Search Results (1,226)

Reliable estimation of the compression index (Cc) is essential for settlement assessment, yet geotechnical databases often contain incomplete soil-index measurements. This study developed a missingness-aware heterogeneous ensemble that combined masked variables with binary availability indicators to predict Cc across eight predefined incomplete-input scenarios. The database comprised 1524 marine-clay specimens from eight coastal sites in South Korea. Five sites were used for model development and internal testing, while three sites were reserved for independent testing. A 12-dimensional representation allowed five artificial neural network seed models and four tree-based learners to process all scenarios using validation-derived weights. The proposed model achieved mean root mean square errors of 0.166 and 0.177 in the internal and independent tests, with corresponding coefficients of determination of 0.772 and 0.723. In the internal test, the proposed model produced more favorable point-estimate metric values than the case-specific artificial neural network and random forest baselines in all 32 comparisons and than XGBoost in 30 comparisons. The corresponding differences were less consistent in the independent test, and only six of the 24 unadjusted bootstrap confidence intervals remained entirely below zero. The framework provides a unified tool for preliminary Cc screening under the predefined incomplete-input scenarios evaluated in this study. Full article

Understanding the distribution of Emys orbicularis in Andalusia is essential for assessing its conservation status within one of the most fragmented sectors of its Iberian range. In this study, we applied a modelling framework integrating environmental favourability, spatial structure, and their intersection to identify the factors determining the species’ presence across the Andalusian territory. All presence records were obtained from field surveys and long-term monitoring programmes conducted by the authors, while environmental and spatial predictors were compiled from publicly available databases and processed following the modelling methodology developed by Santoro et al. A consistent pattern emerged across all models: E. orbicularis is strongly associated with forested riparian systems, stable aquatic habitats, and low levels of disturbance, while its distribution is constrained by a marked north–south gradient associated with the Sierra Morena mountain range. The species also shows a historical and continuous presence in Huelva province, including coastal areas, and traditionally occupied parts of the western Betic systems and the Cádiz coastline. Integrating environmental and spatial components allowed us to capture both habitat suitability and the spatial processes limiting the species’ presence. The intersection model, which synthesises the results of both analyses, provided the most reliable representation of the species’ potential distribution by reducing false positives and highlighting core areas of persistence associated with hydrological stability and landscape connectivity. We also observed a marked reduction in the area of occupancy derived from the records, consistent with the initial hypothesis regarding the species’ current status. Full article

Wellness resorts often market natural scenery without knowing whether a structured way of attending to it changes guests’ immediate experience. We examined cloud noticing and forest-therapy elements as two low-infrastructure forms of nature-directed hospitality programming. This exploratory, randomised field study reports outcomes from three facilitator-led arms of a four-condition parent experiment whose service-evaluation findings were published separately. The parent experiment allocated 120 adult resort guests equally, by individual manual lottery, to cloud noticing, forest-therapy elements, an active-control session, or an ordinary stay. The present complete-case dataset comprised 84 participants with linked measurements immediately before and after an organised activity: cloud noticing (n = 29), forest-therapy elements (n = 28), and active control (n = 27). The ordinary-stay group was outside the analytical scope because no additional session, and therefore no matched session-level measurement interval, was available. Linear mixed-effects models estimated group-by-time contrasts for state anxiety, positive and negative affect, and subjective restoration; perceived stress, connectedness with nature, six activity-specific ratings, resting pulse, and blood pressure were secondary or descriptive variables. Both nature-focused sessions showed more favourable short-term psychological patterns than the active control, with the clearest contrasts for subjective restoration. Cloud noticing was associated descriptively with awe, mental spaciousness, and perspective distancing, whereas forest-therapy elements were associated with sensory immersion, bodily presence, grounding, and the greatest increase in nature connectedness. Pulse and blood pressure are reported only as physiological field descriptors. The earlier companion publication examined experience quality, satisfaction, novelty, added value, and recommendation intention; none of those endpoints are reanalysed here. Applicability is limited by the single-resort setting, lack of blinding, complete-case analysis, absence of preregistration, and lack of follow-up. Full article

Early detection of tree-seedling establishment is essential for monitoring regeneration success in coastal-dune plantations, where conventional field assessments remain labour-intensive and spatially limited. This study presents a deep-learning workflow for detecting early-stage Pinus pinaster seedlings using multispectral UAS-derived point clouds. Field surveys in the Quiaios National Forest, Portugal, mapped approximately 1500 seedlings using RTK GNSS positioning, biometric measurements, and field photographs. Multispectral imagery acquired with a DJI Mavic 3 Multispectral platform was processed through Structure-from-Motion to generate calibrated orthomosaics, terrain products, and dense point clouds. Training-data preparation combined pine-centred buffers, spectral conditioning, manual refinement and point-cloud class assignment. Point Transformer V3 models were trained in ArcGIS Pro and evaluated using field-mapped buffers withheld from model training within plantation-line areas. The Baseline high-recall model achieved 88% object-level recall at the operational threshold of at least three classified Pine-Seedling points per buffer. The refined hard-negative model retained 84% recall while reducing off-buffer detections from 243 to 41. False-negative analysis showed that omissions were associated with reduced crown diameter and limited branch development under the adopted buffer-based retrieval framework. These results support transformer-based multispectral point-cloud classification for scalable monitoring of early-stage pine regeneration in heterogeneous coastal environments. Full article

Accurate estimation of global horizontal irradiance (GHI) is relevant for characterizing solar resources in regions with limited measurement infrastructure. This study compared six machine learning models Polynomial Ridge Regression, Decision Tree, Random Forest, XGBoost, Artificial Neural Network, and K–Nearest Neighbors for the contemporaneous estimation of GHI at a five-minute resolution in an arid coastal climate. After quality control and restriction to daytime periods, 45,024 observations were analyzed using five external chronological blocks with an expanding-window scheme, generating 31,517 out-of-sample estimates. XGBoost achieved the highest $R^2$$(0.657\pm 0.192)$ and the lowest RMSE (108.01 ± 13.67 W m⁻²), whereas Random Forest yielded lower MAE, WMAPE, and MASE values. DM–HAC sensitivity analysis favored XGBoost under squared-error loss for six of the seven evaluated bandwidths, whereas no significant difference between XGBoost and Random Forest was found under absolute-error loss. None of the models achieved the nominal conformal coverage level of 90%; XGBoost showed the highest empirical coverage and the narrowest prediction intervals (PICP = 0.791; PINAW = 0.312). Predictor-set reduction improved the performance of four of the six algorithms. Overall, XGBoost and Random Forest exhibited complementary performance profiles, indicating that model selection should jointly consider predictive accuracy, temporal stability, predictor sensitivity, and uncertainty. Full article

Marine viruses are important components of microbial communities and influence their structure and dynamics, yet their variability remains poorly documented in the northwestern Arabian Gulf. Virus-like particle (VLP) abundance was monitored over four years at three coastal sites representing different environmental settings in Kuwait. Surface and depth samples were collected during each sampling period, along with physicochemical and nutrient data. VLP abundance fluctuated throughout the study and differed among sites and between depths. VLP counts tended to be higher at the southern site and near the surface, while the lowest values were recorded in Kuwait Bay. The three sites had distinct environmental characteristics, but relationships between VLP abundance and individual physicochemical variables varied among sites and seasons. When environmental, spatial, and temporal variables were considered together, Random Forest identified season, dissolved oxygen, and nutrients, particularly nitrate, among the important predictors of VLP abundance. Dissolved oxygen showed a nonlinear association with VLP abundance in the generalized additive model (GAM), and lower abundance at depth remained significant after accounting for the measured environmental variables, while site effects were not significant in this model. Overall, VLP abundance showed marked spatial and temporal variability across Kuwait coastal waters, with patterns associated with multiple environmental and seasonal factors. This four-year record provides a baseline for viral abundance in the northwestern Arabian Gulf and supports further investigation of the biological and environmental processes influencing viral dynamics in this highly variable coastal system. Full article

Mangroves support a diverse insect fauna that plays essential roles in ecosystem functioning; however, the diversity of bark and ambrosia beetles (Curculionidae: Scolytinae) in the mangroves of the Mexican Pacific remains poorly understood. This study aimed to characterize the diversity and community structure of Scolytinae associated with two mangrove ecosystems in the state of Colima, western Mexico: Palo Verde Estuary and Juluapan Lagoon. Beetles were sampled using ethanol-baited traps (70% ethanol). A total of 1869 individuals belonging to 19 species and 10 genera were collected. Hypothenemus was the most species-rich genus, whereas Xyleborus volvulus was the dominant species. Five species are reported for the first time in Colima: Ambrosiodmus obliquus, Corthylus comatus, Premnobius cavipennis, Hypothenemus dolosus, and Hypothenemus indigens. Palo Verde Estuary showed higher beetle abundance, whereas Juluapan Lagoon exhibited greater species diversity and community evenness. Although the community shared several genera with mangrove ecosystems from other regions of Mexico and Brazil, it differed in species composition and dominance patterns. This study provides the first baseline on the Scolytinae fauna associated with the mangroves of Colima and establishes a reference for future ecological monitoring and phytosanitary surveillance programs. Full article

In January 2025, the Palisades and Eaton fires overtook large swaths of Los Angeles County, covering a combined area of approximately 152 km², composed of diverse coastal, urban, and forested environments. Aerial Light Detection and Ranging (LiDAR) surveys were commissioned directly following these fires, and compared against previously unreleased foundational LiDAR surveys captured in 2023 and 2024. The timeliness of these surveys presents a unique opportunity to approach large-scale damage characterization metrologically at sub-meter resolution. Cell-based height differencing across 367 million change-detected cells (on a 0.5 m grid) identifies 49.5 km² of vegetation loss and 1.66 km² of building footprint destruction in the Palisades fire, and 24.3 km² of vegetation loss and 1.48 km² of building footprint destruction in the Eaton fire. From the resulting volumetric loss inventory, we derive bottom-up carbon emission estimates of $255\pm 61$ kt C for the Palisades fire and $162\pm 38$ kt C for the Eaton fire. The Eaton estimate agrees to within 6% of an independent atmospheric inversion estimate derived from ground-based sensor networks, well within the propagated uncertainty of either method, providing an independent cross-validation, at the total-emission level, between LiDAR-based and atmospheric-inversion approaches to wildland–urban interface fire emissions. This paper details the segmentation and characterization methodology, and coincides with ALERTCalifornia’s public release of all described raw and derivative datasets. Full article

Mangrove deforestation is a global problem, but sediment-dominated estuaries can resist it through their own morphodynamic processes. This study reconstructs the spatiotemporal trajectory of the Ujung Pangkah estuary (1995–2025) to weigh natural progradation against anthropogenic pressure. We mapped the estuary in Google Earth Engine using Landsat archives and a Random Forest classifier with four spectral indices (NDVI, mNDWI, EVI, and MVI) and then independently validated all four epochs (Overall Accuracy 92.25–95.00%; Kappa 0.845–0.900). Error-adjusted change-detection analysis shows a non-monotonic trajectory: mangrove extent grew from 898.88 ha (1995) to a 2410.44 ha peak in 2015 and then contracted to 1816.87 ha by 2025 (error-adjusted: 1261.11 to 2626.03 to 2213.71 ha). Across the 30-year record, this is a statistically significant net expansion (z = 3.60, p < 0.001). Spatial attribution shows the 2015–2025 contraction comes mostly from landward anthropogenic conversion (78.1%), not seaward erosion (21.9%). A lagged correlation between a suspended sediment proxy and decadal net change (r = 0.92) offers quantitative support for continued sediment-driven coastal progradation. Ujung Pangkah’s resilience therefore coexists with a real, locatable anthropogenic pressure. Safeguarding this blue carbon ecosystem means targeting policy at the interior conversion zones already underway, alongside continued protection of the coastal frontier. Full article

The protected island of Chrysi, a Natura 2000 site that hosts one of the Mediterranean’s most significant populations of Juniperus spp. dune forests, stands as a critical case study in the conflict between mass tourism and ecosystem survival. For decades, this fragile environment has been subjected to escalating, largely unregulated tourism pressure, culminating in over 151,000 visitors in 2019, resulting in demonstrable environmental degradation and threatening its long-term viability. This study was undertaken to address this critical conflict by establishing a scientifically robust foundation for sustainable management. The primary objectives were (a) to identify if abiotic stress parameters affected the dune forest degradation; (b) conduct a comprehensive assessment of the island’s tourism carrying capacity, defining the maximum number of daily visitors the ecosystem can support without sustaining irreversible damage; and (c) to support the formulation of a detailed, actionable Protection and Supervision Plan to enforce these limits and guide conservation efforts. The methodology employed a multifaceted approach, integrating analyses of long-term visitor data, a synthesis of baseline ecological and climate data, and a social survey to gauge visitors’ perceptions of environmental conditions and their acceptance of management interventions. The study successfully calculated the carrying capacity based on designated visitable zones and trails. Crucially, social surveys indicated a high level of visitor awareness of the environmental problems and strong support for implementing restrictive measures to protect the island. Based on these integrated findings, this study’s principal recommendation is the immediate and permanent implementation of the proposed management framework, centred on strict adherence to the scientifically determined daily visitor limit. This framework is essential to halt ongoing degradation and ensure the long-term conservation of the Chrysi Island ecosystems, and it recommends a potential model for sustainable island tourism management in the Mediterranean and worldwide. Full article

Rice is a staple grain crop central to China’s food security. As the core production region of single-cropping rice, the middle and lower reaches of the Yangtze River face escalating high daytime and nighttime temperatures and compound drought–heat stress amid global warming. The accurate assessment of heat hazards is therefore pivotal for regional yield stability and disaster mitigation. Based on meteorological, remote-sensing, and soil data, together with county-level rice yield statistics from 150 major producing counties spanning 1991 to 2024 (5009 county-year calibration units), we first constructed a composite heat damage index (CHI) by integrating daytime harmful accumulated temperature (Ha), nighttime harmful accumulated temperature (HNa), and the Vegetation Health Index (VHI). We then implemented a gradient boosting decision tree (GBDT) machine learning framework in which yield loss was imposed as a physical constraint. This framework was benchmarked against convolutional neural network (CNN), random forest (RF), and support vector machine (SVM) models, with the Shapley additive explanations (SHAP) method used for attribution analysis and an independent temporal partitioning strategy applied for model validation. The results indicate the following: (1) compared to the single daytime heat damage index, the CHI elevated the yield correlation coefficient from 0.52 to 0.63; (2) with yield constraint calibration, the model attained a balanced accuracy of 92.6% and 94.0% consistency with historical disaster records; (3) regional heat hazard presents a spatial pattern of “high in inland areas and low in coastal areas,” with the heading–flowering stage as the critical sensitive period; and (4) high nighttime temperature accounts for approximately 20% of the model’s relative importance, with higher discriminative sensitivity for high-grade hazards, while the amplifying effect of water deficit on heat stress maintains a stable relative importance of around 16%. In this study, the coupled optimization of traditional assessment paradigms and data-driven approaches is achieved, providing a methodological reference for refined growth stage–specific heat hazard assessment. Its cross-regional portability and independent predictive validity require further validation. Full article

Extensive ecosystem degradation along the coastal areas of Ghana highlights the need for targeted landscape restoration. This study identified areas with high restoration potential by evaluating environmental, climatic, and anthropogenic determinants of mangrove distribution. We tested multiple habitat suitability models, including Random Forest (RF), Generalized Additive Model (GAM), Generalized Linear Model (GLM), Maximum Entropy (MaxEnt), and an ensemble approach. Mangrove occurrence records were compiled from field observations, drone-derived data, and publicly available biodiversity and mapped datasets. The ensemble GAM-RF model emerged as the most robust model, with the highest predictive performance. Hydrological and topographic factors were the strongest predictors of mangrove habitat suitability, particularly proximity to rivers and the coastline, low-elevation terrain (<5 m asl), and temperature-related bioclimatic variables, while precipitation metrics and anthropogenic proxies played secondary roles. Using the ensemble approach, the estimated potentially suitable habitat ranged from 417 to 1313 km², indicating that substantial areas remain available for mangrove restoration. These findings align with ongoing national and international restoration initiatives, including the coastal restoration plan, and have important implications for coastal protection, carbon sequestration, fisheries habitat, and local livelihoods. Our findings demonstrate that multi-model habitat suitability analyses can guide spatially targeted, and evidence-based restoration planning. Full article

The Guangdong Haifeng Coastal Wetlands (Ramsar Site No. 1727) in Shanwei City are a critical habitat for migratory birds, but the long-term evolution and driving mechanisms of ecological vulnerability under rapid urbanization remain insufficiently understood. In particular, few studies have quantitatively characterized the temporal shifts in the relative roles of anthropogenic pressures and conservation policies over decadal scales. This study developed a landscape pattern-based ecological vulnerability assessment model integrating spatial autocorrelation, Geodetector, the PLUS model, and spatial overlay analysis to diagnose the dynamics and driving mechanisms of the wetlands from 1980 to 2018. The results showed that farmland and woodland dominated Shanwei City, while water bodies consistently dominated the Haifeng Wetlands. Ecological vulnerability exhibited significant spatial heterogeneity, with levels higher in the northwest and lower in the southeast. Forest and water expansion overlapped with vulnerability reduction, whereas built-up and farmland expansion matched increases in vulnerability. Land-use intensity, elevation, and mean annual temperature were identified as the primary drivers. The drivers of built-up land expansion shifted from population-related pressures during 1980–2000 to stronger policy regulation effects after 2000, indicating a transition in human–land interactions. Conservation policies, including nature reserve establishment and Ramsar designation, provided important institutional support for ecological management. These findings highlight the importance of integrating long-term land-use dynamics with natural, socioeconomic and policy contexts to improve the understanding and management of coastal wetlands vulnerability under rapid urbanization. Full article

Assessing the soil quality of coastal reclamation areas is fundamental to alleviating land resource scarcity in coastal cities and ensuring food security. However, it remains unclear how to evaluate soil quality in reclaimed areas and identify factors driving its variation. In this study, paddy soils from four typical reclaimed coastal areas in China (Yueqing, YQ; Longgang, LG; Rui’an, RA; Longwan, LW) were investigated to construct a minimum data set via principal component analysis and to calculate soil quality indices (SQIs). YQ had higher contents of soil organic carbon (SOC: 22.82 g kg⁻¹), total nitrogen (TN: 0.14 g kg⁻¹), total water-soluble salts (TWS: 3.09 g kg⁻¹), cation exchange capacity (CEC: 21.77 cmol(+) kg⁻¹), and available Fe (44.23 mg kg⁻¹), Mn (36.63 mg kg⁻¹), Cu (30.31 mg kg⁻¹), and Zn (8.79 mg kg⁻¹) than the other sites. RA exhibited significantly higher activities of β-glucosidase (BG: 32.79 nmol g⁻¹ h⁻¹), xylanase (XYL: 5.88 nmol g⁻¹ h⁻¹), N-acetyl-β-D-glucosaminidase (NAG: 18.00 nmol g⁻¹ h⁻¹), leucine aminopeptidase (LAP: 27.01 nmol g⁻¹ h⁻¹), and acid phosphatase (PHOS: 54.50 nmol g⁻¹ h⁻¹) than the other sites (p < 0.05). LW had the greatest bacterial and fungal abundances, whereas LW displayed the highest fungal diversity. RA showed the highest SQI (SQIw: 0.49; SQIa: 0.48), followed by LW (SQIw: 0.47; SQIa: 0.47), LG (SQIw: 0.43; SQIa: 0.41), and YQ (SQIw: 0.38; SQIa: 0.41). Random forest analysis indicated that soil enzyme activities (XYL, NAG, BG, CB, PHOS), nutrients (TN, SOC, AN), fungal abundance and diversity, and TWS were key SQI predictors (Welch ANOVA p = 0.016), with XYL being the strongest predictor (p < 0.05). Collectively, soil quality in coastal reclamation areas is co-regulated by microbial metabolic activity, nutrients, and salinity, with soil enzyme activity serving as an important indicator for its assessment, thereby deepening the understanding of soil quality dynamics within these areas and enabling their sustainable management. However, the microbial mechanisms underlying these patterns across broader environmental gradients remain to be explored. Full article

This study compared net photosynthesis (A), water-use efficiency (WUE), intrinsic water-use efficiency (iWUE), and A–${\mathrm{C}}_i$ curve-derived photosynthetic parameters (maximum carboxylation rate, $V_{\mathrm{cmax}}$; maximum electron transport rate, $J_{\max }$) across nine mangrove and semi-mangrove species at a tropical coastal site in Bali, Indonesia. Gas exchange measurements were conducted using portable photosynthesis systems (LI-6400 and LI-6800), and A–${\mathrm{C}}_i$ curves were fitted to the Farquhar–von Caemmerer–Berry (FvCB) model. Sonneratia alba Sm. exhibited the highest A (15.29 ± $2.39$ μmol ${\mathrm{m}}^{-2}{\mathrm{s}}^{-1}$), suggesting comparatively high photosynthetic performance, while Hibiscus tiliaceus L. and Pongamia pinnata (L.) Pierre showed the highest iWUE values (88–$97$ μmol ${\mathrm{mol}}^{-1}$), indicating relatively efficient carbon gain per unit stomatal conductance. Significant overall interspecific variation was detected in $V_{\mathrm{cmax}}$ and $J_{\max }$ ($p=0.003$ and $p=0.016$, respectively). The observed interspecific variation in A, iWUE, $V_{\mathrm{cmax}}$, and $J_{\max }$ suggests differences in photosynthetic characteristics and leaf-level water-use efficiency among species. These ecophysiological baseline data may contribute to species selection for mangrove restoration and to understanding physiological responses to environmental change in tropical coastal forests. Full article