Search Results (448)

To explore the characteristics of species diversity and phylogenetic diversity, as well as the dominant processes of community construction, in different forest types (deciduous broad-leaved forest, mixed coniferous and broad-leaved forest, and Chinese fir plantation) in subtropical regions, analyze the specific driving patterns of soil nutrients and other environmental factors on the formation of forest diversity in different forest types, and clarify the differences in response to environmental heterogeneity between natural forests and plantation forests. Based on 48 fixed monitoring plots of 50 m × 50 m in Shouchang Forest Farm, Jiande City, Zhejiang Province, woody plants with a diameter at breast height ≥5 cm were investigated. Species diversity indices (Margalef index, Shannon–Wiener index, Simpson index, and Pielou index), phylogenetic structure index (PD), and environmental factors were used to analyze the relationship between diversity characteristics and environmental factors through variance analysis, correlation analysis, and generalized linear models. Phylogenetic structural indices (NRI and NTI) were used, combined with a random zero model, to explore the mechanisms of community construction in different forest types. Research has found that (1) the deciduous broad-leaved forest had the highest species diversity (Margalef index of 4.121 ± 1.425) and phylogenetic diversity (PD index of 21.265 ± 7.796), significantly higher than the mixed coniferous and broad-leaved forest and the Chinese fir plantation (p < 0.05); (2) there is a significant positive correlation between species richness and phylogenetic diversity, with the best fit being AIC = 70.5636 and R² = 0.9419 in broad-leaved forests; however, the contribution of evenness is limited; (3) the specific effects of soil factors on different forest types: available phosphorus (AP) is negatively correlated with the diversity of deciduous broad-leaved forests (p < 0.05), total phosphorus (TP) promotes the diversity of coniferous and broad-leaved mixed forests, while the diversity of Chinese fir plantations is significantly negatively correlated with total nitrogen (TN); (4) the phylogenetic structure of three different forest types shows a divergent pattern in deciduous broad-leaved forests, indicating that competition and exclusion dominate the construction of deciduous broad-leaved forests; the aggregation mode of Chinese fir plantation indicates that environmental filtering dominates the construction of Chinese fir plantation; the mixed coniferous and broad-leaved forest is a transitional model, indicating that the mixed coniferous and broad-leaved forest is influenced by both stochastic processes and ecological niche processes. In different forest types in subtropical regions, the species and phylogenetic diversity of broad-leaved forests is significantly higher than in other forest types. The impact of soil nutrients on the diversity of different forest types varies, and the characteristics of community construction in different forest types are also different. This indicates the importance of protecting the original vegetation and provides a scientific basis for improving the ecological function of artificial forest ecosystems through structural adjustment. The research results have important practical guidance value for sustainable forest management and biodiversity conservation in the region. Full article

(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate classification methods to support conservation and policy efforts. In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. All models were trained using eight-band SuperDove satellite imagery from PlanetScope, with a spatial resolution of ~3 m, and ancillary geospatial datasets representing topography and soil drainage characteristics, each of which is available globally. (3) Results: All four machine learning models performed well in detecting wetlands from SuperDove imagery and environmental covariates, with varying strengths. The highest accuracy was achieved using all eight image bands alongside features created from supporting geospatial data. For binary wetland classification, the highest F1 scores were recorded by XGB (0.73) and RF/HGB (both 0.72) when including all covariates. MLPC also showed competitive performance (wetland F1 score of 0.71), despite its relatively lower spatial consistency. However, each model over-predicts total wetland area at a national level, an issue which was able to be reduced by increasing the classification probability threshold and spatial filtering. (4) Conclusions: The comparative analysis highlights the strengths and trade-offs of RF, XGB, HGB and MLPC models for wetland classification. While all four methods are viable, RF offers some key advantages, including ease of deployment and transferability, positioning it as a promising candidate for scalable, high-resolution wetland monitoring across diverse ecological settings. Further work is required for verification of small-scale wetlands (<~0.5 ha) and the addition of fine-spatial-scale covariates. Full article

This study aimed to analyse the spatiotemporal evolution of phytoplankton community dynamics and its underlying mechanisms in the Liaoning section of the Liao River Basin in 2010, 2015, and 2020. Phytoplankton species diversity increased significantly, with an increase from three phyla and 31 species in 2010 to six phyla and 74 species in 2020. Concurrent increases in α-diversity indicated continuous improvements in habitat heterogeneity. The community structure shifted from a diatom-dominated assemblage to a green algae–diatom co-dominated configuration, contributing to an enhanced water purification capacity. The upstream agricultural zone (Tieling section) had elevated biomass and low diversity, indicating persistent non-point-source pollution stress. The midstream urban–industrial zone (Shenyang–Anshan section) emerged as a phytoplankton diversity hotspot, likely due to expanding niche availability in response to point-source pollution control. The downstream wetland zone (Panjin section) exhibited significant biomass decline and delayed diversity recovery, shaped by the dual pressures of resource competition and habitat filtering. The driving mechanism of community succession shifted from nutrient-dominated factors (NH₃-N, TN) to redox-sensitive factors (DO, pH). These findings support a ‘zoned–graded–staged’ ecological restoration strategy for the Liao River Basin and inform the use of phytoplankton as bioindicators in watershed monitoring networks. Full article

Biofloc technology (BFT), traditionally centered on feed supplementation and water purification in aquaculture, harbors untapped multifunctional potential as a sustainable resource management platform. This review systematically explores beyond conventional applications. BFT leverages microbial consortia to drive resource recovery, yielding bioactive compounds with antibacterial/antioxidant properties, microbial proteins for efficient feed production, and algae biomass for nutrient recycling and bioenergy. In environmental remediation, its porous microbial aggregates remove microplastics and heavy metals through integrated physical, chemical, and biological mechanisms, addressing critical aquatic pollution challenges. Agri-aquatic integration systems create symbiotic loops where nutrient-rich aquaculture effluents fertilize plant cultures, while plants act as natural filters to stabilize water quality, reducing freshwater dependence and enhancing resource efficiency. Emerging applications, including pigment extraction for ornamental fish and the anaerobic fermentation of biofloc waste into organic amendments, further demonstrate its alignment with circular economy principles. While technical advancements highlight its capacity to balance productivity and ecological stewardship, challenges in large-scale optimization, long-term system stability, and economic viability necessitate interdisciplinary research. By shifting focus to its underexplored functionalities, this review positions BFT as a transformative technology capable of addressing interconnected global challenges in food security, pollution mitigation, and sustainable resource use, offering a scalable framework for the future of aquaculture and beyond. 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

Many components in industry are subjected to high loads during operation and therefore often do not reach their intended service life. Conventional steels frequently do not provide sufficient protection against wear and corrosion. One solution is to coat these components using methods like thermal spraying to apply cermet coatings such as Cr₃C₂-NiCr or WC-Co-Cr. In light of increasingly strict environmental regulations, more eco-friendly alternatives are needed, especially ones that use little or no Cr, Ni, Co, or W. Another alternative is the recycling of powder materials, which is the focus of this research project. This study investigated whether filter dust from an HVOF system could be used to develop a new coating suitable for use in applications requiring resistance to wear and corrosion. This is challenging as the filter dusts have heterogeneous compositions and irregular particle sizes. Nevertheless, this recycled material, referred to as “Green Cermets” (GCs), offers previously untapped potential that may also be of ecological interest. An established WC-Co-Cr coating served as a reference. In addition to friction wear and corrosion resistance, the study also examined particle size distribution, hardness, microstructure, and susceptibility to crack formation at the interface and inside the coating. Even though the results revealed a diminished performance of the GC coatings relative to the conventional WC-CoCr, they may still be applicable in various industrial applications. Full article

Green roofs are increasingly being adopted as sustainable, nature-based solutions for managing urban stormwater, mitigating the urban heat island effect, and saving energy in buildings. However, the long-term performance of their individual components—particularly filter geotextiles—remains understudied, despite their critical role in maintaining system functionality. The filter layer, responsible for preventing clogging of the drainage layer with fine substrate particles, directly affects the hydrological performance and service life of green roofs. While most existing studies focus on the initial material properties, there is a clear gap in understanding how geotextile filters behave after prolonged exposure to real-world environmental conditions. This study addresses this gap by assessing the mechanical and structural integrity of geotextile filters after five years of use in both extensive and intensive green roof systems. By analyzing changes in surface morphology, microstructure, and porosity through tensile strength tests, digital imaging, and scanning electron microscopy, this research offers new insights into the long-term performance of geotextiles. Results showed significant retention of tensile strength, particularly in the machine direction (MD), and a 56% reduction in porosity, which may affect filtration efficiency. Although material degradation occurs, some geotextiles retain their structural integrity over time, highlighting their potential for long-term use in green infrastructure applications. This research emphasizes the importance of material selection, long-term monitoring, and standardized evaluation techniques to ensure the ecological and functional resilience of green roofs. Furthermore, the findings contribute to advancing knowledge on the durability and life-cycle performance of filter materials, promoting sustainability and longevity in urban green infrastructure. Full article

The Advanced Topographic Laser Altimeter System (ATLAS) on the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) employs a photon-counting detection mode with a 532 nm laser to obtain high-precision Earth surface elevation data and offers a new remote sensing method for nearshore bathymetry. The key issues in using ATLAS ATL03 data for bathymetry are achieving automatic and accurate extraction of signal photons in different water environments. Especially for areas with sharply fluctuating topography, the interaction of various impacts, such as topographic fluctuations, sea waves, and laser pulse direction, can result in a sharp change in photon density and distribution at the seafloor, which can cause the signal photon detection at the seafloor to be misinterpreted or omitted during analysis. Therefore, an improved size and direction adaptive filtering (ISDAF) method was proposed for nearshore bathymetry using ATLAS ATL03 data. This method can accurately distinguish between the original photons located above the sea surface, on the sea surface, and the seafloor. The size and direction of the elliptical density filter kernel automatically adapt to the sharp fluctuations in topography and changes in water depth, ensuring precise extraction of signal photons from both the sea surface and the seafloor. To evaluate the precision and reliability of the ISDAF, ATLAS ATL03 data from different water environments and seafloor terrains were used to perform bathymetric experiments. Airborne LiDAR bathymetry (ALB) data were also used to validate the bathymetric accuracy and reliability. The experimental findings show that the ISDAF consistently exhibits effectiveness in detecting and retrieving signal photons, regardless of whether the seafloor terrain is stable or dynamic. After applying refraction correction, the high accuracy of bathymetry was evidenced by a strong coefficient of determination (R²) and a low root mean square error (RMSE) between the ICESat-2 bathymetry data and ALB data. This research offers a promising approach to advancing remote sensing technologies for precise nearshore bathymetric mapping, with implications for coastal monitoring, marine ecology, and resource management. Full article

Animal-borne acoustic sensors provide valuable insights into wildlife behavior and environments but face significant power and storage constraints that limit deployment duration. We present a novel adaptive acoustic monitoring system designed for long-term, real-time observation of wildlife. Our approach combines low-power hardware, configurable firmware, and an unsupervised machine learning algorithm that intelligently filters acoustic data to prioritize novel or rare sounds while reducing redundant storage. The system employs a variational autoencoder to project audio features into a low-dimensional space, followed by adaptive clustering to identify events of interest. Simulation results demonstrate the system’s ability to normalize the collection of acoustic events across varying abundance levels, with rare events retained at rates of 80–85% while frequent sounds are reduced to 3–10% retention. Initial field deployments on caribou, African elephants, and bighorn sheep show promising application across diverse species and ecological contexts. Power consumption analysis indicates the need for additional optimization to achieve multi-month deployments. This technology enables the creation of novel wilderness datasets while addressing the limitations of traditional static acoustic monitoring approaches, offering new possibilities for wildlife research, ecosystem monitoring, and conservation efforts. Full article

Domestic showers are critical points of human exposure to microbial biofilms, which may harbor opportunistic pathogens such as Legionella spp. and nontuberculous Mycobacterium. However, biofilm development in reverse osmosis (RO)-treated drinking water systems remains poorly understood. We tested whether shower plumbing material (flexible polymer hose versus showerhead with inline polyethersulfone filter) and seasonal water variations influence biofilm community assembly. In a controlled field study, commercial shower systems were deployed in households supplied with RO-treated tap water from the KAUST Seawater Desalination Plant; biofilm samples were collected from hoses and filters over 3–17 months. Flow cytometry and 16S rRNA gene amplicon sequencing characterized microbial abundance, diversity, and taxonomic composition. We found that alpha diversity, measured by observed OTUs, was uniformly low, reflecting ultra-low biomass in RO-treated tap water. Beta diversity analyses revealed clear clustering by material type, with hoses exhibiting greater richness and evenness than filters. Core taxa—Pelomonas, Blastomonas, and Porphyrobacter—dominated both biofilm types, suggesting adaptation to low-nutrient, chlorinated conditions. Overall, our results demonstrate that ultra-low-nutrient RO tap water still supports the formation of material-driven, low-diversity biofilms dominated by oligotrophic taxa, underscoring plumbing-material choice as a critical factor for safeguarding shower water quality. These findings advance our understanding of biofilm ecology in RO-treated systems, informing strategies to mitigate potential health risks in shower water. Full article

This study investigates the mechanisms of zooplankton community assembly and their relationship to environmental factors in high-latitude arid regions. We conducted seasonal sampling at four reservoirs in the upper Tarim River Basin from 2023 to 2024: Shangyou Reservoir (SY), Shengli Reservoir (SL), Duolang Reservoir (DL) and Xinjingzi Reservoir (XJZ). The zooplankton community was categorized into five functional groups based on the predominant species, with small crustacean filter feeders (SCF) in all reservoirs except XJZ, where a seasonal shift between rotifer collectors (RC) in the wet season and SCF in the dry season was observed. Pearson correlation and canonical correspondence analysis (CCA) revealed that interspecific competition, pH, conductivity (COND), and salinity (SALIN) were the main determinants of zooplankton community composition. Significant correlations (p < 0.05) were detected among functional groups RC (rotifers carnivora), RF (rotifers filter feeders), SCF (small copepods and claocera filter feeders), and MCC (middle copepods and claocera carnivora). Environmental factors showed significant spatial heterogeneity, while zooplankton biomass was positively correlated with pH and COND. Cluster similarity analyses indicated complex interactions between 29 zooplankton species, with RF identified as an important positive predictor for larger groups. The network of co-occurrences showed predominantly positive relationships, emphasizing the mutual facilitation between the species. Our results suggest that interspecific interactions have stronger effects on community structuring than environmental factors, with mutual facilitation emerging as an important survival strategy. This study provides important insights into the dynamics of zooplankton communities in dry reservoirs and establishes a framework for understanding ecological patterns and assembly mechanisms under drought conditions. Full article

Estuarine ecosystems, characterized by dynamic salinity gradients and complex physicochemical interactions, serve as critical transition zones between freshwater and marine environments. This study investigates the spatial evolution of sediment microbial communities across a freshwater–seawater continuum and their correlations with water quality parameters. Five sampling zones (upstream, midstream, downstream, transition zone, and ocean) were established in a typical estuary (Kuiyu Park, China). High-throughput 16S rRNA sequencing revealed significant shifts in microbial composition, with dominant phyla including Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. Alpha diversity decreased from freshwater to the transition zone but rebounded in seawater, suggesting habitat filtering and niche differentiation. Redundancy analysis identified salinity, dissolved oxygen, nutrients, and heavy metals as key drivers of microbial community structure. Functional predictions highlighted metabolic adaptations such as methanogenesis, sulfur oxidation, and aerobic chemoheterotrophy across zones. This study explores how sediment microorganisms adapt to water quality variations during the freshwater–seawater transition, offering insights into estuarine resilience under global change. These findings elucidate microbial assembly rules in estuarine ecosystems and provide insights for ecological management under global environmental change. Full article

Marine oil spills pose a serious and persistent threat to marine ecosystems, coastal resources, and global environmental health. These incidents not only disrupt ecological balance by damaging marine flora and fauna but also lead to long-term economic consequences for fisheries, tourism, and maritime industries. Owing to their rapid spread and often unpredictable occurrence, timely and accurate detection is essential for effective containment and mitigation. An efficient detection system can significantly enhance the responsiveness of emergency teams, enabling targeted interventions that minimize ecological damage and economic loss. This paper proposes a marine radar-based oil spill detection method that combines the Significance-to-Boundary Ratio (SBR) feature with an improved Sauvola adaptive thresholding algorithm. The raw radar data was firstly preprocessed through mean and median filtering, grayscale correction, and contrast enhancement. SBR features were then employed to extract coarse oil spill regions, which were further refined using an improved Sauvola thresholding algorithm followed by a denoising step to obtain fine-grained segmentation. Comparative experiments using different threshold values demonstrate that the proposed method achieves superior segmentation performance by better preserving oil spill boundaries and reducing background noise. Overall, the approach provides a robust and efficient solution for marine oil spill detection and monitoring. Full article

Municipal wastewater effluent (MWWE) is a common source of nutrient enrichment and provides a route for emerging substances of concern (ESOCs) to enter aquatic systems. Community composition and abundance metrics of benthic macroinvertebrates are commonly utilized to assess ecological impacts associated with nutrient enrichment; however, the responses of these metrics in systems with diverse chemical mixtures from MWWE, are not well understood. This study specifically addresses the effects of cumulative loading of tertiary-treated MWWE through responses in benthic macroinvertebrate communities in experimental control and treatment streams. Treatment streams used source river water previously exposed to upstream wastewater treatment plants but with an additional 5% by volume tertiarily treated MWWE, while control streams used only source river water. Surbers and artificial substrate rock baskets were used to examine impacts on both established and colonizing benthic communities, respectively. No significant differences were observed between the control and treatment streams in any of the community metrics of well-established benthic communities. In contrast, significant decreases in colonizing taxon diversity and evenness were found between treatment and control streams. The dominant taxa (most abundant family, by percentage of sample) in the community, often filter feeders, significantly increased in percentage of the total community in treatment streams. This response was consistent with a nutrient enrichment effect, with no evidence of ESOC related toxicity. This study highlights the need for bioassessment programs to utilize approaches involving varied in-situ sampling methods and controlled exposure systems to gain a better understanding of how various stages of community-level development are impacted by urban pollutants such as MWWE. Full article

Accurately predicting suspended sediment concentrations (SSC) is vital for effective reservoir planning, water resource optimization, and ecological restoration. This study proposes a hybrid ensemble model—VMD-MGGP-NGO-BiLSTM-NGO—which integrates Variational Mode Decomposition (VMD) for signal decomposition, Multi-Gene Genetic Programming (MGGP) for feature filtering, and a double-optimized NGO-BiLSTM-NGO (Northern Goshawk Optimization) structure for enhanced predictive learning. The model was trained and validated using daily discharge and SSC data from the Tangnaihai Hydrological Station on the upper Yellow River. The main findings are as follows: (1) The proposed model achieved an NSC improvement of 19.93% over the Extreme Gradient Boosting (XGBoost) and 15.26% over the Convolutional Neural Network—Long Short-Term Memory network (CNN-LSTM). (2) Compared to GWO- and PSO-based BiLSTM ensembles, the NGO-optimized VMD-MGGP-NGO- BiLSTM-NGO model achieved superior accuracy and robustness, with an average testing-phase NSC of 0.964, outperforming the Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) counterparts. (3) On testing data, the model attained an NSC of 0.9708, indicating strong generalization across time. Overall, the VMD-MGGP-NGO-BiLSTM-NGO model demonstrates outstanding predictive capacity and structural synergy, serving as a reliable reference for future research on SSC forecasting and environmental modeling. Full article