Search Results (7,010)

China has entered an era of population decline, yet urbanization continues as rural-to-urban migration persists. This demographic transition has prompted a strategic shift in urban development from extensive spatial expansion toward quality-oriented, intensive growth models. However, evolving human–land supply–demand dynamics in cities historically characterized by population inflows remain insufficiently understood. This study focuses on Xiamen, a prototypical coastal migrant-receiving city, to investigate land use simulation under demographic transition. By integrating the cohort-component method with the Patch-generating Land Use Simulation (PLUS) model, we project Xiamen’s population under three scenarios by 2030: Stable Continuation (SCS), Natural Development (NDS), and National 2030 Population Planning (NPP), with projected increases of 5.56%, 6.76%, and 24.69%, respectively. Results show continued but decelerating population growth, with adequate labor supply and persistent demographic dividend. Notably, the NPP scenario reveals a negative correlation between population growth and construction land expansion. In NPP-High, prioritizing compact development and ecological conservation, population grows by 1.27 million while construction land decreases by 2.85% and forest land increases by 4.09%. This framework provides empirical evidence for compact urban development under the dual constraints of land-use efficiency and ecological protection. Full article

In this study, a hybrid sensor-based electronic nose circuit was developed using eight metal-oxide semiconductors and 14 quartz crystal microbalance gas sensors. This study included 100 participants: 60 individuals diagnosed with lung cancer, 20 healthy nonsmokers, and 20 healthy smokers. A total of 338 experiments were performed using breath samples throughout this study. In the classification phase of the obtained data, in addition to traditional classification algorithms, such as decision trees, support vector machines, k-nearest neighbors, and random forests, the fuzzy logic method supported by the optimization algorithm was also used. While the data were classified using the fuzzy logic method, the parameters of the membership functions were optimized using a nature-inspired optimization algorithm. In addition, principal component analysis and linear discriminant analysis were used to determine the effects of dimension-reduction algorithms. As a result of all the operations performed, the highest classification accuracy of 94.58% was achieved using traditional classification algorithms, whereas the data were classified with 97.93% accuracy using the fuzzy logic method optimized with optimization algorithms inspired by nature. Full article

Afforestation substantially promotes vegetation restoration and modifies soil physical, chemical, and biological properties. The integrated effects of soil properties on soil quality, expressed via a composite soil quality index (SQI), remain unclear despite variations among individual properties. Here, five vegetation restoration treatments were selected as follows: (1) barren land (BL, control), (2) disturbed short-rotation Eucalyptus plantation (REP); (3) undisturbed long-term Eucalyptus plantation (UEP); (4) mixed native-species plantation (MF); and (5) natural forest (NF) following >50 years of restoration. Soil physicochemical properties and microbial community compositions were investigated, and soil quality was evaluated by an integrated SQI. Our results showed that vegetation restoration had strong effects on soil physicochemical properties, soil quality, and microbial communities. Most of the soil physicochemical properties exhibited significant differences among treatments. Soil dissolved organic carbon, total nitrogen, and ammonium nitrogen were the three key soil quality indicators. The SQI increased significantly with vegetation recovery intensity. In both UEP and MF, it reached levels comparable to NF, and was higher in UEP than in REP, implying that short-rotation practices impede soil restoration. In addition, microbial biomass (bacteria, fungi, arbuscular mycorrhizal fungi, actinomycetes, and total microbe PLFAs) increased from BL to NF. All plantations exhibited lower microbial biomass than NF, revealing incomplete recovery and a greater sensitivity to soil physicochemical properties. Conversely, the fungi-to-bacteria biomass ratio decreased sequentially (REP > BL > UEP > MF > NF). Strong positive correlations between microbial biomass and the SQI were observed. These results collectively indicate that afforestation with mixed tree species is optimal for rapid soil restoration, and undisturbed long-term monocultures can achieve similar outcomes. These findings highlight that tree species mixtures and reducing disturbance should be taken into consideration when restoring degraded ecosystems in the tropics. Full article

Floods are among the most devastating natural disasters; predicting their depth and extent remains a global challenge. Machine Learning (ML) models have demonstrated improved accuracy over traditional probabilistic flood mapping approaches. While previous studies have developed ML-based models for specific local regions, this study aims to establish a methodology for estimating flood depth on a global scale using ML algorithms and freely available datasets—a challenging yet critical task. To support model generalization, 45 catchments from diverse geographic regions were selected based on elevation, land use, land cover, and soil type variations. The datasets were meticulously preprocessed, ensuring normality, eliminating outliers, and scaling. These preprocessed data were then split into subgroups: 75% for training and 25% for testing, with six additional unseen catchments from the USA reserved for validation. A sensitivity analysis was performed across several ML models (ANN, CNN, RNN, LSTM, Random Forest, XGBoost), leading to the selection of the Random Forest (RF) algorithm for both flood inundation classification and flood depth regression models. Three regression models were assessed for flood depth prediction. The pixel-based regression model achieved an R² of 91% for training and 69% for testing. Introducing a pixel clustering regression model improved the testing R² to 75%, with an overall validation (for unseen catchments) R² of 64%. The catchment-based clustering regression model yielded the most robust performance, with an R² of 83% for testing and 82% for validation. The developed ML model demonstrates breakthrough computational efficiency, generating complete flood depth predictions in just 6 min—a 225× speed improvement (90–95% time reduction) over conventional HEC-RAS 6.3 simulations. This rapid processing enables the practical implementation of flood early warning systems. Despite the dramatic speed gains, the solution maintains high predictive accuracy, evidenced by statistically robust 95% confidence intervals and strong spatial agreement with HEC-RAS benchmark maps. These findings highlight the critical role of the spatial variability of dependencies in enhancing model accuracy, representing a meaningful approach forward in scalable modeling frameworks with potential for global generalization of flood depth. Full article

Assessing ecological quality in mining areas is critical for environmental protection and sustainable resource management. However, most previous studies concentrate on large-scale analysis, overlooking fine-scale assessment in mining areas. To address this issue, this study proposed a novel analysis framework for mining areas by integrating high-resolution Landsat data, the Remote Sensing Ecological Index (RSEI), and the Random Forest regression method. Based on the framework, four decades of spatiotemporal dynamics and drivers of ecological quality were revealed in Youjiang River Valley. Results showed that from 1986 to 2024, ecological quality in Youjiang River Valley exhibited a fluctuating upward trend (slope = 0.004/year), with notable improvement concentrated in the most recent decade. Spatially, areas with a significant increasing trend in RSEI (48.71%) were mainly located in natural vegetation regions, whereas areas with a significant decreasing trend (9.11%) were concentrated in impervious surfaces and croplands in northern and central regions. Driver analysis indicates that anthropogenic factors played a crucial role in ecological quality changes. Specifically, land use intensity, precipitation, and sunshine duration were main determinants. These findings offer a comprehensive understanding of ecological quality evolution in subtropical karst mining areas and provide crucial insights for conservation and restoration efforts in Youjiang River Valley. Full article

Soils act as sources or sinks for three major greenhouse gases (CO₂, CH₄, and N₂O). Approximately 20% of global CO₂ emissions are released from soils through the soil respiration process. Soil respiration (soil CO₂ emission) can account for over 85% of ecosystem respiration. The aim of this study was to compare managed and unmanaged stands of pedunculate oak (Quercus robur L.) in order to investigate the impact of forest management on soil CO₂ emissions. We selected one managed and two unmanaged stands. The first stand (S1) represents a managed middle-aged stand, which is the optimal stage of development. The second stand (S2) belongs to the over-mature stage of development in an old-growth oak forest, while the third stand (S3) belongs to the decay stage of development in an old-growth oak forest. The closed chambers method was used for air sampling and the air samples were analyzed using gas chromatography (GC). Multiple regression models that include soil temperature (ST), soil moisture (SM), and their interaction provide a better explanation for variation in soil CO₂ emission (SCDE) (higher R² values) compared to regression models that only involve two variables (ST and SM). The study showed that SCDE in the decay stage of old-growth forest (S3) was significantly lower (p < 0.001) compared to the other two stands (S1 and S2). S3 is characterized by very low canopy cover and intensive natural regeneration, unlike S1 and S2. However, there were no significant differences in SCDE between the managed middle-aged stand (S1) and the over-mature (old-growth) stand (S2). Over a long-term rotation period in pedunculate oak forests, forest management practices that involve the periodic implementation of moderate silvicultural interventions can be deemed acceptable in terms of maintaining the carbon balance in the soil. Full article

High-value patents are a key indicator of new product development, the emergence of innovative technology, and a source of innovation incentives. Multiple studies have shown that patent value exhibits a significantly skewed distribution, with only about 10% of patents having high value. Identifying high-value patents from a large volume of patent data in advance has become a crucial problem that needs to be addressed urgently. However, current machine learning methods often rely on manual hyperparameter tuning, which is time-consuming and prone to suboptimal results. Existing optimization algorithms also suffer from slow convergence and local optima issues, limiting their effectiveness on complex patent datasets. In this paper, machine learning and intelligent optimization algorithms are combined to process and analyze the patent data. The Fire Hawk Optimization Algorithm (FHO) is a novel intelligence algorithm suggested in recent years, inspired by the process in nature where Fire Hawks capture prey by setting fires. This paper firstly proposes the Enhanced Fire Hawk Optimizer (EFHO), which combines four strategies, namely adaptive tent chaotic mapping, hunting prey, adding the inertial weight, and enhanced flee strategy to address the weakness of FHO development. Benchmark tests demonstrate EFHO’s superior convergence speed, accuracy, and robustness across standard optimization benchmarks. As a representative real-world application, EFHO is employed to optimize Random Forest hyperparameters for high-value patent recognition. While other intelligent optimizers could be applied, EFHO effectively overcomes common issues like slow convergence and local optima trapping. Compared to other classification methods, the EFHO-optimized Random Forest achieves superior accuracy and classification stability. This study fills a research gap in effective hyperparameter tuning for patent recognition and demonstrates EFHO’s practical value on real-world patent datasets. Full article

Forest disturbance is a major driver shaping the structure and function of plantation ecosystems. Current research predominantly focuses on single forest types or landscape scales. However, species-level fine-scale assessments of disturbance dynamics are still scarce. In this study, we investigated Chinese fir (Cunninghamia lanceolata), Armand pine (Pinus armandii), and Yunnan pine (Pinus yunnanensis) plantations in the mountainous eastern Yunnan Plateau. We developed a Spatial Coupling Framework of Disturbance Legacy (SC-DL) to systematically elucidate the spatial associations between contemporary species distribution patterns and historical disturbance regimes. Using the Google Earth Engine (GEE) platform, we reconstructed pixel-level disturbance trajectories by integrating long-term Landsat time series (1993–2024) and applying the LandTrendr algorithm. By fusing multi-source remote sensing features (Sentinel-1/2) with terrain factors, employing RFE, and performing a multi-model comparison, we generated 10 m-resolution species distribution maps for 2024. Spatial overlay analysis quantified the cumulative proportion of the historically disturbed area and the spatial aggregation patterns of historical disturbances within current species ranges. Key results include the following: (1) The model predicting disturbance year achieved high accuracy (R² = 0.95, RMSE = 2.02 years, MAE = 1.15 years). The total disturbed area from 1993 to 2024 was 872.7 km², exhibiting three distinct phases. (2) The random forest (RF) model outperformed other classifiers, achieving an overall accuracy (OA) of 95.17% and a Kappa coefficient (K) of 0.93. Elevation was identified as the most discriminative feature. (3) Significant spatial differentiation in disturbance types emerged: anthropogenic disturbances (e.g., logging and reforestation/afforestation) dominated (63.1% of total disturbed area), primarily concentrated within Chinese fir zones (constituting 70.2% of disturbances within this species’ range). Natural disturbances accounted for 36.9% of the total, with fire dominating within the Yunnan pine range (79.3% of natural disturbances in this zone) and drought prevailing in the Armand pine range (71.3% of natural disturbances in this zone). (4) Cumulative disturbance characteristics differed markedly among species zones: Chinese fir zones exhibited the highest cumulative proportion of disturbed area (42.6%), with strong spatial aggregation. Yunnan pine zones followed (36.5%), exhibiting disturbances linearly distributed along dry–hot valleys. Armand pine zones showed the lowest proportion (20.9%), characterized by sparse disturbances within fragmented, high-altitude habitats. These spatial patterns reflect the combined controls of topographic adaptation, management intensity, and environmental stress. Our findings establish a scientific basis for identifying disturbance-prone areas and inform the development of differentiated precision management strategies for plantations. Full article

The glomalin-related soil protein (GRSP), a class of stable glycoproteins produced by arbuscular mycorrhizal fungi, constitute an important microbial-derived carbon pool in terrestrial ecosystems. However, the response of GRSP accumulation to land-use change and quantitative contribution to soil organic carbon (SOC) pools, as well as the environmental and edaphic factors controlling GRSP dynamics in different land-use systems, require further elucidation. To address these knowledge gaps, we systematically collected surface soil samples (0–20 cm depth) from 72 plots across three land-use types—tea plantations (TP; n = 24), artificial forests (AF; n = 24), and natural forests (NF; n = 24) in China’s Huangshan Mountain region between July and August 2024. GRSP was extracted via autoclaving (121 °C, 20 min) in 20 mM citrate buffer (pH 8.0), fractionated into total GRSP (T-GRSP), and quantified using the Bradford assay. Results revealed distinct patterns in soil carbon storage, with NF exhibiting the highest concentrations of both SOC (33.2 ± 8.69 g kg⁻¹) and total GRSP (T-GRSP: 2.64 ± 0.34 g kg⁻¹), followed by AF (SOC: 14.9 ± 2.55 g kg⁻¹; T-GRSP: 1.42 ± 0.25 g kg⁻¹) and TP (SOC: 7.07 ± 1.72 g kg⁻¹; T-GRSP: 0.58 ± 0.11 g kg⁻¹). Although absolute GRSP concentrations were lowest in TP, its proportional contribution to SOC remained consistent across land uses (TP: 8.72 ± 2.84%; AF: 9.69 ± 1.81%; NF: 8.40 ± 2.79%). Statistical analyses identified dissolved organic carbon and microbial biomass carbon as primary drivers of GRSP accumulation. Structural equation modeling further demonstrated that land-use type influenced SOC through its effects on MBC and fine-root biomass, which subsequently enhanced GRSP production. These findings demonstrate that undisturbed forest ecosystems enhance GRSP-mediated soil carbon sequestration, emphasizing the critical role of natural forest conservation in ecological sustainability. Full article

The Amur tiger (Panthera tigris altaica) is a flagship and umbrella species of forest ecosystems in northeastern Asia. Climate change is profoundly and irreversibly affecting wildlife habitat suitability, especially for large mammals. To effectively protect the Amur tiger, it is necessary to understand the impact of climate change on the quality and the connectivity of its habitat. We used the species distribution models combined with the latest Shared Socioeconomic Pathway (SSP) climate scenarios to predict current and future changes in habitats and corridors. We found the following: (1) The total area of the Amur tiger’s suitable habitat currently amounts to approximately 4941.94 km², accounting for 27.64% of the study area represented by two adjacent national parks. Among these habitats, the highly suitable areas are mainly located on the both sides of the Sino-Russian border. The landscape connectivity remains relatively stable, and the degree of fragmentation in highly suitable habitats is low. (2) Although the suitable habitat of the Amur tiger shows an expansion trend under most climate scenarios (excluding SSP3-7.0), the area of suitable habitat within the entire study region does not increase significantly. Therefore, we should implement conservation measures to facilitate the continued expansion of suitable habitat for the Amur tiger. The quantity and length of landscape connectivity corridors are expected to vary in response to changes in core habitat patches, while the centroid of highly suitable habitats is also expected to shift to different extents. In such circumstances, new ecological corridors need to be constructed, while existing natural ecological corridors should be preserved. Full article

This study systematically documented macrofungal diversity in Beijing, China (field surveys conducted from 2020 to 2024) using line-transect and random sampling. A total of 1056 species were identified, spanning 2 phyla, 7 classes, 25 orders, 109 families, and 286 genera. The inventory includes 12 new species, 456 new records for Beijing, 79 new records for China, and comprises 116 edible, 56 edible–medicinal, 123 medicinal, and 58 poisonous species. Among these, 542 species were assessed against China’s Macrofungi Redlist, revealing eight species needing conservation attention (seven Near Threatened, one Vulnerable). Analysis revealed stark differences in dominant taxa between natural ecosystems (protected areas) and urban green spaces/parks. In natural areas, macrofungi are dominated by 31 families (e.g., Russulaceae, Cortinariaceae) and 47 genera (e.g., Russula, Cortinarius). Ectomycorrhizal lineages prevailed, highlighting their critical role in forest nutrient cycling, plant symbiosis, and ecosystem integrity. In urban areas, 10 families (e.g., Agaricaceae, Psathyrellaceae) and 17 genera (e.g., Leucocoprinus, Coprinellus) were dominant. Saprotrophic genera dominated, indicating their adaptation to decomposing organic matter in human-modified habitats and the provision of ecosystem services. The study demonstrates relatively high macrofungal diversity in Beijing. The distinct functional guild composition—ectomycorrhizal dominance in natural areas versus saprotrophic prevalence in urban zones—reveals complementary ecosystem functions and underscores the conservation value of protected habitats for maintaining vital mycorrhizal networks. These findings provide fundamental data and scientific support for regional biodiversity conservation and sustainable macrofungal resource development. Full article

Understanding the integration of newly recorded species into forest ecosystems is essential for evaluating their ecological impacts on native wildlife diversity. In this study, we examined the spatial and temporal niche dynamics of three sympatric squirrel species within the Labagoumen nature reserve, a temperate forest located in northern China. Particular emphasis was placed on the recently documented Eurasian red squirrel (Sciurus vulgaris) and its potential interactions with two native species: Père David’s rock squirrel (Sciurotamias davidianus) and the Siberian chipmunk (Tamias sibiricus). Using camera trapping data from 91 sites (2019–2024), we examined habitat use, activity rhythms, and niche overlap under contrasting levels of human disturbance. A total of 3419 independent effective photos of squirrels were recorded. S. vulgaris showed a broader spatial distribution and a higher relative abundance index (RAI) in the tourist area, while native species were more abundant in the non-tourist area. All three species showed similar annual activity patterns based on the monthly relative abundance index (MRAI), although native species exhibited an additional activity peak in June–July. Temporal niche overlap (C_ih) and the coefficient of overlap (Δ) between S. vulgaris and native species increased during the tourist season, suggesting synchronized activity under high disturbance. In contrast, lower overlap in the non-tourist season indicated stronger temporal partitioning. The daily activity rhythm of S. vulgaris remained stable, while native species displayed more variability, especially in non-tourist areas. S. vulgaris also exhibited a significantly broader spatial niche breadth (B_i), suggesting greater habitat exploitation and adaptability. Non-metric multidimensional scaling (NMDS) revealed no significant spatial segregation among the three species, indicating successful integration of S. vulgaris into the local community. Our findings emphasize the competitive advantage of S. vulgaris and demonstrate how human activities can restructure forest small mammal assemblages by altering spatiotemporal niche partitioning. We recommend long-term ecological monitoring to assess species diversity changes and guide adaptive conservation strategies. Full article

We propose a two-stage statistical learning framework to investigate how financial news headlines posted over weekends affect stock returns. In the first stage, Natural Language Processing (NLP) techniques are used to extract sentiment features from news headlines, including FinBERT sentiment scores and Impact Probabilities derived from Logistic Regression models (Binomial, Multinomial, and Bayesian). These Impact Probabilities estimate the likelihood that a given headline influences the stock’s opening price on the following trading day. In the second stage, we predict over-weekend log returns using various sets of covariates: sentiment-based features, traditional financial indicators (e.g., trading volumes, past returns), and headline counts. We evaluate multiple statistical learning algorithms—including Linear Regression, Polynomial Regression, Random Forests, and Support Vector Machines—using cross-validation and two performance metrics. Our framework is demonstrated using financial news from MarketWatch and stock data for Apple Inc. (AAPL) from 2014 to 2023. The results show that incorporating sentiment features, particularly Impact Probabilities, improves predictive accuracy. This approach offers a robust way to quantify and model the influence of qualitative financial information on stock performance, especially in contexts where markets are closed but news continues to develop. Full article

Heterobasidion root rot fungi represent a major threat to conifer forest stands, and virocontrol (biocontrol) has been proposed as an alternative strategy of disease management in recent years. Here, we investigated the occurrence of RNA viruses and viroid-like genomes in Heterobasidion annosum sensu lato in near-natural forests of Bosnia and Herzegovina (Dinaric Alps), a region previously unexplored in this regard. Seventeen H. annosum s.l. isolates were screened for virus presence by RNA Sequencing and bioinformatic analyses. In total, 32 distinct mycoviruses were discovered in the datasets, 26 of which were previously unknown. The detected viruses represent two dsRNA (Partitiviridae and Curvulaviridae), six linear ssRNA (Mitoviridae, Narnaviridae, Botourmiaviridae, Virgaviridae, Benyviridae, and Deltaflexiviridae) and three circular ssRNA (Dumbiviridae, Quambiviridae, and Trimbiviridae) virus families. In addition to the known circular ambiviruses with their hammerhead (HHRz) and hairpin (HPRz) ribozymes, two other smaller non-coding circular RNAs of ca. 910 bp each were identified encoding HHRz and deltavirus (DVRz) ribozymes in both polarities of their genomes. This study documents the first report of a putative viroid-like RNA agent in Heterobasidion, along with beny-like and deltaflexivirus-like viruses in Heterobasidion abietinum, and expands the known virosphere of Heterobasidion species in Southeastern European forests. Full article

Drought, a complex and frequent natural hazard in the context of global change, poses a major threat to key forest ecosystems in the carbon cycle. However, current research lacks a systematic and quantitative analysis of the multi-factor drivers of drought sensitivity based on lagged and accumulative effects. To address this gap, a drought sensitivity model was established by integrating both lagged and accumulative effects derived from long-term remote sensing datasets. To leverage both predictive power and interpretability, the XGBoost–SHAP framework was employed to model nonlinear associations and identify the threshold effects of driving factors. In addition, the Geodetector model was applied to examine spatially explicit interactions among multiple drivers, thereby uncovering the coupling effects that jointly shape forest drought sensitivity across China. The results reveal the following: (1) Drought had lagged and accumulative effects on 99.52% and 95.55% of forest GPP, with evergreen broadleaf forest showing the strongest effects and deciduous needleleaf forest the weakest. (2) Evergreen needleleaf forests have the highest proportion of extremely high drought sensitivity (16.94%), while deciduous needleleaf forests have the least (1.02%), and the drought sensitivity index declined in 67.12% of forests over decades. (3) Temperature and precipitation are the primary drivers of drought sensitivity, with clear threshold effects. Evergreen forests are mainly driven by climatic factors, while forest age is a key driver in deciduous needleleaf forests. (4) Interactive effects among multiple factors significantly amplify spatial variations in drought sensitivity, with water–heat coupling dominating in evergreen forests and structure–climate interactions prevailing in deciduous forests. Full article