Search Results (5,339)

Landslides are a common geological hazard in mountainous areas, causing significant damage to ecosystems and production activities. Near-natural ecological restoration is considered an effective strategy for post-landslide recovery. To investigate the impact of near-natural restoration strategies on the recovery of plant communities and soil in landslide-affected areas, we selected landslide plots in Lantian County at 1, 6, and 11 years post-landslide as study sites, surveyed plots undergoing near-natural restoration and adjacent undisturbed control plots (CK), and collected and analyzed data on plant communities and soil properties. The results indicate that vegetation succession followed a path from “human intervention to natural competition”: species richness peaked at 1 year post-landslide (D_m = 4.2). By 11 years, dominant species prevailed, with tree species decreasing to 4.1 ± 0.3, while herbaceous diversity increased by 200% (from 4 to 12 species). Soil recovery showed significant temporal effects: total nitrogen (TN) and dehydrogenase activity (DHA) exhibited the greatest increases after 1 year post-landslide (132% and 232%, respectively), and by 11 years, the available nitrogen (AN) in restored plots recovered to 98% of the CK levels. Correlations between plant and soil characteristics strengthened over time: at 1 year, only 6–9 pairs showed significant correlations (p < 0.05), increasing to 21–23 pairs at 11 years. Near-natural restoration drives system recovery through the “selection of native species via competition and activation of microbial functional groups”. The 6–11-year period post-landslide is a critical window for structural optimization, and we recommend phased dynamic regulation to balance biodiversity and ecological functions. Full article

Amidst the intensification of global climate change and the increasing impacts of human activities, ecosystem patterns and processes have undergone substantial transformations. The distribution and evolutionary dynamics of mountain ecosystems have become a focal point in ecological research. The Surkhan River Basin is located in the transitional zone between the arid inland regions of Central Asia and the mountain systems, where its unique physical and geographical conditions have shaped distinct patterns of vertical zonation. Utilizing Landsat imagery, this study applies a hierarchical classification approach to derive land cover classifications within the Surkhan River Basin. By integrating the NDVI (normalized difference vegetation index) and DEM (digital elevation model (30 m SRTM)), an “NDVI-DEM-Land Cover” scatterplot is constructed to analyze zonation characteristics from 1980 to 2020. The 2020 results indicate that the elevation boundary between the temperate desert and mountain grassland zones is 1100 m, while the boundary between the alpine cushion vegetation zone and the ice/snow zone is 3770 m. Furthermore, leveraging DEM and LST (land surface temperature) data, a potential energy analysis model is employed to quantify potential energy differentials between adjacent zones, enabling the identification of ecological transition areas. The potential energy analysis further refines the transition zone characteristics, indicating that the transition zone between the temperate desert and mountain grassland zones spans 1078–1139 m with a boundary at 1110 m, while the transition between the alpine cushion vegetation and ice/snow zones spans 3729–3824 m with a boundary at 3768 m. Cross-validation with scatterplot results confirms that the scatterplot analysis effectively delineates stable zonation boundaries with strong spatiotemporal consistency. Moreover, the potential energy analysis offers deeper insights into ecological transition zones, providing refined boundary identification. The integration of these two approaches addresses the dimensional limitations of traditional vertical zonation studies, offering a transferable methodological framework for mountain ecosystem research. Full article

Alpine scree, a distinctive plateau ecosystem, serves as habitat for numerous rare and endangered species. However, current research does not differentiate it from desert in terms of spatial boundary, hindering biodiversity conservation and ecological monitoring efforts. Using the Tibetan Plateau as a case study, we defined the spatial boundary of alpine scree based on its surface formation process and examined its distribution and long-term evolution. The results show that in 2020, alpine scree on the Tibetan Plateau covered 73,735.34 km², 1.5 times the area of glaciers. Alpine scree is mostly distributed at elevations between 4000 and 6000 m, with a slope of approximately 30–40 degrees. Characterized by low temperature and sparse rainfall, the regions are located in the humid zone. From 1975 to 2020, the area of alpine scree initially increased before declining, with an overall decrease of 560.68 km². Climate warming was the primary driver of these changes, leading to an increase in scree from 1975 to 1995 and a decrease in scree from 1995 to 2020. Additionally, between 1975 and 2020, the Tibetan Plateau’s grasslands shifted upward by 16.47 km². This study enhances our understanding of the spatial distribution and dynamics of this unique ecosystem, alpine scree, offering new insights into climate change impacts on alpine ecosystems. Full article

Investigating land cover patterns, changes in carbon stocks, and forecasting future conditions are essential for formulating regional sustainable development strategies and enhancing ecological and environmental quality. This study centers on Guiyang, a mountainous urban area in southwestern China, to analyze the dynamic changes in land cover and their effects on carbon stocks from 2000 to 2035. A carbon stocks assessment framework was developed using a cellular automaton-based artificial neural network model (CA-ANN), the InVEST model, and the geographical detector model to predict future land cover changes and identify the primary drivers of variations in carbon stocks. The results indicate that (1) from 2000 to 2020, impervious surfaces expanded significantly, increasing by 199.73 km². Compared to 2020, impervious surfaces are projected to increase by 1.06 km², 13.54 km², and 34.97 km² in 2025, 2030, and 2035, respectively, leading to further reductions in grassland and forest areas. (2) Over time, carbon stocks in Guiyang exhibited a general decreasing trend; spatially, carbon stocks were higher in the western and northern regions and lower in the central and southern regions. (3) The level of greenness, measured by the normalized vegetation index (NDVI), significantly influenced the spatial variation of carbon stocks in Guiyang. Changes in carbon stocks resulted from the combined effects of multiple factors, with the annual average temperature and NDVI being the most influential. These findings provide a scientific basis for advancing low-carbon development and constructing an ecological civilization in Guiyang. Full article

Cinnamomum camphora is an ecologically and economically significant species, highly valued for its essential oil production and environmental benefits. Although a tissue culture system has been established for C. camphora, large-scale propagation remains limited due to the inconsistent formation of adventitious roots (ARs). This study investigated AR formation from callus tissue, focusing on associated physiological changes and gene expression dynamics. During AR induction, contents of soluble sugars and proteins decreased, alongside reduced activities of antioxidant enzymes, including superoxide dismutase (SOD), peroxidase (POD), and polyphenol oxidase (PPO). Levels of indole-3-acetic acid (IAA) and abscisic acid (ABA) decreased significantly throughout AR formation. Zeatin riboside (ZR) levels initially declined and then rose, whereas gibberellic acid (GA) levels displayed the opposite trend. Comparative transcriptomic and temporal expression analyses identified differentially expressed genes (DEGs), which were grouped into four distinct expression patterns. KEGG pathway enrichment indicated that 67 DEGs are involved in plant hormone signaling pathways and that 38 DEGs are involved in the starch and sucrose metabolism pathway. Additionally, protein–protein interaction network (PPI) analysis revealed ten key regulatory genes, which are mainly involved in auxin, cytokinin, GA, ABA, and ethylene signaling pathways. The reliability of the transcriptome data was further validated by quantitative real-time PCR. Overall, this study provides new insights into the physiological and molecular mechanisms underlying AR formation in C. camphora and offers valuable guidance for optimizing tissue culture systems. Full article

This study investigates the potential of spaceborne imaging spectroscopy to support the analysis of the status of two major Estonian lakes, i.e., Lake Peipsi and Lake Võrtsjärv, using data from the PRISMA and EnMAP missions. The study encompasses nine specific applications across 12 satellite scenes, including the validation of remote sensing reflectance, optical water type classification, estimation of phycocyanin concentration, detection of macrophytes, and characterization of reflectance for lake ice/snow coverage. Rrs validation, which was performed using in situ measurements and Sentinel-2 and Sentinel-3 as references, showed a level of agreement with Spectral Angle < 16°. Hyperspectral imagery successfully captured fine-scale spatial and spectral features not detectable by multispectral sensors, in particular it was possible to identify cyanobacterial pigments and optical variations driven by seasonal and meteorological dynamics. Through the combined use of in situ observations, the study can serve as a starting point for the use of hyperspectral data in northern freshwater systems, offering new insights into ecological processes. Given the increasing global concern over freshwater ecosystem health, this work provides a transferable framework for leveraging new-generation hyperspectral missions to enhance water quality monitoring on a global scale. Full article

Objectives: The major sugarcane-producing regions of Guangxi represent a critical agricultural zone in China. Investigating the mechanisms of land use change and carbon storage dynamics in this area is essential for optimizing regional ecological security and promoting sustainable development. Methods: Employing the land use transfer matrix, the InVEST model and the Geodetector model to analyze carbon storage changes and identify key driving factors and their interactive effects. Results: (1) From 2011 to 2022, Guangxi’s major sugarcane-producing regions experienced significant land use changes: reductions in cultivated land, grassland and water bodies alongside expansions of forest, bare land and construction land. (2) The total carbon storage in Guangxi’s major sugarcane-producing regions has increased from 2011 to 2018 by 0.99%, representing 1627.03 and 1643.10 million tons, while it has decreased by 0.1% in 2022 (1641.47 million tons) compared to 2018. (3) Cultivated land proportion and forest coverage rate were the primary drivers of spatial heterogeneity, followed by average slope and land urbanization rate. (4) Interaction analysis revealed strong synergistic effects among cultivated land proportion, forest coverage rate, NDVI and average slope, confirming multi-factor control over carbon storage changes. Conclusions: Carbon storage in the Guangxi sugarcane-producing regions is shaped by land use patterns and multi-factor interactions. Future strategies should optimize land use structures and balance urbanization with ecological protection to enhance regional carbon sequestration. Full article

Jute, a vital lignocellulosic fiber crop with substantial industrial and ecological relevance, continues to suffer considerable yield and quality degradation due to pervasive foliar pathologies. Traditional diagnostic modalities reliant on manual field inspections are inherently constrained by subjectivity, diagnostic latency, and inadequate scalability across geographically distributed agrarian systems. To transcend these limitations, we propose DERIENet, a robust and scalable classification approach within a deep ensemble learning framework. It is meticulously engineered by integrating three high-performing convolutional neural networks—ResNet50, InceptionV3, and EfficientNetB0—along with regularization, batch normalization, and dropout strategies, to accurately classify jute leaf diseases such as Cercospora Leaf Spot, Golden Mosaic Virus, and healthy leaves. A key methodological contribution is the design of a novel augmentation pipeline, termed Geometric Localized Occlusion and Adaptive Rescaling (GLOAR), which dynamically modulates photometric and geometric distortions based on image entropy and luminance to synthetically upscale a limited dataset (920 images) into a significantly enriched and diverse dataset of 7800 samples, thereby mitigating overfitting and enhancing domain generalizability. Empirical evaluation, utilizing a comprehensive set of performance metrics—accuracy, precision, recall, F1-score, confusion matrices, and ROC curves—demonstrates that DERIENet achieves a state-of-the-art classification accuracy of 99.89%, with macro-averaged and weighted average precision, recall, and F1-score uniformly at 99.89%, and an AUC of 1.0 across all disease categories. The reliability of the model is validated by the confusion matrix, which shows that 899 out of 900 test images were correctly identified and that there was only one misclassification. Comparative evaluations of the various ensemble baselines, such as DenseNet201, MobileNetV2, and VGG16, and individual base learners demonstrate that DERIENet performs noticeably superior to all baseline models. It provides a highly interpretable, deployment-ready, and computationally efficient architecture that is ideal for integrating into edge or mobile platforms to facilitate in situ, real-time disease diagnostics in precision agriculture. Full article

To investigate the characteristics of rhizosphere soil microbial communities associated with Schisandra sphenanthera across different altitudinal gradients and to reveal the driving factors of microbial community dynamics, this study collected rhizosphere soil samples at four elevations: 900 m (HB1), 1100 m (HB2), 1300 m (HB3), and 1500 m (HB4). High-throughput sequencing and molecular ecological network analysis were employed to analyze the microbial community composition and species interactions. A null model was applied to elucidate community assembly mechanisms. The results demonstrated that bacterial communities were dominated by Proteobacteria, Acidobacteriota, Actinobacteriota, and Chloroflexi. The relative abundance of Proteobacteria increased with elevation, while that of Acidobacteriota and Actinobacteriota declined. Fungal communities were primarily composed of Ascomycota and Basidiomycota, with both showing elevated relative abundances at higher altitudes. Diversity indices revealed that HB2 exhibited the highest bacterial Chao, Ace, and Shannon indices but the lowest Simpson index. For fungi, HB3 displayed the highest Chao and Ace indices, whereas HB4 showed the highest Shannon index and the lowest Simpson index. Ecological network analysis indicated stronger bacterial competition at lower elevations and enhanced cooperation at higher elevations, contrasting with fungal communities that exhibited increased competition at higher altitudes. Altitude and soil nutrients were negatively correlated with soil carbon content, while plant nutrients and fungal diversity positively correlated with soil carbon. Null model analysis suggested that deterministic processes dominated bacterial community assembly, whereas stochastic processes governed fungal assembly. These findings highlight significant altitudinal shifts in the microbial community structure and assembly mechanisms in S. sphenanthera rhizosphere soils, driven by the synergistic effects of soil nutrients, plant growth, and fungal diversity. This study provides critical insights into microbial ecology and carbon cycling in alpine ecosystems, offering a scientific basis for ecosystem management and conservation. Full article

Integrated rice–crayfish (Oryza sativa–Procambarus clarkii) co-culture (RC) systems have gained prominence due to their economic benefits and ecological sustainability; however, the interactions between soil properties and microbial communities in such systems remain poorly understood. This study evaluated the effects of the RC systems on soil physicochemical characteristics and microbial dynamics in paddy fields of southern Henan Province, China, over the 2023 growing season and subsequent fallow period. Using a randomized complete design, rice monoculture (RM, as the control) and RC treatments were compared across replicated plots. Soil and water samples were collected post-harvest and pre-transplanting to assess soil properties, extracellular enzyme activity, and microbial community structure. Results showed that RC significantly enhanced soil moisture by up to 30.2%, increased soil porosity by 9.6%, and nearly tripled soil organic carbon compared to RM. The RC system consistently elevated nitrogen (N), phosphorus (P), and potassium (K) throughout both the rice growth and fallow stages, indicating improved nutrient availability and retention. Elevated extracellular enzyme activities linked to carbon, N, and P cycling were observed under RC, with enzymatic stoichiometry revealing increased microbial nutrient limitation intensity and a shift toward P limitation. Microbial community composition was significantly altered under RC, showing increased biomass, a higher fungi-to-bacteria ratio, and greater relative abundance of Gram-positive bacteria, reflecting enhanced soil biodiversity and ecosystem resilience. Further analyses using the Mantel test and Random Forest identified extracellular enzyme activities, PLFAs, soil moisture, and bulk density as major factors shaping microbial communities. Redundancy analysis (RDA) confirmed that total potassium (TK), vector length (VL), soil pH, and total nitrogen (TN) were the strongest environmental predictors of microbial variation, jointly explaining 74.57% of the total variation. Our findings indicated that RC improves soil physicochemical conditions and microbial function, thereby supporting sustainable nutrient cycling and offering a promising, environmentally sound strategy for enhancing productivity and soil health in rice-based agro-ecosystems. Full article

In ecosystems, spatial structure plays a fundamental role in shaping the observed dynamics. In particular, the availability and distribution of unoccupied sites—potential habitats—can strongly affect species persistence. However, mathematical models of ecosystems based on ordinary differential equations (ODEs) often neglect the explicit representation of these unoccupied sites. Here, probabilistic cellular automata (PCA) are used to reproduce two basic ecological scenarios: competition between two species and a predator–prey relationship. In these PCA-based models, unoccupied sites are taken into account. Subsequently, a mean field approximation of the PCA behavior is formulated in terms of ODEs. The variables of these ODEs are the numbers of individuals of both species and the number of empty cells in the PCA lattice. Including the empty cells in the ODEs leads to a modified version of the Lotka–Volterra system. The long-term behavior of the solutions of the ODE-based models is examined analytically. In addition, numerical simulations are carried out to compare the time evolutions generated by these two modeling approaches. The impact of explicitly considering unoccupied sites is discussed from a modeling perspective. Full article

(1) Background: The development of professional identity in future educators is a dynamic and multifaceted process, shaped by personal experiences, interpersonal relationships, role models, and the values internalized across diverse learning ecologies. This identity formation is closely intertwined with academic achievement, socio-educational integration, peer interactions, and the underlying motivations for choosing a particular educational and professional trajectory. This study explores the connections between students’ educational experiences and their motivation to pursue careers in pedagogy and social education. (2) Methods: Based on 138 autoethnographic narratives written by undergraduate students enrolled in Anthropology of Education courses at two [Spanish] universities. (3) Results: The analysis reveals how reflective engagement with personal educational histories enables students to reconstruct the relational foundations of their learning paths. These reflections inform their academic and career choices, which are understood not as static decisions, but as evolving trajectories shaped by a generational context characterized by uncertainty and change. (4) Conclusions: The findings highlight how students, through their narratives, articulate both their professional aspirations and a vision of the educator as an agent of change—one who emerges in response to, and often in critique of, the educational structures and practices. Full article

The value realization of ecological products is an important part of rural and agricultural development. As a significant force for protecting traditional agricultural systems and promoting rural revitalization, agricultural heritage systems (AHSs) have formed diverse value realization paths of ecological products in the process of dynamic protection and adaptive management. Through theoretical research, this article analyzed the characteristics of ecological products in AHS sites (EPAHSSs) and summarized the framework of value realization paths of EPAHSSs. Then, the Wannian Rice Culture System in China was selected as a case for conducting empirical research. The results showed that EPAHSSs exhibit obvious uniqueness in terms of climate environment, germplasm resources, farming and breeding models, and cultural heritage. The value realization paths of EPAHSSs mainly include industrial development support, such as the extension of agricultural industrial chains and the development of tourism, as well as fiscal transfer payments. The case analysis results indicated that Wannian County contains a rich variety of ecological products and developed a value realization pathway mainly based on the integration of industries and supplemented by fiscal transfer payments during the process of protection and development. However, further optimization is needed to promote the development of tourism and other paths. This study not only contributes to the sustainable development of the Wannian Rice Culture System, but the proposed framework is also applicable to other heritage systems and similar regions. Full article

The remote sensing classification of urban functional zones provides scientific support for urban planning, land resource optimization, and ecological environment protection. However, urban functional zone classification encounters significant challenges in accuracy and efficiency due to complicated image structures, ambiguous critical features, and high computational complexity. To tackle these challenges, this work proposes a novel XT-SECA algorithm employing a strengthened efficient channel attention mechanism (SECA) to integrate the feature-extraction XGBoost branch and the feature-enhancement Transformer feedforward branch. The SECA optimizes the feature-fusion process through dynamic pooling and adaptive convolution kernel strategies, reducing feature confusion between various functional zones. XT-SECA is characterized by sufficient learning of complex image structures, effective representation of significant features, and efficient computational performance. The Futian, Luohu, and Nanshan districts in Shenzhen City are selected to conduct urban functional zone classification by XT-SECA, and they feature administrative management, technological innovation, and commercial finance functions, respectively. XT-SECA can effectively distinguish diverse functional zones such as residential zones and public management and service zones, which are easily confused by current mainstream algorithms. Compared with the commonly adopted algorithms for urban functional zone classification, including Random Forest (RF), Long Short-Term Memory (LSTM) network, and Multi-Layer Perceptron (MLP), XT-SECA demonstrates significant advantages in terms of overall accuracy, precision, recall, F1-score, and Kappa coefficient, with an accuracy enhancement of 3.78%, 42.86%, and 44.17%, respectively. The Kappa coefficient is increased by 4.53%, 51.28%, and 52.73%, respectively. Full article

Global interest in nematode communities and their ecological relationships as unique and complex soil ecosystems has remarkably increased in recent years. As they have a representative role in the soil biota, nematodes present great potential to help understand soil health through analyzing their food chains in different environments. The objective of this study was to analyze the spatial and dynamic distributions of nematode communities and soil properties in two riparian areas of the Caatinga biome: one with native vegetation and the other with a history of agricultural use (modified). The study was carried out in a semi-arid region of Brazil in Parnamirim, PE. In both areas, sampling grids of 60 m × 40 m were established to obtain data on soil moisture, organic matter, particle size, electrical conductivity, and pH, as well as metabolic activity and ecological indices of nematode communities. There was a greater abundance and diversity of nematodes in riparian soils with native vegetation compared to in the modified area due to agricultural use and the dominance of exotic and invasive species. In both areas, bacterivores and plant-parasitic nematodes were dominant, with the genus Acrobeles and Tylenchorhynchus as the main contributors to the community. In the modified area, soil variables (fine sand, clay, and pH) positively influenced Fu4 and PP4 guilds, while in the area with native vegetation, moisture and organic matter exerted a greater influence on Om4, PP5, and Ba3 guilds. Kriging maps showed the soil variables were more concentrated in the center in the areas with native vegetation, in contrast to the area with modified vegetation, where they concentrated more on the margins. The functional guilds in the native vegetation did not exhibit a gradual increase towards the regions close to the riverbank, unlike in the modified area. The presence of plant-parasitic nematodes, especially of the genus Tylenchorhynchus, indicates the need for greater attention in the management of these ecosystems. The study contributes to understanding the interactions between nematode communities and soil in riparian areas of the Caatinga biome, emphasizing the importance of preserving native vegetation to maintain the diversity and balance of this ecosystem, in addition to highlighting the need for appropriate management practices in areas with a history of agricultural use, aiming to conserve soil biodiversity. Full article