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Accurate individual tree segmentation, which is important for forestry investigation, is still a difficult and challenging task. In this study, we developed a climbing algorithm and combined it with a deep learning model to extract forests and achieve individual tree segmentation using lidar point clouds. We tested the algorithm on mixed forests within complex environments scanned by unmanned aircraft system lidar in ecological restoration mining areas along the Yangtze River of China. Quantitative assessments of the segmentation results showed that the forest extraction achieved a kappa coefficient of 0.88, and the individual tree segmentation results achieved F-scores ranging from 0.86 to 1. The climbing algorithm successfully reduced false positives and false negatives with the increased crown overlapping and outperformed the widely used top-down region-growing point cloud segmentation method. The results indicate that the climbing algorithm proposed in this study will help solve the overlapped crown problem of tree segmentation under complex environments. [ABSTRACT FROM AUTHOR]

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A new species of Cuphophyllus (Hygrophoraceae, Agaricales) was discovered in mixed coniferous and broad-leaved forests (Pinaceae and Fagaceae) and grassland margins in Lijiang, North-west Yunnan, and South-west China. The species is described using morphological and molecular methods. Based on on-site habitat observation data and a literature review, the novel species is best described as a saprobic macrofungus that thrives in soil humus within mixed pine forests or grasslands. The basidiomata of the novel species exhibit macroscopic similarities to C. virgineus; however, C. lijiangensis sp. nov. differs primarily in having a smaller pileus, shorter stipe, larger basidiospores, and an intricate trichoderm pileipellis. Phylogenetic analysis, based on the combined rDNA internal transcribed spacer (ITS), nuc28S rDNA (28S), and RNA polymerase II subunit 2 (rpb2) genes, places C. lijiangensis in a clade with C. virgineus, C. borealis, C. yacurensis, and C. russocoriaceus. Both morphological characteristics and phylogenetic data support C. lijiangensis as a distinct new species within Cuphophyllus. This study provides a comprehensive description of the new species, colour photographs of basidiomata, micromorphological structures, and a phylogenetic tree to show the position of the new species. [ABSTRACT FROM AUTHOR]

Soil microbial communities play a vital role in accelerating nutrient cycling and stabilizing ecosystem functions in forests. However, the diversity of soil microbiome and the mechanisms driving their distribution patterns along elevational gradients in montane areas remain largely unknown. In this study, we investigated the soil microbial diversity along an elevational gradient from 650 m to 3,800 m above sea level in southeast Tibet, China, through DNA metabarcode sequencing of both the bacterial and fungal communities. Our results showed that the dominant bacterial phyla across elevations were Proteobacteria, Acidobacteriota and Actinobacteriota, and the dominant fungal phyla were Ascomycota and Basidiomycota. The Simpson indices of both soil bacteria and fungi demonstrated a hollow trend along the elevational gradient, with an abrupt decrease in bacterial and fungal diversity at 2,600 m a.s.l. in coniferous and broad-leaved mixed forests (CBM). Soil bacterial chemoheterotrophy was the dominant lifestyle and was predicted to decrease with increasing elevation. In terms of fungal lifestyles, saprophytic and symbiotic fungi were the dominant functional communities but their relative abundance was negatively correlated with increasing elevation. Environmental factors including vegetation type (VEG), altitude (ALT), soil pH, total phosphorus (TP), nitrate nitrogen (NO3−-N), and polyphenol oxidase (ppo) all exhibited significant influence on the bacterial community structure, whereas VEG, ALT, and the carbon to nitrogen ratio (C/N) were significantly associated with the fungal community structure. The VPA results indicated that edaphic factors explained 37% of the bacterial community variations, while C/N, ALT, and VEG explained 49% of the total fungal community variations. Our study contributes significantly to our understanding of forest ecosystems in mountainous regions with large elevation changes, highlighting the crucial role of soil environmental factors in shaping soil microbial communities and their variations in specific forest ecosystems. [ABSTRACT FROM AUTHOR]

Pine wilt disease (PWD) is a highly destructive worldwide forest quarantine disease that has the potential to destroy entire pine forests in a relatively brief period, resulting in significant economic losses and environmental damage. Manual monitoring, biochemical detection and satellite remote sensing are frequently inadequate for the timely detection and control of pine wilt disease. This paper presents a fusion model, which integrates the Mamba model and the attention mechanism, for deployment on unmanned aerial vehicles (UAVs) to detect infected pine trees. The experimental dataset presented in this paper comprises images of pine trees captured by UAVs in mixed forests. The images were gathered primarily during the spring of 2023, spanning the months of February to May. The images were subjected to a preprocessing phase, during which they were transformed into the research dataset. The fusion model comprised three principal components. The initial component is the Mamba backbone network with State Space Model (SSM) at its core, which is capable of extracting pine wilt features with a high degree of efficacy. The second component is the attention network, which enables our fusion model to center on PWD features with greater efficacy. The optimal configuration was determined through an evaluation of various attention mechanism modules, including four attention modules. The third component, Path Aggregation Feature Pyramid Network (PAFPN), facilitates the fusion and refinement of data at varying scales, thereby enhancing the model's capacity to detect multi-scale objects. Furthermore, the convolutional layers within the model have been replaced with depth separable convolutional layers (DSconv), which has the additional benefit of reducing the number of model parameters and improving the model's detection speed. The final fusion model was validated on a test set, achieving an accuracy of 90.0%, a recall of 81.8%, a map of 86.5%, a parameter counts of 5.9 Mega, and a detection speed of 40.16 FPS. In comparison to Yolov8, the accuracy is enhanced by 7.1%, the recall by 5.4%, and the map by 3.1%. These outcomes demonstrate that our fusion model is appropriate for implementation on edge devices, such as UAVs, and is capable of effective detection of PWD. [ABSTRACT FROM AUTHOR]

Old-growth forests are essential for maintaining biodiversity, as they are formed by the complexity of diverse forest structures, such as broad variations in tree height and diameter (DBH) and conditions of living and dead trees, leading to various ecological niches. However, many efforts of old-growth forest mapping from LiDAR have targeted only one specific forest structure (e.g., stand height, basal area, or stand density) by deriving information through a large number of LiDAR metrics. This study introduces a novel approach for identifying old-growth forests by optimizing a set of selected LiDAR standards and structural metrics. These metrics effectively capture the arrangement of multiple forest structures, such as canopy heterogeneity, multilayer canopy profile, and canopy openness. To determine the important LiDAR standard and structural metrics in identifying old-growth forests, multicollinearity analysis using the variance inflation factor (VIF) approach was applied to identify and remove metrics with high collinearity, followed by the random forest algorithm to rank which LiDAR standard and structural metrics are important in old-growth forest classification. The results demonstrate that the LiDAR structural metrics (i.e., advanced LiDAR metrics related to multiple canopy structures) are more important and effective in distinguishing old- and second-growth forests than LiDAR standard metrics (i.e., height- and density-based LiDAR metrics) using the European definition of a 150-year stand age threshold for old-growth forests. These structural metrics were then used as predictors for the final classification of old-growth forests, yielding an overall accuracy of 78%, with a true skill statistic (TSS) of 0.58 for the test dataset. This study demonstrates that using a few structural LiDAR metrics provides more information than a high number of standard LiDAR metrics, particularly for identifying old-growth forests in mixed temperate forests. The findings can aid forest and national park managers in developing a practical and efficient old-growth forest identification and monitoring method using LiDAR. [ABSTRACT FROM AUTHOR]

The oribatid mite family Achipteriidae is recorded in the Dominican Republic for the first time. A new species of the genus Achipteria—A. (Izuachipteria) dominicanensis sp. nov.—is described, based on adults collected from leaf litter in a mixed forest. The species is characterized by the morphology of the lamella (triangular distally, without strong lateral tooth), the location of the lamellar seta (on ventral side of the lamella), the length of the bothridial seta (long), the ornamentation and morphology of the pteromorph (partially striate, with lateral tooth), the number of the leg claws (one), and the absence of the notogastral saccules. The taxonomic status of the subgenera Achipteria (Cubachipteria), A. (Hokkachipteria), and A. (Izuachipteria) is discussed. An identification key, distribution, and habitat of the known representatives of Achipteria (Izuachipteria) are presented. [ABSTRACT FROM AUTHOR]

Climate conditions have long been recognised as an important factor influencing the start and spread of forest fires in Mediterranean areas. This is partly due to the long dry periods that characterise these regions. Mixed forest ecosystems are more stable than monocultures. This study was conducted at two sites, the city of Makarska (the southern coast of the Croatian Mediterranean) and the island of Rab (the northern island of the Croatian Mediterranean). The main variables examined, flammability, combustion and the moisture content of potential forest fuel, best define the start and spread of fires. The aim of the study was to examine the influence of climate factors on these variables using the example of mock privet (Phillyrea latifolia L.). The results showed that moisture content of fuel was a key variable in direct correlation with the influence of climate factors. Though the Mediterranean region is burdened by fires and they will continue to occur in the future due to changing conditions, the study results can contribute to reducing burned areas in fires for the purpose of preserving Mediterranean ecosystems. [ABSTRACT FROM AUTHOR]

Leaf water potential ( ψ leaf) is a key indicator of plant water status, but its measurement is labor-intensive and limited in spatial coverage. While remote sensing has emerged as a useful tool for estimating vegetation water status, ψ leaf remains unexplored, particularly in mixed forests. Here, we use spectral indices derived from unmanned aerial vehicle-based hyperspectral imaging and machine learning algorithms to assess ψ leaf in a mixed, multi-species Mediterranean forest comprised of five key woody species: Pinus halepensis, Quercus calliprinos, Cupressus sempervirens, Ceratonia siliqua, and Pistacia lentiscus. Hyperspectral images (400–1000 nm) were acquired monthly over one year, concurrent with ψ leaf measurements in each species. Twelve spectral indices and thousands of normalized difference spectral index (NDSI) combinations were evaluated. Three machine learning algorithms—random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM)—were used to model ψ leaf. We compared the machine learning model results with linear models based on spectral indices and the NDSI. SVM, using species information as a feature, performed the best with a relatively good ψ leaf assessment (R2 = 0.53; RMSE = 0.67 MPa; rRMSE = 28%), especially considering the small seasonal variance in ψ leaf ( ± σ = 0.8 MPa). Predictions were best for Cupressus sempervirens (R2 = 0.80) and Pistacia lentiscus (R2 = 0.49), which had the largest ψ leaf variances ( ± σ > 1 MPa). Aggregating data at the plot scale in a 'general' model markedly improved the ψ leaf model (R2 = 0.79, RMSE = 0.31 MPa; rRMSE = 13%), providing a promising tool for monitoring mixed forest ψ leaf. The fact that a non-species-specific, 'general' model could predict ψ leaf implies that such a model can also be used with coarser resolution satellite data. Our study demonstrates the potential of combining hyperspectral imagery with machine learning for non-invasive ψ leaf estimation in mixed forests while highlighting challenges in capturing interspecies variability. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Forests worldwide face significant challenges due to climate change, impacting their health and productivity. In this study, we examined how European beech and Scots pine influence each other's phenology and growth in mixed forests. Our findings indicate that mixing these complementary tree species can increase resource efficiency within forest ecosystems. By leveraging informed species selection, this research highlights the potential for developing knowledge‐based, resource‐efficient forests. These insights are invaluable for policymakers and forest managers in designing forests that are not only productive but also sustainable and adaptable to evolving environmental conditions. Summary: We investigated the effects of interspecific neighbors on crown morphology and growth efficiency in European temperate forests, specifically focusing on European beech (Fagus sylvatica L.) and Scots pine (Pinus sylvestris L.). Our goal was to determine whether the previously reported overyielding in this mixture is primarily due to improved space‐use efficiency and packing density or enhanced resource‐use efficiency.Our methodology involved a detailed analysis of 128 individual felled trees. We assessed the effect of intraspecific and interspecific neighbors on stem volume growth, the allometric relationships of tree crowns and their components, and the allocation of branch and leaf biomass along the trees' vertical structure.Our findings demonstrate that interspecific neighbors significantly influence the allometric relationships of tree crowns, especially altering the vertical biomass distribution in European beech. Additionally, we found that interspecific neighbors can significantly enhance the growth efficiency of European beech but not for Scots pine.This research provides valuable insights for enhancing forest growth models and guiding forest management practices. By understanding the critical role of crown biomass allocation and growth efficiency in mixed‐species stands, policymakers and forest managers can design forests that are both productive and adaptable to changing environmental conditions. This study emphasizes the importance of species interactions in forest dynamics and bridges theoretical concepts with practical applications. [ABSTRACT FROM AUTHOR]

Paleoenvironmental insights gleaned from geological history are profoundly important for the discovery and exploitation of mineral resources. In China's Bohai Bay Basin, the Kongdian Formation represents the principal oil-bearing stratum from the Eocene Epoch. However, a comprehensive understanding of its paleoenvironmental evolution and stratigraphic division has been hindered by the paucity of paleontological data. To address this gap, three sedimentary cores were meticulously collected from the southern extremity of the Liaoxi Uplift within the Bohai Bay. These cores underwent a thorough sporopollen-algae analysis to elucidate their stratigraphic division and to reconstruct the associated paleoenvironmental conditions. The analysis yielded the identification of three distinct sporopollen-algae assemblages of the regional Kongdian Formation: (1) The assemblage of Divisisporites longilaesuratus-Betulaepollenites-Tiliaepollenites microreticulatus is indicative of the lower submember of the Kongdian Formation Ek2; (2) The assemblage of Polypodiaceaesporites-Alnipollenites indicates the upper submember of the Ek2; (3) The assemblage of Pterisisporites undulatus-Taxodiaceaepollenites-Ephedripites corresponds to the Kongdian Formation Ek1. These assemblages reflect a significant evolutionary trajectory of the regional plant communities throughout the Kongdian Formation. Initially, there were evergreen arbor-shrub mixed forests, which transitioned to green algae-herb-evergreen broadleaved biota and finally evolved into evergreen conifer-shrub mixed forests. This botanical evolution mirrors shifts in the paleoclimate, which experienced a progression from conditions of high temperature and high humidity through a phase of warm, semi-humid environments to eventually high temperature and semi-arid conditions. [ABSTRACT FROM AUTHOR]

The spatiotemporal variability of precipitation profoundly influences terrestrial carbon fluxes, driving shifts between carbon source and sink dynamics through gross primary productivity (GPP) and ecosystem respiration (ER). As a result, the sensitivities of GPP and ER to precipitation (SGPP and SER), along with their differential responses, are pivotal for understanding ecosystem reactions to precipitation changes and predicting future ecosystem functions. However, comprehensive evaluations of the spatiotemporal variability and differences in SGPP and SER remain notably scarce. In this study, we utilized eddy covariance flux data to investigate the spatial patterns, temporal dynamics, and differences in SGPP and SER. Spatially, SGPP and SER were generally strongly correlated. Among different ecosystems, the correlation between SGPP and SER was lowest in mixed forest and highest in broadleaf and needleleaf forest. Within the same ecosystem, SGPP and SER exhibited considerable variation but showed no significant differences. In contrast, they differed significantly across ecosystems, with pronounced variability in their magnitudes. For example, shrubland exhibited the highest values for SGPP, whereas needleleaf forest showed the highest values for SER. Temporally, SER demonstrated more pronounced changes than SGPP. Different ecosystems displayed distinct trends: shrubland exhibited an upward trend for both metrics, while grassland showed a downward trend in both SGPP and SER. Forest, on the other hand, maintained stable SGPP but displayed a downward trend in SER. Additionally, SGPP and SER exhibited a notable non-linear response to changes in the aridity index (AI), with both showing a rapid decline followed by stabilization. However, SER demonstrated a wider adaptive range to precipitation changes. Generally, this research enhances our understanding of the spatiotemporal variations in ecosystem carbon fluxes under changing precipitation patterns. [ABSTRACT FROM AUTHOR]

Bamboo and broad-leaved mixed forest is a kind of forestry management that can effectively improve the ecology of bamboo forests. The aerial structure of the stand can reflect the growth status of the stand, as well as the spatial structure of the stand with respect to maintaining and improving the basis of the stand structure. However, the lack of quantitative studies on how different mixing ratios affect the stand spatial structure of bamboo and broad hybrid forests has further disturbed the development of bamboo and broad-leaved mixed-forest management. In this study, we used 10 bamboo and broad mixed forests with different ratios as the research object, determined the stand spatial structure unit by using the weighted Delaunay triangular network, calculated the stand spatial-homogeneity index from the vertical spatial structure of the stand, horizontal spatial structure, and competition, and constructed the stand spatial-homogeneity evaluation system by combining it with the forest health grading system and the rank classification method of the near-natural forest management, dividing it into five evaluation classes. It was divided into five evaluation levels. Finally, a regression model was used to elucidate the effects of stand spatial homogeneity on moso bamboo (Phyllostachys pubescens) biomass. The results showed that the spatial homogeneity of No. 1 (5%–10% mixed) and No. 3 (15%–20%) samples was classified into five grades; No. 2 (10%–15%) samples were classified into four grades; No. 4 (20%–25%) and No. 6 (30%–35%) samples were classified into three grades; No. 5 (25%–30%) samples were classified into two grades; and Nos. 7–10 (more than 35%) samples were all classified into one grade. It was also found that both the degree of hybridization and the competition index in the bamboo and broad-leaved mixed forest showed highly significant negative correlation with the spatial homogeneity index of the stand and the moso bamboo biomass, while the spatial density index showed highly significant positive correlation with the spatial homogeneity index of the stand and the moso bamboo biomass. From the viewpoint of management purpose, for the management of bamboo and broad-leaved mixed forest with economic benefits, the mixing ratio should be 5%–10% and 25%–35%; for the management of bamboo and broad-leaved mixed forest with ecological benefits, the mixing ratio should be more than 35%; and for the management purpose of balancing ecological benefits and economic benefits, the mixing ratio should be 10%–25%. [ABSTRACT FROM AUTHOR]

In recent decades, excessive human activities have led to large-scale rocky desertification in karst areas. Vegetation restoration is one of the most important ways to control rocky desertification. In this study, vegetation surveys were conducted on three typical plantations in Jianshui County, Yunnan Province, a typical karst fault basin area, in 2016 and 2021. The plantations were Pinus massoniana forest (PM), Pinus yunnanensis forest (PY), and mixed forests of Pinus yunnanensis and Quercus variabilis (MF). Plant diversity and soil nutrients were compared during the five-year period. This paper mainly draws the following results: The plant diversity of PM, PY, and MF increased. With the increase of time, new species appeared in the tree layer, shrub layer, and herb layer of the three forests. Tree species with smaller importance values gradually withdrew from the community. In the tree layer, the Patrick index, Simpson index, and Shannon–Wiener index of the three forests increased significantly. The Pielou index changed from the highest for PM in 2016 to the highest for PY in 2021. In the shrub layer, the Pielou index of the three forests increased. The Patrick index changed from the highest for MF in 2016 to the highest for PY in 2021. There was no significant difference in species diversity index for the herb layer. With the increase of vegetation restoration time, the soil bulk density (BD) of the three forests decreased. There was no significant difference in soil total porosity (TP), soil capillary porosity (CP), and non-capillary porosity (NCP). The pH of PM increased significantly from 5.88~6.24 to 7.24~7.34. The pH of PY decreased significantly (p < 0.05). The contents of total nitrogen (TN) and ammonium nitrogen (NH4+-N) in PY and MF decreased. The content of nitrate nitrogen (NO3−-N) in the three forests increased significantly (p < 0.05). Total phosphorus (TP) content decreased in PM and MF. The content of available phosphorus (AP) in PM and PY increased. In general, with the increase of vegetation restoration time, plant diversity and soil physical and chemical properties have also been significantly improved. The results can provide important data support for vegetation restoration in karst areas. [ABSTRACT FROM AUTHOR]

(1) Objective: To improve forest fire prevention, this study provides a reference for forest fire risk assessment in Sichuan Province. (2) Methods: This research focuses on various forest vegetation types in Sichuan Province. Given data from 6848 sample plots, five machine learning models—random forest, extreme gradient boosting (XGBoost), k-nearest neighbors, support vector machine, and stacking ensemble (Stacking)—were employed. Bayesian optimization was utilized for hyperparameter tuning, resulting in machine learning models for predicting forest fuel loads (FLs) across five different vegetation types. (3) Results: The FL model incorporates not only vegetation characteristics but also site conditions and climate data. Feature importance analysis indicated that structural factors (e.g., canopy closure, diameter at breast height, and tree height) dominated in cold broadleaf, subtropical broadleaf, and subtropical mixed forests, while climate factors (e.g., mean annual temperature and temperature seasonality) were more influential in cold coniferous and subtropical coniferous forests. Machine learning-based FL models outperform the multiple stepwise regression model in both fitting ability and prediction accuracy. The XGBoost model performed best for cold coniferous, cold broadleaf, subtropical broadleaf, and subtropical mixed forests, with coefficient of determination (R2) values of 0.79, 0.85, 0.81, and 0.83, respectively. The Stacking model excelled in subtropical coniferous forests, achieving an R2 value of 0.82. (4) Conclusions: This study establishes a theoretical foundation for predicting forest fuel capacity in Sichuan Province. It is recommended that the XGBoost model be applied to predict fuel loads (FLs) in cold coniferous forests, cold broadleaf forests, subtropical broadleaf forests, and subtropical mixed forests, while the Stacking model is suggested for predicting FLs in subtropical coniferous forests. Furthermore, this research offers theoretical support for forest fuel management, forest fire risk assessment, and forest fire prevention and control in Sichuan Province. [ABSTRACT FROM AUTHOR]

Livestock can contribute fecal indicator bacteria (FIB) to waterbodies. However, few studies have examined the relationship between watershed land use or land cover involving livestock production and the genetic diversity of Escherichia coli in freshwater ecosystems. Our major goal was to determine if FIB abundance and E. coli phylogenetic group distributions in headwater streams are related to livestock production in rural watersheds in South Carolina. In both 2017 and 2018, grab samples were collected from streams at summer baseflow. In 2017, we collected samples from watersheds with or without poultry rearing facilities (PRFs). In 2018, we collected samples from streams draining watersheds with mixed forest and pasture cover and from streams in mostly forested watersheds. In both summers, we measured concentrations of total coliforms, E. coli, and Enterococcus. We also categorized E. coli isolates into one of four phylogenetic groups (A, B1, B2, D). Streams with PRFs in their watersheds had significantly higher concentrations of Enterococcus but not total coliforms or E. coli than streams in watersheds without PRFs. Also, B2 isolates were less frequent and B1 isolates were more frequent in watersheds with PRFs than in those without. Streams draining mixed forest/pasture watersheds had significantly higher concentrations of total coliforms and E. coli but not Enterococcus, as well as higher frequencies of B1 isolates, than streams in mostly forested watersheds. Overall, the most frequent E. coli phylogenetic groups in watersheds with animal production appeared consistent with the phylogenetic groups that are especially abundant in poultry or mammalian livestock feces. [ABSTRACT FROM AUTHOR]

Temperate mixed forests are currently experiencing severe drought conditions and face increased risk of degradation. However, it remains unclear how critical tree physiological functions such as sap flow density (SFD) and tree water deficit (TWD, defined as reversible stem shrinkage when water is depleted), respond to extreme environmental conditions and how they interact under dry conditions. We monitored SFD and TWD of three co‐occurring European tree species (Fagus sylvatica, Fraxinus excelsior and Acer pseudoplatanus) in dry conditions, using high temporal resolution sap flow, dendrometer, and environmental measurements. Species‐specific SFD responses to soil drying did not differ significantly, while TWD was significantly higher in F. excelsior. Inter‐specific differences in wood anatomy and water use strategies did not consistently explain these responses. TWD and SFD responded both to soil moisture content (SWC) during wet (SWC ≥ 0.2) and dry (SWC < 0.2) phases, with SFD responding more strongly. There was a significant correlation for TWD and vapour pressure deficit (VPD) only in the wet phase, and for SFD and VPD only in the dry phase. During the dry phase, the incoming PPFD significantly correlated with SFD in all species, and with TWD only in F. sylvatica and F. excelsior. TWD negatively responded to SFD, showing hysteresis effects from which a decreasing sigmoidal phase along the soil drying gradient was observed. The nonlinear correlations between TWD and SFD may result from a time lag between the two variables, and their different sensitivities to SWC and VPD under different drought intensities. We conclude that, under drought stress, TWD cannot be used as a proxy for SFD or vice versa. [ABSTRACT FROM AUTHOR]

Saproxylic organisms play a pivotal role in the decomposition of woody materials, profoundly impacting nutrient cycling within forest ecosystems. With their rich biodiversity and diverse ecological functions, saproxylic species provide unique ecological services and interactions that are closely linked to specific dead wood types. Given the distinctive roles and diversity of saproxylic beetles, forest loss and management practices that reduce dead wood amount and quality pose a risk of greater biodiversity loss. This highlights the critical need for comprehensive research into saproxylic beetle diversity and community structures from various perspectives to support sustainable forest management strategies. We sampled 60 Quercus logs in a temperate mixed forest during both winter and summer, and reared saproxylic beetles using emergence traps. Consequently, we collected a total of 1793 saproxylic beetles from 27 families and 75 species across three decay classes. Season was a key environmental variable, clearly distinguishing saproxylic beetle assemblages between winter and summer. While decay class did not clearly differentiate the beetle assemblages in summer, it distinctly separated the assemblages in winter. Our findings indicate that saproxylic beetles are influenced by changing resource availability and microclimate, driven by environmental variables such as season. This variability reveals the overlooked biodiversity potential, emphasizing the need for continuous and comprehensive research to achieve conservation goals and improve understanding of saproxylic organisms. [ABSTRACT FROM AUTHOR]

From 2020 to 2023, significant leaf spot disease has occurred on the main economic tree species of bamboo and tea (Camellia L.), in Guangde City. After microscopic examination, Alternaria Nees was found. This paper investigates the prevalence patterns of this disease, through comparative investigation, it was found that camellia flowers planted in urban green spaces have obvious disease spots and serious harm, while tea trees planted in mixed forests or under forests have no typical disease spots and are less harmful. The diseased bamboo forest has gradually recovered its health by adopting measures such as suitable stand density and retaining local tree species. Improper cultivation management methods and lack of biodiversity are the main reasons for the occurrence and prevalence of this type of disease. [ABSTRACT FROM AUTHOR]

Results of the study of terrestrial mollusc shells from the unconsolidated deposits of the Tetyukhinskaya Cave, Dalnegorsk, Primorsky Krai, Russia are presented. Brief species descriptions of the molluscs and their images complete the scarce information on the fossil Quaternary mollusc fauna of the region. With the help of malacological analysis, characteristic zones and malacological complexes were identified, which were used as indicators of the habitats near the cave entrance and in the adjacent territory. The data concerning the palaeoecological conditions of individual mollusc species confirm the development of broad-leaved and mixed forests, alongside open meadows with herbage vegetation in the region near the cave, towards the end of the Late Pleistocene and in the Holocene. [ABSTRACT FROM AUTHOR]

The majority of the woodland area in Central Europe is composed of managed forests. Although the species diversity and composition, particularly in older forests, may be similar to those of natural or close-to-nature forests, intensive forestry and other human activities have adverse impacts on biodiversity. The present study focused on bryophyte diversity in managed lowland forests, as these have received less attention compared to other types of forests. Our research targeted the area located within the Borská nížina Lowland in south-western Slovakia. In total, 37 forest sampling plots (SPs) were selected, representing five different forest types, namely Quercus, Fraxinus, Robinia, Pinus, and mixed forests. Multivariate statistical analyses were conducted across these forest types to demonstrate variations in bryophyte species richness, composition, and functional traits. Overall, 60 species of bryophytes were identified, of which five were liverworts and 55 were mosses. Epiphytes were the most abundant substrate group in deciduous and mixed stands, while epigeic species prevailed in Pinus forests. Considering environmental factors, the bryophyte diversity was influenced mostly by the forest type and area size of SPs. Clear differences in species composition were observed when comparing coniferous and deciduous stands, as well as non-native Robinia forests and native tree stands. Overall, intensive forest management results in a high concentration of hemerophilous and nitrophilous species, along with the absence of rare and threatened mosses and liverworts. Nevertheless, by adhering to proper management methods, even managed forests can provide suitable habitats for various bryophytes. [ABSTRACT FROM AUTHOR]

Deciduous fine woody debris (DFWD) is a common deadwood substrate type in boreal conifer-dominated forests, but it is usually present in low volumes, and its importance for deadwood dependent biodiversity is poorly understood. In this study, we investigated how DFWD-associated fungal and beetle diversity depends on local substrate availability and quality, and how species diversity differs between DFWD and coarse deciduous deadwood (birch logs) in boreal mixed spruce-dominated forests in southern Finland. We studied 25 forest plots (each 0.16 ha), measuring and sampling all pieces of DFWD with a diameter of 2–5 cm and minimum length of 50 cm. Wood-inhabiting fungi were surveyed from wood samples by DNA metabarcoding and saproxylic beetles were surveyed by bark sieving. Our results showed a clear positive relationship between DFWD abundance and the diversity of fungi and beetles. Tree species and decay class diversity were not important in explaining fungal and beetle diversity or community composition, possibly due to low degree of variation in DFWD quality among the study plots. DFWD hosted more diverse fungal assemblages than birch logs, including species of conservation concern, while no red-listed beetle species were observed on DFWD. Overall, species assemblages associated with fine and coarse deciduous deadwood were non-nested. Thus, DFWD represents a non-redundant complementary deadwood resource type alongside coarse deciduous deadwood in boreal forests. [ABSTRACT FROM AUTHOR]

ABSTRACT Introduction Methods Results Conclusions Ixodes ricinus, the castor bean tick, is the most prevalent tick species in Europe. It favours habitats such as shrubs, deciduous and mixed forests, but can also be found in urban environments. Due to its high vector competence, it is of enormous veterinary as well as medical importance, transmitting tick‐borne encephalitis (TBE) virus, Borrelia burgdorferi s. l., the causative agent of lyme borreliosis, Rickettsia spp. and Anaplasma phagocytophilum amongst many other pathogens. In Kosovo, I. ricinus is the predominant species and a few studies, mostly based on human and animal seroprevalences, indicate the circulation of tick‐borne pathogens. However, data on pathogens in I. ricinus are scarce in Kosovo, particularly in urban settings. This study aimed to provide first insights into the circulation of tic‐kborne pathogens in I. ricinus from urban and peri‐urban areas in Kosovo.Urban and peri‐urban areas were sampled by flagging 150 m transects. In total, 197 ticks were morphologicaly identified as I. ricinus, and consequently DNA and RNA were isolated. All individuals were screened for the presence of tick‐borne pathogens by using reverse line blotting (RLB) hybridisation.DNA of nine different pathogens from four genera including Borrelia spp., Rickettsia spp., Anaplasma spp. and Babesia spp. was detected in 60 (33.5%) specimens. The most frequently detected pathogens were Rickettsia spp. (16.2%), followed by Borrelia spp. (11.7%). Altogether, 54 single infections, 11 double infections and 1 triple infection were observed.We provide first data on genotyping of B. burgdorferi sensu lato as well as the detection of Anaplasma, Babesia and Rickettsia from I. ricinus in this country. The data underline that particularly recreational (peri‐)urban areas could facilitate the spillover of zoonotic tick‐borne pathogens to humans in Kosovo and provide baseline data for future surveys. [ABSTRACT FROM AUTHOR]

The expansion of Pleioblastus amarus into tea plantations introduces environmental heterogeneity, significantly influencing the growth and quality of bamboo shoots. This study examined the effects of bamboo expansion on the appearance, nutrition, and palatability of bamboo shoots, utilizing partial least squares structural equation modeling (PLS-SEM) to identify key influencing factors. Results revealed that bamboo expansion increased shoot diameter, length, and fresh weight, enhancing overall size and edibility, particularly in the tea-bamboo mixed forest center zone (TBC), where appearance quality peaked. Nutritional analysis revealed substantial increases in protein, fat, starch, and vitamin C content after bamboo expansion, along with the improvements in amino acid score (AAS), essential amino acid index (EAAI), and nutritional index (NI), indicating elevated nutritional value. However, despite the rise in soluble sugars and flavor-enhancing amino acids, higher levels of total acids, oxalic acid, tannins, and cellulose diminished the palatability, notably in TBC site. PLS-SEM further indicated that while bamboo expansion positively influenced shoot appearance and nutrition, soil factors predominantly drove these changes and concurrently detracted from overall palatability. These findings provide a framework for enhancing bamboo shoot quality and optimizing management practices in tea plantation ecosystems. [ABSTRACT FROM AUTHOR]

Introduction: Chronic wasting disease (CWD) among cervids in Kansas has seen a consistent rise over the years, both in terms of the number of infections and its geographical spread. In this study, we assessed the occupancy patterns of CWD among white-tailed deer and mule deer across the state. Methods: Using surveillance data collected since 2005, we applied a dynamic patch occupancy model within a Bayesian framework, incorporating various environmental covariates. Using principal components analysis, 13 fully orthogonal components representing cervid habitat, soil, and elevation were derived. Competing models with different temporal patterns were fit, and the best model selected based on Watanabe-AIC values and AUC value of 0.89. Results: The occupancy pattern produced by this model revealed a steady progression of the disease toward the east and southeast of the state. A random forest analysis of covariates at annual intervals indicated that geographic location, elevation, areas occupied by mixed forests, and several soil attributes (pH, clay content, depth to restrictive layer, available water content, and bulk density) explained most of the variability in the surveillance data (R 2 = 0.96). Discussion: The findings reported in this study are the first for the state of Kansas but are consistent with previous findings from other geographic jurisdictions in the US and Canada. This consistency underscores their value in designing surveillance and management programs. [ABSTRACT FROM AUTHOR]

Forecasting the effects of global change drivers on ecosystems is one of the most pressing challenges for scientists worldwide. Particularly, climate change and exotic pathogens might have a large impact on plant community dynamics and ecosystem functioning through changes in carbon uptake and sinks. Nevertheless, we still have a poor understanding of the combined effects of these two drivers on plant communities.Here, we explored the impact of rainfall reduction and exotic pathogens on the carbon balance of Mediterranean tree species. For this, we performed a 3‐year field experiment taking advantage of rainfall exclusion infrastructures (30% exclusion) installed in the southernmost European oak forests invaded by the aggressive exotic pathogen Phytophthora cinnamomi. We measured a set of 10 variables representative of tree carbon sources (photosynthetic rates) and sinks (primary production, reproduction, defence, and reserves) in adult trees of three species in two forest types: closed forests of Quercus suber and Q. canariensis, and open woodlands of Q. suber and Olea europaea.We found a large variability in the sensitivity of the different carbon sources and sinks to the effects of drought and pathogens, from variables highly sensitive to both factors (carbon fixation and reproduction, root chemistry) to variables only responsive to drought (litter production) or totally unresponsive (tree trunk, leaf chemistry). Although negative effects predominated, positive effects of rainfall exclusion were also detected in wet years, likely due to a reduction of pathogen abundance in drier soil. Trade‐offs between carbon sinks appeared in all tree species, but rainfall exclusion only modified trade‐offs in Q. suber, the species most susceptible to P. cinnamomi.Synthesis. We provide evidence on the complexity of the combined effects of abiotic (drought) and biotic (pathogens) global change drivers on carbon source and sinks of adult trees, including both negative direct effects and positive indirect effects. Our results showed that these effects varied among co‐existing species, particularly for carbon sinks directly related to tree demography (reproduction). Therefore, long‐term changes in the structure of Mediterranean mixed forests might be expected towards the dominance of species highly resistant to both drought and pathogens. [ABSTRACT FROM AUTHOR]

Vegetation changes on the Tibetan Plateau are indicative of the dual impacts of climate change and human activities, with satellite data offering a potent tool for monitoring these alterations. However, the impacts of future land cover change on vegetation changes on the Tibetan Plateau under different climate scenarios remain unclear. This study systematically investigates vegetation trends and their contributions driven by land cover changes under eight future climate scenarios from 2015 to 2100 using remotely sensed land cover and NDVI data. We estimated consistent NDVI data for land cover changes under the climate scenarios and quantified the vegetation trends and the relative contributions of each land cover type using a relative importance matrix. The study found that (1) Grasslands will remain the dominant land cover, increasing by 4.13% from 2015 to 2100, while Forests, particularly Woody Savannas and Mixed Forests, will significantly influence vegetation trends, with maximum contributions of 48–55% across seasons. (2) Vegetation trends under climate scenarios exhibit greening, browning followed by greening, fluctuation, and browning patterns, with greening being predominant. (3) Forests dominate vegetation trends in most scenarios, especially under pathways of sustainability (SSP1) and fossil-fueled development (SSP5). (4) The seasonal patterns of vegetation changes due to land cover changes are generally similar to the annual one; variations in land cover changes under different scenarios lead to differences in vegetation seasonal patterns. Our research promotes the understanding of the interaction between future land cover changes and vegetation changes on the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

A home range determines the resources animals can access, and the size of a home range often reflects resource availability and energetic requirements of individuals. The Flammulated Owl (Psiloscops flammeolus) is a cryptic small forest owl that breeds in mixed conifer forests in western North America. The home ranges of individuals generally comprise open forests with large trees, but we have yet to fully understand temporal variation in this species' use of space and the habitat structures that drive space use at a fine scale. During the 2017 summer breeding season (May–July), we tracked the movement of six territorial males with GPS tags to estimate temporal variation in space use and examined resource selection for fine-scale habitat characteristics within home ranges. Results suggest that individual movement was more constrained around nests early at night, but the area of space used was consistent between the incubation and nestling stages. The owls' activity centers near their nests had denser ground cover than available habitat, indicating that the forest understory may be valuable for this species' breeding ecology. As forested landcover rapidly changes in western North America, understanding the spatial behavior and fine scale habitat associations of this species and others is increasingly important. [ABSTRACT FROM AUTHOR]

Israel's nationwide multiyear campaigns, particularly in the late 18th and early 19th centuries, involved deforestation and the development of natural areas for agriculture and human settlement, and resulted in the local extirpation of several animal species. However, reforestation began in the 1940s and promoted the resurgence of extirpated populations. One species that has benefited from these landscape changes is the Eurasian Sparrowhawk (Accipiter nisus). Based on the analysis of population trends in the Judea region (central Israel), we found that this species' abundance increased by 8.9% over a 5-yr study period (2006–2010). We also found that hatching day was the only reproductive parameter in our study area that varied significantly among years. This breeding parameter depended on three habitat characteristics: (1) the height of the nest; (2) the amount of mixed forest with sparse cover in the area around the nest; and (3) the amount of herbaceous vegetation around the nest. Hatching began earlier in higher nests, and hatching began later in nests surrounded by a high percentage of mixed forest and herbaceous vegetation. However, productivity was independent of the habitat type surrounding the nest site. Our descriptive analysis of prey types suggested that most species in the prey remains and pellets likely came from outside the forest, typically from open areas such as agricultural fields, water reservoirs, or aquaculture ponds. Further, only three species dominated the prey remains and pellets below the nests. We documented reproductive parameters and changes in population size during the early years of this population increase. We predict that the population will continue to thrive and spread into the surrounding areas, as there have been no significant changes in land management in the study area over the past 13 yr. [ABSTRACT FROM AUTHOR]

Background and aims: Growing evidence has shown that nitrogen (N) deposition can lead to soil acidification and tree nutrient imbalance. Tree species-specific differences in plant-soil interactions may render different forest types exhibiting contrasting responses to N deposition, yet this remains largely untested. Methods: We conducted N addition experiments (0 and 50 kg N ha−1 yr−1) separately in a larch (Larix kaempferi) forest and an adjacent larch-broadleaf mixed forest, and examined whether soil N availability, soil acid–base chemistry, leaf nutrients and stoichiometry of these two forests responded differently to four-year N addition. Results: We found that N addition increased soil nitrate concentrations at four soil layers (i.e. Oa + e, 0–10, 10–20, and 20–40 cm), and resulted in soil acidification at Oa + e and 0–10 cm layers characterized by decreased pH and exchangeable base cations and increased hydrolyzing cations in the larch forest. In contrast to soil chemical properties, larch leaf nutrient stoichiometry except the C:N ratio showed no significant responses to N addition in the larch forest. Moreover, N addition did not significantly affect soil inorganic N concentration, soil acid–base chemistry, and tree leaf nutrients in the larch-broadleaf mixed forest. Conclusions: Our results suggest the higher resistance of larch-broadleaf mixed forests than larch forests against soil acidification under N addition, and highlight the establishment of conifer-broadleaf mixed forests is an important silvicultural practice to alleviate soil acidification induced by N deposition. [ABSTRACT FROM AUTHOR]

The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar instrument that collects near-global measurements of forest structure. While expansive in scope, GEDI samples are spatially sparse and cover a small fraction of the land surface. Converting the sparse samples into spatially complete predictive maps is of practical importance for a number of ecological studies. A complicating factor is that GEDI collects measurements over forested and non-forested land alike, with no automatic labeling of the land type. Such classification is important, as it categorically influences the probability distribution of the spatial process and the ecological interpretation of the observations/predictions. We propose and implement a spatial mixture model, separating the observations and the greater spatial domain into two latent classes. The latent classes are governed by a Bernoulli spatial process, with spatial effects driven by a Gaussian process. Within each class, the process is governed by a separate spatial model, describing the unique probabilistic attributes. Model predictions take the form of scalar predictions of the GEDI observables as well as discrete labeling of the class membership. Inference is conducted through a Bayesian paradigm, yielding rich quantification of prediction and uncertainty through posterior predictive distributions. We demonstrate the method using GEDI data over Wollemi National Park, Australia, using optical data from Landsat 8 as model covariates. When compared to a single spatial model, the mixture model achieves much higher posterior predictive densities on the true value. When compared to a random forest model, a common algorithmic approach in the remote sensing community, the random forest achieves better absolute prediction accuracy for prediction locations far from observed training data locations, but at the expense of location-specific assessments of uncertainty. The unsupervised binary classifications of the mixture model appear broadly ecologically interpretable as forest and non-forest when compared to optical imagery, but further comparison to ground-truth data is required. [ABSTRACT FROM AUTHOR]

Long‐term pure forest (PF) management and successive planting has result resulted in "low‐efficiency artificial forests" in large areas. However, controversy persists over the promoting effect of introduction of broadleaf tree species on production efficiency of PF. This study hypothesised that introduced broadleaf tree species can significantly promote both water‐nutrient use efficiency and gross primary productivity (GPP)of PF. Tree ring chronologies, water source, water use efficiency and GPP were analysed in coniferous Cunninghamia lanceolata and broadleaved Phoebe zhennan growing over the past three decades. The introduction of P. zhennan into C. lanceolata plantations resulted in inter‐specific competition for water, probably because of the similarity of the main water source of these two tree species. However, C. lanceolata absorbed more water with a higher nutrient level from the 40–60‐cm soil layer in mixed forests (MF). Although the co‐existing tree species limited the basal area increment and growth rates of C. lanceolata in MF plots, the acquisition of dissolved nutrients from the fertile topsoil layer were enhanced; this increased the water use efficiency and GPP of MF plots. To achieve better ecological benefits and GPP, MFs should be constructed in southern China. Summary Statement: 1)The effects of introduced broadleaf tree species on benefits of pure forest (PF) was evaluated.2)Both species probably have competition for water at low levels in the mixed forests (MF).3)Although the co‐existing tree limited the Basal area increment of MF, the water use efficiency and gross primary productivity increased.4)The introduction of MFs enhances the water and nutrient acquisition resources. [ABSTRACT FROM AUTHOR]

Background and Aims Mixed forest plantations are increasingly recognized for their role in mitigating the impacts of climate change and enhancing ecosystem resilience. Yet, there remains a significant gap in understanding the early-stage dynamics of species trait diversity and interspecies interactions, particularly in pure deciduous mixtures. This study aims to explore the timing and mechanisms by which trait diversity of deciduous species and competitive interactions influence yield, carbon allocation and space occupation in mixed forests, both above and below ground. Methods A forest inventory was conducted in planted monocultures, two-species and four-species mixtures of European Acer , Tilia , Carpinus and Quercus , representing a spectrum from acquisitive to conservative tree species. Effects of competition were assessed with linear mixed-effects models at the level of biomass and space acquisition, including leaf, canopy, stem and fine root traits. Key Results Early above-ground growth effects were observed 6 years post-planting, with significant biomass accumulation after 8 years, strongly influenced by species composition. Mixtures, especially with acquisitive species, exhibited above-ground overyielding, 1.5–1.9 times higher than monocultures. Fine roots showed substantial overyielding in high-diversity stands. Biomass allocation was species specific and varied markedly by tree size and the level of diversity and between acquisitive Acer and the more conservative species. No root segregation was found. Conclusions Our findings underscore the crucial role of species trait diversity in enhancing productivity in mixed deciduous forest plantations. Allometric changes highlight the need to differentiate between (active) acclimatizations and (passive) tree size-related changes, but illustrate major consequences of competitive interactions for the functional relationship between leaves, stem and roots. This study points towards the significant contributions of both above- and below-ground components to overall productivity of planted mixed-species forests. [ABSTRACT FROM AUTHOR]

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The Loess Plateau, with a fragile ecological environment, is one of the most serious water- and soil-eroded regions in the world, which has been improved by large-scale projects involving returning farmland to forest and grassland. This work is mainly aimed at exploring a more reasonable and efficient ecological forest restoration mode and revealing synergistic restoration mechanisms. This study sampled typical Loess Plateau areas and designed the restoration modes for pure forests of Armeniaca sibirica L. (AR), Amygdalus davidiana (Carrière) de Vos ex Henry. (AM), Medicago sativa L. (MS), and mixed forests of apricot–peach–alfalfa (AR&AM&MS), using abandoned land (AL) as a control treatment. The effects of these modes on the physical and chemical properties and enzyme activities of various soils were investigated in detail. Moreover, the soil microbial diversity and community structure, functional gene diversity, and differences in the restoration modes were deeply analyzed by meta-genomic sequencing technology, and the inherent driving correlation and mechanisms among these indicators were discussed. The results showed that the soil water content and porosity of the AR, AM, and AR&AM&MS treatments increased significantly, while the bulk density decreased significantly, compared with AL. Moreover, the total carbon, total nitrogen, nitrate nitrogen, total phosphorus, available phosphorus, total potassium, and available potassium contents of the AR&AM&MS restoration mode increased significantly. Compared to CK, there was no significant change in the catalase content of pure forest and mixed forest; however, the contents of urease, phosphatase, sucrase, B-glycanase, and N-acetylglucosaminidase in the restoration mode of the mixed forest all increased significantly. The species diversity index of the restoration modes is similar, and the dominant bacteria in soil microorganisms include Proteobacteria, Acidobacteria, Actinobacteria, Bacteroidetes, and Gemmatimonadetes. The mixed forest restoration mode had the highest microbial abundance. The functional gene diversity of the different restoration modes was also similar, including kegg genes, eggNOG genes, and carbohydrate enzymes. The functional genes of the mixed forest restoration mode were the most abundant, and their restoration mechanism was related to the coupling effect of soil–forest grass. After evaluation, the restoration mode of mixed forest was superior to that of pure forest or pure grass. This is attributed to the fact that the mode can improve soil structure, retain soil moisture, enhance soil enzyme activity, optimize soil microbial community structure, and improve microbial diversity and functional gene activity. This provides key data for the restoration of fragile ecological areas, and the promotion of sustainable management of forests and grass in hilly areas of the Loess Plateau. [ABSTRACT FROM AUTHOR]

The evaluation of site quality for mixed forests is a comprehensive approach to analyzing forest site conditions and tree species growth performance. Accurate site quality assessment is crucial for understanding and enhancing the ecological functions and productivity potential of forests. This study focuses on mixed forests in Lishui City, Zhejiang Province. Using the Two-way Indicator Species Analysis (TWINSPAN) method, coniferous mixed forest, broadleaved mixed forest, and mixed coniferous–broadleaved forests in the region were classified into 15 forest types. Site form models for each type were then constructed using the Algebraic Difference Approach (ADA) to categorize site quality levels. Subsequently, a site quality classification model was developed by integrating site and climatic factors, employing four machine learning algorithms: Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and XGBoost. This model effectively facilitated the evaluation of site quality in mixed forests. The results showed that, across the 15 forest types, the site form models based on the ADA method achieved R2 values greater than 0.634, indicating accuracy in capturing tree height growth trends in mixed forests. For site quality classification, all four models (RF, KNN, SVM, and XGBoost) achieved overall accuracies above 0.77. Among these, the machine learning models ranked in effectiveness for site quality classification as follows: XGBoost > RF > SVM > KNN. These findings suggest that the site form model is a suitable criterion for classifying site quality in mixed forests in Lishui City, Zhejiang Province, and that the XGBoost-based model demonstrates strong classification accuracy. This study provides a scientific basis for site-adapted tree selection and advances information on mixed forest management. [ABSTRACT FROM AUTHOR]

The mixing of poplar and robinia in coastal saline land is a useful attempt at difficult site afforestation. Investigating the long–term mixing effects of nitrogen–fixing and non–nitrogen–fixing tree species on the spatial heterogeneity of N and P nutrients and their ecological stoichiometric characteristics in the coastal saline–alkali soil can provide a scientific basis for soil improvement and plantation management in the coastal saline–alkali soil. By replacing time with space, poplar and robinia mixed forests and corresponding pure forests with the ages of 3, 7 and 18 years were selected, and soil profiles of 0–20 cm, 20–40 cm and 40–60 cm were dug up to determine the contents of total nitrogen, hydrolyzed nitrogen, total phosphorus and available phosphorus, the activities of soil urease and phosphatase and the number of soil bacteria, fungi and actinomycetes in rhizosphere soil. The mixture of poplar and robinia and the increase in planting years led to the heterogeneity of soil N and P in a coastal saline–alkali forest, which could significantly increase the contents of soil total nitrogen, hydrolyzed nitrogen, total phosphorus and available phosphorus between soil layers. Compared with the pure forest of poplar and robinia at the same age, the soil urease activity in the 0–20 cm soil layer of an 18a poplar and robinia mixed forest increased by 94.75% and 73.36%, and the soil phosphatase activity increased by 30.36% and 70.27%. The mix of poplar and robinia significantly increased the abundance of soil microorganisms in saline–alkali soil. The number of bacteria, fungi and actinomycetes in the 0–20 cm soil layer of the 18a poplar and robinia mixed forest was the highest, which were 703,200, 31,297 and 1903, respectively. Redundancy analysis showed that there was a significant positive correlation between soil N and P nutrient contents, soil enzyme activities and microbial abundance. The soil depth of N and P nutrient decomposition and transformation in the mixed poplar and robinia plantation was expanded. The soil N and P nutrient contents, enzyme activities and microbial abundance in the 40–60 cm soil layer of the mixed forest were higher than those of the pure forest. With the increase in plantation years, the depth of soil that can be used in the forest land is increasing. The mixture of poplar and robinia plantation is an excellent choice for the construction of coastal saline–alkali land plantation, which has a significant mixed gain for the decomposition and transformation of N and P nutrients and increases the depth of the available soil layer in the forest land in coastal saline–alkali land. However, the coastal saline–alkali land soil N/P is < 14 and is still restricted by nitrogen, so the application of nitrogen fertilizer can be increased during the afforestation process. [ABSTRACT FROM AUTHOR]

Intercropping is a promising strategy for sustainable medicinal plant cultivation, but its impact on plant–microbe interactions remains poorly understood. This study investigated the influence that intercropping giant lily (Cardiocrinum giganteum) with bamboo (BG), Chinese fir (FG), and mixed forests (MG) had on the giant lily metabolome and microbiome compared to a monoculture control (GG). Metabolomic analysis revealed that BG significantly increased the accumulation of terpenoids (e.g., yucalexin B22, 19.39-fold), alkaloids (e.g., anabasine, 2.97-fold), and steroids (e.g., O-acetyl-lariciresinol, 4.49-fold), while MG induced the production of stress-related metabolites (e.g., aflatoxin G2, 128.62-fold), and FG enhanced nitrogen metabolism (e.g., putrescine, 2.47-fold). Intercropping altered the rhizosphere and endophytic microbial communities, with BG enriching beneficial bacteria (e.g., Acidobacteria and Alphaproteobacteria) and FG promoting symbiotic fungi (e.g., Serendipita and Xylariales). Network analysis revealed strong correlations between specific microbial taxa (e.g., Bacillus and Ceratobasidiaceae) and key metabolites (e.g., norpandamarilactonine A, methylgingerol), indicating their potential roles in shaping the metabolic profiles of giant lily. These findings highlight the complex interplay between intercropping systems, microbial communities, and medicinal plant metabolism and provide a basis for developing targeted cultivation strategies to enhance the production of bioactive compounds in giant lily and other medicinal plants. [ABSTRACT FROM AUTHOR]

Accurately understanding the stand composition of shelter forests is essential for the construction and benefit evaluation of shelter forest projects. This study explores classification methods for dominant tree species in shelter forests using UAV-derived RGB, hyperspectral, and LiDAR data. It also investigates the impact of individual tree crown (ITC) delineation accuracy, crown morphological parameters, and various data sources and classifiers. First, as a result of the overlap and complex structure of tree crowns in shelterbelt forests, existing ITC delineation methods often lead to over-segmentation or segmentation errors. To address this challenge, we propose a watershed and multi-feature-controlled spectral clustering (WMF-SCS) algorithm for ITC delineation based on UAV RGB and LiDAR data, which offers clearer and more reliable classification objects, features, and training data for tree species classification. Second, spectral, texture, structural, and crown morphological parameters were extracted using UAV hyperspectral and LiDAR data combined with ITC delineation results. Twenty-one classification images were constructed using RF, SVM, MLP, and SAMME for tree species classification. The results show that (1) the proposed WMF-SCS algorithm demonstrates significant performance in ITC delineation in complex mixed forest scenarios (Precision = 0.88, Recall = 0.87, F1-Score = 0.87), resulting in a 1.85% increase in overall classification accuracy; (2) the inclusion of crown morphological parameters derived from LiDAR data improves the overall accuracy of the random forest classifier by 5.82%; (3) compared to using LiDAR or hyperspectral data alone, the classification accuracy using multi-source data improves by an average of 7.94% and 7.52%, respectively; (4) the random forest classifier combined with multi-source data achieves the highest classification accuracy and consistency (OA = 90.70%, Kappa = 0.8747). [ABSTRACT FROM AUTHOR]

Mixed broad-leaved trees are particularly effective in addressing ecological issues such as soil degradation and biodiversity loss caused by the dense planting of Chinese fir. Understanding the changes in soil bacterial communities in fir–broadleaf mixed forests as a function of fir retention density may offer new insights for optimizing management practices and enhancing the ecological functions of the underground components of forest ecosystems. In this study, the diversity and composition of soil bacterial communities in mixed Cunninghamia lanceolata and Betula luminifera forests (CFBFs) with diverse retention densities of Chinese fir (1250, 1560, and 1690 trees/hm2) were analyzed. The results suggested that the soil characteristics and microbial communities' diversity and structure are significantly influenced by the retention densities of Chinese fir in CFBFs. At the aggregate scale, the CFBFs with a retention density of 1560 trees/hm2 presented the greatest soil bacterial community diversity (based on the Chao 1 (3562.75) and Shannon indices (6.58)), and the diversity and richness of soil bacteria initially increase and then decrease as the retention density decreases. In CFBFs, regardless of the retention density, bacterial communities in soil were mainly composed of Acidobacteria, Proteobacteria, and Planctomycetes. The relative abundance of soil Acidobacteria first elevated and afterwards decreased as the retention density decreased, with the highest levels (47.15%) observed in the stand with 1560 trees/hm2 of Chinese fir. The Principal Coordinates Analysis (PCoA) showed that the soil microbial community structure in CFBEs with a retention density of 1560 trees/hm2 is significantly different from CFBEs with a retention density of 1260, and 1690 trees/hm2. Moreover, with different retention densities of Chinese fir, soil organic C, total N concentrations, and soil pH also significantly affected the diversity and composition of CFBF soil bacterial communities. Our results show that the choice of retention densities significantly influences soil microbial diversity and composition in CFBFs. Optimal retention densities (1560 trees/hm2) of Chinese fir in CFBFs can maximize bacterial diversity and stability, providing management guidance for thinning for sustainable management of the soil microenvironment of CFBFs. [ABSTRACT FROM AUTHOR]

In urban industrial area, mining activity directly affects the dynamic of carbon, and consequently, the release of carbon dioxide (CO2) into the atmosphere. The main objective of this research is to study the impact of most important abiotic environmental factors on soil respiration in post-coalmine ecosystems. The moisture and temperature of the soil, along with CO2 outflow from the soil, were measured over three consecutive seasons, using 92 samples from coalmine heaps and 10 samples from deciduous forests in the same urban industrial region. Based on a survey of 396 species, a cluster analysis distinguished all deciduous and 22 forest plots grown on coalmine heaps from herbaceous plots from same coalmine heaps. The lowest soil respiration rate (0.62 mg CO2 per hour per square meter) was recorded in the herbaceous vegetation class on coalmine heaps, compared to (0.76 mg and 0.96 mg) from coalmine-heap forests and deciduous forests, respectively. Species richness and diversity positively affected soil respiration in heap herbaceous plots, though this effect was less pronounced in forests grown on coalmine heaps and in deciduous mixed forests. Unlike soil water content, soil temperature negatively correlated with soil respiration on coalmine heaps, diverging from the well-studied positive impact of soil temperature and respiration in deciduous mixed forests. Our spatial and temporal analyses emphasize that the water content of the substrate is the most significant abiotic element that affects the soil respiration on coalmine heaps positively during early vegetation succession. [ABSTRACT FROM AUTHOR]

The present study demonstrates the importance of biomass as a renewable energy source; the forest ecosystems with high-energy potential have a high carbon storage capacity and the key factor being the amount of biomass stored. The study area was Romania, located in Europe, where the author selected 90 forest ecosystems spread along an altitudinal gradient from the lower Danube floodplain (15 m asl.) to the Carpathian Mountains (2100 m asl.). In each ecosystem type, five circular areas of 500 m2 were selected, where the following parameters were measured and estimated: tree species composition, average age, total timber volume (m3/ha) and annual productivity (m3/ha/year), and wood density (g/cm3). The calorific capacity of the wood was determined by calorimetric methods using the Bomb Calorimeter (Model-IKA C2000), the carbon content was measured with a CE InstrumentsEA-1110 CHNS-O dispositive, and the carbon stock was calculated using CO2FIX31EXE based on woody biomass carbon stocks. From the point of view of standing crop biomass, the most valuable are pure forests, 130-year-old (355 t/ha); mixed beech forests where beech dominates, 110-year-old (300.8 t/ha); and the planted forest, the 35-year-old poplar plantation was the most productive (199.5 t/ha). The highest productivity was in the 10-year-old planted poplar forest (7.14 t/ha/year), followed by the 110-year-old sessile oak forest (2.35 t/ha/year), and 110-year-old mixed beech forests, where 50% beech in composition. In natural forests, in terms of energy, the most efficient are pure beech forests and those mixed with fir and spruce, and for the planted forest, it is the poplar forest. A direct correlation is between the capacity to produce and store energy of the forest ecosystems and the capacity to fix and store the carbon through photosynthesis, and the biomass is a key factor that proves this. [ABSTRACT FROM AUTHOR]

Abstract Mapping dominant tree species in miombo woodlands is essential for enhancing their monitoring and management. We evaluated PlanetScope imagery to map Julbernardia globiflora, Brachystegia spiciformis, and Pterocarpus tinctorius in Tongwe Forest Reserve, Tanzania. The study assessed the effectiveness of PlanetScope bands in discriminating tree species and investigated how different months/seasons influenced tree species classification. Optimal months (seasons) and spectral bands were selected using Principal Component loading, temporal pattern analysis, mean decrease in accuracy, and mean decrease Gini techniques. Random forest classification was employed for tree species classification, and accuracy was assessed using an error matrix. The study identified March, July, and September as optimal months for acquiring imagery, with effective bands including blue, green-1, green, yellow, red, and red-edge. July and September imagery in the dry season achieved higher overall accuracies of 65% and 69%, respectively, while March imagery in the wet season reached 55%. The highest overall accuracy of 72% was achieved using images from different seasons. Producer’s accuracy was highest for Brachystegia spiciformis (79%) and Julbernardia globiflora (95%), whereas Pterocarpus tinctorius had lower accuracy (25%). User’s accuracy varied with 74% for Brachystegia spiciformis, 70% for Julbernardia globiflora, and 67% for Pterocarpus tinctorius. Mapping accuracy was notably high for Brachystegia spiciformis and Julbernardia globiflora, reflecting their high sample size (dominance) and distinct phenology. The yellow and red bands were particularly effective in distinguishing miombo tree species demonstrating PlanetScope’s capability. Future research should focus on scaling up PlanetScope’s application for broad miombo tree species mapping.

Introduction: Human alveolar echinococcosis caused by Echinococcus multilocularis is an important zoonotic disease in the northern hemisphere. The life cycle of E. multilocularis is maintained primarily in wild animals and requires an intermediate host (mainly small mammals). Human can become an intermediate host through accidental ingestion of E. multilocularis eggs. Hokkaido Prefecture is the only area of Japan in which human alveolar echinococcosis is endemic. The purposes of this study were to elucidate the land use ecological factors associated with the density of red fox feces along paved roads and the relationship between the distributions of red fox (Vulpes vulpes) populations and fox feces, which determine the level of hazard from eggs. Methods: A series of surveys was conducted in the central part of the Nemuro Peninsula of Hokkaido, excluding urban areas, over a total of 4 years in May–June in 2014 and 2016–2018 when red foxes remain with their cubs around the dens. Transects of 500 m were set up on paved roads, and feces within the transects were counted. Univariable and multivariable analyses were performed to examine ecological factors including the principal components (PCs) of five land use–type occupancy proportions within 500 m and 1 km, respectively, as explanatory fixed-effect variables. The number of feces in each transect was examined as the response variable using integrated nested Laplace approximation with negative binomial errors with a spatio-temporal autocorrelations structure to separate the effects of similarities of neighboring locations and annual variation. The multivariable models with the lowest widely applicable information criterion values were selected. Results: The feces density was explained by the PC of the 500- m buffer (−0.27, 2.5th and 97.5th percentiles: −0.44, −0.10) characterized by mixed forests (−0.82) and scarcity of residential areas (0.29) and the proximity to the nearest livestock farm house (−0.35, 2.5th and 97.5th percentiles: −0.53, −0.17). This suggested that foxes defecate in the areas where prey is abundant, avoiding humans. Discussion: Policy discussions regarding bait distribution design targeting these conditions should be initiated. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Aims Methods Results Conclusions Soil enzymes are key to predicting nutrient availability and forest fertility.We aimed to evaluate the influence of forest type on the kinetic and thermodynamic characteristics of soil enzymes.Soils were sampled at 0–10 and 10–20 cm depth from two pure forests (Pinus tabulaeformis [PTF] and Quercus acutissima [QAF]) and a mixed forest of PTF and QAF (MF) on the Chinese Loess Plateau. Kinetic parameters (maximum enzyme activity [Vmax], half‐saturation constant [Km], and enzyme efficiency [Kcat]) and thermodynamic parameters (temperature coefficient [Q10] and activation energy [Ea]) of β‐1,4‐glucosidase (BG), β‐1,4‐N‐acetylglucosaminidase (NAG), l‐leucine aminopeptidase (LAP), and alkaline phosphatase (AP) were determined.Forest type exerted significant influence on soil enzyme kinetic parameters. The Vmax and Kcat values of BG, NAG, and AP in PTF of 0–10 cm soil depth were 42.54% and 59.22%, 77.18% and 23.08%, and 62.82% and 58.21% higher than that in MF, respectively. The Vmax of AP and Kcat of NAG in PTF of 10–20 cm depth were 34.61% and 39.90% higher than that in MF. The soil enzyme thermodynamic parameters were significantly influenced by forest type and soil depth. At 0–10 cm depth, low values of Q10–Vmax and Q10–Km of BG, Q10–Vmax and Q10–Kcat of NAG, and Ea of BG and NAG were found in PTF. At 10–20 cm depth, low values of Q10–Vmax, Q10–Kcat, and Ea of BG and NAG were found in MF.PTF was more effective in promoting soil enzymatic reactions, especially in surface soil. MF improved subsoil enzyme thermal stability and reduced temperature sensitivity. The study showed that pure and mixed forests affect soil enzyme characteristics differently, with soil depth as a key factor. [ABSTRACT FROM AUTHOR]

Simple Summary: Little attention has been paid to the efficiency of trap types in capturing bees across taxonomic and functional groups, nor their suitability under varying environmental conditions. Our study evaluated the efficiency, bias, and complementarity of four trap types: yellow, white, and blue pan traps, and blue vane traps for pollinator monitoring in monoculture and mixed forests. We found that bias in trap types was not only detected in taxonomic but also in functional groups. Differences in bee taxonomic, phylogenetic, and functional diversity were also observed, with blue pan traps yielding the highest species richness and phylogenetic diversity, while blue vane traps captured the highest functional richness. When considering the complementarity of different traps, the combination of blue pan and vane traps outperformed the other two-method combinations. Notably, the bias in trap types was most pronounced in mixed forests. The choice of trap in entomological surveys affects the composition of captured insects, though previous comparative studies have been limited in the types of composition measured, and the effects of environmental context. We assessed the sampling bias of several traps commonly used in pollinator monitoring: blue, yellow, and white pan traps, and blue vane traps, towards different taxonomic and functional groups and their efficiency in measuring taxonomic, phylogenetic, and functional diversity. Analyses were performed in monoculture and mixed forests to understand the environmental context of trap efficiency. We found that blue pan traps generally outperformed other types in bee capture and exhibited a preference for Halictidae bees. Blue pan traps yielded the highest species richness and phylogenetic diversity, while blue vane traps captured the highest functional richness. Bias differences were frequently detected in mixed forests compared with monoculture forests. We also found the combination of blue vane and pan traps consistently correlated highest with a complete survey among two-method combinations. Based on our findings, we recommend a combination of blue vane and pan traps to obtain a more comprehensive bee collection in an efficient manner. Additionally, it is crucial to consider habitat type when designing bee trapping protocols to ensure an accurate representation of bee communities. [ABSTRACT FROM AUTHOR]