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Cave microbiota comprise metabolically diverse organisms, including microalgae, among which Bacillariophyta (diatoms) represent one of the most prominent groups, inhabiting a wide range of substrates within cave ecosystems. In contrast to aerophytic cave habitats, aquatic cave environments remain poorly studied. Therefore, the main aims of this study were to determine the diversity, spatial distribution, and seasonal dynamics of diatom assemblages in the Ponorac Stream flowing through Ravništarka Cave, and to assess the influence of environmental variables on diatom diversity and distribution. Samples were collected from six sites along the Ponorac stream in May and November 2023. Physical and chemical water parameters showed only minor variation among sampling sites. In total, 148 diatom taxa belonging to 54 genera were recorded, including several rare diatom taxa. Diatom assemblages in the Ponorac stream were characterized by high taxonomic richness, high α-diversity, and pronounced community heterogeneity. Many taxa occurred in both seasons and across multiple sites, whereas several were restricted to a single season or exhibited clear site specificity. Most diatom index values indicated generally high ecological status. This study highlights the importance of aquatic cave habitats as reservoirs of diatom diversity and their value in studying temporal and spatial variation of their communities. Full article

Northeast China has undergone large-scale cultivation of agricultural land, accompanied by internal restructuring of paddy fields and rain-fed farmland. Such a land change process has an obvious impact on the ecosystem. However, the quantitative effects of long-term cultivation of land/internal structure on the eco-environment are still lacking in the Heilongjiang region, China’s ecological barrier and grain base. To address this academic issue, the integrated method of land update technology, dynamic tracking, remote sensing classification, and improved ecosystem services were applied using satellite imagery and land products. Through satellite monitoring, the area of cultivated land changed from 127,221.71 to 173,665.12 km², with an increment of 46,443.41 km², expanding the central–northern parts and the eastern part over the past half-century. In different regions, all cities have undergone varying degrees of reclamation rate expansion ranging 0.71–29.62%. Regarding the structure, a quarter of the study area was covered by rain-fed farmland (25.29%), but the cultivation level of paddy fields (2.83%) was very low in 1970; after that, only a 13.08% increment in rain-fed farmland but a high increase of 246.14% in paddy fields was monitored from 1970 to 2020. Meanwhile, the source area of cultivated land was 59,271.48 km², with 60.41% from forest and grassland of the agricultural-forestry ecotone. Its destination area was 12,827.11 km², and 78.49% of the total was converted to construction land, forest, and grassland. From 1970 to 2020, the evaluated ecosystem service changed from 15,575.87 to 12,495.72 × 10⁸ yuan, showing a total loss of 3080.15 × 10⁸ yuan and an annual turnover rate of 0.40%. An important calculation indicated that the expansion and shrinkage of cultivated land led to a 2303.46 × 10⁸ yuan loss, which means that three-quarters (i.e., 74.78%) of ecosystem service loss was caused by cultivated land changes. Another key finding was that a large transformation of wetland into paddy fields brought about the huge loss of 847.85 × 10⁸ yuan; by contrast, the process of extensive rain-fed farmland turning into paddy fields was only a small change of 3.38 × 10⁸ yuan. Considering the ecological loss caused by cultivated land, the projects of returning farmland to forests and wetland protection should be implemented. This study provided important references for land system monitoring and environmental impact assessment in high-latitude regions around the world. Full article

Utilizing multi-source meteorological data for lightning identification is crucial for monitoring severe convective weather. However, several key challenges persist in this field: dimensional imbalance and modal competition among multi-source heterogeneous data, model training bias caused by the extreme sparsity of lightning samples, and an imbalance between false alarms and missed detections resulting from complex background noise. To address these challenges, this paper proposes a lightning identification network guided by physical priors and constrained by supervision. First, to tackle the issue of modal competition in fusing satellite (high-dimensional) and radar (low-dimensional) data, a physical prior-guided asymmetric radar information enhancement mechanism is introduced. This mechanism uses radar physical features as contextual guidance to selectively enhance the latent weak radar signatures. Second, at the architectural level, a multi-source multi-scale feature fusion module and a weighted sliding window–multilayer perceptron (MLP) enhanced decoding unit are constructed. The former achieves the coupling of multi-scale physical features at a 2 km grid scale through cross-level semantic alignment, building a highly consistent feature field that effectively improves the model’s ability to detect lightning signals. The latter leverages adaptive receptive fields and the nonlinear modeling capability of MLPs to effectively smooth spatially discrete noise, ensuring spatial continuity in the reconstructed results. Finally, to address the model bias caused by severe class imbalance between positive and negative samples—resulting from the extreme sparsity of lightning events—an asymmetrically weighted BCE-DICE loss function is designed. Its “asymmetric” characteristic is implemented by assigning different penalty weights to false-positive and false-negative predictions. This loss function balances pixel-level accuracy and inter-class equilibrium while imposing high-weight penalties on false-positive predictions, achieving synergistic optimization of feature enhancement and directional suppression. Experimental results show that the proposed method effectively increases the hit rate while substantially reducing the false alarm rate, enabling efficient utilization of multi-source data and high-precision identification of lightning strike areas. Full article

The East China Sea represents a critical coastal wetland region, characterized by complex geomorphology, heterogeneous land-cover composition, and diverse wetland types. Accurate delineation of coastal wetland extent is essential for ecosystem service assessment and sustainable coastal management, directly contributing to wetland-related Sustainable Development Goals (SDGs), particularly SDG 15, on ecosystem conservation and biodiversity protection. However, pronounced spectral similarity and structural heterogeneity among wetland classes pose substantial challenges to reliable classification. To address these challenges, this study developed a hierarchical classification framework integrating Random Forest, K-means clustering, and a decision tree classifier based on multi-source Sentinel-1 and Sentinel-2 imagery. Spectral, polarimetric, texture, and morphological features were systematically constructed to enhance class separability. Using this framework, a 10 m resolution coastal wetland map of the East China Sea was generated for 2023. The proposed approach achieved an overall accuracy of 91.32% and improved the discrimination of spectrally similar wetland types. Feature fusion reduced confusion among water-related classes, while object-based clustering improved the extraction of linear riverine wetlands. The resulting 10 m wetland map provides updated spatial information for ecological assessment and coastal management in the East China Sea. Full article

Unsupervised change detection (CD) in heterogeneous remote sensing images is intrinsically difficult due to severe sensor-specific discrepancies. In the absence of ground truth, these discrepancies result in ambiguous optimization objectives that make it difficult for models to distinguish true land-cover changes from modality-driven pseudo-changes. To address these challenges, we propose MaskUCD, a novel unsupervised framework that reformulates heterogeneous CD as a dynamic mask-driven constraint scheduling problem. Fundamentally distinct from conventional strategies that enforce selective feature alignment, MaskUCD employs a spatially adaptive optimization mechanism. Specifically, the iteratively refined mask serves as a geometric reference to guide optimization. It enforces strict feature alignment in mask-unchanged regions to suppress modality-induced discrepancies, while simultaneously promoting feature divergence in mask-changed regions to emphasize semantic inconsistencies. In this way, explicit optimization objectives are established, together with an intrinsic interpretability constraint that guides the CD process. This strategy treats the mask as a structural guide for representation learning rather than a ground-truth reference, thereby avoiding error accumulation caused by directly using inaccurate masks as supervisory signals. To facilitate this optimization, we design a specialized asymmetric autoencoder with a hybrid encoder architecture, utilizing multi-scale frequency analysis and global context modeling to enhance feature representation capabilities. Consequently, this design enables the generation of refined and semantically consistent masks, which provide increasingly precise structural guidance, yielding converged and discriminative difference maps. Extensive experiments demonstrate that MaskUCD achieves state-of-the-art performance and superior robustness compared to existing advanced methods. Full article

Background: Klebsiella pneumoniae is a Gram-negative bacterium that is found in human microbiota and in diverse environments. This opportunistic pathogen exhibits a highly variable genetic background and is responsible for a broad range of hospital- and community-acquired, multidrug-resistant infections worldwide. To track transmission pathways and understand genetic diversity, single-nucleotide polymorphism (SNP) clustering has become an essential tool. Methods: This study examines data from 2018 to 2024 in the NCBI Pathogen Detection database to determine the temporal and spatial distribution of SNP clusters in clinical K. pneumoniae across Middle East and North Africa (MENA) countries. Results: Among 1858 isolates, a heterogeneous population structure was observed. Of the 478 identified SNP clusters, a few dominant clusters accounted for 37% of the isolates, and numerous low-frequency lineages were detected. The descriptive yearly snapshot revealed a diverse representation of top clusters. Geographical analysis showed the presence of both localized and limited cross-border distribution patterns. Countries with diverse clusters also exhibit higher diversity of carbapenem- and ESBL-resistant genes. Conclusions: These findings provide valuable insights into the dominant, regionally concentrated K. pneumoniae lineage across MENA countries, assisting future genomic surveillance and efforts to combat clinical K. pneumoniae infections in this region. Full article

The rapid growth of Internet of Things (IoT) and Industrial IoT applications has intensified the demand for low-latency and reliable computation support for deadline-constrained periodic real-time tasks. While unmanned aerial vehicles (UAVs) enabling mobile edge computing (MEC) can reduce latency by bringing compute closer to data sources, terrestrial MEC deployments often suffer from limited coverage and poor adaptability to spatially heterogeneous demand. In this paper, we study a multiple-UAV-assisted MEC system serving cluster-based IoT networks, where cluster heads generate deadline-constrained periodic tasks for offloading under strict deadlines. To ensure practical feasibility in dense urban environments, we benchmark UAV mobility using a realistic 3D collision avoidance navigation graph with shortest-path execution, rather than assuming unconstrained continuous UAV motion in free space. On top of this benchmark, we systematically compare three multi-agent reinforcement learning (MARL) paradigms for joint navigation and periodic task offloading: (i) continuous 3D control MARL that outputs motion commands directly; (ii) discrete graph-based MARL that selects collision-free shortest paths; and (iii) asynchronous macro-action MARL. Using a high-fidelity 3D digital twin of San Francisco, we evaluate these paradigms under a unified protocol in terms of offloading success, end-to-end latency, and energy consumption. The results reveal clear performance trade-offs induced by realistic 3D collision avoidance constraints and provide actionable insights for designing UAV-assisted MEC systems supporting periodic real-time task offloading. Full article

The study presents a comprehensive spatio-temporal assessment of meteorological drought in the Upper Vistula basin, a region located in southern Poland. The analysis was based on monthly precipitation data from 30 meteorological stations covering the period 1961–2022. These data were used to calculate the Standardized Precipitation Index (SPI) for accumulation periods of 3, 6, 9, 12, 24, and 48 months. Drought events were identified using run theory, adopting a threshold of SPI < −1 for all accumulation periods. On this basis, drought characteristics were determined, including the number of identified drought episodes (N), average drought duration (ADD), average drought severity (ADS), and average drought intensity (ADI). The multi-scale analysis revealed a clear dependence of drought characteristics on the time scale. Short-term droughts (SPI-3 and SPI-6) occurred frequently and were characterized by high monthly intensity but short duration. In contrast, long-term droughts (SPI-24 and SPI-48) occurred less frequently, but were marked by much longer duration and greater cumulative severity, despite lower average intensity. Spatial analyses showed substantial heterogeneity of drought characteristics within the Upper Vistula basin. The western and south-western parts of the region were particularly exposed to frequent short-term droughts, whereas long-term droughts were less frequent, but more regional in nature and resulted from accumulated, multi-year precipitation deficits affecting groundwater resources and catchment retention. The presented findings provide valuable information for improving drought monitoring systems and adaptation strategies in the Upper Vistula basin and in other climatically diverse regions of Central Europe. Full article

Urban mental health is increasingly influenced by daily environmental exposures, yet limited empirical evidence exists regarding how the spatial configuration of blue–green environments, rather than their mere quantity, relates to emotional behavior in high-density cities. Guided by restoration theories and a perception-based perspective on landscape integration, this study analyzes the urban core of Shanghai by linking blue–green configurations to emotional states inferred from 20,907 geotagged social media facial photographs. Facial expressions serve to derive indices for emotional valence and arousal. The results demonstrate significant spatial clustering of emotional behavior, where hotspots are concentrated in higher-quality and more open settings, while coldspots cluster in dense areas with sparse vegetation. Emotional behavior also exhibits demographic heterogeneity, as females display higher valence and arousal than males. Furthermore, happiness tends to increase with age across both genders, whereas arousal declines specifically among male age groups. Crucially, emotional outcomes align more consistently with landscape integration and configuration than with isolated blue or green areas. Factors such as high connectivity, superior vegetation condition, and configurations featuring water embedded within green space are associated with favorable emotional responses. Conversely, extensive edge-dominated interfaces and high traffic exposure correlate with less favorable outcomes. These findings suggest a shift in blue–green planning from increasing total area toward optimizing spatial composition. Specifically, priority should be given to embedded and cohesive designs alongside the reduction of ambient stressors to foster emotionally supportive environments in dense urban cores. Methodologically, image-derived behavioral traces provide a scalable and ecologically grounded approach for investigating place-based affect at a city scale. Full article

Heterogeneous change detection (HCD) using satellite and Unmanned Aerial Vehicle (UAV) imagery is a pivotal task in remote sensing and Earth observation. However, the effective utilization of such multi-source data is significantly hindered by extreme spatial resolution disparities and distinct radiometric characteristics. Existing deep learning methods, often based on weight-sharing Siamese architectures, struggle to bridge these domain gaps, leading to spectral pseudo-changes and blurred detection boundaries. To address these challenges, we propose a novel Dual-Branch Cross-Scale Fusion Network (DBCF-Net) specifically tailored for heterogeneous satellite–UAV change detection. We introduce a Difference-Aware Attention Module (DAAM) to explicitly align cross-modal feature spaces and suppress domain-related noise through a hybrid local–global attention mechanism. Furthermore, an Adaptive Gated Fusion Module (AGFM) is designed to dynamically weight multi-scale interactions, ensuring the preservation of high-frequency spatial details from UAV imagery while maintaining the semantic consistency of satellite data. Extensive experiments on the Heterogeneous Satellite–UAV Dataset (HSUD) demonstrate that DBCF-Net achieves state-of-the-art performance, reaching an F1-score of 88.75% and an IoU of 80.58%. This study provides a robust technical framework for heterogeneous sensor fusion and high-precision monitoring in complex remote sensing scenarios. Full article

Spatiotemporal heterogeneity of land-use multifunction (LUMF) is crucial for the preservation and management of large-scale national cultural parks in alleviating potential human-land conflicts. Using 28 multidimensional indicators across economic, social, and environmental dimensions, this study established an LUMF index system for the Long March National Cultural Park of China (CLMNCP). LUMF values for 77 prefecture-level cities were quantified from 2008 to 2023, and their spatiotemporal heterogeneity was examined using a spatial autocorrelation model. Subsequently, the Optimal Parameters-based GeoDetector (OPGD) model was applied to identify key driving factors. The main findings are as follows: (1) From 2008 to 2023, the total, economic (EF), social (SF), and environmental (EnF) functions in the CLMNCP exhibited a consistent upward trend. (2) Significant spatial heterogeneity characterized the trade-offs and synergies among these functions. The EF-EnF interaction displayed a concave synergistic relationship, while the EF-SF and SF-EnF interactions showed convex, fluctuating patterns during their transitions between trade-off and synergy. (3) The primary drivers varied across function pairs. The EF-SF synergy was predominantly influenced by agricultural production, resource supply, and cultural service factors. The EF-EnF interaction was mainly shaped by natural conditions and environmental improvement factors. In contrast, the SF-EnF interaction was primarily driven by economic development, cultural services, and resource supply. These findings support functional zoning and targeted management of large-scale national cultural park to balance development and conservation while reducing human-land conflicts. Full article

Artificial light at night (ALAN) data is widely used in urban function analysis and socio-economic activity monitoring, but its application at the micro-scale of cities still faces challenges. This study utilizes high spatial resolution SDGSAT-1 nighttime light data to explore the spatial heterogeneity of ALAN at the street scale in two representative Chinese cities—Beijing and Guangzhou. By integrating multi-source data (such as building vector data, road networks, and point of interest data), a multi-dimensional indicator system covering urban morphology, functional structure, and transportation accessibility is constructed. Based on this, the study employs a Geographically Weighted Random Forest (GWRF) model combined with the Shapley Additive Explanations (SHAP) method to deeply analyze the non-linear relationships between ALAN intensity and multiple driving factors, as well as their spatial variability. Results demonstrate the superiority of the GWRF model over global models in capturing spatial non-stationarity, with R² values of 0.67 for Beijing and 0.74 for Guangzhou, compared to 0.62 and 0.71 for the random forest models, respectively. Road density is the dominant factor influencing nighttime light intensity in both Beijing and Guangzhou. However, the relationship between ALAN and its driving factors varies across these cities. In Beijing, a balanced multi-factor model is observed, whereas in Guangzhou, ALAN intensity is primarily driven by road density, with secondary influences from other factors like sky view factor. This study validates SDGSAT-1 for micro-scale analysis, offering a scientific basis for differentiated urban lighting planning. Full article

Wildfires are a major natural hazard causing extensive ecological damage and endangering human survival. Previous studies on wildfires in China have mostly focused on specific regions or individual drivers, with limited systematic assessments at the long-term and national scales. The spatiotemporal patterns of wildfires and their multiple driving mechanisms under China’s diverse climatic regimes remain insufficiently understood. To bridge this gap, we combined MCD64A1 burned area data (2001–2023) with multi-source natural (meteorological, vegetation, and topographic) and anthropogenic factors, using random forest models at both the national and regional scales to examine the spatiotemporal patterns, dominant drivers, and response mechanisms of wildfires in China. The results revealed that: (1) Spatially, wildfires were concentrated in northeastern and southern China, which accounted for 86.20% of the total burned area. Temporally, northern wildfires were primarily a spring-dominated fire regime, with peak activity in March and April, whereas southern wildfires were winter-dominated, peaking in February. (2) At the national scale, elevation was the key topographic factor influencing wildfire occurrence (relative importance = 0.49), with low-elevation and gentle-slope areas being more fire-prone. At the regional scale, the driving factors exhibit spatial differentiation, forming a spatial pattern of topography-dominated and climate-dominated. (3) Partial dependence plot analysis revealed nonlinear and threshold responses. Fire probability increases rapidly when the soil moisture is below 20 mm, while extremely high land surface temperatures in arid regions suppress fire occurrence due to fuel limitations. This study enhances the understanding of spatially heterogeneous wildfire drivers in China and provides a scientific basis for region-specific wildfire prevention and management strategies. Full article

Although diazotrophy is compatible with low-carbon agriculture and aligned with sustainability goals, its benefits could be expanded by better leveraging associative plant–diazotroph partnerships. Host control, however, remains underemphasized despite increasing resolution of microbial determinants of colonization. Understanding how plants tune permissiveness across fluctuating mineral N landscapes is therefore central to explaining when microbial presence translates into measurable diazotrophic function and plant N gain. Here, we propose an N-mediated host gatekeeping framework that organizes existing evidence into four licensing windows: (i) spatial positioning of permissive sites, (ii) N-sensitive transcriptional thresholding, (iii) local immune tuning at the contact interface, and (iv) carbon energy arbitration sustaining fixation and N transfer. Our model predicts that moderate, spatially heterogeneous mineral N biases the root interface toward permissive states in which microdomain colonization can translate into measurable biological nitrogen fixation, whereas at either extreme one or more windows tend to close. In crops, soil heterogeneity and genotype-linked root functional traits act as filters shaping when functional engagement becomes possible. By reframing N as both a resource and a signal acting through host arbitration, this model clarifies how permissiveness can be tuned to better realize diazotrophic potential and support plant N gain under rational mineral N management. Full article

Spring and autumn phenology jointly regulate terrestrial carbon, water, and energy exchanges, yet the mechanisms linking seasonal transitions remain debated under increasing hydroclimatic stress. Here, we integrate satellite-derived phenology with reanalysis-based indicators of land–atmosphere coupling to examine how spring onset interacts with growing season controlling factors and how these interactions shape autumn senescence at the global scale. Globally, start-of-season (SOS) and end-of-season (EOS) timings are positively coupled, with later SOS generally followed by later EOS, and this relationship becomes stronger when only later-SOS years are considered. However, SOS does not induce coherent global shifts in growing season climate limitation. Piecewise structural equation modeling reveals that SOS influences EOS primarily through a direct phenological pathway, with a mean path coefficient of ~0.4 day·day⁻¹ explaining approximately 26% of global EOS variability. In contrast, energy and water-mediated pathways contribute smaller but spatially heterogeneous effects, together accounting for ~5% of explained variance on average. SOS–EOS coupling is strongest in water-limited regimes, particularly in grasslands and shrublands. Managed croplands exhibit distinct and more heterogeneous responses, reflecting partial decoupling of phenology from natural hydroclimatic constraints. Collectively, our results indicate that spring phenology exerts a robust but spatially variable influence on autumn timing, dominated by direct effects rather than indirect mediation through growing season climate limitations, with regional modulation imposed by background hydroclimatic conditions. Full article