greater than 0.9 were selected as the core of the BA, and used as candidate seeds for region growing to further expand the core and extract complete BA. This two-stage extraction method effectively balances omission and commission errors. To avoid the repeated detection of unrecovered BA, this study developed distinct correction rules based on the differing post-fire recovery characteristics of forests and grasslands. The extracted BA were further categorized into four fire severity levels using the delta normalized burn ratio. In addition, we conducted a quantitative validation of the BA mapping accuracy based on Sentinel-2 data between 2015 and 2023. The results indicated that the BA mapping achieved an overall accuracy of 93.90%, with a Dice coefficient of 82.04%, and omission and commission error rates of 26.32% and 5.25%, respectively. The BA dataset generated in this study exhibited good spatiotemporal consistency with existing products, including MCD64A1, FireCCI51, and GABAM. The BA fluctuated significantly between 1985 and 2010, with the highest value recorded in 1987 (13,315 km²). The overall trend of BA showed a decline, with annual burned areas remaining below 2000 km² after 2010 and reaching a minimum of 92.8 km² in 2020. There was no significant temporal variation across different fire severity levels. The area of high-severity burns showed a positive correlation with the annual total BA. High-severity fire-prone zones were primarily concentrated in the northeastern, southeastern, and western parts of the study area, predominantly within grasslands and forest–grassland ecotone regions. Full article

As a strategic crop of dry farming in northern China, the photosynthetic characteristics and stress resistance of foxtail millet (Setaria italica L.) are crucial to yield formation. This study aimed to explore the physiological characteristics of various foxtail millet varieties and screen high-efficiency varieties adapted to semi-arid climates. In the agro-pastoral ecotone of northern Shanxi Province, the physiological and ecological parameters, etc. of six cultivars were measured. The results showed that different cultivars had bimodal diurnal photosynthetic curves with distinct peak values and midday depression degrees, reflecting varied responses to high midday temperature and light stress. Dabaigu and Jingu 21 performed superiorly, with mean daily net photosynthetic rates (Pn) of 22.99 and 20.72 µmol·m⁻²·s⁻¹, significantly higher than Jinmiao K1 (12.87 µmol·m⁻²·s⁻¹). Chlorophyll fluorescence analysis showed Dabaigu had higher potential activity (F_v/F₀) of 3.98 than Jinmiao K1 (2.40). Jingu 21 synergistically optimized plant height, stem diameter, and biomass accumulation. Dabaigu and Jingu 21 are elite cultivars for the agro-pastoral ecotone of northern Shanxi Province due to high photosynthetic efficiency, strong photoprotection, and morphological plasticity. Full article

Soil salinization limits the growth of agricultural crops in the world, requiring the use of methods to increase the tolerance of agricultural crops to salinity–alkali stress. Arbuscular mycorrhizal fungi (AMF) enhance plant stress adaptation through symbiosis and offer a promising strategy for remediation. However, in non-model crops such as oat (Avena sativa L.), research has mainly focused on physiological assessments, while the key genes and metabolic pathways involved in AMF-mediated growth and saline–alkali tolerance remain unclear. In this study, we employed integrated multi-omics and physiological analyses to explore the regulatory mechanisms of AMF in oats under normal and saline–alkali stress. The results indicated that AMF symbiosis significantly promoted oat growth and physiological performance under both normal and saline–alkali stress conditions. Compared to the non-inoculated group under normal conditions, AMF increased plant height and biomass by 8.5% and 15.3%, respectively. Under saline–alkali stress, AMF enhanced SPAD value and relative water content by 16.7% and 7.3%, reduced MDA content by 35.8%, increased soluble protein by 21.8%, and decreased proline by 13.3%. In addition, antioxidant enzyme activities (SOD, POD, and CAT) were elevated by 18.4%, 18.2%, and 14.8%, respectively. Transcriptomic analysis revealed that AMF colonization under saline–alkali stress induced about twice as many differentially expressed genes (DEGs) as under non-saline–alkali stressed conditions. These DEGs were primarily associated with Environmental Information Processing, Genetic Information Processing, and Metabolic Processes. According to metabolomic analysis, a total of 573 metabolites were identified across treatments, mainly comprising lipids (29.3%), organic compounds (36.8%), and secondary metabolites (21.5%). Integrated multi-omics analysis indicated that AMF optimized energy utilization and antioxidant defense by enhancing phenylpropanoid biosynthesis and amino acid metabolism pathways. This study provides new insights into how AMF may enhance oat growth and tolerance to saline–alkali stress. Full article

Mangrove forests are widely recognized for their effectiveness as carbon sinks and serve as critical ecosystems for mitigating the effects of climate change. Current research lacks comprehensive, large-scale carbon storage datasets for wetland ecosystems, particularly across Mexico and other understudied regions worldwide. Therefore, the objective of this study was to develop a high spatial resolution map of carbon stocks, encompassing both aboveground and belowground components, within the Marismas Nacionales system, which is the largest mangrove complex in northeastern Pacific Mexico. Our approach integrates primary field data collected during 2023–2024 and incorporates some historical plot measurements (2011–present) to enhance spatial coverage. These were combined with contemporary remote sensing data, including Sentinel-1, Sentinel-2, and LiDAR, analyzed using Random Forest algorithms. Our spatial models achieved strong predictive accuracy (R² = 0.94–0.95), effectively resolving fine-scale variations driven by canopy structure, hydrologic regime, and spectral heterogeneity. The application of Local Indicators of Spatial Association (LISA) revealed the presence of carbon “hotspots,” which encompass 33% of the total area but contribute to 46% of the overall carbon stocks, amounting to 21.5 Tg C. Notably, elevated concentrations of carbon stocks are observed in the central regions, including the Agua Brava Lagoon and at the southern portion of the study area, where pristine mangrove stands thrive. Also, our analysis reveals that 74.6% of these carbon hotspots fall within existing protected areas, demonstrating relatively effective—though incomplete—conservation coverage across the Marismas Nacionales wetlands. We further identified important cold spots and ecotones that represent priority areas for rehabilitation and adaptive management. These findings establish a transferable framework for enhancing national carbon accounting while advancing nature-based solutions that support both climate mitigation and adaptation goals. Full article

Although treeline ecotones are significant components of vulnerable mountain ecosystems and key indicators of climate change, treelines of the Southern Hemisphere remain largely outside of research focus. In this study, we investigate, for the first time, the current and future distribution of the treeline species Nothofagus pumilio in the Southern Andes using a Species Distribution Modelling approach. The lack of modelling studies in this region can be contributed to missing occurrence data for the species. In a preliminary study, both point and raster data were generated using a novel Instagram ground truthing approach and remote sensing. Here we tested the performance of the two datasets: a typical binary species dataset consisting of occurrence points and pseudo-absence points and a continuous dataset where species occurrence was determined by supervised classification. We used a Random Forest (RF) classification and a RF regression approach. RF is applicable for both datasets, has a very good performance, handles multicollinearity and remains largely interpretable. We used bioclimatic variables from CHELSA as predictors. The two models differ in terms of variable importance and spatial prediction. While a temperature variable is the most important variable in the RF classification, the RF regression model was mainly modelled by precipitation variables. Heat deficiency is the most important limiting factor for tree growth at treelines. It is evident, however, that water availability and drought stress will play an increasingly important role for the future competitiveness of treeline species and their distribution. Modelling with binary presence–absence point data in the RF classification model led to an overprediction of the potential distribution of the species in summit regions and in glacier areas, while the RF regression model, trained with continuous raster data, led to a spatial prediction with small-scale details. The time-consuming and costly acquisition of complex species information should be accepted in order to provide better predictions and insights into the potential current and future distribution of a species. Full article

Odonata constitute an important invertebrate group that is strongly dependent on water conditions and sensitive to habitat disturbances, rendering them reliable indicators of habitat quality of both aquatic and terrestrial habitats. We studied the compositional and diversity patterns of Odonates in total, and separately for the two suborders (Zygoptera, Anisoptera) in relation to geographic and ecological parameters at the riparian zone of four rivers and one canal within the Axios Delta National Park and the Natura 2000 SAC GR1220002 in northern Greece, using the line transect technique. In total, 6252 individuals belonging to 28 species were identified. The compositional and diversity patterns were significantly different between agricultural and natural sites. Odonata assemblages at croplands were comparatively poorer, dominated by a few, widely distributed, taxonomically proximal species, tolerant to environmental changes, as a result of modifications and consequent alterations of abiotic conditions at croplands, which also led to higher local contribution to β-diversity and species turnover. The absence of several percher, endophytic, and threatened species from agricultural sites led to significantly lower diversity, as a result of environmental filtering due to ecophysiological restrictions. Taxonomic and functional diversity, uniqueness, and Dragonfly Biotic Index (DBI) were significantly higher in riparian forests, due to the sensitivity of damselflies to dehydration, and the avoidance of habitat loss and extreme temperatures by dragonflies, which prefer natural shelters near the ecotone. The newly introduced Conservation Value Index (CVI) revealed 21 conservation hotspots of Odonata (14 at canopy cover sites), widely distributed within the borders of NATURA 2000 SAC GR1220002. Full article

Stand structural configuration dictates ecosystem functional performance. Mangrove ecosystems, located in ecologically sensitive coastal ecotones, require efficient acquisition of stand structure parameters and health assessments based on these parameters for practical applications. Effective assessment of mangrove ecosystem health, crucial for their functional performance in ecologically sensitive coastal ecotones, relies on efficient acquisition of stand structure parameters. This study developed a UAV (Unmanned Aerial Vehicle)-based framework for mangrove health evaluation integrating stand structure parameters, utilizing UAV visible-light imagery, field plot surveys, and computer vision techniques, and applied it to the assessment of a national nature reserve. We obtained the following results: (1) A deep neural network, combining UAV visible-light data with tree height constraints, achieved 88.29% overall accuracy in simultaneously identifying six dominant mangrove species; (2) Stand structure parameters were derived based on individual tree extraction results in seedling zones along forest edges (with canopy individual tree segmentation accuracy ≥ 78.57%), and a stand health evaluation model was constructed; (3) Health assessment revealed that the core zone exhibited significantly superior stand health compared to non-core zones. This method demonstrates high efficiency, significantly reducing the time and effort for monitoring, and offers robust support for future mangrove forest health assessments and adaptive conservation strategies. Full article

Gonghe Basin is an important frontier of resource and energy development and environmental protection on the Qinghai–Tibetan Plateau and upper sections of the Yellow River. As a characteristic ecotone, this area exhibits complex and diverse ecosystem types while demonstrating marked ecological vulnerability. The response of ecosystem services (ESs) to human activities (HAs) is directly related to the sustainable construction of an ecological civilization highland and the decision-making and implementation of high-quality development. However, this response relationship is unclear in the Gonghe Basin. Based on remote sensing data, land use, meteorological, soil, and digital elevation model data, the current research determined the human activity intensity (HAI) in the Gonghe Basin by reclassifying HAs and modifying the intensity coefficient. Employing the InVEST model and bivariate spatial autocorrelation methods, the spatiotemporal evolution characteristics of HAI and ESs and responses of ESs to HAs in Gonghe Basin from 2000 to 2020 were quantitatively analyzed. The results demonstrate that: From 2000 to 2020, the HAI in the Gonghe Basin mainly reflected low-intensity HA, although the spatial range of HAI continued to expand. Single plantation and town construction activities exhibited high-intensity areas that spread along the northwest-southeast axis; composite activities such as tourism services and energy development showed medium-intensity areas of local growth, while the environmental supervision activity maintained a low-intensity wide-area distribution pattern. Over the past two decades, the four key ESs of water yield, soil conservation, carbon sequestration, and habitat quality exhibited distinct yet interconnected characteristics. From 2000 to 2020, HAs were significantly negatively correlated with ESs in Gonghe Basin. The spatial aggregation of HAs and ESs was mainly low-high and high-low, while the aggregation of HAs and individual services differed. These findings offer valuable insights for balancing and coordinating socio-economic development with resource exploitation in Gonghe Basin. Full article

This study quantitatively evaluates the effects of human activities (HAs) and climate change (CC) on the terrestrial ecosystem carbon cycle, providing a scientific basis for ecosystem management and the formulation of sustainable development policies in urban agglomerations located in arid and ecotone regions. Using the LanXi urban agglomeration in China as a case study, we simulated the spatiotemporal variation of vegetation net primary productivity (NPP) from 2000 to 2023 based on MODIS remote sensing data and the CASA model. Trend analysis and the Hurst index were employed to identify the dynamic trends and persistence of NPP. Furthermore, the Geographical Detector model with optimized parameters, along with nonlinear residual analysis, was employed to investigate the driving mechanisms and relative contributions of HAs and CC to NPP variation. The results indicate that NPP in the LanXi urban agglomeration exhibited a fluctuating upward trend, with an average annual increase of 4.26 gC/m² per year. Spatially, this trend followed a pattern of “higher in the center, lower in the east and west,” with more than 95% of the region showing an increase in NPP. Precipitation, mean annual temperature, evapotranspiration, and land use types were identified as the primary driving factors of NPP change. The interaction among these factors demonstrated a stronger explanatory power through factor coupling. Compared with linear residual analysis, the nonlinear model showed clear advantages, indicating that vegetation NPP in the LanXi urban agglomeration was jointly influenced by HAs and CC. These findings can further act as a basis for resource and environmental research in similar ecotone regions globally, such as Central Asia, the Mediterranean Basin, the southwestern United States, and North Africa. Full article

Revealing soil microbial diversity and metabolic limitations in different land uses and soil depths is essential to understanding the regulation processes of soil nutrients. Here, bacterial and fungal microbial diversity and metabolic restriction in the 0–50 cm soil layers of four land uses, namely farmland, grassland, Betula platyphylla secondary forest, and Larix principis-rupprechtii-planted forest in the mountainous forest-grass ecotone of northern China, were determined. The results showed that soil microbial diversity in farmland was the lowest. Soil microorganisms from all land uses are limited by nitrogen, with the highest nitrogen limitation in planted forest. However, microbial nitrogen limitation in farmland increased with increasing soil depth, while microbial nitrogen limitation in grassland, secondary forest, and planted forest decreased with increasing soil depth. The bacterial and fungal community composition was influenced by soil organic carbon, total nitrogen, soil organic carbon:total phosphorus ratio, soil water content, soil organic carbon, and total nitrogen:total phosphorus ratio. The soil organic carbon:total phosphorus ratio has an impact on microbial metabolic limitation. This study shows that soil microbial communities were more affected by land-use type than soil depth. Land use changes the input of soil nutrients from aboveground plants, which affects the physical and chemical properties of soil, microbial community diversity, and microbial metabolic limitation. The vertical filtration effect between soil layers reduces soil nutrients, making the microbial diversity and enzyme activity of surface soil greater than those of deep soil. Our study helps to understand the function of soil microorganisms under different land use types in the forest-grass ecotone of northern China and provides a basis for predicting biogeochemical cycle dynamics in the ecotone in the context of global warming. Full article

The Cerrado-Amazon Transition (CAT) in Brazil represents one of the most ecologically complex and dynamic tropical ecotones globally; however, it remains insufficiently characterized at high spatial resolution, primarily due to its intricate vegetation mosaics and the limited availability of reliable ground reference data. Accurate land cover maps are urgently needed to support conservation and sustainable land-use planning in this frontier region, especially for distinguishing critical vegetation types such as Amazon rainforest, Cerradão (dense woodland), and Savanna. In this study, we produce the first high-resolution land cover map of the CAT by integrating PlanetScope optical imagery, Sentinel-2 multispectral data, and Sentinel-1 SAR data within a U-net deep learning framework. This data fusion approach enables improved discrimination of ecologically similar vegetation types across heterogeneous landscapes. We systematically compare classification performance across single-sensor and fused datasets, demonstrating that multi-source fusion significantly outperforms single-source inputs. The highest overall accuracy was achieved using the fusion of PlanetScope, Sentinel-2, and Sentinel-1 (F1 = 0.85). Class-wise F1 scores for the best-performing model were 0.91 for Amazon Forest, 0.76 for Cerradão, and 0.76 for Savanna, indicating robust model performance in distinguishing ecologically important vegetation types. According to the best-performing model, 50.3% of the study area remains covered by natural vegetation. Cerradão, although ecologically important, covers only 8.4% of the landscape and appears highly fragmented, underscoring its vulnerability. These findings highlight the power of deep learning and multi-sensor integration for fine-scale land cover mapping in complex tropical ecotones and provide a critical spatial baseline for monitoring ecological changes in the CAT region. Full article

Wooded grasslands are agroforestry systems of high biological and cultural value, which are increasingly threatened by land-use abandonment in Mediterranean marginal areas. In the central-southern Apennines, little is known about their ecological dynamics under different management regimes. This study assesses how three management intensities (High: mowing plus grazing; Low: grazing only; and Abandoned: no management for ~50 years) affect the wooded grasslands in a protected area of the Central Apennines. Vascular plant composition and cover were recorded along radial transects from isolated Fagus sylvatica L. trunks to the adjacent grassland, with plots grouped in four positions (Trunk, Mid-canopy, Edge, and Grassland). The canopy cover, shrub height, species richness, and ecological roles of species were analysed. The results show that light availability, driven by canopy and shrub cover, shapes a gradient from shade-adapted species near the trunk to heliophilous grassland species in open areas. In the Abandoned site, shrub encroachment reduces light even beyond the canopy, facilitating the spread of shade-tolerant and pre-forest species, accelerating succession towards a closed-canopy forest. High-intensity management preserves floristic gradients and grassland species, while Low-intensity management shows early signs of succession at the canopy edge. These findings highlight the importance of traditional mowing and grazing in maintaining the biodiversity and ecological functions of wooded grasslands and emphasize the need for timely interventions where management declines. Full article