Search Results (907)

Human activities, especially industrial production and urbanization, have significantly affected vegetation cover, water resource cycles, climate change, and biodiversity in the Qinling-Daba Mountain region and its surrounding areas. These activities contribute to complex and lasting impacts on ecological vulnerability. The Qinling Mountain region exhibits a complex interaction with human activities. The current research on the ecological vulnerability of the Qinling Mountain region primarily focuses on spatial distribution and the driving factors. This study innovatively applies the VSD assessment and Bayesian networks to systematically evaluate and simulate the ecological vulnerability of the study area over the past 20 years, which indicates that the integration of the VSD model with the Bayesian network model enables the simulation of dynamic relationships and interactions among various factors within the study areas, providing a more accurate assessment and prediction of ecosystem responses to diverse changes from a dynamic perspective. The key findings are as follows. (1) Areas of potential and slight vulnerability are concentrated in the Qinling-Daba mountainous regions. Over the past 20 years, areas of extreme and high vulnerability have significantly decreased, while areas of potential vulnerability and slight vulnerability have increased. (2) The key factors impacting ecological vulnerability during this period included industrial water use, SO₂ emissions, industrial wastewater, and ecological water use. (3) Areas primarily hindering the transition to potential vulnerability are concentrated in well-developed small urban regions within basins. Furthermore, natural factors like altitude and temperature, which cannot be artificially regulated, are the major impediments to future ecological restoration. Therefore, this paper recommends natural restoration strategies based on environmental protection and governance strategies that prioritize green development as complementary measures. The discoveries of the paper provide a novel analytical method for the study of ecological vulnerability in mountainous areas, offering valuable insights for enhancing the accuracy of ecological risk prediction, fostering the integration of interdisciplinary research, and optimizing environmental governance and protection strategies. Full article

The water temperature of lakes is one of their fundamental characteristics, upon which numerous processes in lake ecosystems depend. Therefore, it is crucial to have detailed knowledge about its changes and the factors driving those changes. In this article, a neural network model was developed to examine the impact of meteorological variables on lake water temperature by integrating daily meteorological data with data on interday variations. Neural networks were selected for their ability to model complex, non-linear relationships between variables, often found in environmental data. Among various architectures, the Artificial Neural Network (ANN) was chosen due to its superior performance, achieving an R² of 0.999, MSE of 0.0352, and MAE of 0.1511 in validation tests. These results significantly outperformed other models such as Multi-Layer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM). Two lakes (Lake Mikołajskie and Sławskie) differing in morphometric parameters and located in different physico-geographical regions of Poland were analyzed. Performance metrics for both lakes show that the model is capable of providing accurate water temperature forecasts, effectively capturing the primary patterns in the data, and generalizing well to new datasets. Key variables in both cases turned out to be air temperature, while the response to wind and cloud cover exhibited diverse characteristics, which is a result of the morphometric features and locations of the measurement sites. Full article

Dissolved organic matter (DOM), primarily in the form of humic acid (HA), plays a crucial role in trace metal (TM) speciation and their subsequent adsorption dynamics on microplastics (MP) in aquatic environments. This study evaluates the impact of environmentally relevant concentrations of HA on the adsorption behaviors of essential (Co, Cu, Ni, and Zn) and toxic (Cd and Pb) TMs onto polyethylene (PE) and polypropylene (PP) pellets, as well as PP fibers under marine conditions, during a six-week experiment. The HA concentrations were 0.1, 1, and 5 mg/L, while all metals were in the same amounts (10 µg/L). Results reveal that HA significantly influences the adsorption of Cu, Pb, and Zn on MP, particularly on PP fibers, which exhibited the greatest TM adsorption dynamics. The adsorption patterns correspond to the concentrations of these metals in seawater, with the sequence for pellets being Zn > Cu > Pb > Ni > Co~Cd, and for fibers Cu > Zn > Pb > Co~Ni > Cd. Speciation modeling supported these findings, indicating that Cu, Pb, and Zn predominantly associate with HA in seawater, facilitating their adsorption on MP, whereas Cd, Co, and Ni mainly form free ions and inorganic complexes, resulting in slower adsorption dynamics. Statistical analysis confirmed the influence of HA on the adsorption of Cd, Pb, Cu, and Ni. By investigating the dynamics of TM adsorption on plastics, the influence of DOM on these two contaminants under marine conditions was evaluated. The presented results can help in forming a better understanding of synergistic plastic and trace metal pollution in marine systems that are relevant at the global level, since both contaminants pose a serious threat to aquatic ecosystems. Full article

Nitrous oxide (N₂O) ranks as the third most significant greenhouse gas, capable of depleting the ozone layer and posing threats to terrestrial ecosystems. Climate change alters precipitation variability, notably in terms of frequency and magnitude. However, the implications of precipitation variability on N₂O emissions and the underlying mechanisms remain inadequately understood. In this study, employing laboratory incubation methods on three representative sandy soil types (sandy soil, shrub soil, and crust soil), we examined the impacts of diverse precipitation levels (5 mm and 10 mm) and frequencies (7 days and 14 days) on N₂O emissions from these soil types. This study aims to clarify the complex connections between soil N₂O emission fluxes and soil physicochemical properties in the soil environment. Our findings reveal that the N₂O emission flux exhibits heightened responsiveness to 5 mm precipitation events and a 14-day precipitation frequency, and compared to other treatments, the 5 mm precipitation and 14-day precipitation frequency treatment resulted in a 20% increase in cumulative nitrous oxide emissions. Consequently, cumulative N₂O emissions were notably elevated under the 5 mm precipitation and 14-day precipitation frequency treatments compared to the other experimental conditions. The N₂O emission flux in sandy soil displayed a positive correlation with available phosphorus (AP) and a negative correlation with pH, primarily attributed to the exceedingly low AP content in sandy soil. In shrub soil, the soil N₂O emission flux exhibited a significant positive correlation with NH₄⁺-N and a negative correlation with NO₃⁻-N. Conversely, no significant correlations were observed between soil N₂O emission flux and soil physicochemical properties in crust soil, underscoring the importance of considering plant–soil microbial interactions. Our findings suggest that soil nitrous oxide emissions in arid and semi-arid regions will be particularly responsive to small and frequent rainfall events as precipitation patterns change in the future, primarily due to their soil physicochemical characteristics. Full article

The fragmented forests of the Kenya highlands, known for their exceptional species richness and endemism, are among the world’s most important biodiversity hotspots. However, detailed studies on the fauna of these ecosystems—especially specialist species that depend on moist forests, which are particularly threatened by habitat fragmentation—are still limited. In this study, we used mitochondrial genes (cytochrome b and the displacement loop) and a nuclear marker (retinol-binding protein 3) to investigate genetic and morphological diversity, phylogenetic associations, historical divergence, population dynamics, and phylogeographic patterns in two rodent species—the soft-furred mouse (Praomys jacksoni) and the African wood mouse (Hylomyscus endorobae)—across Kenya’s forest landscapes. We found a complex genetic structure, with P. jacksoni exhibiting greater genetic diversity than H. endorobae. The Mt. Kenya P. jacksoni populations are significantly genetically different from those in southwestern forests (Mau Forest, Kakamega Forest, and Loita Hills). In contrast, H. endorobae presented no observable biogeographic structuring across its range. The genetic diversity and geographic structuring patterns highlighted selectively strong effects of forest fragmentation and differing species’ ecological and evolutionary responses to these landscape changes. Our findings further underscore the need for expanded sampling across Kenya’s highland forests to better understand species’ changing diversity and distribution patterns in response to the impacts of human-mediated habitat changes. These insights are critical for informing conservation strategies to preserve biodiversity better in this globally important region. Full article

Dryland ecosystems are highly sensitive to climate change, making vegetation monitoring crucial for understanding ecological dynamics in these regions. In recent years, climate change, combined with large-scale ecological restoration efforts, has led significant greening in China’s arid areas. However, the mechanisms through which seasonal climate variations regulate vegetation growth are not yet fully understood. This study hypothesizes that seasonal climate change affects net primary productivity (NPP) of vegetation by influencing phenology. We focused on China’s Windbreak and Sand-Fixation Ecological Function Conservation Areas (WSEFCAs) as representative regions of dryland vegetation. The Carnegie–Ames–Stanford Approach (CASA) model was used to estimate vegetation NPP from 2000 to 2020. To extract phenological information, NDVI data were processed using Savitzky–Golay (S–G) filtering and threshold methods to determine the start of season (SOS) and end of season (EOS). The structural equation model (SEM) was constructed to quantitatively assess the contributions of climate change (temperature and precipitation) and phenology to variations in vegetation NPP, identifying the pathways of influence. The results indicate that the average annual NPP in WSEFCAs increased from 55.55 gC/(m²·a) to 75.01 gC/(m²·a), exhibiting uneven spatial distribution. The pathways through which seasonal climate affects vegetation NPP are more complex and uneven. Summer precipitation directly promoted NPP growth (direct effect = 0.243, p < 0.001) while also indirectly enhancing NPP by significantly advancing SOS (0.433, p < 0.001) and delaying EOS (−0.271, p < 0.001), with an indirect effect of 0.133. This finding highlights the critical role of phenology in vegetation growth, particularly in regions with substantial seasonal climate fluctuations. Although the overall ecological environment of WSEFCAs has improved, significant regional disparities remain, especially in northwestern China. This study introduces causal mediation analysis to systematically explore the mechanisms through which seasonal climate change impacts vegetation NPP in WSEFCAs, providing new insights into the broader implications of climate change and offering scientific support for ecological restoration and management strategies in arid regions. Full article

Oysters have been recognized as ecological engineers for aquatic ecosystems, as oyster reefs provide critical habitats and foraging locations for other marine species. In the past few decades, anthropogenic activities have negatively impacted oyster reef ecosystems across the globe, resulting in a significant decline in their population. This review critically examines the causes and extent of oyster reef degradation, as well as the effectiveness of restoration initiatives employed to reverse this decline. Furthermore, this review evaluates the effectiveness of restoration strategies employed to rehabilitate oyster reefs. Different approaches, such as genetic improvement, suitable site selection, and oyster seeding to enhance oyster reef restorations, are critically reviewed in this paper. Furthermore, some advanced restoration approaches such as 3D printing, shell recycling, and acoustics technologies are also discussed in this paper, which opens the new doors for researchers in the field of restoration ecology. Challenges and barriers hindering successful restoration are also addressed, including financial constraints, regulatory complexities, and public engagement. The findings and insights presented herein contribute to the growing body of knowledge on oyster reef ecology and serve as a valuable resource for policymakers, scientists, and conservation practitioners seeking effective strategies for restoring these vital coastal ecosystems. Full article

Forest fires pose a significant threat to forest ecosystems, with severe impacts on both the environment and human society. Understanding the post-fire recovery processes of forests is crucial for developing strategies for species diversity conservation and ecological restoration and preventing further damage. The present study proposes applying the EAswin-Mask2former model based on semantic segmentation in deep learning using visible light band data to better monitor the evolution of burn areas in forests after fires. This model is an improvement of the classical semantic segmentation model Mask2former and can better adapt to the complex environment of burned forest areas. This model employs Swin-Transformer as the backbone for feature extraction, which is particularly advantageous for processing high-resolution images. It also includes the Contextual Transformer (CoT) Block to better capture contextual information capture and incorporates the Efficient Multi-Scale Attention (EMA) Block into the Efficiently Adaptive (EA) Block to enhance the model’s ability to learn key features and long-range dependencies. The experimental results demonstrate that the EAswin-Mask2former model can achieve a mean Intersection-over-Union (mIoU) of 76.35% in segmenting complex forest burn areas across different seasons, representing improvements of 3.26 and 0.58 percentage points, respectively, over the Mask2former models using ResNet and Swin-Transformer backbones, respectively. Moreover, this method surpasses the performance of the DeepLabV3+ and Segformer models by 4.04 and 1.75 percentage points, respectively. Ultimately, the proposed model offers excellent segmentation performance for both forest and burn areas and can effectively track the evolution of burned forests when combined with unmanned aerial vehicle (UAV) remote sensing images. Full article

This review explores the complex relationship between gut dysbiosis and hematological malignancies, focusing on graft-versus-host disease (GvHD) in allogeneic hematopoietic stem cell transplantation (allo-HSCT) recipients. We discuss how alterations in microbial diversity and composition can influence disease development, progression, and treatment outcomes in blood cancers. The mechanisms by which the gut microbiota impacts these conditions are examined, including modulation of immune responses, production of metabolites, and effects on intestinal barrier function. Recent advances in microbiome-based therapies for treating and preventing GvHD are highlighted, with emphasis on full ecosystem standardized donor-derived products. Overall, this review underscores the growing importance of microbiome research in hematology–oncology and its potential to complement existing treatments and improve outcomes for thousands of patients worldwide. Full article

The oral microbiome, comprising bacteria, fungi, viruses, and protozoa, is essential for maintaining both oral and systemic health. This complex ecosystem includes over 700 bacterial species, such as Streptococcus mutans, which contributes to dental caries through acid production that demineralizes tooth enamel. Fungi like Candida and pathogens such as Porphyromonas gingivalis are also significant, as they can lead to periodontal diseases through inflammation and destruction of tooth-supporting structures. Dysbiosis, or microbial imbalance, is a key factor in the development of these oral diseases. Understanding the composition and functions of the oral microbiome is vital for creating targeted therapies for these conditions. Additionally, the kynurenine pathway, which processes the amino acid tryptophan, plays a crucial role in immune regulation, neuroprotection, and inflammation. Oral bacteria can metabolize tryptophan, influencing the production of kynurenine, kynurenic acid, and quinolinic acid, thereby affecting the kynurenine system. The balance of microbial species in the oral cavity can impact tryptophan levels and its metabolites. This narrative review aims to explore the relationship between the oral microbiome, oral diseases, and the kynurenine system in relation to certain systemic diseases. Full article

Plant detritus plays a crucial role in regulating belowground biogeochemical processes in forest ecosystems, particularly influencing labile carbon (C) dynamics and overall soil C storage. However, the specific mechanisms by which litter and roots affect soil organic carbon (SOC) and its components in plantations remain insufficiently understood. To investigate this, we conducted a detritus input and removal treatment (DIRT) experiment in a Larix principis-rupprechtii Mayr plantation in the Taiyue Mountains, China, in July 2014. The experiment comprised three treatments: root and litter retention (CK), litter removal (LR), and root and litter removal (RLR). Soil samples were collected from depths of 0–10 cm and 10–20 cm during June, August, and October 2015 to evaluate changes in soil pH, water content (SW), SOC, dissolved organic carbon (DOC), readily oxidizable organic carbon (ROC), and microbial biomass carbon (MBC). The removal of litter and roots significantly increased soil pH (p < 0.05), with pH values being 8.84% and 8.55% higher in the LR and RLR treatments, respectively, compared to CK treatment. SOC levels were significantly reduced by 26.10% and 12.47% in the LR and RLR treatments, respectively (p < 0.05). Similarly, DOC and MBC concentrations decreased following litter and root removal, with DOC content in August being 2.5 times lower than in June. Across all treatments and sampling seasons, SOC content was consistently higher in the 0–10 cm depth, exhibiting increases of 35.15% to 39.44% compared to the 10–20 cm depth (p < 0.001). Significant negative correlations were observed between SOC and the ratios of ROC/SOC, pH, DOC/SOC, and MBC/SOC (R = −0.54 to −0.37; p < 0.05). Path analysis indicated that soil pH had a significant direct negative effect on SOC (p < 0.05), with a standardized path coefficient (β) of −0.36, while ROC had a significant direct positive effect on SOC (β = 0.66, p < 0.05). Additionally, pH indirectly affected SOC by significantly influencing ROC (β = −0.69), thereby impacting SOC indirectly. Random forest analysis also confirmed that the ROC/SOC ratio plays a critical role in SOC regulation. This study reveals the complex interactions between litter and root removal and soil C dynamics in larch plantations, identifying soil pH and ROC as crucial regulator of SOC content. However, the short-term duration and focus on shallow soil depths limit our understanding of long-term impacts and deeper soil C storage. Future research should explore these aspects and consider varying climate conditions to enhance the applicability of our findings. These insights provide a scientific foundation for developing effective forest management strategies and forecasting changes in soil C storage in the context of climate change. Full article

The critical importance of effective industrial symbiosis is emphasized in the rapidly evolving landscape of manufacturing, energy, and environmental sustainability. This study employs the Decision Making Trial and Evaluation Laboratory (DEMATEL) methodology to examine and outline the complex interrelationships among critical success factors (CSFs) pivotal for the successful implementation of industrial symbiosis. Key findings indicate that leadership and technology are the most significant causal CSFs, driving positive outcomes in waste reduction, environmental impact, and economic growth, identified as primary effect factors. Leadership emerges as the predominant influence, guiding strategic alignment, fostering a collaborative and sustainable organizational culture, and affecting all other CSFs. Technological integration acts both as a direct driver of operational efficiency and as a mediator of leadership’s influence, enabling optimized resource flows and data-driven decision-making. Additional CSFs such as clear communication, enhanced training and education, and policy and regulatory support also serve as essential mediators connecting leadership to key outcomes. This research outlines an actionable pathway for stakeholders, including policymakers, engineers, and corporate executives, to strategically prioritize and utilize these CSFs to promote more resilient and sustainable industrial ecosystems. Full article

Severe seasonal droughts driven by global climate change significantly alter the cycling of carbon and nutrients in forest ecosystems, while the investigation into the impacts of floor mass and plant roots on soil microbial biomass within the context of recurrent seasonal droughts is still rare. To investigate the environmental determinants governing soil microbial biomass with the escalating severity of seasonal droughts, we conducted a study in a montane subtropical moist evergreen broad-leaved forest in southwestern China from June 2019 to May 2023. The study results revealed that soil microbial biomass, as well as soil moisture, floor mass, and plant roots, showed an apparent single-hump modal within one year. In the comparative analysis of the soil microbial biomass fluctuation amplitudes across control and watered plots, a discernible disparity was observed, indicating significant differences in microbial biomass dynamics between the respective experimental conditions. The pooled data revealed a statistically significant influence of seasonal drought, floor mass, plant roots, and their reciprocal interactions on the soil microbial biomass, highlighting these factors as pivotal determinants of microbial community dynamics. This study elucidates the interactive regulatory mechanisms by which seasonal drought, floor mass, and plant roots collectively modulate soil microbial biomass within tropical and subtropical forests, offering insights into the complex ecological processes governing microbial community dynamics. This interactive regulation might influence the trajectory of plant species and soil microbial communities, facilitating their adaptive development and evolutionary responses. Full article

This study employed an integrated field monitoring approach, combining chemical analysis and ecotoxicity testing of multiple environmental matrices—water, sediment, and sediment elutriates—to comprehensively assess the environmental health of the Andong watershed, located near a Zn smelter and mining area. The primary objectives were to evaluate the extent of metal contamination, identify key toxicants contributing to ecological degradation, and trace the sources of these pollutants. Our findings revealed severe metal contamination and significant ecotoxicological effects both in proximity to and downstream from industrial sites. Specifically, Cd, Zn, and Pb were strongly linked to the smelter, while Hg, Ni, Cu, and As were predominantly associated with mining activities in the tributaries. To further assess toxicity of field-collected sediment and their elutriates, a logistic regression analysis was employed to estimate benchmark values for distinguishing between toxic and non-toxic samples, using the sum of toxic units for sediment elutriates and the mean probable effect level (PEL) quotient for sediment toxicity. These models demonstrated greater predictive accuracy than conventional benchmarks for determining toxicity thresholds. Our results highlight that integrating chemical and ecotoxicological monitoring with site-specific concentration–response relationships enhances the precision of ecological risk assessments, facilitating more accurate identification of key toxicants driving mixture toxicity in complex, pollution-impacted aquatic ecosystems. Full article

Soil bulk density is a crucial indicator for assessing soil matter storage and soil quality. Due to the complexity of sampling soil bulk density, particularly in deeper layers, it is essential to study the spatial distribution patterns of soil bulk density and their influencing factors. To address the gap in large-scale studies of vertical (from surface to deeper layers) and horizontal (across a broad area) variations in soil bulk density in arid regions, this study focuses on Changji Prefecture, located in the central northern slope of the Tianshan Mountains and characterized by typical vertical zonation. By integrating classical statistics, geostatistics, and geographic information systems (GISs), this study investigates the spatial distribution patterns and driving factors of soil bulk density. The results indicate that soil bulk density in Changji Prefecture increases with soil depth, with significantly lower values in the surface layer than in deeper layers. Spatially, despite minimal variation in latitude, there is considerable elevation difference within the study area, with the lowest elevations in the central region. Soil bulk density exhibits a spatial distribution pattern of higher values in the northeast (desert areas) and lower values in the southwest (forest areas). The nugget effect in the surface layer (0–20 cm) is substantial at 44.9%, while the deeper layers (20–100 cm) show nugget effects below 25%, suggesting that the influence of both natural and anthropogenic factors on deep soil bulk density is limited and mainly affects the surface layer. Stepwise regression analysis indicates that among topographic factors, slope and elevation are the primary controls of spatial variability in soil bulk density across layers. This research demonstrates that, in arid regions, soil bulk density is influenced primarily by natural factors, with limited impact from human activities. These findings provide valuable data support and theoretical guidance for soil management, agricultural planning, and sustainable ecosystem development in arid regions. Full article