Search Results (2,814)

This study analyzed the planting characteristics and spatial patterns of public-type private gardens in urban areas. Five gardens in Daejeon and Ulsan were surveyed using quadrats to record tree locations and sizes and were digitized for layout mapping. Planting and analysis units were defined, and spatial patterns were examined using degree centrality. The gardens were classified into one site under mixed artificial–natural management and four sites under artificial management with commercial linkage. The mixed site featured both canopy and shrub layers, with spontaneous vegetation surrounding Pinus thunbergii, Pinus densiflora, and Prunus yedoensis. The commercial sites included either canopy-only or canopy-shrub structures. Lagerstroemia indica, P. densiflora, and Euonymus japonicus. were predominant in the temperate central region, while P. densiflora and Diospyros kaki. dominated in the southern region. This study identified the potential of public-type private gardens as planting models and their capacity to contribute to urban environmental improvement. Full article

In a survey of Bajiaozhai National Forest Park (Guilin, China), several specimens of an Euseius sp. were collected. These specimens were very similar to Euseius nicholsi and Euseius oolong, based on morphological observations. However, some morphological characters, such as the body size, number of solenostomes on the dorsal plate, calyx shape of the spermatheca, the shape and number of metapodal platelet, teeth number on the fixed digit, length of setae j3, and macroseta Seg IV, Sti IV, and St IV were different between these specimens and E. nicholsi and E. oolong. To ascertain whether these morphological differences were interspecific or intraspecific variations, molecular analyses were conducted using mitochondrial DNA COI, 12S rRNA, and nuclear ITS markers. Based on the three molecular markers, minimal genetic distances were observed (COI 0–4%, 12S rRNA 0–2%, and ITS 0%) among the putative Euseius sp., E. nicholsi (collected from Bauhinia purpurea in IZGAS and from Eurya macartneyi and Ficus hispida in Shaoguan City), and E. oolong (collected from B. purpurea in IZGAS). Amblyseius swirskii was used as the outgroup. Using the maximum likelihood method, the phylogenetic tree showed that these specimens of Euseius sp., E. nicholsi, and E. oolong clustered in a single clade. Therefore, we propose that this putative Euseius sp. is E. nicholsi, and E. oolong is a junior synonym of E. nicholsi. This study demonstrates the importance of integrative taxonomy for the proper identification of phytoseiid mites. Full article

This paper presents a heuristic methodology for the optimal expansion of unbalanced three-phase distribution systems in rural areas, simultaneously addressing feeder routing and conductor sizing to minimize the total annualized cost—defined as the sum of investments in conductors and operational energy losses. The planning strategy explores two radial topological models: the Minimum Spanning Tree (MST) and the Steiner Tree (ST). The latter incorporates auxiliary nodes to reduce the total line length. For each topology, an initial conductor sizing is performed based on three-phase power flow calculations using Broyden’s method, capturing the unbalanced nature of the rural networks. These initial solutions are refined via four metaheuristic algorithms—the Chu–Beasley Genetic Algorithm (CBGA), Particle Swarm Optimization (PSO), the Sine–Cosine Algorithm (SCA), and the Grey Wolf Optimizer (GWO)—under a master–slave optimization framework. Numerical experiments on 15-, 25- and 50-node rural test systems show that the ST combined with GWO consistently achieves the lowest total costs—reducing expenditures by up to 70.63% compared to MST configurations—and exhibits superior robustness across all performance metrics, including best-, average-, and worst-case solutions, as well as standard deviation. Beyond its technical contributions, the proposed methodology supports the United Nations Sustainable Development Goals by promoting universal energy access (SDG 7), fostering cost-effective rural infrastructure (SDG 9), and contributing to reductions in urban–rural inequalities in electricity access (SDG 10). All simulations were implemented in MATLAB 2024a, demonstrating the practical viability and scalability of the method for planning rural distribution networks under unbalanced load conditions. Full article

Microrefugia can be critical in mediating biological responses to climate change, but the location and characteristics of these habitats are often poorly understood. Groundwater-dependent ecosystems (GDEs) represent critical microrefugia for species dependent on cool, moist habitats. However, knowledge of the distribution and stability of GDE microrefugia remains limited. This challenge is typified in the Pacific Northwest, where poorly studied cliff-face seeps harbor exceptional biodiversity despite their diminutive size (e.g., ~1–10 m width). To improve knowledge about these microrefugia, we regionally modeled their distribution and stability. We searched for cliff-face seeps across 1608 km of roads, trails, and watercourses in Washington and Idaho, while monitoring water availability plus air and water temperatures at selected sites. We detected 457 seeps through an iterative process of surveying, modeling, ground-truthing, and then remodeling the spatial distribution of seeps using boosted regression trees. Additionally, we used linear and generalized linear models to identify factors linked to seep thermal and hydrologic stability. Seeps were generally most concentrated in steep and low-lying areas (e.g., edges of canyon bottoms), and were also positively associated with glacial drift, basalt or graywacke bedrock types, high average slope within 300 m, and low average vapor pressure deficit. North-facing slopes were the best predictor of stable air and water temperatures and perennial seep discharge; low-lying areas also predicted stable seep water temperatures. These findings improve possibilities to manage seep microrefugia in the Pacific Northwest and safeguard their associated biodiversity under climate change. Lastly, our iterative method adapts techniques commonly used in species distribution modeling to provide an innovative framework for identifying inconspicuous microrefugia. Full article

English walnut (Juglans regia) is a species that is highly valued for the quality of its wood and the nutritional and nutraceutical properties of its fruit. A severe dieback of J. regia trees was observed recently in orchards located in three geographically distinct areas of Tuscany, central Italy. Symptoms included root and collar rot, necrosis of the under-bark tissue, bleeding cankers, stunted growth, and crown dieback. Four Phytophthora species were obtained from 239 isolates found on symptomatic J. regia individuals. They were identified, on the basis of macro-morphological (colony shape and texture), micro-morphometric (shape and size of oogonia, antheridia, oospores, sporangia, and chlamydospores) and molecular (ITS sequencing) characters, as P. gonapodyides, P. cactorum, P. citricola, and P. plurivora. Among these species, P. plurivora was the species isolated with overwhelming frequency from symptomatic tissue and rhizosphere soil, suggesting it to be the putative etiological agent. Pathogenicity assays were conducted on 20 cm long detached J. regia branches for a definitive establishment of disease causation. Severe symptoms (extended necroses) were exhibited by branches infected with P. plurivora, proving its pathogenicity and high virulence on this host. The other Phytophtora species produced negligible necroses around the inoculation site. P. plurivora was recovered from all the investigated orchards, providing evidence that it is quite widespread. This study highlights the growing threat posed by the polyphagous P. plurivora to walnut cultivation and the sustainable business it fuels in Europe, underscoring the need for integrated management strategies to mitigate its economic and ecological impacts. Full article

Accurate classification of tree species is crucial for forest management and biodiversity conservation. Remote sensing technology offers a unique capability for classifying and mapping trees across large areas; however, the accuracy of extracting and identifying individual trees remains challenging due to the limitations of available imagery and phenological variations. This study presents a novel integrated machine learning (ML) and Geographic Object-Based Image Analysis (GEOBIA) framework to enhance tree species classification in a botanical garden using multi-temporal unmanned aerial vehicle (UAV) imagery. High-resolution UAV imagery (2.3 cm/pixel) was acquired across four different seasons (summer, autumn, winter, and early spring) to incorporate the phenological changes. Spectral, textural, geometrical, and canopy height features were extracted using GEOBIA and then evaluated with four ML models (Random Forest (RF), Extra Trees (ET), eXtreme gradient boost (XGBoost), and Support Vector Machine (SVM)). Multi-temporal data significantly outperformed single-date imagery, with RF achieving the highest overall accuracy (86%, F1-score 0.85, kappa 0.83) compared to 57–75% for single-date classifications. Canopy height and textural features were dominant for species identification, indicating the importance of structural variations. Despite the limitations of moderate sample size and a controlled botanical garden setting, this approach offers a robust framework for forest and urban landscape managers as well as remote sensing professionals, by optimizing UAV-based strategies for precise tree species identification and mapping to support urban and natural forest conservation. Full article

In many hardwood forests, resprouting is a common response to disturbance and basal resprouts may represent a substantial component of the forest understory, especially post-wildfire. Despite this, resprouts are often overlooked in biomass assessments and drivers of resprouting responses in certain species are still unknown. These knowledge gaps are problematic as the contribution of resprouts to understory fuel loads are needed for wildfire risk modeling and effective forest stewardship. Here, we validated the handheld mobile laser scanning (HMLS) of basal resprout volume and field measurements of stem count and clump height as methods to estimate the mass of California Bay Laurel (Umbellularia californica) basal resprouts at Pepperwood and Saddle Mountain Preserves, Sonoma County, California. In addition, we examined the role of tree size and wildfire severity in predicting post-wildfire resprouting response. Both field measurements (clump height and stem count) and remote sensing (HMLS-derived volume) effectively estimated dry mass (total, leaf and wood) of U. californica resprouts, but underestimated dry mass for a large resprout. Tree size was a significant factor determining post-wildfire resprouting response at Pepperwood Preserve, while wildfire severity significantly predicted post-wildfire resprout size at Saddle Mountain. These site differences in post-wildfire basal resprouting predictors may be related to the interactions between fire severity, tree size, tree crown topkill, and carbohydrate mobilization and point to the need for additional demographic and physiological research. Monitoring post-wildfire changes in U. californica will deepen our understanding of resprouting dynamics and help provide insights for effective forest stewardship and wildfire risk assessment in fire-prone northern California forests. Full article

Coniferous forests in Canada play a vital role in carbon sequestration, wildlife conservation, climate change mitigation, and long-term sustainability. Traditional methods for classifying and segmenting coniferous trees have primarily relied on the direct use of spectral or LiDAR-based data. In 2024, we introduced a novel data representation method, pseudo tree crown (PTC), which provides a pseudo-3D pixel-value view that enhances the informational richness of images and significantly improves classification performance. While our original implementation was successfully tested on urban and deciduous trees, this study extends the application of PTC to Canadian conifer species, including jack pine, Douglas fir, spruce, and aspen. We address key challenges such as snow-covered backgrounds and evaluate the impact of training dataset size on classification results. Classification was performed using Random Forest, PyTorch (ResNet50), and YOLO versions v10, v11, and v12. The results demonstrate that PTC can substantially improve individual tree classification accuracy by up to 13%, reaching the high 90% range. Full article

One of the key challenges in the provision of sustainable energy is understanding how younger generations perceive and respond to the relatively higher cost of green energy. This paper examines the attitudes of Generation Z towards paying premium for using products and services made with green power technologies. We surveyed 173 first- and second-year full-time bachelor students from Krakow University of Economics in Poland, combining contingent valuation in daily life scenarios (coffee purchase, apartment rental, travel carbon offset, environmental donation) with measures of connectedness to nature and self-reported tipping behavior. The results show that between 69% and 82% of respondents are willing to pay a premium for green energy. The size of the premium depends on the product that is bought. We find that while respondents are willing to pay a 10.5% premium for coffee prepared in a restaurant that uses only green energy, they are willing to pay just a 3.1% premium for green electricity at home. We also find that respondents are willing to pay three times more for planting a tree than to offset the carbon footprint of a train trip. A stronger emotional and cognitive bond with nature (on a CNS scale) translates into a greater willingness to financially support environmental initiatives. Full article

In Mexico, an emerging tropical nation, where cities have insufficient vegetation cover and there is little information about their provision of ecosystem services; the study of urban vegetation, as a mitigation strategy, is required. The sidewalk trees in the city of Arriaga (CAR), considered one of the warmest cities in the Mexican southeast, were counted, measured, and assessed for their effect on surface and air temperatures. There are 6239 sidewalk trees, distributed in 11 families and 13 species; 136 trees were sampled concentrating 77% in three species: Neem, Country almond and Benjamina fig. Therefore, a low H’ (1.73 nats) was obtained. The mitigating effect of tree shade on surface temperature went from 7 °C to 23 °C, depending on the day and hour, while there was not a significant refreshing effect of air temperature because the height of sidewalk trees is controlled with severe pruning to prevent damage to public wiring, causing a similar-sized stratum that traps air under the tree canopy. Consequently, an integral solution that includes, but is not limited to, urban trees is required without leaving aside increasing tree diversity, health, and equitable distribution of trees at CAR. Full article

Southern Chile has become a prominent region for sweet cherry production. However, environmental constraints and low P availability can adversely affect fruit quality and conditions in southern Chile. Therefore, the aim of this study was to evaluate the effects of foliar phosphorus (P) on fruit quality, condition, and antioxidant content at harvest and postharvest storage in sweet cherry (Prunus avium L.) cv. Regina was cultivated under a plastic cover in Southern Chile. For this, sweet cherry trees were subjected to three treatments: control (no P), 1.5 L ha⁻¹, and 2.2 L ha⁻¹ foliar P. In our study, no significant effects were observed on average fruit weight, size, or total soluble solids among P treatments. However, P applications increased the proportion of large fruit (>32 mm), enhanced dark mahogany coloration, and pulp antioxidant content (total phenols and anthocyanins). Interestingly, the 2.2 L ha⁻¹ treatment reduced postharvest disorders, including pitting (70%), dehydration (31%), orange peel (56%), and internal browning (29%) compared to the control trees. These results suggest that foliar P application could be an agronomic tool to improve fruit quality and condition in sweet cherry production under plastic covers cultivated in soils with low P availability. Full article

Tea tree seeds are highly sensitive to dehydration and cannot be stored for extended periods, making surface defect detection crucial for preserving their germination rate and overall quality. To address this challenge, we propose Cgc-YOLO, an enhanced YOLO-based model specifically designed to detect small-scale and complex surface defects in tea seeds. A high-resolution imaging system was employed to construct a dataset encompassing five common types of tea tree seeds, capturing diverse defect patterns. Cgc-YOLO incorporates two key improvements: (1) GhostBlock, derived from GhostNetV2, embedded in the Backbone to enhance computational efficiency and long-range feature extraction; and (2) the CPCA attention mechanism, integrated into the Neck, to improve sensitivity to local textures and boundary details, thereby boosting segmentation and localization accuracy. Experimental results demonstrate that Cgc-YOLO achieves 97.6% mAP50 and 94.9% mAP50–95, surpassing YOLO11 by 2.3% and 3.1%, respectively. Furthermore, the model retains a compact size of only 8.5 MB, delivering an excellent balance between accuracy and efficiency. This study presents a robust and lightweight solution for nondestructive detection of tea seed defects, contributing to intelligent seed screening and storage quality assurance. Full article

To evaluate the stability dynamics of typical forest plant communities in Northeast China, 57 forest plots were surveyed in 2009 and surveyed again in 2014. By adapting temporary stability (TS) as the community stability indicator, all plots were divided into three groups of low, moderate, and high stability, and the community initial state and state changes in different groups were analyzed. Results showed that the first dominant species in 15.8% (3/19) of plots was replaced by the second dominant species from 2009 to 2014 in the low stability group, but no such changes occurred in the moderate and high stability groups. The TS change amplitude was obvious in the low stability group, while that was slight in the high stability group. The relative basal area of the top two species was close in the low stability group in both 2009 and 2014, while the first dominant species was prominent in the high stability group. Communities in the high stability group had lower tree diversity, and those in the low stability group had more trees in 2009. Furthermore, tree size increased significantly in the low and moderate stability groups, and tree number decreased significantly in the moderate stability group from 2009 to 2014. The TS indicator is feasible in describing the stability state and change processes of forest plant communities on a time scale. Full article

Driven by real-world demands of processing massive high-frequency data and achieving longer forecasting horizons in time series forecasting scenarios, a variety of deep learning architectures designed for time series forecasting have emerged at a rapid pace. However, this rapid development actually leads to a sharp increase in parameter size, and the introduction of numerous redundant modules typically offers only limited contribution to improving prediction performance. Although prediction models have shown a trend towards simplification over a period, significantly improving prediction performance, they remain weak in capturing dynamic relationships. Moreover, the predictive accuracy depends on the quality and extent of data preprocessing, making them unsuitable for handling complex real-world data. To address these challenges, we introduced Treeformer, an innovative model that treats the traditional tree-based machine learning model as an encoder and integrates it with a Transformer-based forecasting model, while also adopting the idea of time–feature two-dimensional information extraction by channel independence and cross-channel modeling strategy. It fully utilizes the rich information across variables to improve the ability of time series forecasting. It improves the accuracy of prediction on the basis of the original deep model while maintaining a low computational cost and exhibits better applicability to real-world datasets. We conducted experiments on multiple publicly available datasets across five domains—electricity, weather, traffic, the forex market, healthcare. The results demonstrate improved accuracy, and provide a better hybrid approach for enhancing predictive performance in Long-term Sequence Forecasting (LSTF) problems. Full article

Zanthoxylum armatum DC. (Z. armatum) is a versatile plant species valued for its aroma oil and nutritional components. However, the variability of chemical composition in Z. armatum fruits in the field remains largely unknown, and it is still unclear how harvest parameters affect the aroma and nutritional quality of the fruits. To address this gap, Z. armatum fruits from varying harvest times, tree ages, and fruiting positions were analyzed for physicochemical properties, nutrients, minerals, aroma profiles, and antioxidant activity. A quality assessment method was developed based on key Z. armatum fruit parameters. Results showed significant differences in the size, weight, total phenol, flavonoid and sanshool content of Z. armatum fruit from different harvest parameters. Z. armatum fruits contained abundant minerals, showing diverse harvest-condition variations. In vitro antioxidant assays showed higher ABTS/DPPH scavenging activity and reducing capacity (23–54 mg/g). HS-SPME-GC-MS identified 64 aroma compounds, encompassing terpenes, alcohols, etc. Linalool was the predominant constituent (46.65%). PLS-DA and Volcano plot analyses highlighted significant differences in VOCs among harvest times and tree ages, while fruit positions showed minimal impact. The Mantel test identified aroma-active compounds associated with antioxidant activity. These findings facilitate a science-based harvesting strategy to standardize Z. armatum fruit quality and marketability. Full article