White Birch Research Articles

Rubisco activity and activation state dictate photorespiratory plasticity in Betula papyrifera acclimated to future climate conditions

Plant metabolism faces a challenge of investing enough enzymatic capacity to a pathway without overinvestment. As it takes energy and resources to build, operate, and maintain enzymes, there are benefits and drawbacks to accurately matching capacity to the pathway influx. The relationship between functional capacity and physiological load could be explained through symmorphosis, which would quantitatively match enzymatic capacity to pathway influx. Alternatively, plants could maintain excess enzymatic capacity to manage unpredictable pathway influx. In this study, we use photorespiration as a case study to investigate these two hypotheses in Betula papyrifera. This involves altering photorespiratory influx by manipulating the growth environment, via changes in CO2 concentration and temperature, to determine how photorespiratory capacity acclimates to environmental treatments. Surprisingly, the results from these measurements indicate that there is no plasticity in photorespiratory capacity in B. papyrifera, and that a fixed capacity is maintained under each growth condition. The fixed capacity is likely due to the existence of reserve capacity in the pathway that manages unpredictable photorespiratory influx in dynamic environments. Additionally, we found that B. papyrifera had a constant net carbon assimilation under each growth condition due to an adjustment of functional rubisco activity driven by changes in activation state. These results provide insight into the acclimation ability and limitations of B. papyrifera to future climate scenarios currently predicted in the next century.

Identify the key factors driving the lignocellulose degradation of litter along a forest succession chronosequence from the perspective of functional genes

The purpose of this paper was to discover the key factors driving the lignocellulose degradation in litter along a forest succession chronosequence from the perspective of functional genes. We investigated four natural successive stages of forests (white birch forest, broad-leaved mixed forest, coniferous-broad-leaved mixed forest, and mixed broadleaved-Korean pine forest). We determined the lignocellulose degradation of litter and the absolute abundance of related functional genes by using high-throughput-qPCR. There was strong degradation of cellulose content, hemicellulose, and lignin contents in the litter decomposition layer in the early and late stages of forest succession, respectively. Furthermore, forest succession changed microbial communities' succession and fungal Shannon diversity, and then enhanced the absolute abundance of lignocellulose-degrading genes and nitrogen-cycling genes. By network analysis, bacterial and fungal module 1 were key modules for producing lignocellulose-degrading genes and nitrogen cycling genes, while fungal module 2 was a key module for lignin-degrading genes. Fungi were strongly correlated with functional genes based on Procrustes analysis. Additionally, cellulose-degrading genes were the key factor driving the cellulose degradation in the early period of forest succession, while fungal diversity and composition were key drivers in promoting the degradation of lignin in the late period of forest succession. Our study provided insight into the mechanisms underlying the soil microbe-driven functional changes in nutrient cycling and an understanding of the decomposition kinetics of litter at a more microscopic level in the process of forest succession.

Stiffness and hardness of thermally modified timber assessed with explainable machine learning

Thermal modification is an environmentally friendly practice to enhance the durability of wood. Despite the method's effectiveness in improving wood durability and dimensional stability, the mechanical properties of wood could be compromised following the heat treatment process. While most of the literature has focused on studying the impact of wood modification on stiffness or strength, less attention was given to characterization and nondestructive prediction of wood hardness following thermal treatment. Hardness, an important quality indicator of wood, has been a subject of limited research in the context of developing predictive models for assessing the hardness of thermally modified wood. The existing research in this area has either been scarce or unsuccessful, with poor predictive performance being a common issue. This research studies the impact of thermal modification on the longitudinal and transverse hardness of some common North American softwoods (Douglas-fir, Western hemlock, red cedar) and hardwoods (red alder, paper birch). It also aims to develop an explainable machine-learning (TreeNet gradient boosting machine) predictive model for hardness prediction using stress wave data combined with wood density and information about the type of wood species and heat treatment level. The impact of heat treatment on the stress wave velocity, density, and hardness was studied, and the dependency of the variables was assessed using correlation and clustering analysis. Machine learning modeling showed that the longitudinal and transverse hardness could be predicted by having a maximum R2 (test data) of 70.62 and 72.78, respectively, with a relative importance of the predictors in descending order as wood species > density > wave velocity >MOEdyn> heat treatment. Using the top 3 critical parameters yielded an R2 (test data) of ∼69 % for both models. The developed model could predict the hardness of various wood species treated at different temperatures, having only the type of wood species and its density followed by the stress wave velocity, which can be measured at an industrial scale.

Aboveground Carbon Stock Estimation Based on Backpack LiDAR and UAV Multispectral Imagery at the Forest Sample Plot Scale

Aboveground carbon stocks (AGCs) in forests play an important role in understanding carbon cycle processes. The global forestry sector has been working to find fast and accurate methods to estimate forest AGCs and implement dynamic monitoring. The aim of this study was to explore the effects of backpack LiDAR and UAV multispectral imagery on AGC estimation for two tree species (Larix gmelinii and Betula platyphylla) and to emphasize the accuracy of the models used. We estimated the AGC of Larix gmelinii and B. platyphylla forests using multivariate stepwise linear regression and random forest regression models using backpack LiDAR data and multi-source remote sensing data, respectively, and compared them with measured data. This study revealed that (1) the diameter at breast height (DBH) extracted from backpack LiDAR and vegetation indices (RVI and GNDVI) extracted from UAV multispectral imagery proved to be extremely effective in modeling for estimating AGCs, significantly improving the accuracy of the model. (2) Random forest regression models estimated AGCs with higher precision (Xing’an larch R2 = 0.95, RMSE = 3.99; white birch R2 = 0.96, RMSE = 3.45) than multiple linear regression models (Xing’an larch R2 = 0.92, RMSE = 6.15; white birch R2 = 0.96, RMSE = 3.57). (3) After combining backpack LiDAR and UAV multispectral data, the estimation accuracy of AGCs for both tree species (Xing’an larch R2 = 0.95, white birch R2 = 0.96) improved by 2% compared to using backpack LiDAR alone (Xing’an larch R2 = 0.93, white birch R2 = 0.94).

Preliminary Insights on Moisture Content Measurement in Square Timbers Using GPR Signals and 1D-CNN Models

Accurately measuring the moisture content (MC) of square timber is crucial for ensuring the quality and performance of wood products in wood processing. Traditional MC detection methods have certain limitations. Therefore, this study developed a one-dimensional convolutional neural network (1D-CNN) model based on the first 8 nanoseconds of ground-penetrating radar (GPR) signals to predict the MC of square timber. The study found that the mixed-species model exhibited effective predictive performance (R2 = 0.9864, RMSE = 0.0393) across the tree species red spruce, Dahurian larch, European white birch, and Manchurian ash (MC range 0%–133.1%), while single-species models showed even higher accuracy (R2 ≥ 0.9876, RMSE ≤ 0.0358). Additionally, the 1D-CNN model outperformed other algorithms in automatically capturing complex patterns in GPR full-waveform amplitude data. Moreover, the algorithms based on full-waveform amplitude data demonstrated significant advantages in detecting wood MC compared to those based on a traditional time–frequency feature parameter. These results indicate that the 1D-CNN model can be used to optimize the drying process and detect the MC of load-bearing timber in construction and bridge engineering. Future work will focus on expanding the dataset, further optimizing the algorithm, and validating the models in industrial applications to enhance their reliability and applicability.

Research on Chaga hypoglycemic functional foods

Chaga, also known as Inonotus obliquus (Fr.) Pilát, belongs to the genera Basidiomycotina, Hymenomycetes, Hymenochaetales, Hymenochaetaceae, and Inonotus. Chaga is a brown polypore fungus that mostly grows under the bark of white birch and silver birch trees. It forms sarcoma-like sclerotia when the bark is damaged. It mainly grows in Northern Europe at the 40° to 50° north latitude, Siberia and the Far East in Russia, Hokkaido in Japan, and the Changbai Mountain area of the Heilongjiang Province and Jilin Province in China. Chaga has various pharmacological activities, such as anti-tumor, hypoglycemic, anti-viral, and anti-inflammatory. Inonotus obliquus, an edible fungus with the same origin as medicine and food, has attracted more and more attention. At present, Chaga has become a raw material with great potential for developing functional foods. In this article, Chaga’s blood sugar-lowering function, functional ingredients, and blood sugar-lowering mechanism and the development status of Chaga functional foods are reviewed and Chaga’s future development is analyzed and forecast.

Reduced representation bisulfite sequencing (RRBS) analysis reveals variation in distribution and levels of DNA methylation in white birch (Betula papyrifera) exposed to nickel.

Research in understanding the role of genetics and epigenetics in plant adaptations to environmental stressors such as metals is still in its infancy. The objective of the present study is to assess the effect of nickel on DNA methylation level and distribution in white birch (Betula papyrifera Marshall) using reduced representation bisulfite sequencing (RRBS). The distribution of methylated C sites of each sample revealed that the level of methylation was much higher in CG context varying between 54% and 65%, followed by CHG (24%-31.5%), and then CHH with the methylation rate between 3.3% and 5.2%. The analysis of differentially methylated regions (DMR) revealed that nickel induced both hypermethylation and hypomethylation when compared to water. Detailed analysis showed for the first time that nickel induced a higher level of hypermethylation compared to controls, while potassium triggers a higher level of hypomethylation compared to nickel. Surprisingly, the analysis of the distribution of DMRs revealed that 38%-42% were located in gene bodies, 20%-24% in exon, 19%-20% in intron, 16%-17% in promoters, and 0.03%-0.04% in transcription start site. RRBS was successful in detecting and mapping DMR in plants exposed to nickel.

Hydraulic conductivity regulates tree growth and drought resistance in semi-arid mixed forests of northern China

With the intensification of climate warming, drought has become increasingly severe. Drought seriously impacts tree growth and may even result in tree mortality. However, our understanding of how the hydraulic structure of xylem in different tree species regulates growth and drought resistance remains incomplete. Here, we collected tree-ring samples from three semi-arid natural Chinese pine (Pinus tabuliformis)-broadleaf mixed forests in northern China. We selected Chinese pine (non-porous wood), white birch (Betula platyphylla, diffuse-porous wood), and Liaodong oak (Quercus liaotungensis, ring-porous wood) as target tree species. Using dendrochronology and wood anatomy methods, we evaluated interspecies differences in tree growth, climate sensitivity, and drought resistance, and analyzed the relationships between tree growth, drought resistance, and theoretical xylem-specific hydraulic conductivity (Ks). Our findings revealed distinct differences in the hydraulic structure of xylem among these three tree species as well as their resilience to drought. Liaodong oak exhibited less sensitive to drought compared to Chinese pine and white birch. Meanwhile both Chinese pine and Liaodong oak demonstrated greater resistance to drought than white birch. The positive correlation between specific xylem hydraulic conductivity and both tree growth and drought resistance were observed for Chinese pine, but Liaodong oak was the opposite. This suggested that specific hydraulic conductivity of xylem cannot serve as a consistent or robust predictor of tree growth or drought resistance across different species. As climate warming intensifies, it is anticipated that white birch may experience severe and significant irreversible decline in growth, while Liaodong oak may exhibit the highest potential growth performance. Given the projected intensification of future warming trends along with more frequent extreme climate events, safeguarding biodiversity and productivity within semi-arid Chinese pine-broadleaf mixed forests may hinge on the prioritizing protection efforts for Chinese pine.

Linking physiological drought resistance traits to growth and mortality of three northeastern tree species.

Climate change is raising concerns about how forests will respond to extreme droughts, heat waves and their co-occurrence. In this greenhouse study, we tested how carbon and water relations relate to seedling growth and mortality of northeastern US trees during and after extreme drought, warming, and combined drought and warming. We compared the response of our focal species red spruce (Picea rubens Sarg.) with a common associate (paper birch, Betula papyrifera Marsh.) and a species expected to increase abundance in this region with climate change (northern red oak, Quercus rubra L.). We tracked growth and mortality, photosynthesis and water use of 216 seedlings of these species through a treatment and a recovery year. Each red spruce seedling was planted in containers either alone or with another seedling to simulate potential competition, and the seedlings were exposed to combinations of drought (irrigated, 15-d 'short' or 30-d 'long') and temperature (ambient or 16days at +3.5°C daily maximum) treatments. We found dominant effects of the drought reducing photosynthesis, midday water potential, and growth of spruce and birch, but that oak showed considerable resistance to drought stress. The effects of planting seedlings together were moderate and likely due to competition for limited water. Despite high temperatures reducing photosynthesis for all species, the warming imposed in this study minorly impacted growth only for oak in the recovery year. Overall, we found that the diverse water-use strategies employed by the species in our study related to their growth and recovery following drought stress. This study provides physiological evidence to support the prediction that native species to this region like red spruce and paper birch are susceptible to future climate extremes that may favor other species like northern red oak, leading to potential impacts on tree community dynamics under climate change.

Fungal Enzymes for Saccharification of Gamma-Valerolactone-Pretreated White Birch Wood: Optimization of the Production of Talaromyces amestolkiae Cellulolytic Cocktail.

Lignocellulosic biomass, the most abundant natural resource on earth, can be used for cellulosic ethanol production but requires a pretreatment to improve enzyme access to the polymeric sugars while obtaining value from the other components. γ-Valerolactone (GVL) is a promising candidate for biomass pretreatment since it is renewable and bio-based. In the present work, the effect of a pretreatment based on GVL on the enzymatic saccharification of white birch was evaluated at a laboratory scale and the importance of the washing procedure for the subsequent saccharification was demonstrated. Both the saccharification yield and the production of cellulosic ethanol were higher using a noncommercial enzyme crude from Talaromyces amestolkiae than with the commercial cocktail Cellic CTec2 from Novozymes. Furthermore, the production of extracellular cellulases by T.amestolkiae has been optimized in 2L bioreactors, with improvements ranging from 40% to 75%. Finally, it was corroborated by isoelectric focus that optimization of cellulase secretion by T.amestolkiae did not affect the pattern production of the mainβ-glucosidases and endoglucanases secreted by this fungus.

Bending and compressive properties of glued-laminated timber composed of Japanese white birch wood using casein adhesive

ABSTRACT Size-reduced model glued-laminated timber (GLT) (1/8 cross-sectional size, six layers, 11 by 18 by 430 mm) was manufactured using birch (Betula platyphylla) wood and a casein adhesive to evaluate the possibility of producing the structural GLT without petrochemical-based adhesives in Mongolia. The same-sized solid lumber and GLT using aqueous vinyl polymer solution-isocyanate adhesives (API) that can be used for structural GLT production in Japan was prepared to evaluate the mechanical performance of GLT using casein adhesive. The effects of lamination configuration and patterns on the mechanical properties (dynamic Young's modulus, modulus of elasticity [MOE], modulus of rupture [MOR], bending work [energy until broken, W], and compressive strength [CS]) were also evaluated using mixed-effect model. Almost no significant differences in the bending and compressive properties were found between GLT using casein and API adhesives, suggesting that, in dry conditions, the bonding performance of casein adhesives closely matches that of API. Modeling results showed that lamina configuration influenced the W and CS even in the Young’s modulus was almost the same, suggesting that the basic wood properties, such as load and deflection characteristics, are important to enhance the mechanical properties of birch GLT with casein adhesive by laminae configurations.

A putative Na+/H+ antiporter BpSOS1 contributes to salt tolerance in birch

White birch (Betula platyphylla Suk.) is an important pioneer tree which plays a critical role in maintaining ecosystem stability and forest regeneration. The growth of birch is dramatically inhibited by salt stress, especially the root inhibition. Salt Overly Sensitive 1 (SOS1) is the only extensively characterized Na+ efflux transporter in multiple plant species. The salt-hypersensitive mutant, sos1, display significant inhibition of root growth by NaCl. However, the role of SOS1 in birch responses to salt stress remains unclear. Here, we characterized a putative Na+/H+ antiporter BpSOS1 in birch and generated the loss-of-function mutants of the birch BpSOS1 by CRISPR/Cas9 approach. The bpsos1 mutant exhibit exceptional increased salt sensitivity which links to excessive Na+ accumulation in root, stem and old leaves. We observed a dramatic reduction of K+ contents in leaves of the bpsos1 mutant plants under salt stress. Furthermore, the Na+/K+ ratio of roots and leaves is significant higher in the bpsos1 mutants than the wild-type plants under salt stress. The ability of Na+ efflux in the root meristem zone is found to be impaired which might result the imbalance of Na+ and K+ in the bpsos1 mutants. Our findings indicate that the Na+/H+ exchanger BpSOS1 plays a critical role in birch salt tolerance by maintaining Na+ homeostasis and provide evidence for molecular breeding to improve salt tolerance in birch and other trees.

Некоторые закономерности накопления микроэлементов в листьях липы (Tilia cordata Mill.) и берёзы (Betula alba L.)

The purpose of the work: to study the patterns of background accumulation of trace elements during the spring-summer-autumn season in the vegetative organs (leaves) of the heart-shaped linden (Tilia cordata Mill.) and white birch (Betula alba L.). Samples were taken on the territory of the Main Botanical Garden of the Russian Academy of Sciences during one season of 2018 from the same tree with an interval of 10–12 days, starting from the opening of the bud (end of April) right up to the time of leaf fall. Soil samples were also taken from birch and linden growing areas. The samples were analyzed in the laboratory of Chemical analytical methods of the GIN RAS using X-ray fluorescence analysis (XRF), instrumental neutron activation analysis (INАA) and atomic absorption spectrometry of cold vapor (AA). The concentration of trace elements in the selected soil samples corresponds to the concept of “background” contents. Two types of trace elements’ distribution in linden and birch leaves during the season have been revealed. The first type is a uniform accumulation of trace elements with a maximum at the end of the season (Na, Ca, Cr, Fe, Co, Br, Sb, La). The second type of distribution is characterized by a maximum concentration of trace elements in the middle of the season with a decrease in concentration by the time of leaf fall (K, As, Zn, Cd, Pb). It should be noted that the range of changes in the concentration of trace elements during the season in both cases can reach 10–15 times. We recommend to take samples of vegetation (leaves) as soon as possible in the middle of the summer season, and to use samples taken at the same time in the same area as “background”.

Seasonal changes in the phenolic compound contents and bioactivities in Betula platyphylla bark determined by soil properties

The white birch (Betula platyphylla Suk.) is widespread in eastern Asia, and its bark is a valuable resource for secondary metabolite extraction. Limited information is available regarding seasonal variations of secondary metabolites in white birch bark. In our study, we analyzed the phenolic compound contents and bioactivities of white birch bark in different seasons and their relationship to climatic factors and soil properties. The data suggested that the levels of total phenol, total flavonoid, seven individual phenolic compounds, and bioactivities all peaked in September. The antioxidant activity was positively correlated with the contents of phenolic compounds. Soil microbial properties like microbial biomass nitrogen (MBN) and soil urease activities (URE) explained more than 40 % of the variation in phenolic compound contents. This study highlighted that seasons regulate the contents of phenolic compounds and bioactivities by influencing soil microbial properties. The soil with low microbial activity was more favorable for the accumulation of secondary metabolites.

Autumn sunlight promotes aboveground carbon loss in a temperate mixed forest

BackgroundPhotodegradation of plant litter plays a pivotal role in the global carbon (C) cycle. In temperate forest ecosystems, the exposure of plant litter to solar radiation can be significantly altered by changes in autumn phenology and snow cover due to climatic change. How this will affect litter decomposition and nutrient dynamic interacting with forest canopy structure (understorey vs. gaps) is uncertain. In the present study, we conducted a field experiment using leaf litter of early-fall deciduous Betula platyphylla (Asian white birch) and late-fall deciduous Quercus mongolica (Mongolian oak) to explore the effect of change in autumn solar radiation on dynamics of litter decomposition in a gap and understorey of a temperate mixed forest.ResultsExposure to the full-spectrum of not only significantly increased the loss of mass, C, and lignin, but also modified N loss through both immobilization and mineralization during the initial decomposition during autumn canopy opening, irrespective of canopy structure and litter species. These effects were mainly driven by the blue-green spectral region of sunlight. Short-term photodegradation by autumn solar radiation had a positive legacy effect on the later decomposition particularly in the forest gap, increasing mass loss by 16% and 19% for Asian white birch and Mongolia oak, respectively.ConclusionsOur results suggest that earlier autumn leaf-fall phenology and/or later snow cover due to land-use or climate change would increase the exposure of plant organic matter to solar radiation, and accelerate ecosystem processes, C and nutrient cycling in temperate forest ecosystems. The study provides a reference for predictive research on carbon cycling under the background of global climate change.

Comparison of Deep and Machine Learning Approaches for Quebec Tree Species Classification Using a Combination of Multispectral and LiDAR Data

Accessing tree species information is required for making appropriate decisions in forest management. 3D photo-interpretation using high spatial resolution aerial imagery is used to provide information on tree species in the province of Quebec. However, the shortage of qualified interpreters and the increasing costs of 3D photo-interpretation have affected the production of the forest inventory. In this study, we employed deep and machine learning models to classify nine tree species (i.e., paper birch, yellow birch, red maple, poplar, black spruce, white spruce, tamarack, jack pine, and balsam fir). We used a combination of spectral and vertical structural information extracted from 30-cm aerial imagery and airborne light detection and ranging (LiDAR) data in a 9 , 100 km 2 forested area in Quebec, Canada. The results indicated that Dense Convolution Network (DenseNet) achieved the best overall accuracy of 78%, outperforming machine learning methods by 5%. In addition, the models’ performance was independently assessed using permanent and temporary ground sample plots, acknowledging the superiority of DenseNet in terms of overall accuracy (73%) in predicting the dominant species. Our results suggest that the combination of aerial imagery and airborne LiDAR data, using deep learning approaches, can be applied to accurately map tree species.

Дендроэкологическое исследование роста осины в условиях Московской области

According to V.A. Dragavtsev's concept, the reaction of the annual ring to the provocative environmental background of different years creates a “portrait” of adaptive polygenic genotype systems. By comparing the reactions of the main forest-forming species from an ecologically homogeneous habitat, it is possible to assess the level of similarity of their ecological properties. Cluster analysis of tree-ring chronologies of aspen and species growing together with it, correlation analysis of chronologies and indexed chronologies, as well as time series of meteorological factors and Fourier spectral analysis have been used for a dendroecological research into aspen growth features in the conditions of the Molokchinskiy Botanical and Entomological Reserve of the Sergiev Posad District of the Moscow Region. To compare with the chronologies of aspen from two stands, the chronologies of European white birch, English oak, grey alder and Norway spruce have been used. It has been established that the cluster analysis of indexed chronologies based on calculating the squared Euclidean distance and combining the chronologies into groups according to the rule of complete linkage distinguishes aspen chronologies into a separate cluster, very distant from the other species under consideration. According to the correlation analysis of indexed chronologies, the dynamics of radial increment of aspen is most closely related to the dynamics of radial increment of European white birch and gray alder. Moreover, the maximum correlation coefficient (0.71) is observed for the chronologies of aspen from two different stands. Among the climatic factors, the main limiting influence on aspen increment is exerted by July precipitation in the year of the annual ring formation and August precipitation in the year preceding the year of the annual ring formation. The response to precipitation in August last year is specific to aspen stands and has several options for ecophysiological interpretation. According to Fourier spectral analysis, aspen chronologies have the most pronounced cyclic component with a period of 10.4 years, which completely coincides with the cyclic component of the time series of Wolf numbers in the time interval under consideration. This distinguishes aspen from the rest of the studied forest-forming species. A conclusion has been made about the specificity of the dynamics of the radial growth of aspen in comparison with other forest-forming species under study.

Molecular characterization of birch toti-like virus, a plant-associated member of the new family Orthototiviridae.

A novel totivirus, named "birch toti-like virus" (BTLV), was discovered in European white birch (Betula pendula) plants. The genome of BTLV is 4,967 nucleotides long and contains two overlapping open reading frames (ORFs) coding for the capsid protein (CP) and an RNA-dependent RNA-polymerase (RdRP). The encoded CP and RdRP proteins shared 46.9% and 60.2% amino acid sequence identity, respectively, with those of Panax notoginseng virus B. The presence of a putative slippery heptamer signal 82 nt upstream of the stop codon of ORF1 suggests that a -1 translational frameshifting strategy is involved in the expression of ORF2, like in other totiviruses. Phylogenetic analysis based on the CP and RdRP amino acid sequences placed this virus within a clade of plant-associated totiviruses, with taro-associated virus as its closest relative. Hence, based on its distinct host and the amino acid sequence similarity between BTLV and its relatives, we conclude that birch toti-like virus is a new member of the genus Totivirus.

Modelling Diameter at Breast Height Distribution for Eight Commercial Species in Natural-Origin Mixed Forests of Ontario, Canada

Diameter at breast height (DBH) is a unique attribute used to characterize forest growth and development for forest management planning and to understand forest ecology. Forest managers require an array of DBHs of forest stands, which can be reconstructed using selected probability distribution functions (PDFs). However, there is a lack of practices that fit PDFs of sub-dominating species grown in natural mixed forests. This study aimed to fit PDFs and develop predictive models for PDF parameters, so that the predicted distribution would represent dynamic forest structures and compositions in mixed forest stands. We fitted three of the simplest forms of PDFs, log-normal, gamma, and Weibull, for the DBH of eight tree species, namely balsam fir (Abies balsamea [L.] Mill.), eastern white pine (Pinus strobus L.), paper birch (Betula papyrifera Marshall), red maple (Acer rubrum L.), red pine (Pinus resinosa Aiton), trembling aspen (Populus tremuloides Michx), and white spruce (Picea glauca [Moench] Voss), all grown in natural-origin mixed forests in Ontario province, Canada. We estimated the parameters of the PDFs as a function of DBH mean and standard deviation for these species. Our results showed that log-normal fit the best among the three PDFs. We demonstrated that the predictive model could estimate the recovered parameters unbiasedly for all species, which can be used to reconstruct the DBH distributions of these tree species. In addition to prediction, the cross-validated R2 for the DBH mean ranged between 0.76 for red maple and 0.92 for red pine. However, the R2 for the regression of the standard deviation ranged between 0.00 for red pine and 0.69 for sugar maple, although it produced unbiased predictions and a small mean absolute bias. As these mean and standard deviations are regressed with dynamic covariates (such as stem density and stand basal area), in addition to climate and static geographic variables, the predicted DBH distribution can reflect change over time in response to management or any type of disturbance in the regime of the given geography. The predictive model-based DBH distributions can be applied to the design of appropriate silviculture systems for forest management planning.

STUDY OF EXTRACT SUBSTANCES OF WHITE BIRCH BARK

The article presents the results of a comparative study of extractive substances and the main substance - betulin content of Turkestan white birch bark, as well as the results of the study of its structure and some physico-chemical parameters. Betula Turkestanica Roth extractives contain 82% betulin.