Search Results (285)

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article

Understanding root decomposition dynamics is essential to address declining carbon sequestration and nutrient imbalances in monoculture plantations. This study elucidates how forest gaps regulate Pinus massoniana root decomposition through comparative methodological analysis, providing theoretical foundations for near-natural forest management and carbon–nitrogen cycle optimization in plantations. The results showed the following: (1) Root decomposition was significantly accelerated by the in situ soil litterbag method (ISLM) versus the traditional litterbag method (LM) (decomposition rate (k) = 0.459 vs. 0.188), reducing the 95% decomposition time (T_0.95) by nearly nine years (6.53 years vs. 15.95 years). ISLM concurrently elevated the root potassium concentration and reconfigured the relationships between root decomposition and soil nutrients. (2) Lower-order roots (orders 1–3) decomposed significantly faster than higher-order roots (orders 4–5) (k = 0.455 vs. 0.193). This disparity was amplified under ISLM (lower-/higher-order root k ratio = 4.1) but diminished or reversed under LM (lower-/higher-order root k ratio = 0.8). (3) Forest gaps regulated decomposition through temporal phase interactions, accelerating decomposition initially (0–360 days) while inhibiting it later (360–720 days), particularly for higher-order roots. Notably, forest gap effects fundamentally reversed between methodologies (slight promotion under LM vs. significant inhibition under ISLM). Our study reveals that conventional LM may obscure genuine ecological interactions during root decomposition, confirms lower-order roots as rapid nutrient-cycling pathways, provides crucial methodological corrections for plantation nutrient models, and advances theoretical foundations for precision management of P. massoniana plantations. 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

Machine predictive maintenance plays a critical role in reducing unplanned downtime, lowering maintenance costs, and improving operational reliability by enabling the early detection and classification of potential failures. Artificial intelligence (AI) enhances these capabilities through advanced algorithms that can analyze complex sensor data with high accuracy and adaptability. This study introduces an explainable AI framework for failure detection and classification using symbolic expressions (SEs) derived from a genetic programming symbolic classifier (GPSC). Due to the imbalanced nature and wide variable ranges in the original dataset, we applied scaling/normalization and oversampling techniques to generate multiple balanced dataset variations. Each variation was used to train the GPSC with five-fold cross-validation, and optimal hyperparameters were selected using a Random Hyperparameter Value Search (RHVS) method. However, as the initial Threshold-Based Voting Ensembles (TBVEs) built from SEs did not achieve a satisfactory performance for all classes, a meta-dataset was developed from the outputs of the obtained SEs. For each class, a meta-dataset was preprocessed, balanced, and used to train a Random Forest Classifier (RFC) with hyperparameter tuning via RandomizedSearchCV. For each class, a TBVE was then constructed from the saved RFC models. The resulting ensemble demonstrated a near-perfect performance for failure detection and classification in most classes (0, 1, 3, and 5), although Classes 2 and 4 achieved a lower performance, which could be attributed to an extremely low number of samples and a hard-to-detect type of failure. Overall, the proposed method presents a robust and explainable AI solution for predictive maintenance, combining symbolic learning with ensemble-based meta-modeling. Full article

Globally, ecosystems are affected by climate change, human activities, and natural disasters, which impact ecosystem quality and stability. Vegetation plays a crucial role in ecosystem material cycle and energy transformation, making it important to monitor its resilience under disturbance stress. The Critical Slowing Down (CSD) indicates that as ecosystems near collapse, the autocorrelation of lag temporal increases and resilience decreases. We used the lag Temporal Autocorrelation (TAC) of long-term remote sensing Leaf Area Index (LAI) to monitor vegetation resilience in the Three Gorges Reservoir Area (TGRA). The Disturbance Event Model (DEM) was used to validate the CSD. The results showed the following: (1) The eastern TGRA exhibited high and increasing vegetation resilience, while most areas showed a decline. (2) Among the various vegetation types, forests demonstrated higher resilience than other vegetation types. (3) Precipitation, temperature, and soil moisture significantly influenced vegetation resilience dynamics within the TGRA. (4) For model accuracy, the CSD’s results were consistent with the DEM, confirming its applicability in the TGRA. Overall, the CSD when applied to long-term remote sensing data, provided valuable quantitative indicators for vegetation resilience. Furthermore, more CSD-based indicators are needed to analyze vegetation resilience dynamics and better understand the biological processes determining vegetation degradation and restoration. Full article

Accelerating the recovery of compacted soils caused by logging machinery using bioengineering techniques is a key goal of Sustainable Forest Management. This research was conducted on an abandoned skid trail with a uniform 15% slope and a history of heavy traffic, located in the Nav forest compartment of northern Iran. The main objectives were to assess (a) soil physical properties 35 years after skidding by a tracked bulldozer, (b) the impact of natural alder regeneration on soil recovery, and (c) the contribution of alder fine-root development to the restoration of compacted soils in beech stands. Soil physical properties and fine root biomass were analyzed across three depth classes (0–10 cm, 10–20 cm, 20–30 cm) and five locations (left wheel track (LT), between wheel tracks (BT), right wheel track (RT)) all with alder trees, and additionally control points inside the trail without alder trees (CPWA), as well as outside control points with alder trees (CPA). Sampling points near alder trees (RT, LT, BT) were compared to CPWA and CPA. CPA had the lowest soil bulk density, followed by LT, BT, RT, and CPWA. Bulk density was highest (1.35 ± 0.07 g cm⁻³) at the 0–10 cm depth and lowest (1.08 ± 0.4 g cm⁻³) at 20–30 cm. The fine root biomass at 0–10 cm depth (0.23 ± 0.21 g dm⁻³) was significantly higher than at deeper levels. Skid trail sampling points showed higher fine root biomass than CPWA but lower than CPA, by several orders of magnitude. Alder tree growth significantly reduced soil bulk density, aiding soil recovery in the study area. However, achieving optimal conditions will require additional time. Full article

As an important ecological barrier in Northwest China, the health of forest ecosystems in Shaanxi Province is crucial to regional ecological balance and sustainable development. However, forest degradation has become increasingly prominent in recent years due to both natural and anthropogenic pressures. This study aims to identify the spatio-temporal pattern of forest degradation in Shaanxi Province, construct an ecological network, and propose targeted restoration strategies. To this end, we first built a structural-functional forest degradation (SFD) assessment system and used the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm to identify degraded areas and types; subsequently, we used morphological spatial pattern analysis (MSPA) and the minimum cumulative resistance (MCR) model to construct a forest ecological network and identify key restoration nodes. Finally, we proposed a differentiated restoration strategy for near-natural forests based on the Miyawaki method as a conceptual framework to guide future ecological recovery efforts. The results showed that (1) in 1991–2020, the total area of forest degradation in Shaanxi Province was 1010.89 km², which was dominated by functional degradation (98%) and structural degradation (87.15%), with significant regional differences; (2) the constructed ecological network contained 189 ecological source sites, 189 ecological corridors, 89 key nodes, and 50 urgently restored; and (3) specific restoration measures were proposed for different degradation conditions (e.g., density regulation and forest window construction for functional light degradation and maintenance of the status quo or full reconstruction for structural heavy degradation). This study provides key data and systematic methods for the accurate monitoring of forest degradation, the optimization of ecological networks, and scientific restoration in Shaanxi Province, which holds great practical significance for establishing a robust ecological barrier in Northwest China. Full article

Fragments of urban forests can host a remarkable diversity of insects, even in environments that have been greatly transformed. This study evaluates the diversity, abundance, and composition of insects that belong to seven families in four urban forest fragments near Panama City, i.e., Ciudad del Saber (CDS), Parque Natural Metropolitano (PNM), Corozal (COR), and Albrook (ALB). A total of 2038 individuals were collected via Malaise traps and categorized into 403 morphospecies, 75 genera, and 43 subfamilies. The highest richness of morphospecies was observed in CDS (223), whereas PNM exhibited the highest abundance of individuals (862). The alpha diversity indices (Shannon-Wiener > 4.3; Margalef > 21; Pielou ≈ 1.0; and Simpson’s inverse > 0.95) reflected communities that were characterized by high levels of diversity and equity. The level of similarity observed among the fragments was moderate (Diserud–Odegaard index = 0.543), thus indicating differences among the sites evaluated as part of this research in terms of their taxonomic composition. These results provide evidence concerning the variability of entomological communities in tropical urban landscapes and the role of forest fragments as possible reservoirs of biodiversity. Full article

Maowusu sandy land is characterized by a fragile ecological environment and extreme sensitivity to external disturbances such as climate change and human activities. Identifying and zoning ecological spaces in this region are crucial for maintaining eco-environmental safety and promoting sustainable regional development. With Maowusu sandy land as the study object, the temporal and spatial characteristics of land use and the driving forces were explored via spatial analysis technology—the geographic information system. Then, a 2D relation judgment matrix was constructed by evaluating the importance of ecosystem service functions and ecological sensitivity. Next, restoration zoning of natural ecological space was performed, and relevant restoration suggestions were put forward accordingly. Results show that the land use in Maowusu sandy land has significantly changed in the past 30 years, with construction land and forest continuously expanding, cropland and grassland being squeezed, and some areas of unutilized land being transformed into other land use types. Ecosystem service functions tend to weaken from southwest to northeast, whereas the ecologically sensitive zones are mainly distributed in the middle of Maowusu sandy land. The high-importance and high-sensitivity zones of natural ecological space account for 3.60% of the total area of natural ecological space, mainly distributed near Ejin Horo Banner. A comprehensive restoration project of soil and water conservation should be conducted in this zone to alleviate soil erosion and maintain the management and restoration of ecological protection red lines. Moderately important sensitive zones account for the largest proportion (80.42%) of the total area of natural ecological space, being widely distributed. In such zones, water resources should be taken as constraints, with emphasis on ecological protection and improvement measures. Low-importance and low-sensitivity zones account for the smallest proportion, in which ecosystem protection, near-natural restoration, and moderate development and utilization should be carried out. This study aims to provide a scientific basis for reasonably protecting natural ecological resources and promoting the healthy and ordered development of natural ecosystems. Full article

Mangrove forests, vital coastal ecosystems that provide critical biodiversity habitats and carbon sequestration services, face increasing heavy metal pollution that threatens their ecological functions through bioaccumulation and toxicity to marine organisms. However, existing studies lack dynamic insights into temporal and spatial variations of heavy metals in mangrove sediments. This study systematically analyzed two mangrove reserves in Hainan Island, China (Hainan Dongzhaigang National Nature Reserve [DZG] and Hainan Qinglan Provincial Nature Reserve [QL]), by collecting sediment samples in 2014 and 2022, analyzing metals (Cr, Cu, Zn, As, Cd, and Pb) via ICP-MS, and applying the geo-accumulation index, potential ecological risk index, Markov transition matrix, and statistical analyses. Results showed that DZG exhibited rising Cu and Zn levels but declining Cr, As, Cd, and Pb, with Cd showing the most significant decrease (66.83%). In contrast, QL saw only a 42.7% reduction in Cd, while other heavy metals increased. Spatial heterogeneity linked higher concentrations to anthropogenic hotspots, DZG’s southeast (industrial/aquaculture inputs), and QL’s northwest (urban/industrial discharges). Although ecological risks were generally low, Cd in QL reached a moderate risk level (E_Cd = 46.44, 40 ≤ E_i < 80). The large-scale pond-to-mangrove conversion significantly increased vegetation cover, which enhanced sedimentation rates and exerted a “dilution effect” on sediment heavy metals. These findings underscore anthropogenic activities as the dominant driver of heavy metal contamination. We recommend (1) stringent wastewater control near QL, (2) enhanced shipping regulation, and (3) the establishment of mangrove buffers in heavy metal accumulation zones to improve ecological status. Full article

In this report, we document and analyze a case in which the Irkut virus (IRKV) (Mononegavirales: Rhabdoviridae) caused a fatal human case following a bat bite in June 2021. Unfortunately, the available data did not permit a detailed taxonomic classification of the carrier bat (Chiroptera). The event occurred in the southwestern part of the Sikhote-Alin mountain region (Russian Far East) covered by the Ussuri taiga forest. The symptoms of the illness began with the following: fever; pronounced psychomotor and motor agitation; tremor of the lower jaw and tongue; aphasia; dyslexia; and dysphagia. These rapidly developed, leading to a severe and fatal encephalitis. The patient was not vaccinated for rabies and did not receive rabies immunoglobulin. Using brain sections prepared from the deceased, molecular diagnostics were performed: immunofluorescence (polyclonal anti-rabies immunoglobulin) indicating the presence of the lyssavirus antigen; and RT-PCR indicating traces of viral RNA. Sectional material (brain) was used for whole-genome sequencing, resulting in a near-complete sequence of the lyssavirus genome. The obtained genomic sequence was identified as the Irkut virus. A comparative analysis of the new sequence and other currently available IRKV sequences (NCBI) revealed differences. Specifically, amino acid differences between antigenic sites in the isolate and those of the rabies vaccine strain used regionally were noted. The patient history and subsequent analysis confirm human IRKV infection following bat contact. Like other fatal cases of IRKV infection described earlier, this case occurred in the southern part of the Russian Far East. Two have occurred in the southwestern part of the Sikhote-Alin mountain region. This indicates the possible existence of an active, natural viral focus. Full article

Marine steel structures face severe corrosion risks due to harsh environmental conditions, posing significant logistical, economic, and safety challenges for inspection and maintenance. Traditional corrosion assessment methods are costly, labor-intensive, and potentially hazardous. This study evaluated the capabilities of visible-to-near-infrared hyperspectral imaging (HSI) for automating corrosion detection and severity classification in steel samples subjected to accelerated corrosion conditions simulating marine exposure. Marine steel coupons were partially coated to simulate protective paint and immersed in natural brackish water from the Panama Canal, creating varying corrosion levels. Hyperspectral images were acquired in controlled illumination conditions, calibrated radiometrically, and reduced in dimensionality via principal component analysis (PCA). Four machine learning models, including k-nearest neighbors, support vector machine, random forest, and multilayer perceptron, were tested for classifying corrosion severity. The multilayer perceptron achieved the highest accuracy at 96.18%, clearly distinguishing among five defined corrosion stages. These findings demonstrate that hyperspectral imaging, coupled with machine learning techniques, provides a viable, accurate, non-destructive methodology for assessing marine steel corrosion, potentially reducing costs, improving safety, and streamlining maintenance procedures. Full article

We report a landscape-scale natural experiment that followed the abundance and demography of forest-floor small mammals and the activity of small mustelids over a 4-year period of an extreme heat wave and abundant coniferous cone crops. Deer mice (Peromyscus maniculatus) and southern red-backed voles (Myodes gapperi) are major species in the coniferous forest-floor small mammal community near Summerland in southern British Columbia, Canada. Their major mammalian predators include the short-tailed weasel (Mustela richardsonii), long-tailed weasel (Neogale frenata), and American marten (Martes americana). We evaluated three hypotheses (H) that may explain the changes in these mammals from 2021 to 2024: (H₁) that large coniferous cone crops in 2022 would have generated high populations of forest-floor small mammals in 2023 owing to enhanced reproductive output and overwinter survival; (H₂) that increased activity of mustelids would have followed population increases, resulting in the decline of small mammal prey in 2024; and (H₃) that the widespread occurrence of cone crops in 2022 would also have elicited the same mammalian responses in 2023 at a second study area (Golden, BC) 276 km and three mountain ranges from Summerland. During the summer periods of each year, small mammal populations were monitored by intensive live-trapping, and mustelid presence was measured via an index of activity based on live traps, fecal scats, and predation events. The mean abundance and reproductive performance of the P. maniculatus and M. gapperi populations increased in response to the coniferous seedfall, thereby supporting H₁. The activity of small mustelids responded positively to increased numbers of small mammal prey and potentially acted in a regulatory and top–down function in these communities, and hence partially support H₂. Similar responses at Summerland and Golden indicated that this seedfall event and changes in the mammalian community occurred at a landscape-scale, thereby providing partial support for H₃. Potential differential effects of large seed crops on consumers did not affect the mean abundance patterns for P. maniculatus but apparently reduced this metric for M. gapperi. Heat waves, induced by anthropogenic climate change, may alter the frequency of coniferous masting events, and their effects may temporarily change the number and species of mammalian seed consumers and their predators. Full article

Recent research underscores the positive effects of nature exposure on health and well-being. Growing evidence also links biodiversity within these environments to enhanced health outcomes, as diverse ecosystems may offer a broader range of multi-sensory stimuli. This experimental field study investigated the effects on psychological and physiological outcomes linked to spending time in a forest compared to an urban environment. Sixty-six healthy participants were randomly assigned to spend 20 min in either a forest environment with alternating tree species richness in the Wienerwald near Vienna, Austria, or an urban environment. Psychological data were collected using validated scales, and saliva cortisol samples were taken before and after the intervention. Findings showed that the forest visit significantly reduced negative emotions, enhanced positive affect, and lowered cortisol levels more effectively than the exposure to the urban environment. However, increased tree diversity within the forest setting did not further amplify these benefits. These results suggest potential mental health and stress reduction benefits of forest exposure in the case of the Wienerwald, supporting the consideration of nature-based interventions in urban public health initiatives. While forest biodiversity appears to have limited additional effects, future research could further investigate its role in nature-based interventions and forest therapy practices. Full article

Armed conflict is a dynamic social phenomenon, yet existing research often overlooks its evolving nature. We propose a method to simulate the dynamic transformations of armed conflicts. First, we enhanced the Spatial Conflict Dynamic Indicator (SCDi) by integrating conflict intensity and clustering, which allowed for the distinction of various conflict types. Second, we established transformation rules for the SCDi, quantifying five types of transformations: outbreak, stabilization, escalation, de-escalation, and maintaining peace. Using the random forest algorithm with multiple covariates, we simulated these transformations and analyzed the driving factors. The results reveal a global trend of polarization in armed conflicts over the past 20 years, with an increase in clustered/high-intensity (CH) and dispersed/low-intensity (DL) conflicts. Stable regions of ongoing conflict have emerged, notably in areas like Syria, the border of Afghanistan, and Nepal’s border region. New conflicts are more likely to arise near these zones. Various driving forces shape conflict transformations, with neighboring conflict scenarios acting as key catalysts. The capacity of a region to maintain peace largely depends on neighboring conflict dynamics, while local factors are more influential in other types of transformations. This study quantifies the dynamic process of conflict transformations and reveals detailed changes. Full article