Search Results (1,614)

Accurately assessing the impact of land use patterns on total phosphorus (TP) concentration in surface water is crucial for protecting the water environment of the Yangtze River Basin (YRB). However, due to the heterogeneity of land use patterns, the regional differences in the intensity and direction of their impacts on TP concentrations in the YRB remain insufficiently understood. This study utilizes water quality monitoring data from state-controlled sections in 2021 and employs spatial autocorrelation analysis, geographic detectors, and Pearson correlation models to identify the impacts of land use on TP concentrations at multiple scales across the YRB. The results indicate that TP concentrations at 98.8% of the monitoring stations in the YRB exceed the Class III standard, with high concentrations of TP concentrated in the Pudu River Basin, Chengdu Plain, Jianghan Plain, and Yangtze River Delta regions. At the YRB scale, the spatial pattern of built-up land, cropland, and industrial and mining land significantly increases TP concentrations, while the pattern of forest and grassland areas exert mitigating effects. At the sub-basin scale, the impact of land use patterns on TP concentrations varies regionally. Specifically, TP concentrations in the Pudu River Basin are primarily attributed to the spatial pattern of industrial and mining land, in the Chengdu Plain to the spatial pattern of cropland and industrial and mining land, and in the Jianghan Plain to the spatial pattern of cropland, built-up land, and industrial and mining land. These results provided decision support for TP concentration control strategies and effective mitigation measures. Full article

This study introduces a novel evaluation framework for predicting web page performance, utilizing state-of-the-art machine learning algorithms to enhance the accuracy and efficiency of web quality assessment. We systematically identify and analyze 59 key attributes that influence website performance, derived from an extensive literature review spanning from 2010 to 2024. By integrating a comprehensive set of performance metrics—encompassing usability, accessibility, content relevance, visual appeal, and technical performance—our framework transcends traditional methods that often rely on limited indicators. Employing various classification algorithms, including Support Vector Machines (SVMs), Logistic Regression, and Random Forest, we compare their effectiveness on both original and feature-selected datasets. Our findings reveal that SVMs achieved the highest predictive accuracy of 89% with feature selection, compared to 87% without feature selection. Similarly, Random Forest models showed a slight improvement, reaching 81% with feature selection versus 80% without. The application of feature selection techniques significantly enhances model performance, demonstrating the importance of focusing on impactful predictors. This research addresses critical gaps in the existing literature by proposing a methodology that utilizes newly extracted features, making it adaptable for evaluating the performance of various website types. The integration of automated tools for evaluation and predictive capabilities allows for proactive identification of potential performance issues, facilitating informed decision-making during the design and development phases. By bridging the gap between predictive modeling and optimization, this study contributes valuable insights to practitioners and researchers alike, establishing new benchmarks for future investigations in web page performance evaluation. Full article

Ecosystem functioning and management are primarily concerned with addressing climate change and biodiversity loss, which are closely linked to carbon stock and species diversity. This research aimed to quantify forest understory (shrub and herb) diversity, tree biomass and carbon sequestration in the Binsar Wildlife Sanctuary. Using random sampling methods, data were gathered from six distinct forest communities. The study identified 271 vascular plants from 208 genera and 74 families. A notable positive correlation (r² = 0.085, p < 0.05) was observed between total tree density and total tree basal area (TBA), shrub density (r² = 0.09), tree diversity (D) (r² = 0.58), shrub diversity (r² = 0.81), and tree species richness (SR) (r² = 0.96). Conversely, a negative correlation was found with the concentration of tree dominance (CD) (r² = 0.43). The Quercus leucotrichophora, Rhododendron arboreum and Quercus floribunda (QL-RA-QF) community(higher altitudinal zone) exhibited the highest tree biomass (568.8 Mg ha⁻¹), while the (Pinus roxburghii and Quercus leucotrichophora) PR-QL (N) community (lower altitudinal zone) in the north aspect showed the lowest (265.7 Mg ha⁻¹). Carbon sequestration was highest in the Quercus leucotrichophora, Quercus floribunda and Rhododendron arboreum (QL-QF-RA) (higher altitudinal zone) community (7.48 Mg ha⁻¹ yr⁻¹) and lowest in the PR-QL (S) (middle altitudinal zone) community in the south aspect (5.5 Mg ha⁻¹ yr⁻¹). The relationships between carbon stock and various functional parameters such as tree density, total basal area of tree and diversity of tree showed significant positive correlations. The findings of the study revealed significant variations in the structural attributes of trees, shrubs and herbs across different forest stands along altitudinal gradients. This current study’s results highlighted the significance of wildlife sanctuaries, which not only aid in wildlife preservation but also provide compelling evidence supporting forest management practices that promote the planting of multiple vegetation layers in landscape restoration as a means to enhance biodiversity and increase resilience to climate change. Further, comprehending the carbon storage mechanisms of these forests will be critical for developing environmental management strategies aimed at alleviating the impacts of climate change in the years to come. Full article

The ecosystem water use efficiency (WUE) plays a critical role in many aspects of the global carbon cycle, water management, and ecological services. However, the response mechanisms and driving processes of WUE need to be further studied. This research was conducted based on Gross Primary Productivity (GPP), Evapotranspiration (ET), meteorological station data, and land use/cover data, and the methods of Ensemble Empirical Mode Decomposition (EEMD), trend variation analysis, the Mann–Kendall Significant Test (M-K test), and Partial Correlation Analysis (PCA) methods. Our study revealed the spatio-temporal trend of WUE and its influencing mechanism in the Yellow River Basin (YRB) and compared the differences in WUE change before and after the implementation of the Returned Farmland to Forestry and Grassland Project in 2000. The results show that (1) the WUE of the YRB showed a significant increase trend at a rate of 0.56 × 10⁻² gC·kg⁻¹·H₂O·a⁻¹ (p < 0.05) from 1982 to 2018. The area showing a significant increase in WUE (47.07%, Slope > 0, p < 0.05) was higher than the area with a significant decrease (14.64%, Slope < 0, p < 0.05). The region of significant increase in WUE in 2000–2018 (45.35%, Slope > 0, p < 0.05) was higher than that of 1982–2000 (8.23%, Slope > 0, p < 0.05), which was 37.12% higher in comparison. (2) Forest WUE (1.267 gC·kg⁻¹·H₂O) > Cropland WUE (0.972 gC·kg⁻¹·H₂O) > Grassland WUE (0.805 gC·kg⁻¹·H₂O) under different land cover types. Forest ecosystem WUE has the highest rate of increase (0.79 × 10⁻² gC·kg⁻¹·H₂O·a⁻¹) from 2000 to 2018. Forest ecosystem WUE increased by 0.082 gC·kg⁻¹·H₂O after 2000. (3) precipitation (37.98%, R > 0, p < 0.05) and SM (10.30%, R > 0, p < 0.05) are the main climatic factors affecting WUE in the YRB. A total of 70.39% of the WUE exhibited an increasing trend, which is mainly attributed to the simultaneous increase in GPP and ET, and the rate of increasing GPP is higher than the rate of increasing ET. This study could provide a scientific reference for policy decision-making on the terrestrial carbon cycle and biodiversity conservation. Full article

In the southern taiga of Siberia, periodic outbreaks of the Siberian moth Dendrolimus sibrircus Tschetv. have been observed. The outbreaks result in the defoliation of Siberian fir Abies sibirica Ledeb. and Siberian pine Pinus sibirica Du Tour. stands across approximately one million hectares, leading to dieback of the affected forests. This is largely attributable to the inability to promptly identify the onset of the pest population growth in a timely manner, particularly in the context of expansive forest areas with limited accessibility. It is feasible to enhance the efficacy of monitoring Siberian moth populations by discerning stands with the highest propensity for damage and concentrating efforts on these areas. To achieve this, we employed machine learning techniques, specifically gradient boosting, support vector machines, and decision trees, training models on two sets of predictors. One of the datasets was obtained through a field study conducted in forest stands during the previous outbreak of the Siberian moth (2015–2018), while the other was derived from the analysis of remote sensing data during the same period. In both 2015 and 2016, the defoliation was most accurately predicted using gradient boosting (XGB algorithm), with ROC-AUC values reaching 0.89–0.94. The most significant predictors derived from the ground data were the proportions of Siberian fir, Siberian spruce Picea obovata Ledeb., and Scots pine Pinus sylvestris L., phytosociological data, tree age, and site quality. Among the predictors obtained from the analysis of remote sensing data, the distance to disturbed forest stands was identified as the most significant, while the proportion of dark coniferous species (A. sibirica, P. sibirica, or Picea obovata Ledeb.), the influx of solar radiation (estimated through the CHILI index), and the position in the relief (mTPI index) were also determined to be important. Full article

Fertilization can improve soil nutrition and increase the yield of Sapindus mukorossi, but the response of soil microbial communities to fertilization treatments and their correlation with soil nutrition and Sapindus mukorossi yield are unclear. In order to investigate the characteristics of soil physicochemical qualities and the bacterial community, we carried out a field experiment comparing various quantities of nitrogen (N), phosphorus (P), and potassium (K) fertilizers to the unfertilized control treatments and the yield of Sapindus mukorossi in raw material forests in response to different applications of fertilizers and to try to clarify the interrelation among the three. Results showed that (1) there are significant differences in the effects of different fertilization treatments on the soil properties of Sapindus mukorossi raw material forests. The increase in the application rates of nitrogen or phosphorus fertilizers significantly reduced the soil pH value. (2) Compared with control, the α-diversity of bacterial communities was significantly lower in N₃P₂K₂ and N₁P₁K₂ treatments. Among the dominant groups of soil bacteria at the phylum level, the relative abundance of Chloroflexi showed an increase and then a decrease trend with the increase in N application. The relative abundance of Firmicutes, Bacteroidota, and Fusobacteriota was positively correlated with the application of P and K fertilizers, while the relative abundance of Acidobacteriota and Verrucomicrobiota decreased with the increase in P and K fertilizers. (3) The N₂P₂K₂ treatment produced the highest sapindus yield (1464.58 kg/ha), which increased by 258.67% above the control. (4) Redundancy analysis (RDA) showed that the primary determinants of bacterial community structure were soil pH, total K, and effective P concentration. (5) Structural equation modeling (SEM) showed that soil nutrient content was the main direct factor driving the yield of Sapindus mukorossi, whereas the bacterial community attributes (e.g., diversity and structure) had minor effects on the yield. In summary, the rational use of formulated fertilization can change the bacterial community structure, improve the bacterial diversity, and increase the soil nutrient content, with the latter exerting a significant effect on the improvement of the yield of Sapindus mukorossi. Full article

Empirically based approaches, like the Universal Soil Loss Equation (USLE), are appropriate for estimating mass movement attributed to rill erosion. USLE and its associates become widespread even in spatially extended studies in spite of its original plot-level concept, as well as with certain constraints on the supply of suitable input spatial data. At the same time, there is a continuously expanding opportunity and offer for the application of remote sensing (RS) imagery together with machine learning (ML) techniques to model and monitor various environmental processes utilizing their versatile benefits. The present study focused on the applicability of data-driven geospatial models for predicting soil erosion in three vineyards in the Upper Pannon Wine Region, Central Europe, considering the seasonal variation in influencing factors. Soil loss was formerly modeled by USLE, thus providing non-observation-based reference datasets for the calibration of parcel-specific prediction models using various ML methods (Random Forest, eXtreme Gradient Boosting, Regularized Support Vector Machine with Linear Kernel), which is a well-established approach in digital soil mapping (DSM). Predictions used spatially exhaustive, auxiliary, and environmental covariables. RS data were represented by multi-temporal Sentinel-2 satellite imagery data, which were supplemented by (i) topographic covariates derived from a UAV-based digital surface model and (ii) digital primary soil property maps. In addition to spatially quantifying soil erosion, the feasibility of transferring the inferred models between nearby vineyards was tested with ambiguous outcomes. Our results indicate that ML models can feasibly replace the empirical USLE model for erosion prediction. However, further research is needed to assess model transferability even to nearby parcels. Full article

Species belonging to the Pezizales are mainly saprobes in nature. They are most commonly observed in woodlands and humid environments. As a result of recent research conducted on the distribution of species in sandy areas and some National Forests Parks, five new species belonging to three genera were identified. A total of five species of disk fungi from Northeast China were identified and described based on morphological classification and molecular phylogenetics. These included Pulvinula (Pulvinula elsenensis, Pulvinula sublaeterubra), Microstoma (Microstoma jilinense, Microstoma changchunense), and Sarcoscypha (Sarcoscypha hongshiensis). Maximum likelihood and Bayesian analyses were performed using a combined nuc rDNA internal transcribed spacer region (ITS) and nuc 28S rDNA (nrLSU) dataset for the construction of phylogenetic trees. Morphological descriptions, line illustrations, and photographs of the ascocarps of these new species are provided, along with lists of the salient attributes exhibited by the species in the three genera under consideration. Full article

The key to geospatial data integration lies in identifying corresponding objects from different sources. Aiming at the problem of the low matching accuracy of geospatial entities under a single feature attribute, a geospatial entity matching method based on multi-feature value calculation is proposed. Firstly, when dealing with POI (point of interest) data, the similarity of POI data in terms of name, address, and distance is calculated by combining the improved hybrid similarity method, the Jaccard method, and the Euclidean metric method. Secondly, the random forest algorithm is utilized to dynamically determine the information weights of each attribute and calculate the comprehensive similarity. Finally, taking the area within the Second Ring Road in Beijing as the experimental area, the POI data of Tencent Maps and Amap are collected to verify the method proposed in this paper. The experimental results show that, compared with the existing POI matching methods, the accuracy and recall rate of the results obtained by the POI matching and fusion method proposed in this paper are significantly improved, which verifies the accuracy and feasibility of the matching. Full article

Research Highlights: We report a 45-year time-line of forest restoration after harvesting and responses of red-backed voles (Myodes gapperi), an indicator species of closed-canopy forests. Background and Objectives: We have a unique long-term window to test four hypotheses that evaluated the relationship of M. gapperi with old forest structural attributes. Materials and Methods: The study began in old-growth lodgepole pine (Pinus contorta var. latifolia) through clearcutting, regeneration, stand thinning, fertilization, and growth to mature forest (1979 to 2024) in southern BC, Canada. Populations of red-backed voles were monitored in all phases of forest restoration. Results: Clearcutting resulted in the extirpation of M. gapperi followed by small (≤2 voles/ha) populations in young (13–23 years) thinned and fertilized stands. At age 33, the mean annual abundance of M. gapperi (6.5 to 8.7/ha) was highest in the heavily thinned and old-growth stands. At age 43, mean numbers of M. gapperi ranged from 2.7 to 4.2/ha in these same stands. Heavily thinned stands had large trees, multi-layered canopies of conifers, and understory patchiness. Conclusions: This is the first report of long-term responses of M. gapperi to the restoration of mature forest. M. gapperi is a suitable species for monitoring the recovery of some old forest structural features. Full article

Global climate change projections highlight the Mediterranean Basin as one of the most susceptible areas to the effects of intense and prolonged droughts, as well as increasing air temperatures. Accordingly, the productivity and survival of forests in this area will depend on their ability to resist and adapt to increasingly drier conditions. Different climatic conditions across the Mediterranean Basin could drive differences in forest functioning, requiring trees to acclimate to them. Sea breeze dynamics along orographic valleys can also influence climatic conditions, accentuating differences between inland and coastal forests. However, there is limited information on whether the climatic factors regulating tree transpiration in Aleppo pine forest in orographic valleys vary according to climate. This study aims to identify and compare the climatic factors that regulate tree transpiration along a gradient and determine the thresholds at which these factors affect transpiration rates. This study was carried out by means of sap flow gauges, since this technique is a key feature for quantifying and understanding tree transpiration. It was conducted in two Aleppo pine dry sub-humid forests (inland and coastal, 750 and 675 trees ha⁻¹, respectively) and in two pine semi-arid forests (inland and coastal, 600 and 400 trees ha⁻¹, respectively) in the western Mediterranean basin during January–November of 2021. No significant rainfall events or droughts were recorded during the period of study, indicating a standard climatic condition in these areas. The main findings demonstrated that the variability in sap flow could be attributed to the interaction between soil water content and vapour pressure deficit in all the forests studied. However, the highest threshold values of these climatic factors in relation to the increase or decrease in maximum sap flow (i.e., less sensitivity) were exhibited in semi-arid forests, highlighting the adaptability of Aleppo pine to more limiting climatic conditions. These findings are relevant for the consequences of the predicted increase in harsh climatic conditions and the balance among vapour pressure deficit, temperature and soil water availability. Future research will be essential to confirm forest acclimatisation in the transitional dry to semi-arid forest ecosystems predicted by global climate change projections, given their potential to strongly alter ecosystem function and water cycles. Full article

The Amazon ecosystem plays a vital role in global climate regulation and biodiversity conservation but faces escalating threats from deforestation and degradation. The resulting secondary forests (SFs) provide a promising opportunity for Transformative Territorial Management, fostering restoration and enhancing conservation values. This study evaluated aboveground biomass (AGB), species diversity, forest structure, and soil properties in SFs of the Colombian Amazon along a chronosequence, from early to mature successional stages, in landscapes of mountains and of hills to identify key indicators for effective restoration management. The results show a consistent increase in AGB, species diversity, forest structure, and soil quality with forest age, though recovery patterns varied between both landscapes evaluated. Topographic differences influenced successional dynamics, with mountainous landscapes showing faster early recovery compared to the steadier, linear growth observed in hill areas. In hills, AGB at 10 years reached 12.65% of the biomass expected in a mature forest, increasing to nearly 42% by 40 years of abandonment, at a rate of 0.708 Mg C ha⁻¹ year⁻¹. In contrast, in the mountain landscape, AGB at 10 years reached approximately 8.35% of the carbon in a mature forest and increased to nearly 63.55% at 40 years. Forest age and soil properties emerged as primary drivers of AGB recovery, while diversity and forest structure played indirect but significant roles. In hill areas, soil conservation practices are critical for maintaining steady growth, whereas mountain regions benefit from assisted natural regeneration (ANR) to accelerate recovery. These findings highlight the importance of prioritizing the management of SFs as a central strategy for achieving restoration goals. Such practices are essential to enhance the ecological resilience of SFs and ensure their long-term sustainability, fostering their role as key contributors to restoration efforts and the provision of ecosystem services. Full article

This study aims to identify the best deep learning and machine learning models to identify the unethical behavior patterns of learners using distance education exam data of an educational institution. One hundred twenty-nine online exam data were analyzed by the researcher with three different scenarios to reveal the best model performance in regression and classification. For regression and classification, deep neural network (DNN) from deep learning algorithms and support vector machine (SVM), decision trees (DTs), k-nearest neighbor (KNN), random forest (RF), logistic regression (LR), and extreme gradient boosting (XGBoost) algorithms from machine learning algorithms were used. In the regression analysis conducted within the scope of Scenario-1, the model we proposed to detect “cheating” behavior, which is one of the unethical learner behaviors, was found to be a 5-layer DNN model with a test performance success of 80.9%. In the binary classification analysis for Scenario-2, students who “copied” from unethical behaviors were obtained with an accuracy rate of 96.9% by the model established by the 10-layer DNN algorithm we proposed. In the triple classification analysis for Scenario-3 defined in the study, the XGBoost model was found to have the highest accuracy rate of 97.7% for students who “cheated” due to unethical behaviors and the highest performance in all other metric values. In addition, SHAP and LIME methods, which are explanatory methods for the XGBoost model, which is one of the best-performing models, were applied, and the attributes and percentages affecting the model were shared. As a result of this study, it has been shown that the application of the most appropriate layer functions and parameter selection that will increase performance can be effective in estimating complex problems and target values that cannot be solved using classical mathematical models. The proposed models can provide educational institutions with a roadmap and insight in evaluating online examination practices and ensuring academic integrity. Future researchers may need more data sets and different analyses for better performance of the established models. Full article

The determination of diameter at breast height (DBH) is critical in forestry, serving as a key metric for deriving various parameters, including tree volume. Light Detection and Ranging (LiDAR) technology has been increasingly employed in forest inventories, and the development of cost-effective, user-friendly smartphone and tablet applications (apps) has expanded its broader use. Among these are augmented reality (AR) apps, which have already been tested on mobile devices for their accuracy in measuring forest attributes. In February 2024, Apple introduced the Mixed-Reality Interface (MRITF) via the Apple Vision Pro (AVP), offering sensor capabilities for field data collection. In this study, two apps using the AVP were tested for DBH measurement on 182 trees across 22 sample plots in a near-natural forest, against caliper-based reference measurements. Compared with the reference measurements, both apps exhibited a slight underestimation bias of −1.00 cm and −1.07 cm, and the root-mean-square error (RMSE) was 3.14 cm and 2.34 cm, respectively. The coefficient of determination (R²) between the reference data and the measurements obtained by the two apps was 0.959 and 0.978. The AVP demonstrated its potential as a reliable field tool for DBH measurement, performing consistently across varying terrain. Full article

Intercropping has the potential to improve phosphorus (P) uptake and crop growth, but the potential benefits and relative contributions of root morphology and arbuscular mycorrhizal fungi (AMF) colonization are largely unknown for the intercropping of rice and soybean under dry cultivation. Both field and pot experiments were conducted with dry-cultivated rice (Oryza sativa L.) and soybean (Glycine max L. Merr.) grown alone or intercropped under two P levels. Two root separation modes between rice and soybean were employed to explore the contribution of AMF association and root plasticity on P uptake in intercrops. The results showed that rice/soybean intercropping resulted in a notable increase in the total biomass and yield compared to monoculture in the field. In the potted experiment, compared to the plastic root separation treatment (PS), the no root separation treatment (NS) increased the total biomass and P uptake by 9.4% and 19.9%, irrespective of the P levels. This was primarily attributable to a considerable enhancement in biomass and phosphorus uptake in soybean by 40.4% and 49.7%, which offset a slight decline in the rice of NS compared to PS by 26.8% and 18.0%, respectively. The results of random forest analysis indicate that the P uptake by the dominant species, soybean, was mainly contributed by root morphology, while rice was more dependent on AMF colonization in the intercropping system. Therefore, dry-cultivated rice/soybean intercropping enhances P uptake and productivity by leveraging complementary belowground strategies, with soybean benefiting primarily from root morphological adjustments and rice relying more on arbuscular mycorrhizal fungi colonization. Full article