Search Results (109)

To explore the characteristics of species diversity and phylogenetic diversity, as well as the dominant processes of community construction, in different forest types (deciduous broad-leaved forest, mixed coniferous and broad-leaved forest, and Chinese fir plantation) in subtropical regions, analyze the specific driving patterns of soil nutrients and other environmental factors on the formation of forest diversity in different forest types, and clarify the differences in response to environmental heterogeneity between natural forests and plantation forests. Based on 48 fixed monitoring plots of 50 m × 50 m in Shouchang Forest Farm, Jiande City, Zhejiang Province, woody plants with a diameter at breast height ≥5 cm were investigated. Species diversity indices (Margalef index, Shannon–Wiener index, Simpson index, and Pielou index), phylogenetic structure index (PD), and environmental factors were used to analyze the relationship between diversity characteristics and environmental factors through variance analysis, correlation analysis, and generalized linear models. Phylogenetic structural indices (NRI and NTI) were used, combined with a random zero model, to explore the mechanisms of community construction in different forest types. Research has found that (1) the deciduous broad-leaved forest had the highest species diversity (Margalef index of 4.121 ± 1.425) and phylogenetic diversity (PD index of 21.265 ± 7.796), significantly higher than the mixed coniferous and broad-leaved forest and the Chinese fir plantation (p < 0.05); (2) there is a significant positive correlation between species richness and phylogenetic diversity, with the best fit being AIC = 70.5636 and R² = 0.9419 in broad-leaved forests; however, the contribution of evenness is limited; (3) the specific effects of soil factors on different forest types: available phosphorus (AP) is negatively correlated with the diversity of deciduous broad-leaved forests (p < 0.05), total phosphorus (TP) promotes the diversity of coniferous and broad-leaved mixed forests, while the diversity of Chinese fir plantations is significantly negatively correlated with total nitrogen (TN); (4) the phylogenetic structure of three different forest types shows a divergent pattern in deciduous broad-leaved forests, indicating that competition and exclusion dominate the construction of deciduous broad-leaved forests; the aggregation mode of Chinese fir plantation indicates that environmental filtering dominates the construction of Chinese fir plantation; the mixed coniferous and broad-leaved forest is a transitional model, indicating that the mixed coniferous and broad-leaved forest is influenced by both stochastic processes and ecological niche processes. In different forest types in subtropical regions, the species and phylogenetic diversity of broad-leaved forests is significantly higher than in other forest types. The impact of soil nutrients on the diversity of different forest types varies, and the characteristics of community construction in different forest types are also different. This indicates the importance of protecting the original vegetation and provides a scientific basis for improving the ecological function of artificial forest ecosystems through structural adjustment. The research results have important practical guidance value for sustainable forest management and biodiversity conservation in the region. Full article

Little information is available about the ecosystem carbon (C) storage among coniferous and broadleaved plantations with similar stand ages in North China. The aim of the present research was to estimate the C storages of the components of plants, litter, and soil in two coniferous plantations (Pinus tabulaeformis and Larix principis-rupprechtii) and two broadleaved plantations (Betula platyphylla and Populus davidiana) on Yanshan Mountain, North China. Allometric equations of diameter at breast height (DBH) and height (H) were used to quantify the biomass of the tree organs. The C storage of trees, herbs, litter, and soil were estimated based on the measured C contents. The C storage varied from 24.0 to 51.9 Mg ha⁻¹, 0.3 to 0.7 Mg ha⁻¹, and 1.9 to 4.0 Mg ha⁻¹ in the tree, herbs, and litter layers, respectively. The ecosystem C storages were as follows: B. platyphylla (164.1 Mg ha⁻¹) > P. davidiana (150.4 Mg ha⁻¹) > L. principis-rupprechtii (122.3 Mg ha⁻¹) > P. tabulaeformis (106.7 Mg ha⁻¹), 65.7%–75.6% of which was stored in the soil layer. Broadleaf plantations stored higher C than coniferous plantations in this study. These results indicate that ecosystem C storage varied among various plantation types, and broadleaf plantations had considerable ecosystem C sequestration potential with even-aged plantation stands. Full article

Numerous studies on biodiversity–ecosystem functioning (BEF) have shown that mixed plantations can improve the ecological benefits of forest ecosystems. However, few studies have employed a multi-dimensional approach to study the integrated ecological benefits of mixed plantations. This study aims to evaluate the stand characteristics and ecological benefits of different forest types by examining various ecological indicators, including trees, shrubs, herbs, and soil properties. Focusing on typical mixed broadleaf–conifer plantations (MBCPs), mixed coniferous plantations (MCPs), and pure Cunninghamia lanceolata (Lamb.) Hook plantations (PCLs) at the Guiyang Plantation Farm, Suichang, we analyzed growth performance, spatial structure, understory vegetation diversity, and soil physicochemical properties across these forest types. For each forest type, one 100 × 100 m plot was established. Within each plot, five 20 × 20 m subplots were selected for investigation. Our results show that the aboveground biomass of MCPs is higher than that of MBCPs and PCLs, with increases of 46.58% and 177.29%, respectively. Furthermore, both mixed plantations offer better stand structure compared to pure plantations. In mixed plantations, the MBCPs exhibited a high degree of niche overlap, indicating that interspecific competition outweighed complementarity, whereas the MCPs demonstrated a more favorable stand structure. MCPs also exhibit significantly greater understory vegetation diversity compared to MBCPs and PCLs, with increases of 4.19%–13.04% and 10.34%–36.99%, respectively. Additionally, mixed plantations enhance soil moisture retention and fertility. With the onset of global warming and the increasing prevalence of extreme weather events, the establishment of artificial mixed plantations is an essential strategy to address climate change and enhance the ecological benefits of plantations. Full article

Forest structural diversity, referring to the variety of physical structural traits, has been identified as a critical indicator of both plant species and environmental diversity. Mapping structural diversity serves as a cost-effective proxy for monitoring forest biodiversity and large-scale ecosystem functions like productivity. Light detection and ranging (LiDAR) carried by unmanned aerial vehicles (UAVs) can achieve precise quantification of structural parameters with a resolution of sub-meter at the stand scale, providing robust support for accurately depicting three-dimensional forest structural features. Since forest management influences biodiversity and ecological functions by shaping the physical structure of forests, this study investigates how different forest management strategies affect structural diversity in China’s red soil hilly region. Using point cloud data obtained by unmanned aerial vehicle laser scanning (UAV-LS), we derived structural metrics including canopy volume diversity (CVD), and tree height diversity (THD), which were then used as variables to calculate the Shannon diversity index (SDI) of forests. The study focused on three forest types: close-to-nature broadleaf forest (CNBF), coniferous mature plantations (CPM), and close-to-nature coniferous forest (CNCF). Results revealed that CNBF exhibited the highest structural diversity, with superior values for canopy volume (CVD = 2.09 ± 0.35), tree height (THD = 1.72 ± 0.53), and canopy projected area diversity (CAD = 2.13 ± 0.32), approaching the upper range of the theoretical maximum for SDI (theoretical maximum ≈ 2.3; typical range: 0.5–2.0). This was attributed to optimal understory vegetation and higher biomass. Despite exhibiting greater tree height, CPM demonstrated lower structural diversity, while CNCF recorded a CVD (1.81 ± 0.39) similar to that of CPM but lower than that of CNBF. These results indicate that close-to-nature forest management enhances forest structural diversity. It is implied that the forest structural diversity can serve as an effective tool for evaluating forests biodiversity under different forest management strategies. The study also suggests that improving understory vegetation is a direction in the future management of coniferous plantations. Full article

Currently, it is very important to accurately estimate growing stock volumes; it is crucial for quantitatively assessing forest growth and formulating forest management plans. It is convenient and quick to use the Structure from Motion (SfM) algorithm in computer vision to obtain 3D point cloud data from captured highly overlapped stereo photogrammetry images, while the optimal algorithm for estimating growing stock volume varies across different data sources and forest types. In this study, the performance of UAV stereo photogrammetry (USP) in estimating the growing stock volume (GSV) using three machine learning algorithms for a coniferous plantation in Northern China was explored, as well as the impact of point density on GSV estimation. The three machine learning algorithms used were random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM). The results showed that USP could accurately estimate the GSV with R² = 0.76–0.81, RMSE = 30.11–35.46, and rRMSE = 14.34%–16.78%. Among the three machine learning algorithms, the SVM showed the best results, followed by RF. In addition, the influence of point density on the estimation accuracy for the USP dataset was minimal in terms of R², RMSE, and rRMSE. Meanwhile, the estimation accuracies of the SVM became stable with a point density of 0.8 pts/m² for the USP data. This study evidences that the low-density point cloud data derived from USP may be a good alternative for UAV Laser Scanning (ULS) to estimate the growing stock volume of coniferous plantations in Northern China. Full article

The accurate point cloud completion of individual tree crowns is critical for quantifying crown complexity and advancing precision forestry, yet it remains challenging in dense plantations due to canopy occlusion and LiDAR limitations. In this study, we extended the scope of conventional point cloud completion techniques to artificial planted forests by introducing a novel approach called Multi−feature Fusion Completion of Populus (MFCPopulus). Specifically designed for Populus Tomentosa plantations with uniform spacing, this method utilized a dataset of 1050 manually segmented trees with expert−validated trunk−canopy separation. Key innovations include the following: (1) a hierarchical adversarial framework that integrates multi−scale feature extraction (via Farthest Point Sampling at varying rates) and biologically informed normalization to address trunk−canopy density disparities; (2) a structural characteristics split−collocation (SCS−SCC) strategy that prioritizes crown reconstruction through adaptive sampling ratios, achieving a 94.5% canopy coverage in outputs; (3) a cross−layer feature integration enabling the simultaneous recovery of global contours and a fine−grained branch topology. Compared to state−of−the−art methods, MFCPopulus reduced the Chamfer distance variance by 23% and structural complexity discrepancies (ΔD_b) by 33% (mean, 0.12), while preserving species−specific morphological patterns. Octree analysis demonstrated an 89−94% spatial alignment with ground truth across height ratios (HR = 1.25−5.0). Although initially developed for artificial planted forests, the framework generalizes well to diverse species, accurately reconstructing 3D crown structures for both broadleaf (Fagus sylvatica, Acer campestre) and coniferous species (Pinus sylvestris) across public datasets, providing a precise and generalizable solution for cross−species trees’ phenotypic studies. Full article

Essential oils are mixtures of volatile compounds often found in the leaves, wood, and fruits of coniferous trees and shrubs. The composition and abundance of individual oil components vary across different plant parts. Terpenes (monoterpenes, sesquiterpenes, diterpenes, triterpenes) dominate in the essential oils of many plants. They are the most abundant class of secondary metabolites, with plants containing over a hundred of them at varying concentrations. The terpene profile of certain species consists of a few dominant (abundant) components and numerous less abundant ones. It is believed that the biological activity of essential oil mostly depends on the dominant terpene components. In most of the analyzed Pinus species, the most abundant terpene compounds are α-pinene, β-pinene, δ-3-carene, β-caryophyllene, limonene/β-phellandrene, and germacrene D. In certain taxa, additional dominant compounds include α-cedrol, bornyl acetate, caryophyllene oxide, α-phellandrene, trimethylbicyclo [3.1.1]hept-2-ene, 2H-benzocyclohepten-2-one, phenylethyl butyrate, 4-epi-isocembrol, β-thujene, and thunbergol. Moreover, compounds with abundances exceeding 15% include methyl chavicol (=estragole), geranylene, myrcene, γ-muurolene, sabinene, and abieta-7,13-diene. It can be concluded that the terpene profiles of the needles of the analyzed pine taxa depend on the type of chromatographic columns, the method of obtaining essential oils, the origin of the trees (in natural habitat or artificial plantation), the age of the needles, the variety, and the season in which the needles were collected. Full article

There is an increasing number of studies providing evidence that multi-species tree plantations possess more advantages in terms of species-specific tree diameter, growth rates, and soil properties than monocultures. In order to clarify the effect of a multi-species tree plantation on leaf nutrition and soil fertility, a statistical analysis was carried out on the leaf and soil properties, as well as soil enzyme activity, from two repeatedly measured stands in the Qingliangsi Forest District of the Dengfeng Forest Station. For the analysis, the plots were categorized into type A and type B according to the different forest structures. Type A was the mixed plantation of Quercus variabilis and Platycladus orientalis, while type B was a pure stand of Quercus variabilis. The results clearly showed that the leaf water content of P. orientalis was greater than that of Q. variabilis. The total water, free fatty acids, soluble sugar, flavonoid, tannin, lignin, cellulose, and hemicellulose contents of P. orientalis were higher than those of Q. variabilis in type A. Furthermore, the soil moisture of the mixed plantations was apparently higher than that of the pure stand. Soil peroxidase activity was the highest both in type A and type B among the 10 soil enzyme activities. Meanwhile, there was a significant difference between soil catalase and acid phosphatase activities. Soil urease, leucine aminopeptidase, and sucrase were significantly correlated with multiple soil enzyme activities. In addition, based on the correlation analysis results, we noted that type A had more complex relationships than type B in the leaf and soil properties and soil enzyme activity. Compared to the monoculture stands, multi-species tree stands appeared to have more complicated and preferable soil and water conservation capabilities. These results further verify the beneficial role of mixed plantations in water holding capacity and in improving soil quality. Q. variabilis is a broad-leaved deciduous tree species, and P. orientalis is an evergreen coniferous tree species. Our study indicates that these two native Chinese tree species are suitable as the target species when constructing mixed forests. They can increase the interaction of leaf and soil properties, enhance the soil enzyme activities, and improve the soil. Full article

The agroforestry system provides a new option for P. notoginseng cultivation. However, the effects on soil health and microbial communities are still incomplete when monoculture coniferous forests are converted to P. notoginseng–pine plantations. To assess soil health, samples from five P. notoginseng–pine plantations were collected, including healthy plants and root-diseased plants, as well as plantation control soil. The samples were analyzed for physical, chemical, and biological aspects and soil microbial communities. Our results suggested that P. notoginseng planted under pine forest was more conducive to increased microbial biomass carbon, soil dissolved organic nitrogen, particulate organic nitrogen, and soil enzyme activities compared with uncultivated control soil. A quantitative assessment of soil health demonstrated that the comprehensive soil quality index (SQI) of P. notoginseng-cultivated soil exhibited marked increases of 79.41% and 119.85% in comparison with the control soil. The observed alterations in soil characteristics could be attributed to variations in the soil microbiome. This implies that changes in SQI positively regulate bacterial and fungal abundance in P. notoginseng–pine agroforestry ecosystems mainly through biological properties. This comprehensive SQI assessment helps to guide the cultivation of P. notoginseng under forest and improve soil quality for P. notoginseng–pine agroforestry ecosystems. Full article

Girdling is a crucial technique for promoting the close-to-nature transformation of plantation forests in Hainan Tropical Rainforest National Park (HNNP). It has shown effectiveness in aspects such as community structure and biodiversity restoration. However, its impacts on ecological functions like eco-hydrology still require further in-depth investigation. This study analyzes the impact of girdling on the eco-hydrological indices of three plantations—Acacia mangium, Pinus caribaea, and Cunninghamia lanceolata—through field investigations and laboratory tests. The data was evaluated using a game theory combination weighting-cloud model. The results show that the eco-hydrological indicators of leaf litter in A. mangium increased by 5.77% while those of P. caribaea and C. lanceolata decreased by 11.86% and 5.29%, respectively. Soil bulk density decreased slightly across all plantations while total porosity increased, with A. mangium showing the highest increase of 20.31%. Organic carbon content increased by 76.81% in A. mangium and 7.24% in C. lanceolata, whereas it decreased in P. caribaea. Saturated hydraulic conductivity increased by 33.32% in P. caribaea and 20.91% in A. mangium but decreased in C. lanceolata. Based on the cloud model, the eco-hydrological function of A. mangium improved from ‘medium’ to ‘good’, while that of P. caribaea and C. lanceolata declined towards the ‘poor’ level. In summary, during the process of close-to-nature transformation of tropical rainforests, girdling is an effective method to enhance the ecohydrological functions of broadleaf planted forests. However, for coniferous species, the ecohydrological functions of the planted forests weaken in the short term following the transformation. Full article

The large pine weevil (Hylobius abietis L.) is a major pest in European and Asian coniferous forests, particularly in managed plantations where clear-felling practices create ideal conditions for its population growth. Traditional management practices involving synthetic insecticides have limited efficacy in terms of reducing pest populations and pose environmental risks. This study evaluated the effectiveness of a wild entomopathogenic fungus (EPF) and the commercial entomopathogenic nematode Steinernema carpocapsae (EPN) as biological control agents (BCAs) against H. abietis in clear-felled spruce plantations in Wales and Scotland. Field trials used a randomised block design with three treatments (EPN full dose, EPF full dose and a combination of EPF+EPN at half doses each) compared to a control. Emergence trapping and destructive sampling were employed to assess treatment efficacy. All treatments significantly reduced weevil emergence, with the mixed treatment showing the greatest impact. Destructive sampling revealed strong associations between treatment type and infection outcomes in H. abietis, with a small but significant relationship between weevil developmental stages and infection types. Importantly, the treatments had no significant impact on the total abundance or taxon richness of non-target invertebrates. These findings suggest that wild EPFs alone and combined with EPNs are effective and environmentally safe alternatives to synthetic insecticides for managing H. abietis populations in managed forests. Full article

Heterobasidion, a significant forest pathogen affecting coniferous forests in the Northern Hemisphere, can infect 27 species of coniferous trees, leading to widespread forest mortality. It has already caused considerable damage to both natural and plantation forests in Europe. As essential components of the global ecosystem, forests are increasingly affected by ongoing environmental changes. The ability to accurately predict and effectively respond to pathogen outbreaks across different habitats is becoming increasingly critical. This study employs an optimized MaxEnt model in conjunction with six Global Climate Models (GCMs) to simulate and predict the potentially suitable distributions and changes of three Heterobasidion species in Europe (Heterobasidion abietinum, Heterobasidion annosum sensu stricto, Heterobasidion parviporum) under current conditions and four future climate scenarios (SSP126, SSP245, SSP370, and SSP585) for the period 2081–2100. The objective of this analysis is to assess the potential effects of climate change on the distribution of Heterobasidion species. The results indicate that the distributions of the three Heterobasidion species are influenced by factors such as temperature and precipitation. By 2081–2100, under most climate scenarios, except for the SSP585 scenario, the distribution areas of the three Heterobasidion species show an expansion trend. Notably, Heterobasidion abietinum showed the most significant expansion of its suitable habitat, while the expansion of low-suitability areas for Heterobasidion parviporum and Heterobasidion annosum sensu stricto was more pronounced. Heterobasidion abietinum is projected to shift southward due to factors such as precipitation, while Heterobasidion parviporum and Heterobasidion annosum sensu stricto are expected to migrate northward, influenced by factors such as temperature and host tree species. Full article

Eucalyptus plantations with fast growth and short rotation play an important role in improving economic conditions for local farmers and governments. It is necessary to map and update eucalyptus distribution in a timely manner, but to date, there is a lack of suitable approaches for quickly mapping its spatial distribution in a large area. This research aims to develop a uniform procedure to map eucalyptus distribution at a regional scale using the Sentinel-2 imagery on the Google Earth Engine (GEE) platform. Different seasonal Senstinel-2 images were first examined, and key vegetation indices from the selected seasonal images were identified using random forest and Pearson correlation analysis. The selected key vegetation indices were then normalized and summed to produce new indices for mapping eucalyptus distribution based on the calculated best cutoff values using the ROC (Receiver Operating Characteristic) curve. The uniform procedure was tested in both experimental and test sites and then applied to the entire Fujian Province. The results indicated that the best season to distinguish eucalyptus forests from other forest types was winter. The composite indices for eucalyptus–coniferous forest separation (${\mathrm{C}\mathrm{I}}_{\mathrm{E}\mathrm{C}}$) and for eucalyptus–broadleaf forest separation (${\mathrm{C}\mathrm{I}}_{\mathrm{E}\mathrm{B}}$), which were synthesized from the enhanced vegetation index (EVI), plant senescing reflectance index (PSRI), shortwave infrared water stress index (SIWSI), and MERIS terrestrial chlorophyll index (MTCI), can effectively differentiate eucalyptus from other forest types. The proposed procedure with the best cutoff values (0.58 for ${\mathrm{C}\mathrm{I}}_{\mathrm{E}\mathrm{C}}$ and 1.29 for ${\mathrm{C}\mathrm{I}}_{\mathrm{E}\mathrm{B}}$) achieved accuracies of above 90% in all study sites. The eucalyptus classification accuracies in Fujian Province, with a producer’s accuracy of 91%, user’s accuracy of 97%, and overall accuracy of 94%, demonstrate the strong robustness and transferability of this proposed procedure. This research provided a new insight into quickly mapping eucalyptus distribution in subtropical regions. However, more research is still needed to explore the robustness and transferability of this proposed method in tropical regions or in other subtropical regions with different environmental conditions. Full article

To improve our understanding of the carbon balance, it is significant to study long-term variations of all components of carbon exchange and their driving factors. Gross primary production (GPP), respiration (Re), and net ecosystem productivity (NEP) from the hourly to the annual sums in a subtropical coniferous forest in China during 2003–2017 were calculated using empirical models developed previously in terms of PAR (photosynthetically active radiation), and meteorological parameters, GPP, Re, and NEP were calculated. The calculated GPP, Re, and NEP were in reasonable agreement with the observations, and their seasonal and interannual variations were well reproduced. The model-estimated annual sums of GPP and Re over 2003–2017 were larger than the observations of 11.38% and 5.52%, respectively, and the model-simulated NEP was lower by 34.99%. The GPP, Re, and NEP showed clear interannual variations, and both the calculated and the observed annual sums of GPPs increased on average by 1.04% and 0.93%, respectively, while the Re values increased by 4.57% and 1.06% between 2003 and 2017. The calculated and the observed annual sums of NEPs/NEEs (net ecosystem exchange) decreased/increased by 1.04%/0.93%, respectively, which exhibited an increase of the carbon sink at the experimental site. During the period 2003–2017, the annual averages of PAR and the air temperature decreased by 0.28% and 0.02%, respectively, while the annual average water vapor pressure increased by 0.87%. The increase in water vapor contributed to the increases of GPP, Re, and NEE in 2003–2017. Good linear and non-linear relationships were found between the monthly calculated GPP and the satellite solar-induced fluorescence (SIF) and then applied to compute GPP with relative biases of annual sums of GPP of 5.20% and 4.88%, respectively. Large amounts of CO₂ were produced in a clean atmosphere, indicating a clean atmospheric environment will enhance CO₂ storage in plants, i.e., clean atmosphere is beneficial to human health and carbon sink, as well as slowing down climate warming. Full article

Human activities have both positive and negative impacts on forests, altering the extent and composition of various forest vegetation types, and increasing uncertainty in ecological management. A detailed understanding of the historical distribution of forest vegetation is crucial for local conservation efforts. In this study, we integrated phenological features with climatic and terrain variables to enhance the mapping accuracy of forest vegetation in Yunnan. We mapped the historical distributions of five forest vegetation type groups and nine specific forest vegetation types for 2001, 2010, and 2020. Our findings revealed that: (1) rubber plantations can be effectively distinguished from other forest vegetation using phenological features, coniferous forests and broad-leaved forests can be differentiated using visible spectral bands, and environmental variables (temperature, precipitation, and elevation) are effective in differentiating forest vegetation types under varying climate conditions; (2) the overall accuracy and kappa coefficient increased by 14.845% and 20.432%, respectively, when climatic variables were combined with phenological features, and by 13.613% and 18.902%, respectively, when elevation was combined with phenological features, compared to using phenological features alone; (3) forest cover in Yunnan increased by 2.069 × 10⁴ km² (10.369%) between 2001 and 2020. This study highlights the critical role of environmental variables in improving the mapping accuracy of forest vegetation in mountainous regions. Full article