Search Results (399)

Mosquitoes (Diptera: Culicidae) exhibit vertical stratification patterns in forest environments, a fundamental ecological aspect for understanding niche occupation patterns, host-seeking behavior, and consequently arbovirus transmission mechanisms. Despite the relevance of this topic, available studies mostly focus on genera such as Aedes, Haemagogus, and Sabethes which are traditionally associated with arbovirus transmission. There are still important gaps regarding stratification and seasonality in the Mansoniini tribe, whose biology and epidemiological role remain underexplored, especially in highly biodiverse ecosystems such as the Atlantic Forest. This study evaluated the influence of seasonality and vertical stratification on the mosquito community, with a detailed focus on the Mansoniini tribe, in an Atlantic Forest fragment in Brazil, between May 2023 and December 2024. Captures were performed monthly using CDC light traps positioned at 1.5 m and 10 m heights, and specimens were morphologically identified. A total of 880 mosquitoes from nine genera and 24 species were captured, of which 91 (10.3%) belonged to the Mansoniini tribe. The most abundant species were Coquillettidia fasciolata and Mansonia titillans, recorded in both strata. Our results indicate no marked vertical segregation for the studied mosquito community in this specific location, but a strong influence of seasonality, particularly for the Mansoniini tribe, reinforcing the role of meteorological data on the population structure of these species. These site-specific findings offer a foundational ecological portrait and a robust methodological template for a neglected taxon. They generate critical, testable hypotheses about niche partitioning in fragmented forests and underscore the necessity for broader spatial replication to disentangle the relative influence of seasonal versus vertical drivers in similar ecosystems. Full article

Passive protection is widely assumed to preserve biodiversity and ecological integrity, yet the evidence for long-term vegetation stability in protected temperate forests remains inconclusive. We resurveyed two deciduous forests in SW Poland after 30 years of strict protection to assess temporal changes in their understory vegetation, functional structure, and habitat conditions. Using paired phytosociological relevés (n = 40), collected using the Braun-Blanquet method, we compared baseline (1989–1991) and recent (2022) data with respect to species frequency, Ellenberg indicator values, basic functional traits, and functional diversity. Species composition proved highly stable: only 10% of vascular plant species exhibited significant changes in frequency in particular layers, largely reflecting the vertical redistribution of woody species rather than species turnover. Habitat conditions showed no significant temporal changes. In contrast, the functional structure of the herb layer changed markedly, with significant increases in community-weighted means of seed mass, plant height, and specific leaf area, accompanied by a significant rise in functional diversity. These shifts were partly driven by the increasing abundance of woody species and some opportunistic and invasive species. Our results demonstrate that functional traits may reveal directional ecological changes in passively protected forests even when species composition and habitat indicators remain unchanged, highlighting the importance of trait-based approaches for long-term forest surveys. Full article

Tropical Andean forests are biodiversity hotspots that have been transformed by anthropogenic activities, making ecosystem regeneration and restoration essential for their recovery. This study evaluated floristic composition, forest structure, and diversity in three land cover types within tropical Andean ecosystems: riparian forest (RF), natural regeneration (NR), and ecological restoration areas (RE). Vegetation was inventoried using standardized plots, recording species composition, diameter, and height. Basal area, size class distribution, and vertical structure were estimated. The Shannon Wiener and Simpson indices were evaluated. RF showed the highest structural complexity and basal area among the evaluated cover types, followed by ER, whereas NR showed the lowest values. NR showed the highest diversity values and a predominance of individuals in lower diameter and height classes, reflecting active recruitment and intermediate successional stages. Segment ER exhibited lower diversity and intermediate structural development, consistent with shorter recovery periods and limitations in restoration design. Overall, the integration of floristic, structural, and diversity attributes indicates distinct successional trajectories, conditioned by land-use history, disturbance intensity, and environmental heterogeneity. These findings highlight the great potential for natural regeneration under reduced anthropogenic pressure and emphasize the need to integrate passive and active restoration strategies to enhance biodiversity and resilience in Andean tropical forests. Full article

Accurate Digital Terrain Models (DTMs) are essential for managing forest drainage networks as a crucial element of water management, yet dense canopies and complex micro-topography challenge Airborne Laser Scanning (ALS) precision. This study evaluates the vertical accuracy of an ALS-derived DTM specifically within forest drainage ditches, utilizing 706 GNSS and total station measurements for validation. The results indicate a positive elevation bias, with a mean elevation error of 0.415 m and an RMSE of 0.464 m, 54.7% higher than the 0.3 m declared in the DTM technical report. Forest height, acting as a proxy for forest structural density and canopy closure, was significantly associated with a reduction in ground reflection density and an increase in the distance to the nearest ground reflection (p < 0.05). Mixed-effects ANOVA confirmed that there are significantly more ground reflections in low vegetation (0–1 m). Crucially, multiple regression analysis revealed that forest height was not the primary driver of elevation error; instead, ditch geometry was the most significant predictor. Narrower ditches exhibited substantially higher errors than wider ones, regardless of the canopy height. Furthermore, while ground reflection density decreased in mature stands, this reduction did not significantly diminish DTM vertical accuracy, suggesting that some of the LiDAR reflections of low vegetation could be misclassified as ground reflections, decreasing accuracy. These findings suggest that while ALS is effective for general forest topography and mapping drainage infrastructure, its application may require corrections for ditch dimensions rather than vegetation height alone to mitigate systematic overestimation of ditch bed elevations. Full article

Accurate estimation of Total Phosphorus, referred to as “Phosphorus, Total” (PPUT; µg/L) in the sourced monitoring data, is essential for understanding eutrophication dynamics and guiding water-quality management in inland lakes. However, lake-wide PPUT mapping at high resolution is challenging to achieve using conventional in-situ sampling, and nearshore gradients are often poorly resolved by medium- or low-resolution satellite sensors. This study exploits multi-generation PlanetScope imagery (Dove Classic, Dove-R, and SuperDove; 3–5 m, near-daily revisit) to develop a hybrid AI framework for PPUT retrieval in Lake Simcoe, Ontario, Canada. PlanetScope surface reflectance, short-term meteorological descriptors (3 to 7-day aggregates of air temperature, wind speed, precipitation, and sea-level pressure), and in-situ Secchi depth (SSD) were used to train five ensemble-learning models (HistGradientBoosting, CatBoost, RandomForest, ExtraTrees, and GradientBoosting) across eight feature-group regimes that progressively extend from bands-only, to combinations with spectral indices and day-of-year (DOY), and finally to SSD-inclusive full-feature configurations. The inclusion of SSD led to a strong and systematic performance gain, with mean R² increasing from about 0.67 (SSD-free) to 0.94 (SSD-aware), confirming that vertically integrated optical clarity is the dominant constraint on PPUT retrieval and cannot be reconstructed from surface reflectance alone. To enable scalable SSD-free monitoring, a knowledge-distillation strategy was implemented in which an SSD-aware teacher transfers its learned representation to a student using only satellite and meteorological inputs. The optimal student model, based on a compact subset of 40 predictors, achieved R² = 0.83, RMSE = 9.82 µg/L, and MAE = 5.41 µg/L, retaining approximately 88% of the teacher’s explanatory power. Application of the student model to PlanetScope scenes from 2020 to 2025 produces meter-scale PPUT maps; a 26 July 2024 case study shows that >97% of the lake surface remains below 10 µg/L, while rare (<1%) but coherent hotspots above 20 µg/L align with tributary mouths and narrow channels. The results demonstrate that combining commercial high-resolution imagery with physics-informed feature engineering and knowledge transfer enables scalable and operationally relevant monitoring of lake phosphorus dynamics. These high-resolution PPUT maps enable lake managers to identify nearshore nutrient hotspots, tributary plume structures. In doing so, the proposed framework supports targeted field sampling, early warning for eutrophication events, and more robust, lake-wide nutrient budgeting. Full article

Mountain treelines are among the most climate-sensitive ecosystems on Earth, yet their fine-scale structural and species level dynamics remain poorly resolved in the Himalayas. In particular, the absence of three-dimensional, crown level measurements have hindered the detection of structural thresholds and species turnover that often precede treeline shifts. To bridge this gap, we introduce UAV LiDAR—applied for the first time in the Hindu Kush Himalayas—to quantify canopy structure and tree species distributions across a steep treeline ecotone in the Manang Valley of central Nepal. High-density UAV-LiDAR data acquired over elevations of 3504–4119 m was used to quantify elevation-dependent changes in canopy stature and cover from a canopy height model derived from the 3D point cloud, while individual tree segmentation and species classification were performed directly on the 3D, height-normalized point cloud at the crown level. Individual trees were delineated using a watershed-based segmentation algorithm while tree species were classified using a random forest model trained on LiDAR-derived structural and intensity metrics, supported by field-validated reference data. Results reveal a sharply defined treeline characterized by an abrupt collapse in canopy height and cover within a narrow ~60–80 m vertical interval. Treeline “threshold” was quantified as a breakpoint elevation from a piecewise model of tree cover versus elevation, and the elevation span over which modeled cover and height distributions rapidly declined from forest values to near-zero. Segmented regression identified a distinct structural breakpoint near 3995 m elevation. Crown-level species predictions aggregated by elevation quantified an ordered turnover in dominance, with Pinus wallichiana most frequent at lower elevations, Abies spectabilis peaking mid-slope, and Betula utilis concentrated near the upper treeline. Species classification achieved high overall accuracy (>85%), although performance varied among taxa, with broadleaf Betula more difficult to discriminate than conifers. These findings underscore UAV LiDAR’s value for resolving sharp ecological thresholds, identifying elevation-driven simplification in forest structure, and bridging observation gaps in remote, rugged mountain ecosystems. Full article

Coconut coir, a key substrate in soilless cultivation, presents challenges for accurate moisture detection because of its complex internal structure, which limits the understanding of water infiltration and redistribution. This study employed RGB image recognition techniques combined with machine learning algorithms to systematically investigate the effects of initial moisture content (10%, 20%, and 30%), coarse-to-fine coir volume ratio (1:0, 1:1, and 0:1), and emitter discharge rate (1.0, 1.5, and 2.0 L h⁻¹) on wetting front morphology, water transport dynamics, and moisture variation within coir substrates. Morphological features of the wetting front were extracted from images and incorporated into three machine learning models—Support Vector Regression (SVR), Random Forest (RF), and Polynomial Regression—to construct a predictive framework for coir moisture estimation. The results showed that the SVR model achieved the best predictive performance in coarse coir substrates (R² = 0.89, RMSE = 3.37%), whereas Polynomial Regression performed best in mixed substrates (R² = 0.861, RMSE = 4.34%). All models exhibited lower accuracy in fine coir, particularly at high moisture levels. Under the same irrigation volume, increasing the initial moisture content enhanced both the water transport rate and the wetting front extent, with the aspect ratio (AR) decreasing from approximately 2.0 to 1.3, indicating a morphological transition of the wetting front from a “thumb-shaped” to a “hemispherical” pattern. Coarse particles facilitated vertical infiltration, while fine particles exhibited stronger water retention. By integrating RGB image recognition with machine learning approaches, this study achieved reliable prediction of coir moisture content and proposed an optimal management strategy using mixed substrates with an initial moisture content of 20–30% to balance infiltration efficiency and water-holding capacity while minimizing percolation risk. These findings provide a robust technical pathway for precise water management in coir-based cultivation systems. Full article

Severe erosion persists in the red soil region of southern China despite dense vegetation. Stratified vegetation volume (SVV), which integrates horizontal and vertical vegetation density, better captures understory structure than fractional cover. Here, we established and surveyed 75 forest stands (10 m × 10 m) spanning an erosion-intensity gradient in Changting County, Fujian Province, China. Within each stand, soil was sampled by depth (0–20 cm), and living and dead vegetation volumes in the canopy, shrub–herb, and litter layers were quantified to derive SVV. Relative to slightly eroded soils, moderate and severe erosion reduced the soil water content by 38–41% and soil organic matter by 19–34%, while increasing bulk density by 25–30% and pH by 6–8%. Severe erosion increased the sand content by 20–31% and decreased the gravel content by ≤15%. SVV declined sharply with erosion, with the largest loss in the shrub–herb layer (66–97%). Erosion was most strongly associated with shrub–herb SVV, soil water content, organic matter, and bulk density (r = 0.5–0.6, p < 0.05). The shrub–herb layer was the key component resisting surface erosion. Overall, understory degradation accelerates erosion and soil coarsening, reinforcing a constrained vegetation–soil system; restoring native shrubs and grasses, coupled with targeted canopy thinning, may improve soil and water conservation. Full article

Unmitigated climate change, coupled with habitat loss, has made the grassland and shrubland bird communities particularly vulnerable to extinction. Climate change-induced drought reduces net primary productivity, food availability, habitat quality, and alters vegetation structure. These factors collectively increase mortality in grassland and shrubland birds. However, limited data on habitat use by tropical birds hampers the development of effective management plans for drought-affected landscapes. We examined the foraging sites of 18 shrubland bird species, including two endemic and four declining species, across three shrubland forest sites in the Eastern Ghats of India during the dry season. We recorded microhabitat features within an 11 m radius of observed foraging points and compared them with random plots. Additionally, we examined the association between bird species and plant species where a bird was observed foraging. Foraging sites differed significantly from random plots, indicating active selection of microhabitats by shrubland birds. Using linear discriminant analysis, we found that the microhabitat features important for the bird species were presence of ground cover, shrub density, vegetational height, and vertical foliage stratification. Our results show that diet guild and foraging strata influence the foraging microhabitat selection of a species. Microhabitat attributes selected by shrubland specialist species differed from those of generalist shrubland users. Thirteen out of 18 focal species showed a significant association with at least one plant species. Birds were often associated with plants that were green during the dry season. Based on habitat selection and plant associations, we identified several habitat attributes that can be actively managed. Despite being classified as wastelands, the heavily degraded shrub forests can be rehabilitated through strategic and selective harvesting of forest products, targeting invasive species, and a spatially and temporally controlled livestock grazing regime. Full article

Long-term human cultivation activities are the key factors of the vertical distribution and storage dynamics of soil organic carbon (SOC) in cropland. Based on a 45-year long-term field experiment, this study systematically compared SOC dynamics and carbon storage characteristics in soil profiles (0–200 cm) between cultivated land and adjacent natural forest. The findings reveal the hierarchical regulatory effects of tillage management on the soil carbon pool. The results show that: (1) Under both land use types, SOC content decreased exponentially with depth, but values in cultivated soils were 0.35–1.54% lower than in forest soils at each layer. SOC content in surface soil (0–78 cm) was significantly higher than in the subsoil (78–158 cm) and substratum layers (158–200 cm) (p < 0.01). At equivalent depths, SOC in cultivated land was significantly lower than in forest land (p < 0.01). Over 45 years, the SOC accumulation rate in the surface soil of cropland (0.07 g·kg⁻¹·yr⁻¹) was only half that of forest land (0.14 g·kg⁻¹·yr⁻¹). (2) The controls of soil physicochemical properties on SOC differed with land use: in forest soils, SOC correlated positively with clay content (r = 0.63, p < 0.01), whereas in cultivated soils, SOC was primarily regulated by total nitrogen (r = 0.94, p < 0.01) and sand content (r = 0.60, p < 0.01) and negatively correlated with bulk density (r = −0.55, p < 0.01) and pH value (r = −0.45, p < 0.05). (3) Long-term tillage significantly reshaped soil profile structure, thickening the plough layer from 20 cm to 78 cm. Surface carbon storage reached 20.76 t·ha⁻², an increase of 11.13 t·ha⁻² compared with forest soil (p < 0.01). However, storage decreased by 4.99 t·ha⁻² and 7.60 t·ha⁻² in the subsoil and substratum layers, respectively (p < 0.01). The SOC storage increment rate was 50.95 t·ha⁻²·yr⁻¹ higher than that of forest soil in the surface layer but 46.81 t·ha⁻²·yr⁻¹ and 11.12 t·ha⁻²·yr⁻¹ lower in deeper layers. These results confirm that cultivation alters soil structure and material cycling, enhancing carbon enrichment in surface soils while accelerating depletion of deeper carbon pools. This provides new insights into the vertical differentiation mechanisms of SOC under long-term agricultural management. Full article

Acacia melanoxylon R.Br. demonstrates strong biological nitrogen–fixation capacity and favourable economic returns, making it a promising candidate for the development of subtropical forestry in South Asia. It is a fast–growing leguminous tree species widely promoted for cultivation in China, and it is also one of the ideal tree species for improving soil fertility in forest lands. What are the synergistic mechanisms between A. melanoxylon-Eucalyptus stands and pure Eucalyptus spp.? Current theories regarding A. melanoxylon–Eucalyptus systems remain relatively fragmented due to the lack of effective silvicultural measures, resistance studies, and comprehensive ecological–economic benefit evaluations. The absence of an integrated analytical framework for holistic research on A. melanoxylon–Eucalyptus systems makes it difficult to summarise and comprehensively analyse their growth and development, thereby limiting the optimisation and widespread application of their models. This study employed CiteSpace bibliometric analysis and qualitative methods to explore ideal tree species combination patterns, elucidate their intrinsic eco–economic synergistic mechanisms, and reasonably reveal their collaborative potential. This study systematically reviewed silvicultural management, stress physiology, ecological security, and economic policy using the Chinese and English literature published from 2010 to 2025. The narrative synthesis results indicated that strip intercropping (7:3) is widely documented as an effective model for creating vertical niche complementarity, whereby canopy light and thermal utilisation by A. melanoxylon species improve subsoil nutrient cycling by enhancing stand structure. A conceptual full–cycle economic assessment framework was proposed to measure carbon sequestration and timber premiums. Correspondingly, this conversion of implicit ecological services into explicit market values acted as a critical tool for decision–making in assessing benefit. A three–dimensional “cultivation strategy–physiological ecology–value assessment” assessment framework was established. This framework demonstrated how to move from wanting to maximise the output of an individual component to maximising the value of the whole system. It theorised and provided guidance on resolving the complementary conflict between “ecology–economy” in the management of sustainable multifunctional plantations. Full article

Timely and accurate urban change detection is vital for sustainable urban development, infrastructure management, and disaster response. Traditional two-dimensional approaches often overlook vertical and structural variations in dense urban areas. This study proposes a three-dimensional (3D) change detection framework that integrates high-resolution optical imagery and Digital Surface Models (DSMs) from two time points to capture both horizontal and vertical transformations. The method is based on a deep learning architecture combining a ResNet34 encoder with a UNet++ decoder, enabling the joint learning of spectral and elevation features. The research was carried out in two stages. First, a binary classification model was trained to detect areas of change and no-change, allowing direct comparison with conventional methods such as Principal Component Analysis (PCA), Change Vector Analysis (CVA) with thresholding, K-Means clustering, and Random Forest classification. In the second stage, a multi-class model was developed to categorize the types of structural changes, including new building construction, complete destruction, building height increase, and height decrease. Experiments conducted on a high-resolution urban dataset demonstrated that the proposed CNN-based framework significantly outperformed traditional methods, achieving an overall accuracy of 96.58%, an F1-score of 96.58%, and a recall of 96.7%. Incorporating DSM data notably improved sensitivity to elevation-related changes. Overall, the ResNet34–UNet++ architecture offers a robust and accurate solution for 3D urban change detection, supporting more effective urban monitoring and planning. Full article

Addressing the limitations of traditional acoustic turbidity inversion models in complex marine environments—specifically their reliance on empirical parameters and lack of vertical resolution—this study presents a novel CNN-ResNet-RF hybrid model based on the simultaneous ADCP and turbidity observations in the Chengshantou sea area. Unlike conventional approaches, the proposed framework integrates deep spatio-temporal features automatically extracted by a ResNet-enhanced CNN, utilizing a Random Forest (RF) regressor for final prediction, thereby avoiding the limitations of artificial feature engineering. To ensure rigorous evaluation and mitigate stochastic bias, the model was validated using a 5-fold cross-validation strategy with dynamic Z-score normalization. Experimental results demonstrate that the proposed model significantly outperforms benchmark methods (CNN, RF, and CNN-RF), achieving an average R² of 0.782, an MAE of 4.454, and a MAPE of 15.42% on the test sets. This study confirms that the hybrid framework successfully combines the feature extraction power of deep learning with the robustness of ensemble learning, providing a robust and high-precision tool for the vertical structural analysis of ocean turbidity. Full article

During the construction of deep foundation pits, closely monitoring the deformation of the foundation pit retaining structure is of vital importance for ensuring the stability and safety of the foundation pit and reducing the risk of structural damage caused by foundation pit deformation. While theoretical and numerical methods exist for displacement prediction, their practical application is often hindered by the complex, non-linear nature of soil behavior and the numerous influencing parameters involved, making direct calculation methods challenging for real-time prediction and control. To address this, this study proposes a novel and interpretable machine learning framework for modeling both vertical and horizontal displacements in foundation pit engineering. Six widely used machine learning algorithms—Decision Tree (DT), Random Forest (RF), Extremely Randomized Trees (ET), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM)—were developed and compared. To improve model performance, the Sparrow Search Algorithm (SSA) was employed for hyperparameter optimization, leading to the creation of hybrid models such as SSA-XGB and SSA-LGBM. The SSA-optimized XGBoost (SSA-XGB) model achieved superior performance, with R² values of 0.988 and 0.990 for vertical and horizontal displacement prediction, respectively, alongside the lowest RMSE (0.785 and 5.684) and MAE (0.562 and 2.427). Notably, the study also found that hyperparameter tuning does not consistently enhance model performance; in some cases, simpler baseline models such as unoptimized ET performed better in noisy environments. Furthermore, SHAP-based interpretability analysis revealed a strong mutual dependency between vertical and horizontal displacements: horizontal displacement was the most influential feature in predicting vertical displacement, and vice versa. Overall, the proposed SSA-XGB model offers a reliable, cost-effective, and interpretable tool for excavation-induced displacement prediction. Full article

Ground settlement is a multifaceted geological phenomenon driven by natural and man-made forces, posing a significant impediment to sustainable urban development. Thus, ground settlement susceptibility (GSS) mapping has emerged as a critical tool for understanding and mitigating cascading hazards in seismically active and anthropogenically modified sedimentary basins. Here, we develop an integrated framework for assessing GSS in the Pohang region, South Korea, by integrating Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR)-derived vertical land motion (VLM) data with seismological, geotechnical, and topographic parameters (i.e., peak ground acceleration (PGA), effective shear-wave velocity (V_s³⁰), site period (T_s), general amplification factor (A_F), seismic vulnerability index (K_g), soil depth, topographic slope, and landform classes) through ensemble machine learning models such as Random Forest (RF), XGBoost, and Decision Tree (DT). Analysis of 56 Sentinel-1 SLC images (2017–2023) revealed persistent subsidence concentrated in Quaternary alluvium, reclaimed coastal plains, and basin-fill deposits. Among the tested models, RF achieved the best performance and strongly agreed with field evidence of sand boils, liquefaction, and structural damage from the 2017 Pohang earthquake. The very-high-susceptibility zones exhibited mean subsidence rates of −3.21 mm/year, primarily within soft sediments (V_s³⁰ < 360 m/s) and areas of thick alluvium deposits. Integration of the optimal RF-based GSS index with regional building inventories revealed that nearly 65% of existing buildings fell within high- to very-high-susceptibility zones. The proposed framework demonstrates that integrating PSInSAR and ensemble learning provides a robust and transferable approach for quantifying ground settlement hazards and supporting risk-informed urban planning in seismically active and complex geological coastal environments. Full article