Search Results (3,616)

Scaevola taccada, as a key dominant plant in coastal ecosystems, plays an irreplaceable role in sand fixation, shoreline protection, and maintaining the ecological stability of coastal zones. To investigate the effects of drought stress on the Binghai plant Scaevola taccada seedlings, a natural drought treatment was applied. Physiological indicators were measured at 0, 10, 25, and 40 days of stress, and 5 days after rewatering. Transcriptome sequencing and long non-coding RNA (lncRNA) analysis were also conducted to reveal the drought response mechanisms and molecular regulatory networks. The results showed that: (1) Prolonged drought significantly inhibited growth, with relative height increase, leaf number, and relative water content declining by 46.8%, 37.2%, and 63.4%, respectively, at T40 compared to the control. (2) In terms of photosynthetic physiology, Rubisco activity, RCA activity, SPAD value, Fv/Fm, and qP all continuously declined with increasing stress, while NPQ increased, suggesting damage to the photosynthetic system but also the activation of energy dissipation mechanisms to alleviate photooxidative stress. (3) The antioxidant system played a crucial role in the drought response. Under drought stress, the activities of SOD, POD, and CAT, and MDA content, underwent significant changes, with antioxidant enzyme activities rebounding notably after rewatering. (4) Transcriptome analysis revealed that differentially expressed mRNAs and lncRNA-targeted genes were significantly enriched in the ‘photosynthesis’ and ‘carbon metabolism’ pathways. Key genes involved, including PSAD-1, PSAL, NPQ4, six LHCs, BAM3, BAM1, SSII-A, and FRK1, were identified as core components of the regulatory network. In summary, Scaevola taccada effectively responds to drought stress through multi-level mechanisms, including photosynthetic regulation, carbon metabolism regulation, antioxidant defense, and transcriptional reprogramming, demonstrating strong drought resistance and post-rewatering recovery potential. These findings provide scientific evidence for plant selection and application in ecological restoration projects in coastal areas in the context of global climate extremes. Full article

As a key spatial platform for implementing China’s Northeast Revitalization Strategy, coordinated development of production–living–ecological (PLE) functions in the Changchun Metropolitan Area is crucial for high-quality regional development. This study uses 24 counties (districts) in the metropolitan area as analytical units and develops a quantitative indicator system to evaluate PLE functions. We integrate the entropy-weighted TOPSIS method, social network analysis (SNA), and geographically and temporally weighted regression (GTWR) to examine the spatiotemporal dynamics, spatial correlation networks, and driving mechanisms of the three functions from 2013 to 2023. Temporally, the production function follows a growth–decline–recovery trajectory, the living function increases overall despite fluctuations, and the ecological function strengthens continuously. Overall, the three functions increasingly exhibit coupling and synergy. Spatially, the production function concentrates in core areas and diffuses along major axes. The living function is led by the core and followed by county-level catch-up. The ecological function is higher in the east, relatively stable in the west, and connected by corridors, together forming a multi-center, axis-based synergistic pattern. In the spatial correlation networks, densities of the production and ecological networks remain largely stable, whereas the living network becomes markedly denser. The three networks display distinct topologies and continue to evolve structurally. For driving mechanisms, the GTWR model provides the best fit. Geographic proximity positively contributes to the formation of all three functional networks, while the eight explanatory factors show pronounced spatiotemporal heterogeneity. These findings provide an evidence base for optimizing functional coordination and implementing differentiated spatial governance in metropolitan areas. Full article

Bioconversion of lignocellulosic biomass into edible, nutrient-rich products using low-cost forestry waste offers substantial ecological and economic benefits. Composting forestry waste as a substrate for oyster mushroom (Pleurotus ostreatus) cultivation is an effective recovery strategy. However, the specific microbial-driven mechanisms by which nitrogen sources regulate lignocellulose degradation and compost quality during forestry waste composting for Pleurotus ostreatus substrate preparation remain to be elucidated. We evaluated three organic nitrogen sources (bran, soybean meal, and chicken manure) and one inorganic source (diammonium phosphate, DAP) during composting of forest-waste-based substrates. Composting performance and cultivation outcomes were assessed using physicochemical analyses, lignocellulose degradation measurements, high-throughput sequencing of bacterial 16S rRNA and fungal ITS, and biological efficiency. Organic nitrogen sources enhanced compost temperature and lignocellulose degradation by providing sustained nitrogen release, promoting stable colonization of core microbial communities and cooperative bacteria–fungi networks. In contrast, inorganic nitrogen resulted in slower heating, minimal lignocellulose degradation (0.75%), and unstable, competition-dominated microbial networks. Nitrogen sources indirectly shaped microbial communities by regulating the C/N ratio, pH, and electrical conductivity. Lignocellulose degradation and bacterial diversity significantly influenced mushroom biological efficiency, with bacterial diversity strongly regulating degradation rates. The forest waste–bran treatment achieved the highest biological efficiency (78.35%). These findings offer a practical strategy for optimizing forestry waste bioconversion into fungal protein. Full article

The discharge of untreated paper mill effluent poses significant ecological risks due to its high organic and nutrient loads. This study aimed to assess the phytoremediation potential of Azolla pinnata for treating paper mill effluent. Response Surface Methodology (RSM) and Artificial Neural Network (ANN) modeling approaches were applied and optimization was used for pollutant removal and plant biomass production. Experiments were designed using a Central Composite Design with two independent variables: effluent concentration (0, 50, and 100%) and plant density (10, 20, and 30 g per container). The responses measured were biochemical oxygen demand (BOD), chemical oxygen demand (COD) removal efficiencies, and final biomass yield after 16 days of exposure. RSM produced statistically significant (p < 0.05) second-order regression models for all three responses (coefficient of determination; R² > 0.98), while ANN showed slightly lower prediction errors within the experimental range studied. Maximum observed removal efficiencies were 91.74% for BOD, 80.91% for COD, and 92.66 g biomass yield under 50% effluent concentration and 30 g plant density. Optimization via both models suggested closely comparable operating conditions (79% effluent concentration and 29 g biomass) for optimal performance. The results indicate that A. pinnata demonstrates potential as a low-cost, nature-based treatment system for industrial effluent remediation under controlled conditions. The integration of data-driven optimization with biological treatment contributes to sustainable effluent management strategies by reducing chemical inputs, minimizing energy demand, and enabling biomass generation with potential downstream valorization. Full article

Sustainable urban development increasingly depends on understanding how digital activity is distributed across space and time, yet the spatiotemporal dynamics of the urban digital landscape remain poorly mapped by conventional data sources. This study uses Beijing as an empirical testbed, applying a multi-dimensional analytical framework to massive mobile network traffic data to decode the metabolic rhythms, distributional laws, and functional organization of the urban digital landscape. The results reveal three findings. First, the urban digital landscape exhibits a sleepless trapezoidal temporal rhythm characterized by continuous saturation without a midday trough and a quantifiable weekend activation lag, indicating that digital metabolism is structurally decoupled from physical mobility patterns. Second, digital traffic follows a skew-normal distribution consistent with a 20/70 rule of spatial polarization, in which the top 20% of super-connector nodes sustain approximately 70% of total urban digital flow, yielding a Gini coefficient of 0.68 as a measurable indicator of infrastructure inequality and systemic vulnerability. Third, four distinct functional prototypes are identified—ranging from continuously active metropolitan cores to inverse-tidal ecological peripheries—empirically validating Beijing’s polycentric transformation through the lens of digital flows. These findings demonstrate that large-scale mobile network traffic data offers a replicable and structurally distinct lens for sustainable urban digital governance, supporting resilient network planning, equitable allocation of digital resources, and evidence-based monitoring of urban functional transformation in rapidly growing megacities. Full article

Urban areas in arid environments are increasingly affected by the urban heat island (UHI) effect, which intensifies thermal stress, disrupts ecological balance, and poses challenges for sustainable urban development. Understanding and predicting spatiotemporal variations in land surface temperature (LST) and land use dynamics is therefore critical for effective urban planning. This study develops a predictive framework for Riyadh, Saudi Arabia, using long-term Landsat time series data (1993–2023) and deep learning models to evaluate urban thermal patterns via the Urban Thermal Field Variation Index (UTFVI). Artificial Neural Networks (ANNs) with six hidden layers for LST and seven for UTFVI forecast future trends up to 2043. The results indicate that urban areas expanded by 521.62 km², increasing from 8.73% to 19.56% between 1993 and 2023, and are projected to reach 1509.40 km² (25.28%) by 2043, while vegetation coverage declined from 0.771% to 0.674%. The highest average summer LST increased from 56.73 °C in 1993 to 59.89 °C in 2023 and is predicted to rise to 60.79 °C by 2033 and 61.52 °C by 2043. Winter temperatures exhibited a comparable upward trend, rising from 30.75 °C to 32.33 °C in 2023 and projected to reach 34.48 °C by 2043. UTFVI analysis revealed a substantial expansion of weak thermal field zones, which covered 2778 km² in 2023 and are expected to reach 3018.44 km² (57%) by winter 2043, accompanied by a marked contraction of strong thermal field areas. The ANN models achieved a high predictive performance, with RMSE values of 0.759 (summer) and 0.789 (winter) for UTFVI and correlation coefficients of 0.91 and 0.89, respectively. Projections further indicate that, by 2043, approximately 39.31% of the study area will experience summer temperatures between 48 °C and 53 °C, compared to 5.59% in 2023. These findings highlight the accelerating interaction between urban growth and thermal intensification in arid cities. The proposed modeling framework provides a robust decision-support tool for urban planners and policymakers to mitigate UHI impacts and promote climate-resilient and sustainable urban development. Full article

Ecological connectivity is a key element in biodiversity conservation and the spatial organization of protected areas. In response to environmental disasters, policy frameworks such as the European Biodiversity Strategy 2030, the European Green Deal, the Nature Restoration Law, the European Strategy on Adaptation to Climate Change, and the Urban Agenda for the EU support on-the-ground actions such as ecological restoration and biodiversity conservation to improve best practices. Despite the availability of indicators and discussions for measuring successful activities, real-world assessments have failed to be consistent. This highlights the gap between planning and expected results. Integrating scientific research with the regulatory initiatives, particularly those supported by ISPRA (Italian Institute for Environmental Protection and Research), addresses persistent gaps in Italian planning guidelines (implementing Action 1.3.B of the National Biodiversity Strategy). This study explores the LIFE projects that have been completed in Italy through a mixed-method analysis within the LIFE programme database. This analysis explores specific keywords that have an impact on connectivity and planning actions that contribute to the development of coherent urban landscapes, where habitats and biodiversity can be restored, and the ecological quality improved. The expected outcomes can promote healthy environments within urban and peri-urban areas. Full article

Accurate urban green space (UGS) mapping is essential for assessing urban ecosystem health and supporting sustainable development planning. However, deep learning-based UGS segmentation from Red–Green–Blue (RGB) remote sensing imagery faces two major challenges. First, the absence of near-infrared (NIR) information in RGB imagery hinders the ability to discriminate spectrally similar classes, such as vegetation and non-vegetation. Second, conventional convolutions with fixed receptive fields struggle to model the complex and irregular boundaries characteristic of UGS. To address these challenges, this study combined the Normalized Green–Red Difference Index with the Deformable Convolutional Network Lab (NGRDI-DCNLab) model, a semantic segmentation model tailored specifically for RGB-only imagery. Based on the DeepLabV3+ framework, the model introduced three core improvements: (1) The Normalized Green–Red Difference Index (NGRDI) was incorporated to compensate for the absence of NIR information, enhancing the spectral separability of vegetation pixels. (2) Standard convolutions in the decoder were replaced with deformable convolutions, enabling the network to more effectively adapt to irregular boundaries of UGS. (3) An NGRDI-weighted loss function was designed to assign higher weights to challenging samples and uncertain boundary regions, guiding the model toward more accurate edge delineation. Comprehensive evaluations on two public high-resolution datasets—the Wuhan Dense Labeling Dataset (WHDLD) and the Beijing subset of the Urban Green Space-1m dataset (UGS-1m_Beijing)—demonstrated that the NGRDI-DCNLab model outperformed existing popular deep learning models (like Unet++, etc.). Specifically, the deformable convolution effectively enhances the feature modeling capability for irregular boundaries; incorporating the NGRDI vegetation index as a fourth channel strengthens spectral feature representation and improves the distinction between vegetation and non-vegetation; and adding the dynamic NGRDI-weighted loss enables targeted learning for challenging samples. Through the synergistic effect of these three modules, the model achieves mean Intersection over Union (MIoU) scores of 84.77% and 77.66%, as well as F1-scores of 91.75% and 87.27%, on the WHDLD and UGS-1m_Beijing datasets, respectively. Furthermore, the model exhibited certain generalization capability on the unmanned aerial vehicle (UAV) dataset, the Urban Drone Dataset 6 (UDD6), attaining an MIoU of 87.43%. Our results confirm that high-precision UGS extraction is achievable using only RGB remote sensing imagery, providing a cost-effective and practical technical solution for refined urban governance and ecological monitoring. Full article

Sustainable tourist route design is a critical challenge in industrial heritage planning. While prior tourism routing algorithms predominantly minimize physical distance, and conventional heritage planning focuses on the static preservation of abandoned sites, both lack the multi-objective adaptability required for “living” industrial landscapes. In such dynamic environments, active production, tourism, and ecological conservation intricately coexist. To address this gap, this study proposes a novel, data-driven route planning framework, taking the Tianjin Changlu Salt Field as a case study. The genuine novelty lies in integrating multi-objective network optimization with spatial design implementation. The site is abstracted into a topological network comprising 13 nodes and 19 edges. Multi-attribute edge weights—incorporating spatial distance, travel time, landscape attractiveness, and ecological sensitivity—are quantified using entropy weighting fused with subjective preferences. Using the Floyd–Warshall algorithm, three theme-based touring routes are generated. Unlike traditional methods, this workflow actively translates algorithmic outputs into concrete spatial strategies, such as bypassing ecologically sensitive zones and transforming production facilities into perceptible landscape nodes. Comparative evaluations demonstrate that these optimized routes achieve higher comprehensive utility than baseline and designer-generated schemes, offering a pioneering, reproducible paradigm for the sustainable renewal of living industrial heritage. Full article

To address the limitations of traditional forest above-ground biomass (AGB) retrieval methods—namely, the restricted accuracy of physical models and the limited generalization ability of purely data-driven models—this study proposes a differentiable physical modeling (DPM) approach for forest AGB estimation. The method adopts the water cloud model (WCM) as a physics-based framework, grounded in radiative transfer theory, and integrates C-band synthetic aperture radar (SAR) data with multispectral imagery. Within the PyTorch tensor computation framework, automatic differentiation (AD) is employed to seamlessly couple the WCM with the deep fully connected neural network (DFCNN), enabling a differentiable implementation of the WCM. Using mean squared error (MSE) as the loss function, the neural network parameters are optimized through backpropagation and gradient descent, thereby constructing an end-to-end trainable DPM model that effectively retrieves forest AGB while preserving physical interpretability and generalization capability. To validate the proposed method, two representative test sites were selected: Simao in Pu’er, Yunnan Province, and Genhe in Inner Mongolia. GF-3 PolSAR and RADARSAT-2 data were used to extract backscattering coefficients and compute the radar vegetation index (RVI), while Landsat 8 OLI imagery was employed to calculate the normalized difference vegetation index (NDVI), difference vegetation index (DVI), and soil-adjusted vegetation index (SAVI). These datasets, together with ASTER GDEM, field-measured biomass, and other relevant datasets, were integrated to construct a multisource dataset combining remote sensing and ground observations. The performance of the DPM model was then compared with the traditional WCM and several data-driven models, including the fully connected neural network (FNN), generalized regression neural network (GRNN), RF, and Adaptive Boosting (AdaBoost). The results indicate that the DPM model achieved R² = 0.60, RMSE = 24.23 Mg/ha, Bias = 0.4 Mg/ha, and ubRMSE = 22.43 Mg/ha in Simao, and R² = 0.48, RMSE = 33.29 Mg/ha, Bias = 0.87 Mg/ha, and ubRMSE = 33.28 Mg/ha in Genhe, demonstrating consistently better performance than both the WCM and all tested data-driven models. The DPM model demonstrated consistent performance across ecologically contrasting forest regions. It alleviated the systematic overestimation bias of purely data-driven models and overcame the limitations in predictive accuracy resulting from the simplified structure of the WCM. The differentiability of the WCM enables the loss function errors to be backpropagated through the neural network, thereby allowing the optimization of the physical model parameters. Overall, the DPM framework integrates the advantages of both physical models and data-driven approaches, providing an estimation method with acceptable accuracy for forest AGB retrieval. It also offers theoretical and practical insights for the integration of deep learning and physical knowledge in other research fields. Full article

Understanding how Sustainable Development Goals (SDGs) interact—through synergies or trade-offs—is critical for coordinating economic growth, social equity, and environmental protection at the regional scale. However, empirical evidence on the structure, directionality, and spatial heterogeneity of SDG interactions remains limited, particularly in policy-supported regions undergoing development transitions. This study addresses this gap by examining SDG interactions in the former Central Soviet Area of Jiangxi Province, China. Using panel data from eight prefecture-level cities spanning 2001–2022, we construct a multi-dimensional SDG evaluation framework encompassing economic development, social equity and livelihood security, resource utilization and environmental protection, and sustainable cities and communities. A two-stage analytical approach is employed: Spearman’s rank correlation analysis is used to identify synergistic and trade-off relationships among SDGs, and a Geographically and Temporally Weighted Regression (GTWR) model is applied to estimate directional influences and their spatiotemporal heterogeneity among development dimensions. The results indicate that synergistic relationships dominate the regional SDG interaction network, while trade-offs are comparatively limited and selectively concentrated in specific goal pairings. Marked spatial heterogeneity is observed, with stronger synergies in the east than in the west. Functional-zone analysis reveals that ecological and cultural conservation zones exhibit the strongest synergies, whereas industrial transformation zones face pronounced trade-offs, particularly between food security (SDG2) and income inequality (SDG10). GTWR results further demonstrate directional asymmetry among development dimensions, with social equity exerting a stronger influence on economic development than the reverse, and relatively weaker feedback from economic growth to resource and environmental outcomes. Overall, this study provides a systematic, spatiotemporally explicit assessment of SDG interactions in a policy-supported regional context. By integrating interaction analysis with spatiotemporal modeling, it offers a robust empirical basis for understanding where, how, and in which direction SDGs interact, thereby contributing to more context-sensitive approaches to regional sustainable development. Full article

Microbial community structure and its carbon, nitrogen, and sulfur metabolic potentials are playing crucial roles in biogeochemical cycles within river ecosystems. However, in karst terrain regions, the impact of the distinctive baijiu industry on these ecosystems remains incompletely understood. This study integrates hydrogeochemical and metagenomic techniques to elucidate how microbial communities and their metabolic potentials respond to the baijiu industry. The results indicate that microbial community richness was higher in the downstream section than in the upstream and core zones. Microbial network modularity decreased from 0.832 upstream to 0.439 downstream, indicating reduced network stability. The migration rate decreased from upstream to downstream, suggesting that species diffusion limitation was gradually enhanced. The NST index gradually decreased from upstream to downstream, reflecting a weakening of random processes and strengthening of deterministic processes within the community. We found significant enrichment of genes associated with dissimilatory nitrate reduction, sulfur oxidation, carbon fixation, and methanogenesis in the core zone, whereas the abundance of denitrification genes decreased. Environmental factor analysis revealed that pH, DO, and elevation are the key hydrochemical parameters driving changes in microbial community structure and metabolic functions. This study reveals the potential impact mechanisms of the baijiu industry on karst river ecosystems from the perspectives of microbial community ecology and metabolic functions, providing a scientific basis for watershed ecological conservation and sustainable management. Full article

Populus euphratica is a critical constructive species in arid desert regions, serving as a “natural barrier” for oasis protection. The sustainable management of Populus euphratica forests is directly related to regional ecological security, and the fine identification of sparse Populus euphratica forests is essential for the conservation of natural Populus euphratica forests. Currently, most mapping studies on Populus euphratica distribution focus on the extraction of dense, contiguous Populus euphratica forests, with insufficient attention paid to the identification of sparse Populus euphratica forests. This study utilizes Gaofen-2 (GF-2) satellite imagery as the data source and takes a typical sparse Populus euphratica forests distribution area in the Tarim River Basin as the study site. It systematically evaluates the performance of nine mainstream deep learning models, including U-Net, DeepLabV3+, and SegFormer, in the task of sparse Populus euphratica forests identification. The results indicate that: (1) The false-color sample set, synthesized from near-infrared, red, and green bands, contributes to improved model accuracy. Compared to the true-color (red, green, blue bands) dataset, the average Intersection over Union (IoU) of the nine models shows a relative improvement of approximately 20%. (2) For the sparse Populus euphratica forests identification task based on the false-color dataset, four models—U-Net, U-Net++, MA-Net, and DeepLabV3+—exhibited excellent performance, with IoU exceeding 75%. (3) Using U-Net as the baseline model, this study integrated the max-pooling indices mechanism, atrous spatial pyramid pooling, and residual connection modules to construct a semantic segmentation network tailored for sparse Populus euphratica forests, named Sparse Populus euphratica Segmentation Network (SPS-Net). This model achieved an IoU of 80%, a relative improvement of approximately 6.3% over the baseline model, and demonstrated good stability in large-scale classification tests. The identification scheme for sparse Populus euphratica forests constructed using GF-2 imagery and deep learning models proposed in this study can provide effective technical support for the refined monitoring and protection of natural Populus euphratica forests. Full article

Assessing and optimizing regional ecological networks is critical for mitigating fragmentation-driven ecological risks and informing evidence-based territorial spatial planning in China. In this study, we developed a comprehensive evaluation framework integrating ecosystem services, ecological sensitivity, and landscape connectivity to identify ecological sources in Anyang City, China. We then extracted ecological corridors and nodes using circuit theory and constructed the city’s ecological network. Notably, we applied complex network theory combined with topological robustness analysis for optimization to enhance network stability. The analysis identified 43 ecological sources (820.72 km²; 11.16% of the region), predominantly distributed in western Anyang. A total of 82 corridors (460.35 km), 62 pinch points, and 120 barrier points were mapped—primarily in the west, revealing critical connectivity deficits. Network optimization through the addition of 10 strategic corridors significantly enhanced structural balance and functionality, with average degree, closeness centrality, clustering coefficient, eigenvector centrality, and graph density increasing by 5.55–12.19%, and their standard deviations decreasing by an average of 19.32%. Global efficiency (+8.74%), the largest connected component ratio (+0.73%), and node/edge recovery robustness (+17.44%/+18.08%) also improved markedly, confirming greater connectivity and resilience. Our methodology comprehensively integrates ecosystem functional services, disturbance resistance, and spatial structural stability, providing a practical reference for the construction and optimization of regional ecological networks in mountainous–plain transition zones of China. Full article

The giant panda holds a critical position in global biodiversity conservation, yet the ongoing fragmentation of its habitat poses a severe threat to the long-term viability of its survival. This study focused on the giant panda habitat in the Xiangling region and systematically analyzed the mechanisms through which human socioeconomic activities drive habitat fragmentation. The analysis was based on data from 2000 to 2023, encompassing land use, population density, transportation networks, mining activities, and nighttime light emissions, utilizing a methodology that integrated Principal Component Analysis, the Moving Window method, trend analysis, and the Geodetector model. The findings reveal the following: First, the degree of habitat fragmentation has intensified over time with significant spatial heterogeneity, exhibiting a pattern of “low fragmentation in the core areas and high fragmentation in the periphery,” where areas of very high fragmentation have expanded markedly along the habitat edges. Second, the trend in fragmentation demonstrates an overall improvement in the core zones, particularly within the Giant Panda National Park, where over 70% of the area shows reduced fragmentation; conversely, nearly 30% of the peripheral areas continue to degrade. Third, the driving factors of habitat fragmentation exhibit bi-factor enhancement and nonlinear enhancement effects, with land use identified as the dominant factor. The study recommends enhancing the overall connectivity and ecological functionality of the habitat through measures such as refining land-use planning, constructing ecological corridors, implementing hierarchical management, and promoting community co-management. Full article