Tree Age Research Articles

Empirical Relationships of the Characteristics of Standing Trees with the Dynamic Modulus of Elasticity of Japanese Cedar (Cryptomeria japonica) Logs: Case Study in the Kyoto Prefecture

With growing worldwide interest in constructing larger and taller wooden buildings, wood properties, such as the dynamic modulus of elasticity (MOEdyn), have become increasingly important. However, the MOEdyn of trees and logs has rarely been considered in forest management because a method for estimating the MOEdyn of logs based on standing tree characteristics has been lacking. Herein, we explored the multiple relationships between the MOEdyn of logs and standing tree characteristics of Japanese cedar (Cryptomeria japonica) such as tree height, diameter at breast height (DBH), and tree age, including the stress-wave velocity of the tree, which is known to be correlated with the MOEdyn of logs. The relationship between the MOEdyn of logs and standing tree characteristics was investigated by considering the bucking position. Different trends between the bottom logs and upper logs were found for all characteristics, showing a multiple trend of tree characteristics with the MOEdyn of logs based on the bucking position. The top three generalised linear mixed models for the prediction of the MOEdyn of logs showed relatively high accuracies when the bucking position was considered as a random effect. Although the contribution of the stress-wave velocity of the tree was relatively high, adding tree age improved the accuracy of the model, and this model was selected as the top model. The model for the bottom log, utilising the stress-wave velocity and age of the tree as explanatory variables, was highly explanatory (R2 = 0.70); however, the best model for upper logs was only moderately explanatory (R2 = 0.44). In addition, tree height and DBH were selected as explanatory variables along with tree age in the second and third models, which suggested the importance of growth rate rather than tree size. Therefore, adding correlates associated to characteristics related to height growth, such as site index, and DBH growth, such as stand density, is expected to improve model accuracy.

Bee Forage Tree Resources for Wild Honeybees: A Case Study from Kodagu's Sacred Groves and Coffee Agroecosystems

Wild honeybee populations are fundamental to biodiversity conservation and agricultural productivity, particularly as pollinators in tropical regions. This study investigated the diversity and seasonal availability of bee forage trees within sacred groves and coffee agro ecosystems in Kodagu, Karnataka, a biodiversity-rich area in the Western Ghats. Sacred groves, protected for cultural reasons, feature high floral diversity, providing continuous nectar and pollen resources, crucial for wild bee populations throughout the year. In contrast, coffee plantations, particularly in semi-evergreen systems, offer valuable yet seasonally limited forage, supplemented by selective native tree retention. By integrating traditional ecological knowledge associated with sacred groves and modern conservation practices, the research highlights how culturally protected landscapes contribute to year-round floral resources, crucial for sustaining wild bee populations. Sacred groves were found to consistently surpass coffee plantations in floral diversity and forage stability, particularly in semi-evergreen groves, which recorded the highest diversity and richness (H' = 3.75, 52 species). In contrast, coffee plantations, enhanced by selective retention of native trees, offer seasonally valuable but limited forage, with moderate diversity (H' = 3.35, 54 species). The study identifies key forage species unique to sacred groves and underscores their role in maintaining ecological resilience. These findings have practical implications for guiding community-based conservation programs, demonstrating that leveraging sacred groves and ecologically managed coffee agroecosystems can effectively balance biodiversity conservation with agricultural productivity. This integrated approach offers a model for sustaining pollinator populations vital to ecosystem health and crop yields.

Fire, land use, and the long-term dynamics of a pitch pine woodland in coastal Maine, USA

BackgroundPitch pine is a globally rare tree species that occurs on xeric sites in the largely mesic landscape of eastern North America. Supporting a rich assemblage of rare species, pitch pine communities are undergoing succession to more mesophytic species in the absence of natural and anthropogenic disturbance. We examined the multi-millennial dynamics of pitch pine in relation to fire and land use across a local moisture gradient in southern coastal Maine, USA.ResultsFossil pollen revealed that pitch pine has inhabited the study area for at least 9000 years. A positive correlation of pitch pine pollen with macroscopic charcoal (> 250 μm) demonstrated the historical importance of fire for this species. Euro-American settlement profoundly altered the forest. The sediment record revealed a pulse of fire, settlement-associated weeds, and pitch pine establishment as intensive land use commenced, followed by a recent decline as forests regenerated after pasture abandonment and fire suppression. Tree age structures and current regeneration patterns revealed ongoing succession of pitch pine to more mesophytic species, such as red oak and red maple, except on the most xeric sites, where the species appears to be self-perpetuating.ConclusionsThe results support a conceptual model of varying pitch population dynamics across a xeric to mesic gradient. They also reveal current mesophication and contraction of pitch pine woodland, a pattern found across the species’ range. If maintenance of large areas of pitch pine woodlands in the study area is an important management goal, then active management to reduce competing vegetation should be considered. If other goals, such as carbon sequestration or restoration of pre-colonial conditions, when pitch pine was apparently less abundant are paramount instead, then allowing natural succession to more mesic forest would make sense.

Changes in the Timing of Autumn Leaf Senescence of Maple and Ginkgo Trees in South Korea over the Past 30 Years: A Comparative Assessment of Process-Based, Linear Regression, and Machine-Learning Models

Changes in vegetation activities driven by climate change serve as both a sensitive indicator and a key driver of climate impacts, underscoring the need for accurate phenological predictions. Delays in leaf senescence due to rising air temperatures increase the risk of damage from early frost, potentially affecting growth and survival in subsequent years. This study aimed to quantify long-term changes in leaf senescence timing for palmate maple and ginkgo trees, explore their associations with environmental factors, and compare the performance of multiple modeling approaches to identify their strengths and limitations for phenological predictions. Using data from 48 sites across South Korea (1989–2020), this study analyzed trends in the timing of leaf senescence for maple and ginkgo trees and compared the performance of process-based models (CDD_T, CDD_P, TP_T, TP_P), a linear regression model, and machine-learning models (random forest, RF; gradient-boosting decision tree, GBTD). Leaf senescence timing for both species has progressively been delayed, with ginkgo trees showing a faster rate of change (0.20 vs. 0.17 days per year, p < 0.05). Delayed senescence was observed in most regions (81% for maple and 75% for ginkgo), with statistically significant delays (p < 0.05) at half of the sites. Machine-learning models demonstrated the highest training accuracy (RMSE < 4.0 days, r > 0.90). Evaluation with independent datasets revealed that the RF and process-based TP_P (including minimum temperature and photoperiod) using a site-specific approach performed best (RMSE < 5.5 days, r > 0.75). Key environmental factors identified by RF included autumn minimum or mean temperatures and a summer photoperiod. By conducting this comparative assessment, the study provides insights into the applicability of different modeling approaches for phenology research and highlights their implications for vegetation management and climate change adaptation.

Risk Modeling for the Emergence of the Primary Outbreak Area of the Siberian Moth Dendrolimus sibiricus Tschetv. in Coniferous Forests of Central Siberia

In the southern taiga of Siberia, periodic outbreaks of the Siberian moth Dendrolimus sibrircus Tschetv. have been observed. The outbreaks result in the defoliation of Siberian fir Abies sibirica Ledeb. and Siberian pine Pinus sibirica Du Tour. stands across approximately one million hectares, leading to dieback of the affected forests. This is largely attributable to the inability to promptly identify the onset of the pest population growth in a timely manner, particularly in the context of expansive forest areas with limited accessibility. It is feasible to enhance the efficacy of monitoring Siberian moth populations by discerning stands with the highest propensity for damage and concentrating efforts on these areas. To achieve this, we employed machine learning techniques, specifically gradient boosting, support vector machines, and decision trees, training models on two sets of predictors. One of the datasets was obtained through a field study conducted in forest stands during the previous outbreak of the Siberian moth (2015–2018), while the other was derived from the analysis of remote sensing data during the same period. In both 2015 and 2016, the defoliation was most accurately predicted using gradient boosting (XGB algorithm), with ROC-AUC values reaching 0.89–0.94. The most significant predictors derived from the ground data were the proportions of Siberian fir, Siberian spruce Picea obovata Ledeb., and Scots pine Pinus sylvestris L., phytosociological data, tree age, and site quality. Among the predictors obtained from the analysis of remote sensing data, the distance to disturbed forest stands was identified as the most significant, while the proportion of dark coniferous species (A. sibirica, P. sibirica, or Picea obovata Ledeb.), the influx of solar radiation (estimated through the CHILI index), and the position in the relief (mTPI index) were also determined to be important.

Climate signal age effects in Pinus uncinata tree-ring density data from the Spanish Pyrenees

Key messageThe temperature sensitivity of maximum latewood density measurements in pine trees from a high-elevation site in the Spanish Pyrenees increases with tree age. Detrending modulates the intensity of the effect. Tree-rings are the prime archive for high-resolution climate information over the past two millennia. However, the accuracy of annually resolved reconstructions from tree-rings can be constrained by what is known as climate signal age effects (CSAE), encompassing changes in the sensitivity of tree growth to climate over their lifespans. Here, we evaluate CSAE in Pinus uncinata from an upper tree line site in the Spanish central Pyrenees, Lake Gerber, which became a key location for reconstructing western Mediterranean summer temperatures at annual resolution. We use tree-ring width (TRW) and maximum latewood density (MXD) measurements from 50 pine trees with individual ages ranging from 7 to 406 years. For MXD, temperature sensitivity increases significantly (p < 0.01) with tree age from r = 0.31 in juvenile rings with a cambial age < 100 years to r = 0.49 in adult rings > 100 years. Similar CSAE are not detected in TRW, likely affected by the overall lower temperature signal (rTRW = 0.45 vs. rMXD = 0.81 from 1951 to 2020). The severity of CSAE is influenced by the approach used to remove ontogenetic trends, highlighting the need to assess and consider potential biases during tree-ring standardization. Our findings reveal CSAE to add uncertainty in MXD-based climate reconstructions in the Mediterranean. We recommend studying CSAE by sampling diverse age classes in dendroclimatic field campaigns.

Changes in carbon sequestration with age of trees in guava orchards at different locations in a tropical climate on Alfisols

Changes in carbon sequestration with age of trees in guava orchards at different locations in a tropical climate on Alfisols

Influence of loblolly pine anatomical fractions and tree age on oil yield and composition during fast pyrolysis

Fast pyrolysis of woody materials is a technology pathway for producing renewable fuels and chemicals.

Functional traits of Ziziphus mucronata below mistletoe-infected trees in a semi-arid African savanna

Mistletoes are increasingly associated with elevated leaf litter, soil nutrients, and soil moisture, resources which influence variations in plant functional traits. Despite this recognition, variations in the functional traits of understorey plants with overstorey (host) tree mistletoe infection are yet to be examined. Here, we measured the different functional traits (height, stem diameter, canopy area, leaf area, specific leaf area, whole-leaf thickness, leaf dry matter content, and chlorophyll content) of Ziziphus mucronata, a dominant woody plant beneath mistletoe-infected Vachellia karroo trees in semi-arid savanna. Two-sample t-tests were used to compare host size, mistletoe infection intensity, and trait variables between low and high mistletoe-infected trees. The relationships between Ziziphus mucronata functional traits vs. host tree diameter and the number of mistletoes per tree were explored using regression analysis and visualized using a regression biplot based on a redundancy analysis (RDA). Host tree height, canopy area, and canopy volume were strongly positively (r > 0.7, p < 0.05) related to mistletoe infection intensity. While most of the traits did not vary with mistletoe infection, the chlorophyll content and leaf area of understorey Z. mucronata increased with host tree size, being greater beneath high than low mistletoe-infected trees. These variations are linked to changes in limiting resources such as light, soil nutrients, and soil moisture due to accumulation of mistletoes and increase in size as the host tree ages. As a result, the understorey plants shifted from being resource conservative to being acquisitive as limiting resources increased. The general lack of trait plasticity in understorey Z. mucronata suggests that plastic allocation responses may not be a general consequence of mistletoe infection.

Advances in molecular genetics have increased knowledge of Tuber species' life cycle and population genetic structure, indicating ways to improve yield.

Truffles are possibly the only high-value cultivated organisms for which some aspects of the habit and life cycle have only recently been elucidated or remain unknown. Molecular techniques have helped explain the biological basis for some traditional empirical management techniques, such as inoculating soil with ascospores to improve yield, and have enhanced the detection of competitive or pathogenic soil microorganisms. Improved precision of assessment of the quality of inoculated seedlings is now possible. New knowledge of the genetic structure of populations has indicated that as trees age, the genotypes of mycorrhizae on inoculated trees change, and that there are large differences in the number of female and male genotypes participating in ascocarp formation. The plasticity of Tuber species has also been revealed, with maternal genotypes growing as an ectomycorrhiza in host tree roots and as surface mycelium or an endophyte in roots of adjacent non-mycorrhizal species. Refinement of management techniques has resulted from applying the new information, and the tools are now available to resolve the many outstanding gaps in our knowledge of Tuber biology.

Carbon storage potentiality in successional and secondary old growth forests

AbstractAs the capacity of old-growth forests to store carbon until very old ages has been proved, their conservation has become a mitigation strategy to reduce net CO2 emissions and moderate climate warming. We investigated the effect of tree age, competition and climate on aboveground standing biomass and C stocks over a 50-years period in two Spanish forest stands (successional Pinus pinea forests with old-growth attributes, OGFA and secondary Pinus nigra old-growth Forest, SOGF), combining dendroecological methods with forest inventory data, using semiparametric modeling. P. nigra SOGF stored 69.9 t C ha−1 in standing volume, while P. pinea OGFA stored 58.2 t C ha−1. Carbon stored during the last 50 years increased in both forests, with a steeper increase in P. pinea OGFA. The fraction of annual C stored by the oldest trees was 20–25% in P. nigra SOGF and 17–23% in P. pinea OGFA. The different patterns of biomass growth increment in the two forests were explained by different biophysical environments, climate and history effects. The response to contrasted climate events was forest-specific. Results for P.nigra showed a similar response to climate irrespective of tree age, while biomass growth in P. pinea increased in humid periods compared to dry periods in trees older than 100-years. The negative effect of drought is evidenced in P. pinea trees over 100-years old, while P. nigra showed a cumulative negative effect of drought for all ages. A lower effect of competition was recorded in the oldest trees in both forests. The interaction of competition with climate and tree age showed attenuated climate-mediated differences when competition was high.

Stable Diversity but Distinct Metabolic Activity of Microbiome of Roots from Adult and Young Chinese Fir Trees

The tree-associated microbiome is vital for both individual trees and the forest ecosystem. The microbiome is dynamic; however, it is influenced by the developmental stages and environmental stresses experienced by host trees. Chinese fir (Cunninghamia lanceolata) is an economically important tree species in the subtropical regions of China. This study investigated the diversity of microbial communities, including bacteria and fungi, in the roots and bulk soil of young (2 years old) and old (46 years old) Chinese fir. It specifically examined the functional characteristics of these microbial communities. Through a non-metric multidimensional scaling (NMDS) analysis, we examined differences in microbial community structures among root and soil samples of Chinese fir. Evaluations using α-diversity metrics (Chao1, Shannon, Pielou, etc.) confirmed significant differences in diversity and structure between soil and root samples but high similarity between young and old tree samples. A network analysis identified key bacterial and fungal genera, such as Burkholderia and Russula, which play pivotal roles in the microbiome structure. We also demonstrated significant variations in microbial metabolic functions, such as dioxin and benzoic acid degradation metabolic pathways, which might relate to stress alleviation for tree fitness. Additionally, for the detection of endophytic microorganisms in Chinese fir seeds, only small amounts (less than 10%) of fungal endophytes and bare bacterial endophytes were identified. In summary, this study revealed that the stable structure of the rhizosphere microbiome was established in the early stage of tree life in Chinese fir, which mostly originated from surrounding soil rather than seed endophytes. The associated microbial metabolic activity naturally decreased with tree aging, implicating the tree microbial dynamics and the need for the addition of an actively functional synthetic community for tree fitness.

Variation of physical wood properties and effect of dasometric variables in Ochroma pyramidale trees growing in plantation.

Physical properties were studied in commercial plantation of balsa established in Costa Rica. Among other variables studied, physical properties varied mainly for tree age, spacing, stand density, diameter, and height of trees, which we named dasometric conditions. The aim of this study was (i) to determine the variation of specific gravity (SG), air-dry density (AD), green density (GD), and green moisture content (GMC), (ii) to know the site effect and dasometric conditions on these properties, and (iii) to establish the relationship between the four physical properties. The results showed that: SG from 0.08 to 0.21, AD from 90 to 250kg/m3, GD varied 203-274kg/m3, and GMC from 38.8 to 137.1%. Tree age affected statistically all physical properties, it was positively correlated with SG, AD, and GD, but negatively correlated with GMC. Diameter breast height and total height were weakly correlated with SG and AD, respectively. Commercial height and stand density were highly correlated with SG and AD, besides stand density was positively correlated with GMC. AD was positively correlated with SG and GD but negatively correlated with GMC. According to the results, balsa tree plantations exhibited significant variation in physical properties (SG, AD, GD, and GMC) in trees aged between 30 and 40 months and this variation was primarily attributed to the site-specific growth conditions, mainly temperature and precipitation. These wood properties can be selected by site and growing conditions.

Ground cover management enhances soil extracellular enzyme activities across Chinese orchards.

The impacts of ground cover management (GCM) on orchard soil properties have been extensively studied. However, the quantitative assessment of soil extracellular enzyme activities (EEAs) in mulch agriculture remains understudied. In this study, we investigated EEAs related to GCM to assess microbial metabolic activity, soil health, and nutrient status, based on 81 studies focusing on orchards in China. Our findings show that GCM significantly increases carbon acquisition (C-acq, 37%), nitrogen acquisition (N-acq, 34%), phosphorus acquisition (P-acq, 26%), and oxidative decomposition (OX, 14%) enzymes compared to continuous clean tillage. A subgroup analysis and a random forest model were conducted to further identify the effects and potential mechanisms through which soil EEAs respond to GCM in orchards under various moderators. The significant changes in EEAs induced by GCM vary with experimental and environmental factors. Tree age, climate conditions, and soil depth are the primary contributors to the variation in soil EEAs. Overall, our results suggest that the implementation of GCM positively affects EEAs, thereby enhancing microbe-mediated soil ecosystem functions and soil fertility. This meta-analysis provides comprehensive evidence of GCM-induced effects on hydrolase and oxidase activity, improving our understanding of the underlying mechanisms by which orchard mulching impacts soil nutrient cycling.

Tree Health Analysis of Old Vatica pauciflora (Korth.) Blume in Bogor Botanical Gardens Using Forest Health Monitoring Method

Abstract Vatica pauciflora (Korth.) Blume (Dipterocarpaceae), also known as resak rawa, is a vital tree classified as vulnerable by the IUCN Red List of Threatened Species. Resak rawa can be found in the Bogor Botanical Gardens (BBG) for research and tourism purposes. It is essential to analyze the tree’s damage to assess its health and ensure the safety of visitors. The Forest Health Monitoring (FHM) method is used in this study. The study aims to evaluate the health of old V. pauciflora trees (&gt;50 years) in the BBG and investigate the relationship between the tree’s age and health level. Fourteen V. pauciflora trees aged 50–117 years were selected for the study. The parameters observed include the type, location, and severity of damage. The results indicate that the older V. pauciflora trees in BBG are generally healthy, with an average Tree Health Index (THI) of 4.24. There is a moderate correlation between the age of the tree and its health level (y=10,680+470.42lnX; R2=0.34; r=0.59). Recommendations for treating V. pauciflora include mechanical, chemical, and biological control methods to address tree damage. These findings can help mitigate the risk posed by damaged V. pauciflora trees.

The trade-off between soil water recovery and nitrate leaching following the orchard-to-cropland conversion in the Chinese Loess Plateau

A large-scale conversion of apple orchards into farmland has occurred in the tableland region of the Chinese Loess Plateau due to the aging of apple trees and the increase in pests and diseases. However, the impact of this conversion on soil desiccation recovery and soil nutrient transportation remains unclear, posing a new challenge for sustainable agricultural development in the region. The study employed the space-time substitution approach to select a long-standing orchard and croplands that has been growing maize for 1-, 3-, 5-, and 10-years post-orchard conversion as sampling sites, to investigate the effects of recovery durations of orchard-to-cropland conversion on deep soil water recharge and residual nitrate dynamics, as well as the key factors driving these changes. The results indicated that within 5 years, the conversion led to a rapid recharge of desiccated deep soil (6–9 m), followed by a stable and slow increase in subsequent years. The annual soil water recovery rate in the deep soil was as high as 5.90 mm m−1 a−1. While, the increased water input also caused rapid leaching and accumulation of nitrate in the deep soil, with its peak depth increasing significantly from 3.4 m to 7.0 m over time (R2 = 0.92). Soil water was identified as the key factor influencing nitrate leaching, with a correlation coefficient of 0.48 (P < 0.05). In conclusion, orchard-to-cropland conversion effectively replenished the deep soil water in the short term but also accelerated soil nitrate leaching. Therefore, while large-scale conversion of orchards to farmland is undertaken, it is crucial to acknowledge the trade-off relationship involving the recharge of deep soil water and the subsequent increase in deep nitrogen leaching. The findings of this study hold significant implication for the management of water and nutrient resources after the conversion of orchards to farmland, highlighting the necessity to mitigate nitrogen leaching while soil water is being restored.

Silvicultural Systems and Their Practices in Terai Forests of Nepal: A Review

It is more than eight decades that Nepalese foresters initiated appropriate silvicultural systems to obtain goods and services from forest, in perpetuity. Several forest management plans were prepared in the past. Unfortunately, these forest management plans were not implemented until 2012 AD. Government of Nepal had introduced a silviculture-based forest management procedure, known as Scientific Forest Management (SciFM) in 2014. One of the objectives of this procedure was to provide management guidelines for Community Forests (CFs) and government managed forests. It has been more than a decade that SciFM was implemented in Terai forests (CF and government managed forests) but how far the procedure supported in achieving its goal is not well documented. This paper examined the forest management system that was implemented in Terai districts of Nepal, irrespective of its management regime. For the study, field visits were carried out in five Terai districts (Morang, Kapilvastu, Dang, Kailali and Kanchanpur) during March and October 2021 to examine the procedure followed in managing the forests and observe the outcomes aimed by the procedure. Both published and unpublished literature on silviculture, silvicultural systems, and forest management, especially devoted to Nepalese context were reviewed. Operational Forest Management Plans (OFMPs) developed by government entities and Community Forests User Groups implemented “irregular shelter-wood system” of forest management in their respective forests, retaining some mother trees for shelter and obtaining quality seeds, based on 80 years of rotation period for Sal (Shorea robusta) although the basis of fixing the rotation is not very clear. The review indicates that, in most sites of Terai parts of Nepal, it may not be necessary to retain mother trees, for ensuring Sal regeneration. There is some possibility of estimating Sal rotation due to appearance of hollowness in the trees, which may help decide the harvestable age of Sal trees in future.

Landslide susceptibility evaluation and determination of critical influencing factors in eastern Sichuan mountainous area, China

Landslide susceptibility evaluation and determination of critical influencing factors is a prerequisite for preventing hazardous risks, especially in landslide-prone mountainous areas. However, in densely vegetated Southwest mountainous areas, identifying assessment approach of shallow landslides susceptibility and their major inducing factors is still a huge challenge. To address this challenge, we applied five advanced machine learning models (Logistic Regression Model, Generalized Additive Model, Random Forest Model, Support Vector Machine Model, Artificial Neural Network Model) to assess the spatial distribution of shallow landslide susceptibility, considering several relevant factors that affect landslide occurrence. These factors include geological, topographic and vegetation factors, as well as four new vegetation factors: stock volume, stand density, average tree age, and stand types. Furthermore, we employed SHAP algorithm and Structural Equation Models to quantify the relative importance and explanatory power of these factors on shallow landslide susceptibility and to clarify the interaction mechanisms among various factors in Huaying Mountain. The results shown that Random Forest Model proves to be the most accurate (95.1 %) in assessing the spatial distribution of shallow landslides susceptibility, followed by the Artificial Neural Network model (78.6 %), the Support Vector Machine model (69.8 %), the Generalized additive model (68.1 %) and the Logistic Regression model (67.6 %).The area with high susceptible landslide possibility was 25.3 km2 occupying 14.8 % of the study region, it is mainly distributed in the west of Tianchi Lake, southeast of Huaying City and west of the study area, along with Xiangyu Railway. Geographical environment and vegetation features were found to significantly explain 67.4 % and 32.6 % of the total effects in shallow landslides susceptibility, respectively. Specifically, the spatial distribution of shallow landslides susceptibility were primarily influenced by geological engineering rock group, distance to faults、stand types and distance to river. Geographical environment factors could indirectly affect changes in vegetation features, thereby indirectly affecting the spatial distribution of shallow landslides susceptibility. Findings from this research could be helpful for scientific decision-making and technical assistance for early warning, prevention, and control of rainstorm-induced landslides in highly vegetation covered areas.

A Mathematical Model for Estimating Carbon Storage Dynamics of Forest Communities

Using the JABOWA single-tree growth model, a program was designed to estimate carbon storage dynamics in the aboveground biomass of a mixed forest community. The developed model incorporates the parameters of tree species that are common to the forests of Central Russia: pedunculate oak (Quercus robur L.), silver birch (Betula pendula Roth), common aspen (Populus tremula L.), small-leaved lime (Tilia cordata Mill.), Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst.), and fir (Abies Mill.). A differential equation for tree diameter at breast height (D) was solved. The results were compared with the forest inventory data. The amount of carbon stored in the aboveground biomass of trees was calculated following the methodology suggested by the Intergovernmental Panel on Climate Change. The dynamics of tree volume were analyzed. An analytical formula was proposed to describe the dependence of tree volume and stored carbon on tree age. The differences in the rates of tree volume growth and carbon accumulation were identified among the species studied. The analytical and numerical results on stored carbon and tree age showed a good agreement for a test plot with the known species composition and tree count, which is located within the forest part of the carbon polygon of Kazan Federal University. The formula offers an accurate estimation and prediction of carbon storage dynamics in mixed forest communities with trees varying in age and, hence, is a valuable tool for managing forestry activities. However, when predicting tree biomass growth and carbon storage dynamics, one should also consider forest site quality classes reflecting the actual growth conditions of trees. Developing a mathematical model based on forest site quality classes as a key variable would help increase the reliability of biomass growth and carbon storage predictions for forest communities. Notably, the obtained model applies to actual forest communities with known species composition and fails to account for natural regeneration. To incorporate this parameter, spatial diffusion models that describe forest regeneration in non-forest areas should be utilized.

Long-Term Cumulative Effect of Management Decisions on Forest Structure and Biodiversity in Hemiboreal Forests

We evaluated the long-term impacts of various forest management practices on the structure and biodiversity of Estonian hemiboreal forests, a unique ecological transition zone between temperate and boreal forests, found primarily in regions with cold winters and moderately warm summers, such as the northern parts of Europe, Asia, and North America. The study examined 150 plots across stands of different ages (65–177 years), including commercial forests and Natura 2000 habitat 9010* “Western Taiga”. These plots varied in stand origin—multi-aged (trees of varying ages) versus even-aged (uniform tree ages), management history—historical (practices before the 1990s) and recent (post-1990s practices), and conservation status—protected forests (e.g., Natura 2000 areas) and commercial forests focused on timber production. Data on forest structure, including canopy tree diameters, deadwood volumes, and species richness, were collected alongside detailed field surveys of vascular plants and bryophytes. Management histories were assessed using historical maps and records. Statistical analyses, including General Linear Mixed Models (GLMMs), Multi-Response Permutation Procedures (MRPP), and Indicator Species Analysis (ISA), were used to evaluate the effects of origin, management history, and conservation status on forest structure and species composition. Results indicated that multi-aged origin forests had significantly higher canopy tree diameters and deadwood volumes compared to even-aged origin stands, highlighting the benefits of varied-age management for structural diversity. Historically managed forests showed increased tree species richness, but lower deadwood volumes, suggesting a biodiversity–structure trade-off. Recent management, however, negatively impacted both deadwood volume and understory diversity, reflecting short-term forestry consequences. Protected areas exhibited higher deadwood volumes and bryophyte richness compared to commercial forests, indicating a small yet persistent effect of conservation strategies in sustaining forest complexity and biodiversity. Indicator species analysis identified specific vascular plants and bryophytes as markers of long-term management impacts. These findings highlight the ecological significance of integrating historical legacies and conservation priorities into modern management to support forest resilience and biodiversity.