Search Results (476)

Background: Phoebe zhennan and Phoebe chekiangensis are valuable evergreen trees recognized for their unique aromas and ecological significance, yet the organ-related distribution and functional implications of aroma-active volatiles remain insufficiently characterized. Methods: In this study, we applied an integrated GC-MS-based volatile metabolomics approach combined with a relative odor activity value (rOAV) analysis to comprehensively profile and compare the volatile metabolite landscape in the seeds and leaves of both species. Results: In total, 1666 volatile compounds were putatively identified, of which 540 were inferred as key aroma-active contributors based on the rOAV analysis. A multivariate statistical analysis revealed clear tissue-related separation: the seeds were enriched in sweet, floral, and fruity volatiles, whereas the leaves contained higher levels of green leaf volatiles and terpenoids associated with ecological defense. KEGG pathway enrichment indicated that terpenoid backbone and phenylpropanoid biosynthesis pathways played major roles in shaping these divergent profiles. A Venn diagram analysis further uncovered core and unique volatiles underlying species and tissue specificity. Conclusions: These insights provide an integrated reference for understanding tissue-divergent volatile profiles in Phoebe species and offer a basis for fragrance-oriented selection, ecological trait evaluation, and the sustainable utilization of organ-related metabolic characteristics in breeding and conservation programs. Full article

Urban metabolism frameworks offer insight into flows of energy, materials, and services in cities, yet tree species selection is seldom treated as a metabolic indicator. In Amman, Jordan, we integrate spatial metabolic metrics to critique monocultural greening policies and demonstrate how species choices forecast long-term urban metabolic performance. Using ENVI-met 5.61 simulations, we compare Melia azedarach, Olea europaea, and Ceratonia siliqua, mainly assessing urban flow related elements like air temperature reduction, CO₂ sequestration, and evapotranspiration alongside rooting depth, isoprene emissions, and biodiversity support. Melia delivers rapid cooling but shows other negatives like a low biodiversity value; Olea offers average cooling and sequestration but has allergenic pollen issues in people as a flow; Ceratonia provides scalable cooling, increased carbon uptake, and has a high ecological value. We propose a metabolic reframing of green infrastructure planning to choose urban species, guided by system feedback rather than aesthetics, to ensure long-term resilience in arid urban climates. Full article

This study examines the impact of vegetation structure on bird species richness and diversity across four urban parks in Chaoyang District, Beijing. Throughout the year, using the Point Count Method (PCM), a total of 68 bird species and 4279 individual observations were recorded, with surveys conducted across all four seasons to capture seasonal variations. The parks with more complex vegetation, such as those with a higher tree canopy cover of species like poplars, ginkgo, and Chinese pines, exhibited higher bird species richness. For example, Olympic Forest Park, with its dense vegetation structure, hosted 42 species, whereas parks with less diverse vegetation supported fewer species. An analysis using PERMANOVA revealed that bird communities in the four parks were significantly different from each other (F = 2.76, p = 0.04075), and every comparison between parks showed significant differences as well (p < 0.001). Variations in the arrangement and level of disturbance within different plant communities likely cause such differences. Principal component analysis (PCA) identified tree canopy cover and shrub density as key drivers of bird diversity. These findings underscore the importance of preserving urban green spaces, particularly those with a diverse range of native tree species, to conserve biodiversity and mitigate the adverse effects of urbanisation. Effective vegetation management strategies can enhance avian habitats and provide ecological and cultural benefits in urban environments. Full article

Inselbergs are rocky outcrops with specialized vegetation, including woody species growing in poorly developed soils. We investigated whether populations of the lithophytic tree Pseudobombax petropolitanum A. Robyns (Malvaceae), a key species endemic to Atlantic Forest inselbergs, have convergent or divergent patterns of functional traits related to leaf chemistry. This study was carried out on three inselbergs located in southeastern Brazil. Green and senescent leaves from nine healthy trees and soil samples were collected in each inselberg. The carbon, nitrogen, phosphorus, potassium, calcium, and magnesium concentrations, and the natural abundances of δ¹³C and δ¹⁵N, were measured in leaves and soil, and the C/N, C/P, and N/P ratios were calculated. The specific leaf area (SLA) was measured, and the nutrient retranslocation rate between green and senescent leaves was estimated. Divergences between populations were observed in the concentrations of potassium and magnesium in the green and senescent leaves, as well as in the C/P and N/P ratios in senescent leaves. Our results suggest that nutrient and water dynamics may differ in some inselbergs due to specific nutrients or their relationships, even though there were convergences in most functional traits related to leaf chemistry among the Pseudobombax populations. The divergences among the populations could have important implications for species selection in the ecological restoration context. Full article

Insects’ body coloration may be indirectly influenced by their host plants. Perina nuda (Lepidoptera: Lymantriidae), commonly known as the Banyan Tussock Moth and a serious pest of banyan trees (Ficus spp.) in southern China, exhibits light body coloration during its first- to third-instar stages, with its coloration progressively darkening as it matures, but little is known of the relationship between larval body coloration and host plants. To address this gap, we examined the R (red), G (green), B (blue), and L (lightness) values of the head, dorsal thorax and abdomen, stripe, dorsal mid-line, and tail of larvae fed on different hosts and host endogenous substance by using quantitative image analysis and chemical determination. Our results revealed that larval body coloration exhibited conserved ontogenetic patterns but varied significantly with host species, developmental age, and anatomical region. Redundancy analysis identified chlorophyll-b as the dominant driver, strongly associating with dorsal thorax–abdomen pigmentation. Flavonoids exhibited subthreshold significance, correlating with darker dorsal mid-line coloration, while nutrients (sugars, proteins) showed negligible effects. Linear regression revealed weak but significant links between leaf and larval body coloration in specific body regions. These findings demonstrate that host plant endogenous substances play a critical role in shaping larval body coloration. This study provides a foundation for understanding the ecological and biochemical mechanisms underlying insect pigmentation, with implications for adaptive evolution and pest management strategies. Full article

Urban green spaces are increasingly recognized as important habitats for pollinators, and wildflower seed mixes marketed as pollinator-friendly are gaining popularity, though their actual conservation value remains poorly understood. This study provides the first systematic screening of commercially available seed mixes in Portugal, evaluating their taxonomic composition, origin, life cycle traits, and potential to support pollinator communities. A total of 229 seed mixes were identified. Although these have a predominance of native species (median 86%), the taxonomic diversity was limited, with 91% of mixes comprising species from only one or two families, predominantly Poaceae and Fabaceae, potentially restricting the range of floral resources available to pollinators. Only 21 seed mixes met the criteria for being pollinator-friendly, based on a three-step decision tree prioritizing native species, extended flowering periods, and visual diversity. These showed the highest percentage of native species (median 87%) and a greater representation of flowering plants. However, 76% of all mixes still included at least one non-native species, although none is considered invasive. Perennial species dominated all seed mix types, indicating the potential for the long-term persistence of wildflower meadows in urban spaces. Despite their promise, the ecological quality and transparency of the seed mix composition remain inconsistent, with limited certification or information on species origin. This highlights the need for clearer labeling, regulatory guidance, and ecologically informed formulations. Seed mixes, if properly designed and implemented, represent a largely untapped yet cost-effective tool for enhancing the pollinator habitats and biodiversity within urban landscapes. Full article

Individual tree crown detection (ITCD) and tree species classification are critical for forest inventory, species-specific monitoring, and ecological studies. However, accurately detecting tree crowns and identifying species in structurally complex forests with overlapping canopies remains challenging. This study was conducted in a complex mixed conifer–broadleaf forest in northern Japan, aiming to improve ITCD and species classification by employing two machine learning models and different combinations of metrics derived from very high-resolution (2.5 cm) UAV red–green–blue (RGB) and multispectral (MS) imagery. We first enhanced ITCD by integrating different combinations of metrics into multiresolution segmentation (MRS) and DeepForest (DF) models. ITCD accuracy was evaluated across dominant forest types and tree density classes. Next, nine tree species were classified using the ITCD outputs from both MRS and DF approaches, applying Random Forest and DF models, respectively. Incorporating structural, textural, and spectral metrics improved MRS-based ITCD, achieving F-scores of 0.44–0.58. The DF model, which used only structural and spectral metrics, achieved higher F-scores of 0.62–0.79. For species classification, the Random Forest model achieved a Kappa value of 0.81, while the DF model attained a higher Kappa value of 0.91. These findings demonstrate the effectiveness of integrating UAV-derived metrics and advanced modeling approaches for accurate ITCD and species classification in heterogeneous forest environments. The proposed methodology offers a scalable and cost-efficient solution for detailed forest monitoring and species-level assessment. Full article

Urban environments pose challenges for pollinators due to habitat loss and limited floral resources. However, green infrastructure, particularly street and ornamental trees, can play a critical role in supporting urban pollinator communities. In this study, we evaluated nectar volume, sugar content, and amino acid composition across eight urban tree species commonly planted in South Korea. Using standardized productivity metrics at the flower, tree, and hectare scales, we compared their nutritional contributions. Our results revealed substantial interspecific differences in nectar quantity and composition. Tilia amurensis, Heptacodium miconioides, Aesculus turbinata, and Wisteria floribunda exhibited high nectar yields or amino acid productivity, whereas species such as Cornus kousa, though lower in nutritional yield, may offer complementary value due to their distinct flowering periods or other phenological traits. These findings underscore the importance of selecting tree species not only for aesthetic value but also for ecological function, providing an evidence-based approach to pollinator-friendly urban biodiversity planning and landscape management. Full article

Tree species classification provides invaluable information across various sectors, from forest management to conservation. This task is most commonly performed using remote sensing; however, this method is prone to classification errors, which modern computational approaches aim to minimize. Convolutional neural networks (CNNs) used to model tabular data have recently gained popularity as a highly efficient classification tool. In the present study, a variation of this method is used to classify satellite multispectral data from the Sentinel-2 mission to distinguish between 18 common Polish tree species. The novel model is trained and tested on data from species-homogeneous forest stands. The data form a multi-seasonal time series and cover five years of observations. The model achieved an overall accuracy of 80% and Cohen Kappa of 0.80 of the raw output and increased to 93% with post-processing procedures. Considering the large number of species classified, this is a promising and encouraging result. The presented results indicate the importance of early vegetation season reflectance data in model training. The spectral bands representing the infrared, red-edge and green wavelengths had the greatest impact on the model. Full article

Canada’s vast forests play a substantial role in the global carbon balance but require laborious and expensive forest inventory campaigns to monitor changes in aboveground biomass (AGB). Light detection and ranging (LiDAR) or reflectance observations onboard airborne or unoccupied aerial vehicles (UAVs) may address scalability limitations associated with traditional forest inventory but require simple forest structures or large sets of manually delineated crowns. Here, we introduce a deep learning approach for crown delineation and AGB estimation reproducible for complex forest structures without relying on hand annotations for training. Firstly, we detect treetops and delineate crowns with a LiDAR point cloud using marker-controlled watershed segmentation (MCWS). Then we train a deep learning model on annotations derived from MCWS to make crown predictions on UAV red, blue, and green (RGB) tiles. Finally, we estimate AGB metrics from tree height- and crown diameter-based allometric equations, all derived from UAV data. We validate our approach using 14 ha mixed forest stands with various experimental tree densities in Southern Ontario, Canada. Our results show that using an unsupervised LiDAR-only algorithm for tree crown delineation alongside a self-supervised RGB deep learning model trained on LiDAR-derived annotations leads to an 18% improvement in AGB estimation accuracy. In unharvested stands, the self-supervised RGB model performs well for height (adjusted R², R_a² = 0.79) and AGB (R_a² = 0.80) estimation. In thinned stands, the performance of both unsupervised and self-supervised methods varied with stand density, crown clumping, canopy height variation, and species diversity. These findings suggest that MCWS can be supplemented with self-supervised deep learning to directly estimate biomass components in complex forest structures as well as atypical forest conditions where stand density and spatial patterns are manipulated. Full article

Understanding species-specific mechanisms governing symbiotic fungal responses to plant traits and soil factors is critical for optimizing urban tree “plant-soil-fungus” systems under pollution stress. To address this gap, we combined δ¹³C/δ¹⁵N isotope analysis and ITS sequencing for three common street trees in Beijing: Sophora japonica, Ginkgo biloba, and Populus tomentosa. In S. japonica, symbiotic fungal abundance was positively associated with leaf δ¹⁵N, indicating root exudate-mediated “plant-microbe” interactions during atmospheric NO_x assimilation. G. biloba, with weak NO_x assimilation, exhibited a negative correlation between fungal abundance and soil available N/P, suggesting mycorrhizal nutrient compensation under low fertility. P. tomentosa showed decreased fungal abundance with increasing soil N/P ratios and specific leaf area, reflecting carbon allocation trade-offs that limit mycorrhizal investment. These results demonstrate that symbiotic fungi respond to atmospheric and edaphic drivers in a tree species-dependent manner. Urban greening strategies should prioritize S. japonica for its NO_x mitigation potential and optimize fertilization for G. biloba (nutrient-sensitive fungi) and P. tomentosa (nutrient balance sensitivity). Strategic mixed planting of P. tomentosa with S. japonica could synergistically enhance ecosystem services through complementary resource acquisition patterns. This study provides mechanism-based strategies for optimizing urban tree management under atmospheric pollution stress. Full article

Forest ecosystems represent a natural repository of biodiversity, bioenergy, food, timber, water, medicine, wildlife shelter, and pollution control. In many countries, forests offer great potential to provide biogenic resources that could be utilized for large-scale biotechnological synthesis and products. The evolving nanotechnology could be an excellent platform for the transformation of forest products into value-added nanoparticles (NPs). It also serves as a tool for commercial production, placing the forest at the heart of conservation and sustainable management strategies. NPs are groups of atoms with a size ranging from 1 to 100 nm. This review analyzes the scholarly articles published over the last 25 years on the forest and woody plant-based green synthesis of NPs, highlighting the plant parts and applications discussed. The biosynthesis of nanomaterials from plant extracts provides inexpensiveness, biocompatibility, biodegradability, and environmental nontoxicity to the resultant NPs. The leaf is the most critical organ in woody plants, and it is widely used in NP biosynthesis, perhaps due to its central functions of bioactive metabolite production and storage. Most biosynthesized NPs from tree species have been used and tested for medical applications. For sustainable advancements in forest-based nanotechnology, broader species coverage, expanded applications, and interdisciplinary collaboration are essential. Full article

Low-cost unmanned aerial vehicle (UAV) visible light remote sensing provides new opportunities for plant community monitoring, but its practical deployment in different ecosystems is still limited by the lack of standardized vegetation index (VI) optimization for multi-species coverage extraction. This study developed a universal method integrating four VIs—Excess Green Index (EXG), Visible Band Difference Vegetation Index (VDVI), Red-Green Ratio Index (RGRI), and Red-Green-Blue Vegetation Index (RGBVI)—to bridge UAV imagery with plant communities. By combining spectral separability analysis with machine learning (SVM), we established dynamic thresholds applicable to crops, trees, and shrubs, achieving cross-species compatibility without multispectral data. The results showed that all VIs achieved robust vegetation/non-vegetation discrimination (Kappa > 0.84), with VDVI being more suitable for distinguishing vegetation from non-vegetation. The overall classification accuracy for different vegetation types exceeded 92.68%, indicating that the accuracy is considerable. Crop coverage extraction showed a minimum segmentation error of 0.63, significantly lower than that of other vegetation types. These advances enable high-resolution vegetation monitoring, supporting biodiversity assessment and ecosystem service quantification. Our research findings track the impact of plant communities on the ecological environment and promote the application of UAVs in ecological restoration and precision agriculture. Full article

Ambrosia beetles (Coleoptera: Curculionidae: Scolytinae) in the tribe Xyleborini are economically important pests of woody ornamentals, tree nuts, and fruit orchards, including pecans in the United States. Among them, the granulate ambrosia beetle, Xylosandrus crassiusculus (Motschulsky), is the most common species in pecan orchards in Georgia. Various traps, including ethanol-mediated Lindgren multi-funnel traps, panel traps, bottle traps, sticky cards, and ethanol-infused wooden bolts, are used in ambrosia beetle monitoring programs. Trap color and placement height are important factors that increase trap effectiveness. To improve trap effectiveness for ambrosia beetles, we conducted a color and height preference experiment under field conditions using six different colored sticky cards, including black, blue, green, red, transparent, and yellow, placing them at three different heights (15, 60, and 120 cm from ground level). The results show that red and transparent sticky cards consistently captured a higher number of ambrosia beetles, whereas yellow-colored sticky cards consistently captured a lower number of ambrosia beetles compared to all other tested colors of sticky cards. A similar trend was observed with X. crassiusculus in field and laboratory settings. Among the evaluated trap heights, more ambrosia beetles, including X. crassiusculus, were consistently captured in the sticky cards placed at a height of 60 cm from the ground surface. Additionally, we monitored natural infestations of ambrosia beetles in commercial pecan orchards in Georgia and found more damage to pecan trees near the ground surface (45 cm) compared to the upper parts. We also recorded three ambrosia beetle species, X. crassiusculus, the black stem borer, X. germanus (Blandford), and the Southeast Asian ambrosia beetle, Xylosandrus amputatus (Blandford). Among them, X. crassiusculus (90.50%) was the most abundant species in the pecan orchards. Therefore, red and transparent sticky cards placed at a height of 45 to 60 cm could improve the trap efficacy and can be used for monitoring ambrosia beetles in pecan orchards. Full article

The management and identification of forest species in a city are essential tasks for current administrations, particularly in planning urban green spaces. However, the cost and time required are typically high. This study evaluates the potential of RGB point clouds captured by unnamed aerial vehicles (UAVs) for automating tree species classification. A dataset of 809 trees (crowns) for eight species was analyzed using a random forest classifier and deep learning with PointNet and PointNet++. In the first case, eleven variables such as the normalized red–blue difference index (NRBDI), intensity, brightness (BI), Green Leaf Index (GLI), points density (normalized), and height (maximum and percentiles 10, 50, and 90), produced the highest reliability values, with an overall accuracy of 0.70 and a Kappa index of 0.65. In the second case, the PointNet model had an overall accuracy of 0.62, and 0.64 with PointNet++; using the features Z, red, green, blue, NRBDI, intensity, and BI. Likewise, there was a high accuracy in the identification of the species Populus alba L., and Melaleuca armillaris (Sol. ex Gaertn.) Sm. This work contributes to a cost-effective workflow for urban tree monitoring using UAV data, comparing classical machine learning with deep learning approaches and analyzing the trade-offs. Full article