Search Results (2,165)

Pharomachrus mocinno breeds in the cloud forests of the El Triunfo Biosphere Reserve, and migrates annually for six months to elevations of 900–1600 m. On the Gulf slope, temperate forests were identified as habitats for migration, but the forests on the Pacific slope have not been similarly described. In this study we described the emergent properties and phenological behavior of the plant communities of five sites identified as migration habitats, in order to test if the number of fruit-bearing species is related to the migration period. At each site, 10,000 m² was sampled, for which PBH (perimeter at breast height) and the height of shrubs and trees were annotated, including the number of palms and ferns included. We identified 25 orders, 41 families, 71 genera, and 94 species; 86.6% of these species produce fleshy fruits or fruits with modified structures that are eaten by Quetzals. During the migration period, 25–43% of these species have fruits. Eight woody species included 49% of the total individuals, which produce Quetzals’ feeding resources. The sites differed in vertical structure, composition and diversity levels. The rarefaction curve indicated that the upper site (1600 m) required more sampling. We identified three plant communities that were distributed either in montane rain forest or in the temperate forest. Since nearly 84% of the plant species are listed in the IUCN (International Union for Conservation of Nature), these forests have an intrinsic importance. The number of fruit-bearing species did not differ between migration and breeding seasons (X² (1, N = 76) 0.57; p = 0.32. Lauraceae did not stand out for the number of fruit-bearing species in any of the migration sites. Full article

Most ash trees (Fraxinus excelsior) in Germany are infected with Hymenoscyphus fraxineus, the causative agent of ash dieback (ADB). This study investigates the phenolic content of ash leaves to evaluate their potential as indicators for monitoring ADB and to assess how this potential is affected by site and year. Fresh leaf samples were collected and immediately frozen from 14 forest plots across Germany over a period of up to four years. Phenolic compounds were quantified using both photometric assays and HPLC. The results reveal strong site-specific differences in both total phenolic content and individual phenolic profiles. Temporal differences between sampling years were less pronounced, but were frequently significant. In contrast, crown condition—a key indicator of ADB damage—had only a weak effect on phenolic content. This suggests that mature ash trees do not exhibit a clear phenol-based defence response to H. fraxineus under field conditions. Our findings underscore the complexity of phenolic dynamics in natural stands and demonstrate that no robust of phenolic biomarker for ADB could be identified in mature trees. Full article

Longleaf pine forests are economically important habitats that stabilize and enrich the soil and store carbon over long periods. When mixed with oaks, these forests provide an abundance of lichen habitats. The tree canopy lichens promote greater moisture capture and retention and encourage canopy insects. Ground lichens limit some vascular plant germination and growth, promoting a more open and healthy pine community. There is a longstanding mutualistic relationship between longleaf pine habitat and lichens. Longleaf pine habitat has a long history of natural summer burning, which promotes a diverse understory and limits tree densities. Lichen diversity exceeds vascular plant diversity in many mature longleaf pine habitats, yet information on the impacts of prescribed fire on lichen species in these habitats is limited. We assessed lichen diversity and abundance before and after a prescribed ground fire in a longleaf pine/wiregrass habitat near Ocala, Florida. Pre-burn, we found greater lichen abundance and diversity on hardwoods, primarily oak species, than on pines. Post-burn, lichen abundance on hardwoods dropped overall by 28%. Lichen abundance on conifers dropped overall by 94%. Ground lichen species were basically eliminated, with a 99.5% loss. Our study provides insights into retaining lichen diversity after a prescribed burn. Hardwood trees, whether alive or standing dead, help retain lichen biodiversity after burning, whereas conifer trees do not support as many species. Landscapes may need to be actively managed by raking pine needle litter away from ground lichen beds, moistening the ground, or removing some lichen material before the burn and returning it to the site post-fire. Based on these results, we suggest retaining some oaks and conducting burns in a mosaic pattern that retains unburned areas. This will allow for lichens to recover between burns, significantly enhancing biodiversity and the ecological health of these longleaf pine communities. Full article

Intra-specific variation in functional traits and their inter-relationships reflect how plants allocate resources, adapt, and evolve in response to environmental changes. This study investigated eight functional traits—leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC), chlorophyll content (CHL), leaf nitrogen content (LNC), leaf phosphorus content (LPC), twig tissue density (TTD), and wood density (WD)—in Cunninghamia lanceolata plantations of three stand ages (15, 30, and 50 years), using a space-for-time substitution approach. We examined differences in trait values, intra-specific variation, and trait correlations across forest ages and diameter classes. The results showed that (1) Functional traits exhibited varying degrees of intra-specific variation, with LA having the highest coefficient of variation (21.66%) and LPC is lowest (9.31%). (2) Forest age had a stronger influence on trait variation than diameter class, with all traits differing significantly across ages, while only WD varied significantly among diameter classes. (3) PC1 (25.5%) and PC2 (19.4%) together explained approximately 44.9% of the total variation, with PC1 primarily reflecting functional trait changes driven by forest age. PCA results showed that LA and CHL tended to exhibit higher values in young forests, whereas SLA, LDMC, LPC, and LNC had relatively higher values in mature forests. This pattern suggests a shift in functional trait expression from resource acquisition to resource conservation strategies with increasing forest age. (4) Significant positive correlations between LNC and LPC, and negative correlations between SLA and LDMC, were observed in most groups, except in large-diameter trees at the over-mature stage. C. lanceolata adjusts trait combinations to enhance fitness across developmental stages. Juvenile trees adopt traits favoring efficient light and nutrient use to support rapid growth and competition. Middle-aged trees prioritize balanced water and nutrient use to maintain productivity and resist disturbances. Mature trees focus on sustained resource use and offspring protection to support ecosystem stability and regeneration. These findings reveal age-specific adaptive strategies and provide insights into the coordination and trade-offs among traits in response to environmental conditions. Full article

Accurate estimation of plantation aboveground biomass (AGB) is critical for quantifying carbon cycles and informing sustainable forest resource management, but enhancing estimation accuracy remains a key challenge. Although tree height and stand age are recognized as critical predictors for enhancing AGB models in addition to spectral vegetation indices, their individual and combined contributions in regional plantation forests remain insufficiently quantified, especially concerning the potential for leveraging the distinct characteristics of fast-growing plantations to facilitate AGB estimation. This study developed multi-source remote sensing-based Eucalyptus AGB estimation models for Nanning, Guangxi, integrating stand age and tree height to assess their impacts. Stand age was mapped from Landsat time-series imagery, and tree height was derived from UAV-LiDAR data. Plot-level reference AGB was obtained using fused UAV and terrestrial LiDAR point clouds. A random forest model, incorporating these variables with Sentinel-2 spectral information and topography, then achieved regional AGB estimation. The findings demonstrate that (1) tree height serves as the most influential predictor for AGB estimation at the regional scale, yielding a robust model performance (R² = 0.84). (2) Tree height captures the majority of the explanatory power associated with stand age. Once tree height was included as a predictor, the subsequent addition of stand age offered no significant improvement in model accuracy (R² = 0.85). (3) Given the challenges in obtaining precise tree height data and the robust correlation between stand age and tree height in fast-growing plantations, the integration of stand age substantially improved the accuracy of AGB estimations (from the spectral model of R² = 0.54 to R² = 0.74), with performance approaching that of tree height-based models (ΔR² = 0.10). Consequently, in fast-growing plantations, which are often characterized by high stand homogeneity, a hybrid model incorporating stand age can offer a reliable and cost-effective solution for AGB estimation. Full article

The estimation of rubber plantation aboveground biomass (AGB) is crucial for carbon sequestration assessment and management optimization. Unmanned Aerial Vehicles (UAVs) fitted with multispectral sensors present an economical approach for local-scale AGB monitoring. However, the prevailing studies primarily concentrate on spectral characteristics and algorithmic enhancements, failing to incorporate key ecological parameters such as stand age. Moreover, the current approaches remain constrained to local-scale assessments due to the absence of reliable upscaling methodologies from UAV to satellite platforms, limiting their applicability for regional monitoring. Thus, this study aims to establish an improved estimation model for rubber plantation AGB based on UAV multispectral imagery and stand age, develop an upscaling algorithm to bridge the gap between UAV and satellite scales, and ultimately achieve accurate regional-scale monitoring of rubber forest AGB. Combining optimized multispectral features, Landsat-derived stand age, and machine learning techniques yields the most accurate UAV-scale AGB estimates in this study, with performance metrics of R² = 0.90, an RMSE = 13.24 t/ha, and an MAE = 11.09 t/ha. Notably, the novel ‘UAV-satellite’ upscaling approach proposed in this study enables regional-scale AGB estimation using Sentinel-2 imagery, with remarkable consistency (correlation coefficient of 0.93). The developed framework synergistically combines Landsat-derived stand age data with spectral features, effectively improving rubber plantation AGB estimation accuracy through machine learning and enabling UAVs to replace manual measurements. This cross-scale upscaling framework demonstrates applicability beyond rubber plantation AGB monitoring, while providing novel insights for estimating critical parameters, including regional-scale stock volume and leaf area index, across diverse tree species. Full article

The Mass Ratio Hypothesis states that the growth dynamics of dominant species influence forest species diversity by regulating the niches of subordinate and transient species. However, this prediction has not yet been empirical confirmed in subtropical forests over long term. Using data from 1995 to 2017, we examined how dominant tree species regulate species evenness and richness by analyzing their height and diameter growth in three clear-cut forests (Castanopsis carlesii (Hemsl.) Hayata, Castanopsis fissa (Champ. ex Benth.) Rehder & E. H. Wilson, and Cunninghamia lanceolata (Lamb.) Hook. stands), combined with the mean value of species niche breadth (measures the diversity of resources a species utilizes) across the community, including separate analyzes for subordinate (persistently coexisting with dominants species) and transient species (temporarily occurring species). Our results showed that an increase in height and diameter growth of dominant species had a negative effect on niche breadth of subordinate species, which in turn reduced species evenness (p < 0.01) but showed no significant relationship with species richness (p ≥ 0.05). Growth dynamics of dominants had no significant influence on the niche breadth of transient species. The early-fast growing dominant C. lanceolata significantly restricted the niche breadth of subordinate species (1.16 ± 0.23), resulting in relatively low evenness (0.49 ± 0.11). Conversely, the late-fast growing dominant C. carlesii promoted niche expansion (6.62 ± 1.20), resulting in higher evenness (0.81 ± 0.02). C. fissa -dominated strands with intermediate growth increments, exhibited moderate species evenness. These findings provide long-term empirical support for the Mass Ratio Hypothesis by demonstrating that growths of dominant species modulate niche partitioning in subordinates and thereby shape species diversity in subtropical forest communities. Full article

Companies are increasingly giving more attention to chatbots as an innovative solution to transform the customer service experience, redefining how they interact with users and optimizing their support processes. This study analyzes the acceptance of conversational robots in customer service within the insurance sector, using a conceptual model based on well-known new information systems adoption groundworks that are implemented with a combination of machine learning techniques based on decision trees and so-called importance–performance map analysis (IPMA). The intention to interact with a chatbot is explained by performance expectancy (PE), effort expectancy (EE), social influence (SI), and trust (TR). For the analysis, three machine learning methods are applied: decision tree regression (DTR), random forest (RF), and extreme gradient boosting (XGBoost). While the architecture of DTR provides a highly visual and intuitive explanation of the intention to use chatbots, its generalization through RF and XGBoost enhances the model’s explanatory power. The application of Shapley additive explanations (SHAP) to the best-performing model, RF, reveals a hierarchy of relevance among the explanatory variables. We find that TR is the most influential variable. In contrast, PE appears to be the least relevant factor in the acceptance of chatbots. IPMA suggests that SI, TR, and EE all deserve special attention. While the prioritization of TR and EE may be justified by their higher importance, SI stands out as the variable with the lowest performance, indicating the greatest room for improvement. In contrast, PE not only requires less attention, but it may even be reasonable to reallocate efforts away from improving PE in order to enhance the performance of the more critical variables. Full article

Mangroves play a pivotal role in carbon sequestration. To investigate the characteristics and driving factors of carbon sequestration in planted mangrove forests, we focused on planted mangrove forests in Wenzhou City, Zhejiang Province, China. Through a statistical analysis of soil physicochemical properties and plant morphological characteristics, we assessed carbon stock distribution patterns and identified key influencing factors, providing scientific support for the northward expansion of mangroves. The results demonstrated significant differences in soil properties and plant morphological characteristics among different stands (p < 0.05). The mean soil carbon stock of restored planted mangroves was 78.75 Mg C/ha (mature stands: 87.84 Mg C/ha; middle-aged stands: 74.09 Mg C/ha; young stands: 74.31 Mg C/ha), while the average plant carbon stock was 12.28 Mg C/ha, indicating that soil is the primary contributor to carbon sequestration in mangroves. Compared to natural mangroves, the restored planted mangroves still exhibited a lower carbon sequestration capacity. The variations in carbon sequestration levels among the planted mangrove forests were mainly attributed to differences in tree species and age composition, hydrothermal conditions, and biomass carbon quantification methods. Key drivers of soil carbon sequestration included total phosphorus content, bulk density, and clay content. Carbon storage in restored planted mangroves depends on short-term soil carbon accumulation and long-term biomass carbon accumulation. Ultimately, we recommend optimal species selection and planting design, improved soil carbon storage mechanisms, and integrated conservation monitoring systems to enhance carbon sequestration in mangrove plantations. Full article

Urban areas face a significant challenge with water pipeline leaks, resulting in resource wastage and economic consequences. The application of noise logger sensors, integrated with ensemble machine learning, emerges as a promising real-time monitoring solution, enhancing efficiency in Water Distribution Networks (WDNs) and mitigating environmental impacts. The paper investigates the integrated use of Noise Loggers with machine learning models, including Support Vector Machines (SVMs), Random Forest (RF), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LogR), Multi-Layer Perceptron (MLP), and YamNet, along with ensemble models, for effective leak detection. The study utilizes a dataset comprising 2110 sound signals collected from various locations in Hong Kong through wireless acoustic Noise Loggers. RF model stands out with 93.68% accuracy, followed closely by KNN at 93.40%, and MLP with 92.15%, demonstrating machine learning’s potential in scrutinizing acoustic signals. The ensemble model, combining these diverse models, achieves an impressive 94.40% accuracy, surpassing individual models and YamNet. The comparison of various machine learning models provides researchers with valuable insights into the use of machine learning for leak detection applications. Additionally, this paper introduces a novel method to develop a robust ensemble leak detection model by selecting the most performing machine learning models. Full article

Forest spatial structure is of significant importance for studying forest biomass accumulation and management. However, above-ground biomass (AGB) estimation based on satellite remote sensing struggles to capture forest spatial structure information, which to some extent affects the accuracy of AGB estimation. To address this issue, this study focused on Chinese fir (Cunninghamia lanceolata) plantations in Zhejiang Province. Using UAV-LiDAR (unmanned aerial vehicle light detection and ranging) data and a seed-point-based individual tree segmentation algorithm, information on individual fir trees was obtained. Building on this foundation, structural parameters such as neighborhood comparison (U), crowding degree (C), uniform angle index (W), competition index (CI), and canopy openness (K) were calculated, and their distribution characteristics analyzed. Finally, these parameters were integrated with UAV-LiDAR point cloud features to build machine learning models, and a geographical detector was used to quantify their contribution to AGB estimation. The research findings indicate the following: (1) The studied stands exhibited a random spatial pattern, moderate competition, and sufficient growing space. (2) A significant correlation existed between the U and AGB (r > 0.6), followed by CI. The optimal stand structure for AGB accumulation was C = 0.25, U < 0.5, CI in (0, 0.8], and K > 0.3. (3) The four machine learning models constructed by coupling spatial structure with point cloud features all improved the accuracy of AGB estimation for the fir forest to some extent. Among them, the XGBoost model performed best, achieving a model accuracy (R²) of 0.92 and a relatively low error (RMSE = 14.02 kg). (4) Geographical detector analysis indicated that U and CI contributed most to AGB estimation, with q-values of 0.44 and 0.37, respectively. Full article

The cumulative impact of decades of oil and gas exploration has left Alberta’s boreal forests densely fragmented by seismic lines, which are expected to naturally regenerate; however, recovery is often highly variable and generally poor in peatlands due to increased wetness and reduced microtopography. In this study, we evaluated seismic lines in lowland ecosites with some degree of successful natural regeneration to gain a better understanding of the natural recovery process in these areas. We compared stand characteristics between the seismic line (23 to 48 years post-disturbance) and the adjacent undisturbed forest. We found that soil properties were similar, but seedling (height < 1.3 m) density was significantly higher on the seismic line, with 252% more tamarack and 65% more black spruce than in the adjacent forest. Relative to the adjacent forest, there were significantly fewer trees (height > 1.3 m) on the seismic line, with an 84% and 50% reduction in black spruce and tamarack, respectively. By analyzing tree ring data from seismic lines, we found that the length of time before tree establishment was 10 years for black spruce and 8 years for tamarack. On average, it took 12 years for tree density to reach 2000 stems per hectare (sph). We modeled growth rates for black spruce and tamarack and found that they were growing faster than their adjacent forest counterparts, reaching 3 m after an average of 38 and 33 years, respectively. Stands on seismic lines were projected to a final stand age of 61 years using the Mixedwood Growth Model (MGM) to evaluate future stand characteristics. Full article

Human activities are increasing the occurrence of megafires that alter ecological dynamics in forest ecosystems. The objective of this study was to understand the impacts of a 610 km² megafire on patterns of tree regeneration and herbivory across three forest types (aspen/fir, oak/maple, and pinyon/juniper). Seventeen transect pairs in adjacent burned/unburned forest stands (6 aspen/fir, 5 oak/maple, and 6 pinyon/juniper) were measured. Sapling density, meristem removal, and height were measured across the transect network over a three-year period from 2019 to 2021. Tree species able to resprout from surviving roots (oak and aspen) generally responded positively to fire while species that typically regenerate by seeding showed little post-fire regeneration. Browse pressure was concentrated on deciduous tree species and was greater in burned areas but the effect diminished over the three-year study period. Meristem removal by herbivores was below the critical threshold, resulting in vertical growth over time. Our results indicate that forest regeneration within the megafire scar was generally positive and experienced sustainable levels of ungulate browsing that were likely to result in forest recruitment success. Full article

Under cool and moist climates, timely implementation of ditch network maintenance (DNM) is crucial for sustaining productivity of drained forests, thus reducing operational costs, while mitigating environmental risks. This underscores the need to understand tree growth responses to DNM. This study evaluated the effects of DNM on tree radial increment in sites with both organic and mineral drained soils, focusing on regionally commercially important species: Scots pine (Pinus sylvestris), Norway spruce (Picea abies), and silver birch (Betula pendula). Responses of relative growth changes over eight years post-DNM to site and tree characteristics were assessed using a linear mixed-effects model. Species- and site-specific growth responses to DNM were indicated by significant interactions between tree species, site type, and distance from the drainage ditch. While growth responses were generally neutral (non-significant), variability among sites and species suggests that both organic and mineral soils might be prone to site-level moisture depletion near drainage infrastructure in the post-DNM period. The effect of stand age and density suggested higher responsiveness of older and less dense stands, hence positive effects of thinning to resilience of stands to DNM. These findings highlight the importance of adapting DNM strategies to local site conditions and stand characteristics to minimize drought-related growth limitations. Full article

Industrial tree plantations (ITPs) are increasingly recognized as a sustainable response to deforestation and the decline in native wood resources in the Philippines. This study presents LUNTIAN (Labor, UNiversity, Timber Investment, and Agent-based Nexus), an agent-based model that simulates an experimental ITP operation within a mountain forest managed by University of the Philippines Los Baños. The model integrates biophysical processes—such as tree growth, hydrology, and stand dynamics—with socio-economic components such as investment decision making based on risk preferences, employment allocation influenced by local labor availability, and informal harvesting behavior driven by job scarcity. These are complemented by institutional enforcement mechanisms such as forest patrolling, reflecting the complex interplay between financial incentives and rule compliance. To assess the model’s validity, its outputs were compared to those of the 3PG forest growth model, with results demonstrating alignment in growth trends and spatial distributions, thereby supporting LUNTIAN’s potential to represent key ecological dynamics. Sensitivity analysis identified investor earnings share and community member count as significant factors influencing net earnings and management costs. Parameter calibration using the Non-dominated Sorting Genetic Algorithm yielded an optimal configuration that ensured profitability for resource managers, investors, and community-hired laborers while minimizing unauthorized independent harvesting. Notably, even with continuous harvesting during a 17-year rotation, the final tree population increased by 55%. These findings illustrate the potential of LUNTIAN to support the exploration of sustainable ITP management strategies in the Philippines by offering a robust framework for analyzing complex social–ecological interactions. Full article