Search Results (9,237)

The rapid growth of IoT ecosystem has significantly increased the potential threats and attack vectors in the recent times, thereby requiring intrusion detection mechanisms that are highly accurate and scalable in nature. This paper presents a hybrid intrusion detection system that involves the usage of both supervised and unsupervised machine learning methods to detect different kinds of attacks present in the IoT network. In the first step, Random Forest-based feature extraction is adopted to determine the most important features from the highly dimensional network traffic data. After this, the extracted features are compressed using the Deep AutoEncoder model into latent features that are fed into multiple classifiers to classify the traffic into various IoT attack classes and normal traffic class. Specifically, the classifiers used in the process include XGBoost, SVM, Logistic Regression, Naive Bayes and Multilayer Perceptron models. Multiple IoT benchmark datasets, such as N-BaIoT and CICIoT2023, are used to evaluate the performance of the proposed hybrid intrusion detection system. It was found that the XGBoost classifier performed better than others, obtaining an accuracy rate of 99.63% and 98.94% on the N-BaIoT and CICIoT2023 datasets, respectively. The above-discussed results show the high potential of the proposed architecture for generalization in various IoT environments. From the results, one can see that it is highly effective to integrate deep learning for extracting features from data and using boosting techniques for classification to develop an efficient IDS system. Full article

The administrative boundaries of ecosystems do not necessarily align with natural watershed boundaries, which is a significant reason for the current inefficiency and pronounced conflicts in ecological governance. Using the watershed as the fundamental unit, this study assessed the forest ecosystem services (FES) of the Beihai Wetland watershed in Tengchong (As of 2025). Forest vegetation was classified to the formation level, and the functional value method was employed. The results showed the following order of service values: regulating services > provisioning services > supporting services > cultural services. Biodiversity was identified as the most valuable ecosystem function. The study further revealed that factors such as stand type, stand age, and altitude influence the total FES value within the watershed. Analysis of FES per unit stand (1 ha) indicated that Lithocarpus variolosus Franch. Chun (natural forest) exhibited the highest value. Through in-depth analysis of linear correlations and spatial associations of FES per unit stand, a synergy-trade-off visualization was constructed. This revealed that natural forests in the upper watershed may exert systemic effects on nutrient cycling in the lower watershed. The results obtained at the formation level provide support for the development of watershed-based forest tending plans. Moreover, studying FES using the watershed as a unit represents a practical exploration of the “life community of mountains, rivers, forests, farmlands, lakes, grasslands, and deserts” and offers a potential reference for maintaining the ecological security and supporting the ecological protection and restoration of the Beihai watershed. Full article

Pine wilt disease (PWD) is a devastating biological forest disturbance, making its large-scale and high-precision remote sensing monitoring crucial for epidemic prevention and control. However, the performance of existing deep learning methods in high-resolution imagery is often limited by the confusion of spectral features among disparate ground objects and the complexity of forest boundaries. To address these challenges, this study proposes an innovative, end-to-end deep learning architecture termed MBA-Former. Built upon the robust Swin Transformer V2 backbone, the model systematically integrates two highly adaptable functional modules: (1) a front-end intelligent fusion module designed to adaptively fuse heterogeneous features, and (2) a back-end boundary refinement module that refines segmentation contours via dual-task learning. To train and evaluate the model, fine-grained manual annotations were first performed on Gaofen-2 satellite imagery acquired from multiple typical epidemic areas across northern and southern China. Information-enhanced datasets were constructed by fusing the original spectral bands, typical vegetation indices, and texture features. A comprehensive performance evaluation was then conducted, specifically targeting typical challenging scenarios characterized by complex ground object boundaries. The experimental results demonstrate that the Multi-modal Boundary-Aware Transformer (MBA-Former) significantly outperforms current state-of-the-art models. It achieved a mean Intersection over Union (mIoU) of 81.74%, an IoU of 77.58% for the most critical infected tree category, and a Boundary F1-Score of 78.62%. Compared to the best-performing baseline model, Swin-Unet, these three metrics exhibited notable improvements of 2.88%, 3.55%, and 4.46%, respectively. These findings convincingly demonstrate that MBA-Former provides a highly accurate and robust solution for the large-scale, automated remote sensing monitoring of forest diseases, offering immense value in preventing significant economic losses and preserving forest ecosystem integrity. Full article

The unchecked expansion of tea plantations onto steep, forest-adjacent slopes in subtropical mountains engenders a conflict between agricultural productivity and ecosystem integrity, particularly by exacerbating habitat fragmentation and soil erosion. While precise monitoring is essential to navigate this trade-off for sustainable management, accurate inventorying remains a challenge due to the plantations’ strong phenological variability, heterogeneous canopy structures, and high spectral confusion with surrounding vegetation. This study proposes a feature-optimized deep learning framework for mapping and characterizing tea plantations in complex landscapes, using Xinyang City, China, as a study area. The framework integrates multi-temporal Sentinel-1/2 observations with a sequential Jeffries-Matusita (JM)-Pearson feature filtering strategy. This approach effectively condenses a 132-variable high-dimensional pool (including optical spectra, vegetation indices, textures, and SAR polarimetry) into a compact 28-feature subset (a 78.8% reduction), preserving critical phenological and structural cues while minimizing redundancy. These optimized predictors drive a hybrid VGG16–UNet++ segmentation network, which couples transfer-learning-based semantic encoding with detail-preserving dense skip fusion. Extensive experiments across 18 model–feature configurations demonstrate that the optimal setting achieves an Overall Accuracy of 97.82%, an F1-score of 0.9093, and a mean IoU of 0.7968. Notably, the method significantly reduces misclassification in rugged, cloud-prone terrain, yielding a User’s Accuracy of 91.14% for tea. Based on the generated wall-to-wall map, we derived two decision-support indicators: multi-threshold steep-slope exposure and a normalized tea–forest interface density. This framework provides actionable, high-precision spatial products to support slope-based zoning, ecological restoration, and sustainable management in fragile mountain agroforestry systems. Full article

Vegetation restoration is critical for ecosystem recovery in abandoned mining areas, yet how restoration age affects soil multifunctionality (SMF) and the underlying microbial regulatory mechanisms remains poorly understood. The space-for-time substitution method was employed in this study. Along a revegetation chronosequence (Restoration 1 year (R1), Restoration 10 year (R10), Restoration 30 year (R30), Restoration 45 year (R45)) in copper mine wasteland in Tongling, China, the dynamics of soil functions, SMF, and microbial communities were quantified, with the key factors influencing soil functions and the most important predictors of SMF subsequently identified. The results showed that the soil moisture regulation function recovered relatively slowly, whereas nutrient cycling functions and SMF were generally enhanced with advancing revegetation. Specifically, these functions all reached their maximum values at R30 (0.39, 0.45, and 0.28, respectively), followed by declines at R45 (−0.74, −0.09, and −0.20, respectively). Furthermore, the soil microbial communities exhibited successional characteristics of increased diversity but reduced dominance. Redundancy analysis indicated that aboveground biomass (AGB), belowground biomass (UGB), and soil total copper were key environmental variables associated with variations in multiple soil functions. Linear regression analysis showed that fungal diversity indices, plant biomass (AGB and UGB), soil total cadmium, and soil total zinc exhibited significant linear relationships with SMF. Random forest analysis further identified UGB, bacterial Simpson index, and fungal Shannon–Wiener index as key predictors of SMF. Importantly, bacterial communities played a more important role in influencing SMF than fungal communities. These results advance the understanding of key drivers of ecosystem functional recovery in mine lands and provide a theoretical basis for optimizing soil function restoration strategies. Ultimately, these findings provide new insights for advancing efforts aimed at halting land degradation and safeguarding biodiversity in degraded mining ecosystems. Full article

Morphological traits, as core components of functional traits, are fundamental in determining environmental adaptability. However, under climate warming, the adaptive morphological changes and associated ecological risks of locust populations migrating to higher altitudes remain poorly understood. Here, we investigated Calliptamus italicus, the dominant locust species in the desert steppes of the Ili River Basin, to explore the response patterns of its morphological functional traits along an altitudinal gradient and their relationships with environmental factors. Morphological measurements revealed that forewing area, width, and length, as well as hindwing width, exhibited highly significant positive correlations with altitude (p < 0.01); in contrast, body length, head width, head height, pronotum length, pronotum width, hind femur length, and hind tibia length displayed significant negative correlations with altitude (p < 0.05). All morphological indicators presented highly significant sexual dimorphism (p < 0.001). Ratio analysis showed that the pronotum width-to-head width ratio (M/C), pronotum height-to-head width ratio (H/C), and forewing length-to-hind tibia length ratio (E/F) were significantly positively correlated with the altitudinal gradient (p < 0.05), with all ratios exhibiting significant sexual differences (p < 0.05). Random Forest analysis showed that PC1 (75.5% of variation) reflected traits for feeding, jumping, and reproduction, whereas PC2 (5.6%) represented flight-related traits, with significant sexual dimorphism. This study demonstrates that trait variation in C. italicus along an altitudinal gradient is closely linked to environmental factors. Our findings provide critical data for predicting habitat adaptation responses in locust populations, thereby enhancing the precision and efficacy of locust plague management and contributing to the conservation and restoration of desert steppe ecosystems. Full article

Under the combined pressures of climate change and irrigated cropland expansion, groundwater tables are declining rapidly across arid regions, thereby intensifying water limitation in riparian ecosystems. However, the mechanisms by which dominant riparian tree species coordinate multiple functional traits to maintain carbon–water balance remains poorly understood. This study investigated coordinated ecophysiological trait shifts of Populus euphratica Oliv. along a groundwater-depth gradient (2.19, 4.88, and 7.45 m) in the middle reaches of the Tarim River (China), hereafter referred to as shallow, middle, and deep groundwater depths, respectively. We quantified photosynthetic, hydraulic, stomatal, leaf anatomical and nutrient traits, and estimated long-term intrinsic water-use efficiency (WUE_i) from foliar δ¹³C. As the groundwater table declined, (1) photosynthetic capacity and photochemical performance decreased, whereas WUE_i increased markedly from 38.5 ± 2.9 to 54.2 ± 1.0 μmol mmol⁻¹, accompanied by the lowest transpiration rate at the deep groundwater depth (4.6 ± 0.5 mmol m⁻² s⁻¹); (2) stomatal and anatomical adjustments consistent with water-loss reduction were observed, including a significant decline in stomatal density from 93.5 ± 14.5 to 79.3 ± 17.4 pores mm⁻², and reduced stomatal size and stomatal area fraction (−20.3% and −32.7%, respectively); (3) the percentage loss of hydraulic conductivity increased, whereas sapwood-specific hydraulic conductivity declined, accompanied by greater sapwood investment relative to leaf area, with Huber value rising from 0.06 ± 0.02 to 0.11 ± 0.04 mm² cm⁻² at deep water depth; and (4) chlorophyll concentrations and leaf water content declined, whereas structural investment increased, as reflected by higher specific leaf mass and leaf dry matter content, and leaf nutrients were enriched, with total nitrogen and total phosphorus increasing by 67.1% and 42.0%, respectively. Trait-WUE_i relationships further indicated that WUE_i covaried most strongly with leaf anatomical and nutrient traits. These results demonstrate that increasing groundwater depth was associated with coordinated shifts in carbon assimilation, water-use regulation, hydraulic function, and nutrient allocation in P. euphratica. Such trait coordination may help explain how this species persists under chronic water limitation in arid riparian forests. Full article

The eddy covariance technique has become one of the most popular methods for measuring CO₂ exchange between ecosystems and the atmosphere. Flux averaging intervals typically range from 15 to 60 min, with 30 min being the most commonly adopted setting. However, due to variations in site conditions and turbulent regimes, the choice of averaging interval can substantially influence flux calculations. In this study, we applied the eddy covariance method to examine how different averaging intervals affect CO₂ flux measurements in a subtropical Moso bamboo forest during winter in Jinggangshan, Jiangxi Province, China. The results showed that the bamboo forest maintained a relatively high CO₂ uptake rate even in winter. When relative humidity exceeded 80%, the averaging interval had a pronounced effect on CO₂ flux estimates, and in some cases even altered the direction of the flux. Based on a comparative analysis, an average interval of 60 min is recommended. These findings offer practical guidance for eddy covariance observations in subtropical Moso bamboo forests and provide useful insights for flux measurements in humid environments more broadly. Full article

Soil health is critical for the sustainability of tropical plantation ecosystems, However, the ecological factors driving productivity gradients remain inadequately understood. This study investigated rubber plantations on Hainan Island with varying yield levels to assess soil health and its underlying ecological mechanisms using a minimum data set (MDS) approach. Twenty-seven soil physical, chemical, and biological indicators were analyzed at two depths (0–20 cm and 20–40 cm). Principal component analysis identified seven key indicators for the MDS: soil organic matter (OM), alkaline-hydrolyzable nitrogen (AN), cation exchange capacity (CEC), dissolved organic carbon (DOC), microbial biomass phosphorus (MBP), acid phosphatase activity (ACP), and microbial diversity (Shannon-Wiener index, SHDI). The soil health indices derived from the MDS showed strong correlations with those generated from the total data set (TDS) (p < 0.001), confirming the reliability of the MDS framework. Overall, soil health levels were rated low to moderate with no significant differences across low-yield plantations (≤900 kg·ha⁻¹), medium-yield plantations (900–1200 kg·ha⁻¹), and high-yield plantations (≥1200 kg·ha⁻¹)., suggesting a decoupling of soil health and rubber productivity under uniform management practices. Random forest analysis identified microbial-driven phosphorus cycling, particularly MBP and ACP, as the primary determinant of soil health across soil layers, with DOC and SHDI also contributing significantly. These findings highlight the critical role of microbial-mediated nutrient cycling in maintaining soil health in rubber plantations and suggest that current management practices prioritize short-term yields over long-term soil ecological stability. Enhancing microbial activity and increasing organic matter inputs may be essential for improving soil health and ensuring the sustainability of rubber production in tropical agroecosystems. Full article

Riparian ecosystems are being increasingly threatened by hydrological alteration and biological invasions, yet the role of local environmental heterogeneity in shaping invasion dynamics remains poorly understood. To address this, we tested the hypothesis that invasion patterns are spatially structured and therefore cannot be fully captured by global statistical models. We evaluated this hypothesis by analysing the relationship between soil sand content and the abundance of Gleditsia triacanthos in a riparian forest of the Esteros de Farrapos and Islands of the Uruguay River National Park, Uruguay. Generalized Linear Mixed Model revealed no significant relationship between soil sand content and G. triacanthos abundance (χ² = 1.93, p = 0.17). In contrast, spatially explicit analyses showed that relationships between sand content and abundance were spatially contingent. Positive linear relationships predominated in areas with low sand content (mean 24.5%, n = 12), while negative relationships were restricted to the highest sand levels (mean 87.6%, n = 3). Intermediate sand-content zones (mean 47%, n = 16) showed no consistent patterns. These results suggest that invasion patterns vary across spatial contexts and may reflect the influence of different processes operating locally, indicating that relying solely on global analyses risks misinterpreting drivers and overlooking fine-scale variation. Our findings emphasize that understanding invasive species in heterogeneous systems requires considering whether mechanisms operate at local or broad scales, and that explicitly analyzing spatial structure can guide both hypothesis formulation and field study design. Full article

Urban trees are increasingly deployed as nature-based infrastructure to mitigate heat and manage stormwater, yet quantitative guidance on how species traits and site context shape transpiration remains fragmented. We conducted a systematic metadata analysis of seven field studies that measured daily transpiration rate in urban settings using heat-pulse methods. The units and spatial scales reported were harmonized with the sap flow density across active sapwood (J_s, g H₂O/${\mathrm{cm}}^2$/day) by converting reported stand transpiration and the outer 2 cm of sapwood sap flux using established Gaussian radial distribution functions for angiosperms and gymnosperms, which account for the non-linear decline in sap flux from the vascular cambium to the heartwood boundary. We then summarized distributions and tested group differences with Kruskal–Wallis and Dunn post hoc comparisons across wood anatomy, climate, soil texture, and planting configuration. Conifers exhibited significantly lower median $J_s$ (39.76 g/${\mathrm{cm}}^2$/day) than angiosperms, while the ring-porous group (median $J_s$ = 92.25 g/${\mathrm{cm}}^2$/day) and diffuse-porous groups (median $J_s$ = 96.70 g/${\mathrm{cm}}^2$/day) had similar distributions overall. Climate-modulated responses within wood anatomy groups differed, with diffuse-porous species exhibiting the highest median $J_s$ (152.59 g/${\mathrm{cm}}^2$/day) in semi-arid regions, ring-porous species maintaining comparatively stable median $J_s$ across climates (varying slightly between 80.72 and 99.32 g/${\mathrm{cm}}^2$/day), and conifers reaching their highest median $J_s$ (69.90 g/${\mathrm{cm}}^2$/day) in humid continental sites. Soil texture effects were consistent with moisture availability: sandy loam generally reduced $J_s$ relative to loam or silt loam for conifers and diffuse-porous species. Across anatomies, single trees transpired more than clustered trees or closed canopies. For example, planting as single trees increased median $J_s$ by 86% in conifers (from 33.01 to 61.37 g/${\mathrm{cm}}^2$/day) and by 45% in diffuse-porous species (from 81.31 to 118.25 g/${\mathrm{cm}}^2$/day). These results provide actionable ranges and contrasts to inform species selection and planting design for urban greening and runoff reduction, while highlighting data gaps for future research. Ultimately, by matching specific wood anatomies and planting configurations to local soil and climatic conditions, urban planners and ecohydrologists can strategically optimize urban forests to maximize targeted ecosystem services. Full article

Ledum palustre (L. palustre) is widely used in drug development because of its antibacterial and analgesic effects. However, wild L. palustre is often affected by wildfires, resulting in unstable yields. Forest fires represent a major disturbance in northern forest ecosystems and profoundly affect shrub vegetation and its associated rhizosphere microbial communities. In this study, we investigated a fire chronosequence (1991, 2004, 2012, 2017, and 2020) to systematically examine the morphological traits of L. palustre, rhizosphere soil physicochemical properties, and microbial community characteristics and to identify the key drivers underlying these patterns. The results revealed that postfire recovery time significantly influenced the morphological traits of L. palustre. The biomass, branch number, basal diameter, and plant height of the shrubs at the 1991 burned site increased by 270.49%, 36.11%, 79.32%, and 191.36%, respectively (p < 0.05). From unburned soils, 29 bacterial and 29 fungal isolates were obtained, with Bacillus sp. and Oidiodendron sp. being the dominant culturable bacterial and fungal taxa, respectively. With increasing postfire recovery time, soil moisture, total nitrogen, ammonium, nitrate, soil organic carbon, acid phosphatase (AP) and N-acetyl-β-D-glucosaminidase (NAG) activity significantly decreased. Early fire disturbance markedly altered soil microbial abundance and community composition, leading to an overall decrease in bacterial α diversity. The bacterial community structure at the 2020 burn site and the fungal community structure at the 2012 burn site significantly differed. Mantel tests revealed significant positive correlations between branch number and basal diameter (p < 0.01) and significant negative correlations between plant height and stem density (p < 0.001). Soil carbon and hydrolysable nitrogen were significantly positively correlated with AP and NAG activities (p < 0.001). Moreover, soil physicochemical properties significantly shaped soil microbial community structures, with bacterial communities in early postfire sites driven by total carbon and nitrogen (p < 0.05), whereas fungal communities in the 2012 burned site were influenced primarily by β-N-acetylglucosaminidase (BG) activity (p < 0.05). Fire disturbance drives successional changes in the rhizosphere microbial community structure and function by altering the soil nutrient status and enzyme activity, which in turn influences the morphological traits of L. palustre. This study provides a theoretical basis for improving the yield of L. palustre by exploring the variation in rhizosphere microorganisms. Full article

Nothofagus obliqua forests in south-central Chile are increasingly threatened by outbreaks of a native leaf-miner complex, dominated by the moth Heterobathmia pseuderiocrania. Despite the high ecological and economic value of these forests, landscape-scale monitoring of forest–insect interactions remains limited, particularly regarding the attribution of phenological anomalies to biotic disturbances. This study aimed to detect and quantify the late-2022 outbreak and evaluate its effects on Land Surface Phenology (LSP), addressing signal attribution challenges associated with remote-sensing-based monitoring of insect defoliation. Using MODIS Enhanced Vegetation Index (EVI) time series (2003–2024), Seasonal-Trend decomposition (STL) was applied to isolate long-term trend anomalies. An EVI condition index was developed to compare 2022–2023 observations against a historical baseline, and synchrony between vegetation condition loss and larval developmental phases was assessed. Additionally, Highest Density Regions (HDR) were used to quantify the statistical probability of spectral anomalies. Results revealed a sharp decline in EVI trend during late 2022, reaching the lowest recorded value in the 20-year time series. Phenological decoupling began in November, coinciding with larval development and peak defoliation, with impacts extending across two growing seasons. Ecosystem condition declined to a minimum of 42%, falling with the 4% historical probability region. Notably, exceptional pre-outbreak vigor (160% condition) preceded the disturbance. By integrating spectral anomaly detection with insect life-cycle dynamics, this multi-layered approach strengthens biotic disturbance attribution and provides a scalable framework for remote forest health monitoring. The findings also address key knowledge gaps in Southern Hemisphere Forest entomology and improve early detection strategies for native insect outbreaks. Full article

The once-extensive coastal forested wetlands (CFWs) of the Mississippi River Delta (MRD) are declining under the combined pressures of pervasive hydrologic change, unregulated harvesting, relative water level rise (due to the combination of geological subsidence and sea-level rise—SLR), and climate change. We synthesize here over 50 years of research conducted in the MRD to examine the history of the CFWs and their management, their ecosystem functions and services, and the nature, extent, and severity of ongoing changes. Seedling recruitment failure and increasing salinity levels are the most immediate threats to forest persistence, necessitating management that restores hydrologic function and sediment and nutrient supply to allow seedling survival and minimizes saltwater intrusion. Collectively, the evidence indicates that managed inflows can bolster accretion and sustain forest function, and long-term resilience requires hydrologic restoration at landscape scales coupled with site-level actions that secure recruitment and address local degradation trajectories. These include freshwater and sediment introduction, protection from herbivory, and, in some cases, planting. Our research findings have important implications for worldwide CFWs, and tidal freshwater ecosystems in general, which occur mainly in tropical deltas. Full article

Wildfires are among the key drivers of transformation in boreal ecosystems; however, the mechanisms of postfire recovery in the arid regions of Eurasia remain insufficiently understood. The aim of this study was to identify the role of burn severity and associated edaphic and hydrological factors in shaping the natural regeneration trajectories of Scots pine forests in the Kostanay region of northern Kazakhstan. This study is based on the integration of field data on seedling regeneration and soil conditions with the analysis of long-term satellite-derived indices (NDVI, NDMI, and NBR). Sample plots were grouped according to fixed burn severity classes, which enabled a consistent statistical comparison and reduced the interpretative ambiguity that has characterized previous studies in the region. The results indicate that pine forest regeneration is most successful under low and moderate burn severity, where seed sources are preserved and favourable moisture conditions are maintained. In contrast, high burn severity leads to a reduction in regenerative potential and a shift in recovery trajectories toward deciduous or sparsely vegetated communities. The spectral indices derived from the remote sensing data strongly agreed with the field-based indicators, confirming their suitability for assessing postfire vegetation dynamics. Soil properties act as important modifying factors of recovery processes, particularly under conditions of limited water availability. These findings enhance the current understanding of postfire recovery mechanisms in the arid part of the boreal zone and highlight the need for differentiated management of postfire landscapes. The integration of field observations with remote sensing data provides a robust framework for monitoring and forecasting recovery processes under an increasingly intensified fire regime. Full article