Search Results (2,223)

Populus adenopoda, an endemic tree species in China with considerable ecological and industrial value, is threatened by climate change-induced habitat loss. Understanding its spatial response is critical for conservation. This study employed the MaxEnt model with 181 occurrence records and seven environmental variables to project its current and future suitable habitats under multiple climate scenarios (SSP126, SSP245, SSP370, SSP585 for the 2050s and 2090s). The model exhibited high predictive performance (AUC = 0.947 and TSS = 0.817). Annual precipitation and the minimum temperature of the coldest month were the dominant factors shaping its distribution. Currently, the total suitable habitat spans approximately 228.19 × 10⁴ km², predominantly in subtropical China. Future projections consistently revealed a stark degradation of highly suitable habitat, with losses of up to 78.81% under SSP585 by the 2090s, partially offset by an expansion of low-suitability areas. A pronounced northwestward shift of the habitat centroid indicates a potential migration toward higher elevations. These results provide a critical scientific foundation for developing climate-adaptive conservation strategies, including identifying priority areas and planning assisted migration, to ensure the long-term sustainability of P. adenopoda. Full article

This study presents an integrated framework for spatiotemporal mapping of carbon stocks in rubber plantations in Rayong Province, Eastern Thailand—an area undergoing rapid agricultural transformation and rubber expansion. Unlike most existing assessments that rely on Tier 1 IPCC defaults or coarse plantation age classes, our framework combines annual plantation age derived from Landsat time series, age-specific allometric growth models, and Tier 2 soil organic carbon (SOC) accounting. This enables fine-scale, age- and site-sensitive estimation of both tree and soil carbon. Results show that tree biomass dominates the carbon pool, with mean tree carbon stocks of 66.94 ± 13.1% t C ha⁻¹, broadly consistent with national field studies. SOC stocks averaged 45.20 ± 0.043% t C ha⁻¹, but were overwhelmingly inherited from pre-conversion land use (43.7 ± 0.042% t C ha⁻¹). Modeled SOC changes ($\mathsf{\Delta }\mathrm{SOC}$) were modest, with small gains (2.06 t C ha⁻¹) and localized losses (−9.96 t C ha⁻¹), producing a net mean increase of only 1.44 t C ha⁻¹. These values are substantially lower than field-based estimates (5–15 t C ha⁻¹), reflecting structural limitations of the global empirical ΔSOC model and reliance on generalized default parameters. Uncertainties also arise from allometric assumptions, generalized soil factors, and Landsat resolution constraints in smallholder landscapes. Beyond carbon, ecological trade-offs of rubber expansion—including biodiversity loss, soil fertility decline, and hydrological impacts—must be considered. By integrating methodological innovation with explicit acknowledgment of uncertainties, this framework provides a conservative but policy-relevant basis for carbon accounting, subnational GHG reporting, and sustainable land-use planning in tropical agroecosystems. Full article

Recalcitrant species are highly sensitive to drought and climate stress, posing urgent challenges for their conservation. Propagation for ex situ management and habitat restoration depends on adequate fruit handling, yet postharvest protocols remain insufficiently examined to support practical implementation. Cryptocarya alba, a dominant tree of the Chilean Mediterranean biome, reflects this gap. Despite its ecological relevance and central role in forest planning, the biological basis of its recalcitrant behavior has yet to be fully elucidated, constraining informed decision-making on its propagation. Accordingly, this study examined the progressive breakdown of fruit integrity under two contrasting storage conditions—refrigeration (5 °C) and room temperature (20 °C)—over 150 days, using a multiscale approach combining physical measurements, histology, and scanning electron microscopy. Fruit weight, moisture, pericarp thickness, and cotyledon starch exhibited a significant linear decline over time. The rate was consistently higher at room temperature—except for starch, which showed no quantitative differences across treatments, though the severity of granule alterations was greater. Overall evidence indicates a close association among these variables, suggesting that desiccation and metabolism-driven degradation result in the structural collapse of C. alba fruits. These findings highlight the need to integrate environmental conditions alongside complementary strategies targeted at physiological regulation, guiding the development of robust, science-based handling protocols to support the species’ conservation. Full article

This investigation focuses on urban ornamental greenery, a field of research that is still relatively unexplored in Italy but is becoming increasingly important both from a botanical point of view and in relation to sustainable land management and planning. A checklist of the ornamental vascular flora of Basilicata (Southern Italy) is reported here. A total of 281 taxa were recorded, including trees, shrubs, herbaceous plants, and succulents cultivated in parks, gardens, and street trees. Such taxa (including 265 species s. str., 6 varieties, 5 subspecies, and 11 forms) belong to 201 genera, included in 94 families, among which the most represented are Rosaceae, Oleaceae, Asteraceae, Pinaceae, Cupressaceae, and Fabaceae. Phanerophytes represent the dominant growth form, and the chorological spectrum is composed mainly of Asian and American taxa. Taxa from subtropical and tropical biomes also showed a significant presence. This study highlighted the clear prevalence in the Basilicata ornamental flora of alien taxa (approximately 80%, of which 21% are naturalized aliens) compared to native ones, which is a phenomenon that is unfortunately widespread and observed worldwide. Full article

The fragile ancient ‘Shuikoulin’ forests, which provide critical habitats for the critically endangered Blue-crowned Laughingthrush, are increasingly degraded by soil contamination and heavy metal pollution. This study examines the rhizosphere environment of four key ancient tree species in the bird’s core habitat, focusing on soil properties, heavy metal accumulation, and the structure of arbuscular mycorrhizal (AM) fungal communities. The results revealed that Liquidambar formosana showed the highest total nitrogen (TN) and available phosphorus (AP), whereas Quercus chenii had the lowest soil organic matter (SOM). The primary heavy metal contaminant across all tree species was Cd (Igeo > 2), followed by the metalloid As. We detected 41 AM fungal species spanning 7 genera, with Glomus dominating (84.19% relative abundance). OTU richness was highest in Cinnamomum camphora and L. formosana (110 each), followed by Q. chenii (88) and Castanopsis sclerophylla (75). Structural equation modeling indicated that soil nutrients (TN, TP, AP, SOM) suppressed the accumulation of V, Cr, Ni, and Cu, thereby indirectly favoring Glomus and Paraglomus. In contrast, higher pH and total potassium (TK) levels promoted Co and Zn bioavailability and negatively affected Acaulospora and other minor genera. Tree species identity directly modulated these interactions. Our findings demonstrate that ancient tree species shape AM fungal assembly through distinct rhizosphere geochemical niches, providing a mechanistic basis for restoring degraded habitats critical to endangered species conservation. Full article

The use of LiDAR in estimating tree growth is a current and practical research topic that is important from both an ecological and forest management perspective. The aim of this study was to assess the possibility of applying publicly available LiDAR data to assess the growth of forest stands. This study focused on forests in northern Poland, where pine trees dominate, but deciduous trees such as alders and birches are also partially present. The research used generally available point clouds from airborne LiDAR data from the years 2013 and 2022 with an average density of 4 pts/m² and an accuracy of 0.15–0.25 m. Inventory data were obtained for the same dates. A methodology was developed to determine height increments from these data, and 216 corresponding tree stands were compared. The Pearson correlation coefficient was 0.6, showing a moderate correlation between height increments determined from LiDAR and inventory data. Performing LiDAR measurements during the growing season could minimize errors in determining stand heights and increase the correction between airborne laser scanning data and inventory data. Our experiment confirms that it is possible to improve forest inventory and forest management using airborne LiDAR data. Full article

The integration of Soft Open Points (SOPs) into distribution networks has been an essential method for enhancing operational flexibility and efficiency. But simultaneous optimization of network reconfiguration and SOP scheduling constitutes a difficult mixed-integer nonlinear programming (MINLP) problem that is likely to suffer from premature convergence with standard metaheuristic solvers, particularly in large power networks. This paper proposes a novel hybrid algorithm, hTLBO–CO, which synergistically integrates the exploitative capability of Teaching–Learning-Based Optimization (TLBO) with the explorative capability of the Cheetah Optimizer (CO). One of the notable contributions of our framework is an in-depth problem formulation that enables SOP locations on both tie and sectionalizing switches with an efficient constraint-handling scheme, preserving topo-logical feasibility through a minimum spanning tree repair scheme. The evolved hTLBO–CO algorithm is systematically validated across IEEE 33-, 69-, and 119-bus test feeders with differential operational scenarios. Results indicate consistent dominance over established metaheuristics (TLBO, CO, PSO, JAYA), showing significant efficiency improvement in power loss minimization, voltage profile enhancement, and convergence rate. Remarkably, in a situation with a large-scale 119-bus power grid, hTLBO–CO registered a significant 50.30% loss reduction in the single-objective reconfiguration-only scheme, beating existing state-of-the-art approaches by over 15 percentage points. These findings, further substantiated by comprehensive statistical and multi-objective analyses, confirm the proposed framework’s superiority, robustness, and scalability, establishing hTLBO–CO as a robust computational tool for the advanced optimization of future distribution networks. Full article

Efficient public transportation systems are fundamental to achieving sustainable urban development. As the backbone of urban mobility, the coordinated development of rail transit and bus systems is crucial. The opening of a new rail transit line inevitably reshapes urban travel patterns, posing significant challenges to the existing bus network. Understanding passenger switch behavior is key to optimizing the competition and cooperation between these two modes. However, existing methods on the switch behavior of bus passengers along the newly opened rail transit line cannot balance the predictive accuracy and model interpretability. To bridge this gap, we propose a CART (classification and regression tree) decision tree-based switch behavior model that incorporates both predictive and interpretive abilities. This paper uses the massive passenger swiping-card data before and after the opening of the rail transit to construct the switch dataset of bus passengers. Subsequently, a data-driven predictive model of passenger switch behavior was established based on a CART decision tree. The experimental findings demonstrate the superiority of the proposed method, with the CART model achieving an overall prediction accuracy of 85%, outperforming traditional logit and other machine learning benchmarks. Moreover, the analysis of factor significance reveals that ‘Transfer times needed after switch’ is the dominant feature (importance: 0.52), and the extracted decision rules provide clear insights into the decision-making mechanisms of bus passengers. Full article

Phyllotaxy is a key determinant of intraspecific variation in leaf functional traits, with different leaflet positions often representing distinct strategies of resource acquisition and utilization. Yet, the extent to which such phyllotactic differentiation is modulated by ontogenetic stage remains poorly understood. Here, we examined saplings and adult trees of Fraxinus mandshurica, a dominant compound-leaved species in temperate broadleaf forests, by quantifying four leaf economic traits and four stomatal traits across six phyllotactic positions. We further assessed the relative influences of phyllotaxy and environmental factors, including soil total nitrogen, soil water content, and canopy openness, on trait variation at different ontogenetic stages. Our results showed that economic traits varied significantly along phyllotaxy, whereas stomatal traits were relatively conservative. The effects of ontogenetic stage on traits at a given phyllotactic position were trait-specific. Within-group correlations of economic traits and of stomatal traits remained stable across ontogenetic stages and were consistently stronger than between-group correlations. Sapling traits were more strongly affected by soil total nitrogen and soil water content, whereas those in adult trees were primarily shaped by soil water content and canopy openness. Moreover, both trait–trait and trait–environment associations were weaker at the leaflet level than at the compound-leaf level. Our study highlights the critical role of ontogenetic stage in shaping leaf trait responses to phyllotaxy and environmental change, providing new insights into the mechanisms underlying intraspecific trait variation in compound-leaved tree species. Full article

Accurately quantifying the impact of weather on dynamic grid carbon intensity is crucial for power system decarbonization. This study proposes a novel, interpretable machine learning framework integrating tree-based models with SHapley Additive exPlanations (SHAP) to quantify this impact across multiple timescales via a standardized “Weather Share” metric. Applied to city-level hourly data from China, the analysis reveals that meteorological variables collectively explain 21.64% of the hourly variation in carbon intensity, with air temperature and solar irradiance being the dominant drivers. Significant temporal variations are observed: the weather share is higher in summer (29.8%) and winter (23.5%) than in transition seasons and increases markedly to 32.7% during extreme high-temperature events. The proposed framework provides a robust, quantitative tool for grid operators, offering actionable insights for weather-aware carbon reduction strategies and highlighting critical time windows for targeted interventions. Full article

Moso bamboo (Phyllostachys heterocyclas) has rapidly expanded in subtropical broadleaf forests of eastern China, raising concerns about biodiversity loss and community restructuring. We investigated how the expansion of this native bamboo influences species diversity and phylogenetic diversity across forest strata (trees, shrubs, herbs) by surveying 16 plots along a gradient from bamboo-free to bamboo-dominated stands. We measured soil properties, calculated multiple α-diversity indices, and constructed a community phylogeny to assess phylogenetic metrics. We also constructed a phylogenetically informed Resistance Index (RI) to evaluate species-specific responses to bamboo expansion. The results showed that overstory tree species richness and Faith’s phylogenetic diversity declined sharply with increasing bamboo cover, accompanied by significant losses of evolutionary lineages. In contrast, understory shrub and herb layers exhibited stable or higher species richness under bamboo expansion, although functional redundancy among new colonists suggests limited gains in ecosystem function. Soil conditions shifted substantially along the expansion gradient: pH increased by approximately 0.5 units, while total organic carbon and total nitrogen each decreased by about 30% (p < 0.01). Redundancy analysis and variance partitioning indicated that bamboo’s impacts on community diversity are mediated primarily through these soil changes. Species-level trends revealed that formerly dominant canopy trees (e.g., Schima superba, Pinus massoniana) were highly susceptible to bamboo, whereas certain shade-tolerant taxa (e.g., Cyclobalanopsis glauca, Rubus buergeri) showed resilience. In conclusion, the aggressive expansion of Moso bamboo drastically alters multi-layer forest diversity and community assembly processes. Our findings point to a need for targeted management (e.g., reducing bamboo density, soil restoration, and enrichment planting of native species) to mitigate biodiversity loss, underscoring the importance of considering phylogenetic diversity in expansion ecology and forest conservation. Full article

In graph theory and network design, the minimum cut is a fundamental measure of system connectivity and communication capacity. While prior research has largely focused on computing the minimum cut for a fixed source–sink pair, practical scenarios such as data center communication often demand a different objective: identifying the source node whose minimum cut to a designated sink is maximized. This task, which we term the Global Maximum Minimum Cut with Fixed Sink (GMMC-FS) problem, captures the goal of locating a high-capacity source relative to a shared sink node that aggregates multiple servers. The problem is of significant engineering importance, yet it is computationally challenging as it involves a nested max–min optimization. In this paper, we present a recursive reduction (RR) algorithm for solving the GMMC-FS problem. The key idea is to iteratively select pivot nodes, compute their minimum cuts with respect to the sink, and prune dominated candidates whose cut values cannot exceed that of the pivot. By recursively applying this elimination process, RR dramatically reduces the number of max-flow computations required while preserving exact correctness. Compared with classical contraction-based and Gomory–Hu tree approaches that rely on global cut enumeration, the proposed RR framework offers a more direct and scalable mechanism for identifying the source that maximizes the minimum cut to a fixed sink. Its novelty lies in exploiting the structural properties of the sink side of suboptimal cuts, which leads to both theoretical efficiency and empirical robustness across large-scale networks. We provide a rigorous theoretical analysis establishing both correctness and complexity bounds, and we validate the approach through extensive experiments. Results demonstrate that RR consistently achieves optimal solutions while significantly outperforming baseline methods in runtime, particularly on large and dense networks. Full article

Aboveground biomass (AGB) is a key indicator of forest structure and carbon sequestration, yet its dynamics under concurrent anthropogenic disturbances remain poorly understood. This study investigates the spatiotemporal dynamics and driving mechanisms of AGB in the Jianfengling tropical lowland rainforest (JFLTLR) within Hainan Tropical Rainforest National Park (NRHTR) from 2015 to 2023. Six machine learning models—Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF)—were evaluated, with RF achieving the highest accuracy (R² = 0.83). Therefore, RF was employed to generate high-resolution annual AGB maps based on Sentinel-1/2 data fusion, field surveys, socio-economic indicators, and topographic variables. Human pressure was quantified using the Human Influence Index (HII). Threshold analysis revealed a critical breakpoint at ΔHII ≈ 0.1712: below this level, AGB remained relatively stable, whereas beyond it, biomass declined sharply (≈−2.65 mg·ha⁻¹ per 0.01 ΔHII). Partial least squares structural equation modeling (PLS-SEM) identified plantation forests as the dominant negative driver, while GDP (−0.91) and road (−1.04) exerted strong indirect effects through HII, peaking in 2019 before weakening under ecological restoration policies. Spatially, biomass remained resilient within central core zones but declined in peripheral regions associated with road expansion. Temporally, AGB exhibited a trajectory of decline, partial recovery, and renewed loss, resulting in a net reduction of ≈ 0.0393 × 10⁶ mg. These findings underscore the urgent need for a “core stabilization–peripheral containment” strategy integrating disturbance early-warning systems, transportation planning that minimizes impacts on high-AGB corridors, and the strengthening of ecological corridors to maintain carbon-sink capacity and guide differentiated rainforest conservation. Full article

Concerns about potential negative impacts of human activity on macrofungal diversity are spreading globally, yet research on this topic remains scarce. This study focuses on forest grazing (silvopasture), a popular economic practice whose impacts on macrofungal diversity are underexplored. Through investigation and comparison of macrofungal diversity and selected environmental factors in three types of subtropical forests (secondary mixed forests, dense-tree plantations and sparse-tree plantations) before and after two years of grazing at an intensity of 10 goats per hectare in South China, three key findings emerged: (1) Macrofungal alpha-diversity increased significantly after grazing, associated with an increase in large plant remains and a decrease in litterfall thickness; (2) dominance was monopolized by few taxa before grazing but became more balanced among a number of taxa after grazing; and (3) dominance of endemic taxa decreased in two of the three types of forests after grazing. Such findings suggest that grazing may create additional niches through foraging, trampling and excretion by livestock and thus recruit diverse macrofungi but may also lead to homogenization of fungal florae across regions and thus result in recessive beta-diversity loss. As this study heavily relies on taxonomy, allied updates for ambiguous taxa recognized in analyses are additionally proposed. Full article

Pinus yunnanensis, is an ecologically and economically important tree species in southwestern China. However, its natural renewal is relatively lagging behind, and it is difficult to achieve sustainable development. Apical removal (top-pruning) can eliminate apical dominance, stimulate sprouting, and provide high-quality scions for clonal propagation. Root systems are a critical foundation for sprouting capacity. In this study, one-year-old P. yunnanensis seedlings were subjected to four treatments: removal of 3/4 (H1), 2/4 (H2), or 1/4 (H3) of the seedling height, and a non-topped control group (CK). The objective was to investigate the seedlings’ responses in terms of root morphology, biomass allocation, and allometric growth. The results showed that by May, biomass allocation in the topped treatments increased by 13.37%, 11.01%, and 7.86%, respectively, compared with the control, and also exhibited higher coefficients of variation. Under the H2 treatment, both fine and coarse roots accounted for a higher proportion of total root biomass and displayed stronger water-retention stability. With increased top-pruning intensity and time, root volume, specific root length, root tissue density, and root tip number were the first to respond, indicating the onset of allometric growth. Notably, in May, the growth rate of specific root surface area followed the order: H3 > H1 > CK > H2. These findings suggest that the root system adapts to environmental changes by modulating growth patterns among various indicators to optimize resource allocation and enhance adaptability. Full article