Search Results (608)

Climate change is reshaping climatic regimes worldwide, with direct consequences for species distributions and ecosystem services, including those provided by wild edible fungi. In Mexico, these fungi represent a resource of ecological, cultural, and economic importance, yet their vulnerability to future climate scenarios remains poorly understood. This study evaluated projected changes in the potential distributions of ten frequently consumed edible fungal species in central Mexico under current and future climate scenarios (2061–2080 and 2081–2100). Ecological niche models were performed using Maxent with 19 bioclimatic variables, spatial block cross-validation, and model tuning based on the AICc and partial ROC curves. Additionally, associations between species suitability and land use and vegetation variables were assessed through multivariate analyses. The most influential predictors were the mean temperature of the warmest quarter (71.929%), temperature seasonality (47.589%), and annual precipitation (41.962%). Current models identify high environmental suitability primarily within the TMVB, Sierra Madre Occidental, and southern mountainous regions such as Chiapas. Future projections revealed heterogeneous, species-specific responses. Suitability gains were projected for Cantharellus cibarius (21–50%), Infundibulicybe gibba (20–34%), Lactarius deliciosus (13–48%), and Lyophyllum decastes (8–141%), whereas Helvella crispa (1–99%), Agaricus campestris (2–88%), and Russula brevipes (74–100%) showed marked contractions under high-emission scenarios. These contrasting patterns suggest that climate change may restructure the spatial availability of edible fungi in Mexico, potentially affecting forest sustainability and the biocultural practices of communities that depend on these resources. Integrating species-specific climatic sensitivity into conservation and sustainable management strategies will be essential under future climate conditions. Full article

Accurate prediction of winter oilseed rape yield is essential for optimising crop management and improving production efficiency. However, the reliability of commonly reported model performance remains uncertain due to the widespread use of random validation strategies. This study evaluated the predictive potential of multi-temporal Normalised Difference Vegetation Index (NDVI) metrics collected between September 2023 and May 2024 for yield estimation across multiple Lithuanian fields, while explicitly addressing spatial generalisation. The analytical dataset comprised dry yield (t ha⁻¹), monthly NDVI, and field identifiers, and underwent quality control, including outlier removal. Four modelling approaches were compared: ordinary least squares (OLS) regression, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and a Deep Neural Network (DNN). Model performance was assessed using both random (80/20) and a spatially independent field-wise (GroupSplit) validation schemes designed to assess model transferability to previously unseen fields, further extended by repeated group-based resampling to quantify variability in model generalisation. Under random sampling, RF and XGBoost achieved the highest accuracy (RMSE ≈ 0.85 t ha⁻¹, R² ≈ 0.55). However, under spatially independent validation, predictive performance declined markedly for all models, with tree-based ensembles showing near-zero R² values, indicating limited transferability to unseen fields. In contrast, the DNN demonstrated more consistent generalisation (RMSE = 1.09 t ha⁻¹, R² = 0.28). Repeated field-wise validation confirmed that performance estimates based on random splits substantially overestimate true predictive capability. Feature importance analyses consistently identified spring NDVI, particularly from March to May, as the dominant predictor of yield, whereas autumn NDVI showed weaker and less consistent relationships with yield. These findings demonstrate that a large portion of the predictive skill reported in NDVI-based yield modelling may arise from spatial information leakage rather than transferable crop-environment relationships. By explicitly quantifying the gap between random and spatial validation, this study provides a more realistic benchmark for model performance and highlights the necessity of spatially robust evaluation frameworks for operational yield prediction in precision agriculture. Full article

Background: Occult lymph node metastasis (LNM) occurs in 30–80% of patients with clinically node-negative papillary thyroid carcinoma (cN0-PTC), partly owing to the limited sensitivity of current preoperative nodal assessment, and may contribute to postoperative recurrence. Conventional sentinel lymph node (SLN) biopsy, typically performed with a single tracer, has limited reliability for detecting occult metastatic nodes, which can result in either overtreatment or undertreatment with lymph node dissection. We aimed to develop a highly accurate multimodal prediction framework to accurately identify second-echelon lymph node metastasis (SeLNM) and non-sentinel lymph node metastasis (NsLNM). Methods: We prospectively enrolled 301 patients with cN0-PTC between April and October 2024, of whom 131 met the inclusion criteria. Intraoperatively, a dual-tracer technique combining carbon nanoparticles and indocyanine green was applied, and near-infrared imaging was used to record the entire SLN visualization process in real time. For each case, a 3 min video clip (150 frames) was captured. Two senior surgeons delineated regions of interest to generate 19,650 mask images. A total of 2048 spatial features and 20 temporal features were extracted, combined with 32 clinical variables, including demographics, ultrasound characteristics, and gene mutation status. Nine deep learning models were developed and evaluated using 10-fold cross-validation. Model performance was quantified using receiver operating characteristic curves, decision curve analysis curves, calibration curves, precision–recall curves, learning curves, and 12 metrics. Statistical comparisons were performed using the DeLong test, and models were further evaluated using a probability-based ranking approach. Shapley Additive Explanations (SHAP) analysis was applied to interpret key predictive features. The primary outcomes were SeLNM and NsLNM, defined based on postoperative histopathology. Results: The Long Short-Term Memory (LSTM) + Transformer model showed the best performance for both prediction tasks, with stable AUCs across training and testing (SeLNM: 0.980/0.982; NsLNM: 0.986/0.983). In the testing set, the model reached the same accuracy for both outcomes (94.7%) and showed strong sensitivity/specificity for SeLNM (94.7%/94.6%) and NsLNM (96.4%/91.5%). SHAP analysis indicated that time-series fluorescence flow features were the most influential predictors, followed by spatial structural features and SLN status. Conclusions: Dual-tracer SLN mapping with deep learning demonstrated encouraging intraoperative prediction of lymph node metastasis with interpretable features in this single-center cohort. Independent multicenter validation and prospective outcome studies are needed before considering clinical adoption. Full article

Coral reefs around Hainan Island in the northern South China Sea represent a marginal reef system exposed to interacting climatic and anthropogenic stresses. This study used an optimized MaxEnt model, remote-sensing-derived coral reef occurrence data, key environmental variables, and CMIP6 climate projections to assess habitat suitability, identify key environmental thresholds associated with suitability change, and examine areas with potential refugial significance. The optimized model showed high predictive performance (mean AUC = 0.947). Bathymetry was the dominant predictor of habitat suitability, while sea surface temperature (SST) and dissolved oxygen (DO) concentration were also important predictors. Predicted suitability declined markedly when water depth exceeded 8.9 m or when multiannual mean SST exceeded 26.8 °C. Under current climate conditions, suitable habitat was limited in extent and showed strong spatial heterogeneity. Future projections indicated severe habitat contraction under SSP2-4.5 and SSP5-8.5, whereas under SSP1-1.9 suitable habitat contracted sharply by the 2050s but partially re-emerged by the 2090s. Under SSP1-1.9, parts of eastern Hainan, especially the coastal waters of southern Wenchang, Qionghai, and Wanning, may retain refugial potential. These results help clarify future spatial patterns of habitat persistence and decline, providing a scientific reference for regional conservation prioritization and adaptive management. Full article

This study evaluates the performance of six feature selection methods (BORUTA, LASSO, RFE, XGBoost, FSM, and SEV) and five predictive modelling techniques (ANN, MARS, RST, SRT, and SVM) for the spatial estimation of municipal waste accumulation rates across 79 districts in the Slovak Republic. Using a 2022 cross-sectional dataset comprising 45 socio-economic and demographic variables, the study focuses on spatial prediction for unseen districts rather than temporal forecasting. Feature selection results indicate that BORUTA, RFE, and XGBoost consistently identify key predictors, notably the share of three-person households, the density of transport and warehousing companies, and average monthly wages. Model robustness was ensured through repeated random sub-sampling (30 iterations, 70/30 split) and validated using the Friedman test with Nemenyi post hoc comparisons (α = 0.05). The highest accuracy was achieved by MARS and ANN models coupled with SEV selection (MAE ≈ 28–30 kg/(person·year), MAPE ≈ 6%, R² > 0.88), and by SVM with XGBoost (MAE ≈ 30 kg/(person·year), R² ≈ 0.90). Reducing the predictor set from ten to five resulted in only minor performance degradation (MAPE increase < 1 pp), confirming the effectiveness of dimensionality reduction. The proposed approach enables accurate, computationally efficient waste generation estimation, thereby supporting regional planning and evidence-based policy development. In a broader context, the findings contribute to the implementation of the European Green Deal and circular economy objectives by providing tools for spatially targeted waste management strategies, directly aligned with United Nations Sustainable Development Goal 11 (Sustainable Cities and Communities) and Goal 12 (Responsible Consumption and Production). Full article

Sustainable groundwater management is essential for water security and human health protection. Fluoride contamination is a serious concern for the sustainable drinking water supply in many parts of Pakistan, including Balochistan, where arid climate conditions and geological formations support the enrichment of fluoride. The toxic nature of fluoride contamination has resulted in negative health impacts on the local population. Conventional geostatistical techniques are usually ineffective to delineate the nonlinear relationships that affect the distribution of fluoride. This study aims to develop a machine learning-driven spatial modelling framework for classifying the spatial distribution of fluoride contamination in groundwater across the study area. The model will help to understand the spatial variability of fluoride contamination and its controlling factors, essential for effective mitigation and early warning systems. Physiochemical elements were used as predictive features in this study, utilizing a unified feature importance framework combining hydrogeochemical analysis, spatial distribution assessment, and ensemble SHAP-based interpretation to identify consistent predictors. Model performance was evaluated using a nested cross-validation framework, followed by validation on an independent geology-informed spatial holdout test set to ensure realistic generalization. Among machine learning models, the Logistic Regression (LR), Support Vector Classifier (SVC), XGBoost (XGB), Decision Tree (DT), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbours (KNN) were evaluated. Support Vector Classifier (SVC) demonstrated a high predictive performance. On the independent spatial holdout dataset, SVC achieved an overall accuracy of 0.75 and an area under the receiver operating characteristic curve (AUC) of 0.821. In addition to classification, a human health risk assessment was conducted using chronic daily intake (CDI) and hazard quotient (HQ) calculations for children and adults, identifying several high-risk water supply schemes. The prediction maps successfully delineated high-risk fluoride points across specific areas, offering a tool for sustainable groundwater management. This study helps to achieve a Sustainable Development Goal (Clean Water and Sanitation, SDG#6) and promotes long-term sustainable planning in water-stressed areas by integrating spatial machine learning mapping and health risk assessment. Full article

Coastal tidal flat soil organic carbon (SOC) is significantly affected by reclamation activities. However, the limited availability of historical SOC data constrains the reconstruction of past SOC. SOC data were integrated in current time-point and remote sensing data during the last two decades by applying machine learning (ML) methods such as random forest (RF), boosted regression trees (BRT), and extreme gradient boosting (XGBoost) to map the spatiotemporal distribution of tidal flat reclamation and the spatial distribution of SOC content in the western coastal region of the Bohai Rim over the last two decades and to explore how the period and type of reclamation affect SOC content. The results show that: (1) The area of tidal flats decreased by 61.92% from 2000 to 2020 due to reclamation activities. (2) Among the ML methods, the XGBoost model demonstrated the best performance (R² = 0.71, MAE = 0.93 g/kg, RMSE = 1.32 g/kg, d-Willmott = 0.98), with the modified normalized difference water index (MNDWI) being the most important predictor variable. (3) The SOC content of tidal flats decreased from 4.11 g/kg in 2000 to 3.33 g/kg in 2020, a reduction of 18.98%. (4) The reclamation of tidal flats into marshes, forest lands, grasslands, farmlands, and bare lands led to an increasing trend in SOC content, with the greatest increase observed in regions converted to farmlands. This study provides data support for the control of reclamation activities, creation of tidal flat conservation policies, and strategic decision-making for climate change mitigation. Full article

Does where an incident happens affect how quickly first responders arrive? Timely emergency responses are important to urban safety. However, the combined influence of street-level environments, operational conditions, and neighborhood contexts on dispatch performance remains unclear. We examined such geographical complexity by modeling geographic predictors for whether emergency vehicles successfully arrived at incidents in the city of Dallas within the city’s eight-minute benchmark. Using 250,647 incidents and 56 million GPS points along emergency dispatch routes in 2016, we compiled fourteen spatial and operational variables for every incident to train a Bayesian-optimized random forest classifier. The fourteen variables characterized street network topology, roadway attributes, land use, and socioeconomic status, and the model achieved an accuracy of 77.26% in predicting whether emergency response arrived at an incident within eight minutes. A longer distance to dispatch stations, dispatching from non-nearest stations, and low street–network integration were the strongest predictors of unsuccessful responses. Higher-income areas showed slightly elevated unsuccessful rates linked to frequent construction-related disruptions. These findings highlight emergency response as a coupled spatial–operational–temporal process and underscore the need for context-sensitive dispatch strategies and coordinated urban planning. Full article

Mediterranean dehesas are open agroforestry systems in which tree configuration and climatic regime condition the organisation of biodiversity. In these landscapes, ants are commonly used as ecological indicators, although the relative importance of local versus regional factors in structuring their communities remains poorly defined. Ant assemblages were sampled using pitfall traps at 15 farms in southern Spain, and the influence of environmental variables defined at two spatial scales was analysed: microhabitat, distinguishing between areas under tree canopy and open areas, and farm as a unit representative of the regional context. The multivariate analyses applied (dbRDA, PERMANOVA and variance partitioning) reveal a hierarchical organisation of community assemblages. At the local scale, community variation was primarily explained by structural attributes of the tree layer, particularly canopy cover and distance to trees. At the farm scale, environmental predictors explained a modest proportion of community variation, with strong overlap among climatic, vegetation and structural variables. Overall, the structure of ant communities in dehesas follows a scale-dependent pattern, in which climate sets the regional framework and tree structure modulates assemblage organisation at a fine scale. Full article

This study evaluated the potential of Sentinel-2-derived NDVI (Normalized Difference Vegetation Index) for predicting within-field variability of winter wheat grain yield in central Lithuania during the 2024 growing season. Reliable within-field yield prediction remains challenging in regions with heterogeneous soils and limited region-specific models, particularly in northeastern Europe. Grain yield data were obtained from combine harvesters equipped with GPS yield monitoring across 13 fields with a total area of 283.6 ha. NDVI values were calculated for four half-monthly periods from March to May, corresponding to key phenological stages (from tillering to spike emergence). Spatial and temporal variability in NDVI–yield relationships was observed, with early May consistently showing the strongest correlations (r up to 0.49), particularly in lower-fertility fields, indicating its critical role in yield prediction. Machine learning models (Random Forest, XGBoost, and Deep Neural Networks), along with linear regression, were applied to predict yields based on NDVI from four growth stages. Random Forest achieved the highest predictive accuracy (MAE = 0.951 t/ha), outperforming the other models. The model also showed the highest correlation with observed yields (Pearson r = 0.717), indicating strong agreement between predicted and measured values. Feature importance analysis confirmed NDVI from 1 to 15 May as the most influential predictor across all models. Predicted yield maps closely matched observed patterns, with the largest discrepancies near field edges due to combine harvester effects. These findings highlight the utility of mid-season NDVI for precise estimation of within-field grain yield variability and demonstrate that Random Forest models can effectively capture the NDVI–yield relationship, particularly under heterogeneous field conditions. Full article

Bovine brucellosis is an endemic zoonotic disease in South Africa with significant consequences for livestock productivity and public health. Although routine laboratory surveillance data from the Rose Bengal Test (RBT) are widely collected, they are seldom used to investigate temporal and spatial patterns of disease detection. This study aimed to examine temporal, seasonal, and spatial predictors of RBT positivity for bovine brucellosis in Mpumalanga Province, South Africa. A retrospective observational study was conducted using routine laboratory records from the Mpumalanga Provincial Veterinary Laboratory between January 2021 and December 2024. The dataset included all bovine serum samples with complete information on testing date, municipality, and RBT results. Laboratory submissions were recorded as batches, defined as groups of serum samples submitted together to the laboratory as part of a single surveillance or investigation event. The primary outcome was batch-level RBT positivity, defined as the presence of at least one RBT-positive serum sample within a submission batch. Temporal (year of testing), seasonal (season of submission), and spatial (local municipality area) variables were evaluated as predictors of RBT positivity using logistic regression models. Mixed-effects logistic regression accounted for the clustering of submissions within municipalities. A total of 568 submission batches comprising 67,974 serum samples were analysed, of which 6182 tested positive, yielding an overall positivity of 9.1%. RBT positivity increased significantly in 2023 compared with 2021 (AOR = 2.47; 95% CI: 2.27–2.68). Seasonal variation was observed, with higher odds of positivity in spring (AOR = 1.80; 95% CI: 1.65–1.97) and lower odds in autumn and winter relative to summer. Mixed-effects modelling indicated significant residual spatial heterogeneity in RBT positivity across municipalities. Routine laboratory surveillance data can provide valuable epidemiological insights into the temporal, seasonal, and spatial dynamics of bovine brucellosis detection and support risk-based surveillance strategies in endemic livestock systems. Full article

Rapid urbanization, environmental degradation and climate variability are intensifying the exposure of urban populations to multiple, interacting hazards in megacities. In India’s capital, Delhi, extreme heat, worsening air quality and flood-related stress overlap in impacted areas, exacerbated by high population density in low-lying zones and extensive built-up cover. This study develops an integrated spatial framework for assessing relative multi-hazard risk potential in Delhi by combining remote sensing, climate reanalysis, land use and demographic datasets into a predictive modeling system to support urban resilience planning. A comprehensive suite of twenty-two predictors representing thermal stress, air quality, surface indices, topography, hydrology, land use land cover (LULC), and demographic data was derived from diverse Earth observation sources. A cloud-native workflow leveraging Google Earth Engine (GEE) and Python 3 harmonized these predictors to train a Light Gradient Boosting Machine (LightGBM) model with five-fold spatial cross-validation. Quantile regression was used to estimate lower (P10) and upper (P90) predictive bounds, which are interpreted here as empirical predictive intervals around the modeled risk surface rather than as a strict separation of different uncertainty types, while SHapley Additive exPlanations (SHAP) decomposed the non-linear contributions of individual features. The model achieved predictive accuracy (R² = 0.98, MAE = 0.01), with residuals centered near zero and consistent performance across spatial folds, demonstrating strong generalizability. Road density (63.4%) and population density (25.9%) emerged as the primary predictors of the modeled risk surface, followed by building density and NO₂ concentration. Conversely, vegetation cover (NDVI) functioned as a critical mitigating buffer. Spatial risk maps identified persistent high-risk clusters in eastern and northeastern Delhi, coinciding with dense transport networks and industrial zones. The integrated P90 mapping framework provides spatially explicit and uncertainty-aware information on relative multi-hazard risk potential to guide targeted interventions, such as transport corridor mitigation and urban greening in Delhi and other rapidly urbanizing cities. Full article

Urban road collapses are characterized by sudden occurrence and strong spatial heterogeneity, posing substantial challenges for proactive infrastructure management. Susceptibility mapping can provide spatially explicit evidence to support targeted inspection and early-warning strategies. Using Futian District, Shenzhen (China) as a case study, a total of 315 road collapse events recorded during 2019–2023 were compiled to develop an integrated framework for urban road collapse relative susceptibility mapping based on multi-source remote sensing and urban spatial data. First, an indicator-based susceptibility index (SI) was constructed using eight conditioning factors, including PS-InSAR-derived deformation, topographic–hydrological conditions, and distance-based infrastructure variables (distance to underground utilities, metro lines, and roads). Factor weights were determined by coupling the Analytic Hierarchy Process (AHP) with the Entropy Weight Method (EWM), producing a comprehensive SI for historical collapse locations. Subsequently, a set of 17 remote-sensing predictors, including Sentinel-2 spectral bands, Sentinel-2 GLCM texture features, and Sentinel-1 SAR backscatter variables, was used to train a Random Forest model to predict SI and generate continuous susceptibility maps at the urban road-network scale. The influence of neighborhood window size on predictive performance was systematically evaluated. Results show that the Random Forest model performed best at the 5 × 5 window scale (R² = 0.70, RMSE = 0.0172, MAE = 0.0122), outperforming both pixel-based inputs (1 × 1) and larger windows. Uncertainty analysis further indicated that the 5 × 5 RF configuration yielded the most stable and spatially coherent predictions, whereas overly small windows and less robust learners produced more fragmented or higher-uncertainty susceptibility patterns. Spatiotemporal analysis indicates that susceptibility patterns remained broadly stable from 2019 to 2023, with moderate susceptibility accounting for 50.82–57.89% and high susceptibility for 21.94–23.30%, while very high susceptibility consistently remained below 1%. Overall, this study demonstrates that integrating multi-source remote sensing with scale-optimized machine learning provides an effective approach for fine-scale susceptibility mapping of urban road collapses, offering practical guidance for differentiated monitoring and risk prevention along critical road corridors. Full article

Stephania tetrandra is a medicinal plant with ecological, germplasm, and economic value whose wild resources are increasingly constrained by overexploitation and climate change. To support conservation planning and sustainable cultivation, we quantified current and future potential habitat suitability across China using an ensemble species distribution modeling (SDM) framework and translated the outputs into climate-based priority areas for protection, germplasm safeguarding, monitoring, and phased cultivation trials. Occurrence records were compiled from multiple sources and preprocessed via cleaning and spatial thinning to reduce sampling bias. Current predictors were derived from WorldClim (1970–2000) and complemented with topographic and edaphic variables; future climates were represented by CMIP6 projections for the 2050s, 2070s, and 2090s under SSP1-2.6, SSP2-4.5, and SSP5-8.5. Multiple algorithms were trained in a consistent cross-validation workflow and filtered using AUC (ROC) and TSS before generating a weighted ensemble (EMwmean). Current projections indicate a well-defined suitability core in the humid subtropical monsoon region south of the Yangtze River. Nationally, high-, moderate-, and low-suitability areas were estimated at 51.90 × 10⁴ km², 22.95 × 10⁴ km², and 31.05 × 10⁴ km², respectively. Future impacts are dominated by suitability-grade reallocation rather than a collapse of total suitable extent. Under SSP5-8.5 in the 2090s, high suitability declines to 13.32 × 10⁴ km² (≈74% reduction), accompanied by contraction of stable habitat (48.95 × 10⁴ km²) and expansion of loss areas (33.64 × 10⁴ km²), while gains remain limited (4.30 × 10⁴ km²). Extrapolation diagnostics (Multivariate Environmental Similarity Surface, MESS; Most Dissimilar Variable, MoD) highlight elevated uncertainty in northwestern arid/high-elevation and strongly seasonal transition zones. Environmental-space niche overlap decreases moderately (Schoener’s D = 0.51–0.67), indicating niche displacement and a narrowing suitability window. These results represent potential climatic habitat suitability rather than guaranteed future occupancy. They support prioritizing in situ protection and germplasm safeguarding in areas that are currently highly suitable and remain comparatively stable under future climates, while treating marginal gain zones as candidates for monitoring and carefully phased cultivation or introduction trials. Full article

Background and Objectives: Accurate prediction of lip prominence changes following orthodontic treatment remains challenging because traditional profile analyses rely on isolated reference lines that do not account for combined nasal and chin morphology. The aesthetic triangle framework integrates these structures and may provide a more comprehensive evaluation of lip position. Materials and Methods: This correlative clinical study evaluated 82 orthodontic patients undergoing bimaxillary incisor repositioning. Lateral cephalograms and standardized profile photographs were obtained before and after treatment. Lip position was assessed relative to the aesthetic triangle boundaries, and dentoalveolar changes were quantified using standard incisor measurements. Lip thickness was also analyzed as a potential modulating factor. Results: Mandibular incisor inclination demonstrated a moderate positive correlation with anterior displacement of the lower lip within the aesthetic triangle (Pearson r = 0.45, p < 0.01). Multiple linear regression analysis confirmed IMPA as a significant predictor of lower lip migration (β = 0.41), explaining approximately 21% of the observed variance (R² = 0.21). In contrast, maxillary incisor inclination (U1–SN) showed weaker and statistically inconsistent associations with upper lip position. Compartment analysis revealed that approximately 32% of patients exhibited anterior migration of the lower lip from the posterior to the central aesthetic triangle compartment following treatment. These findings suggest that mandibular incisor inclination exerts a measurable influence on lower lip prominence, whereas upper lip positional changes appear to be less directly related to maxillary incisor variables. Conclusions: The aesthetic triangle provides a clinically meaningful framework for interpreting orthodontic soft-tissue changes as spatial migration rather than isolated linear measurements. Lower lip prominence responds predictably to dentoalveolar mechanics, whereas upper lip position also depends on soft tissue morphology. Full article