Search Results (437)

The spongy moth is a significant Lepidopteran species across Europe, where it occurs in oak stands. Tree species composition has a crucial effect on larval development, population density, and outbreaks. Host switching is more likely to occur in a mixed forest than in a monospecific forest. We aimed to better understand the effect of host alternation on the development of the spongy moth. In a laboratory, we reared spongy moth larvae on either (a) Turkey oak (Quercus cerris L.) or (b) European hornbeam (Carpinus betulus L.) only or on host plants that were changed from Turkey oak to European hornbeam (c) in the early (L₃) or (d) late (L₅) larval instar. Both Q. cerris and C. betulus proved suitable hosts for the spongy moth larvae. However, the larvae fed exclusively on Turkey oak leaves had better developmental indicators than the others. The groups that switched hosts had weaker developmental indicators than the larvae fed only on Turkey oak but showed better development than the group reared only on Hornbeam leaves. The results of our laboratory research on host switching may offer valuable insights into the developmental dynamics of spongy moths in monospecific forests versus those with higher biodiversity. Full article

While thinnings immediately reduce aboveground biomass, they promote growth by releasing the remaining trees from competition. The biomass removed in thinnings can be used for energy, thus enabling financial returns prior to final harvest and contributing to the global share of renewable energies. In this study, the effects of thinning on stand structure dynamics and potential residential bioheat utilisation scenarios are assessed for a broadleaved mixed even-aged stand. The results demonstrate that ten years after thinning, aboveground biomass increased, ensuring system sustainability and carbon stocks. Furthermore, an average potential yield of 1.1 Mg·ha⁻¹·a⁻¹ (dry basis) of low-ash forest by-products was obtained, offering a sustainable supply of solid biofuels. However, the energy conversion route chosen has major impacts on the solid bioenergy demand and sustainability. Based on theoretical scenarios, upgrading from traditional fireplaces to more efficient combustion systems may reduce the specific biomass consumption up to eight times for residential heat production. The results obtained in this study highlight the challenge and need to use thinning biomass sustainably in the face of growing bioenergy demands. Full article

Allometric equations are essential tools for estimating sustainable biomass and carbon dynamics in riparian tree species. This study derived and validated log–log transformation regression equations that relate diameter at breast height (DBH) to the dry weight, stem volume, and total biomass of Salix chaenomeloides Kimura across five river systems in Korea (Byeongcheon, Andong, Boseong, Topyeong, and Yeongdong). DBH was significantly correlated with biomass components and whole-tree biomass, with explanatory power ranging from 0.47 (Byeongcheon-root) to 0.99 (Topyeong-stem) (R²). Model evaluation metrics (RMSE, MAE, MPE) indicated high predictive accuracy across sites. Using the derived allometric equations, net primary productivity (NPP) of individual was 9.40 kg·tree⁻¹·yr⁻¹ and 2.45 ton C·ha⁻¹·yr⁻¹ at the stand level, with site-specific variability reflecting environmental differences. Biomass conversion coefficients, expansion factors, and root-to-aboveground biomass ratios were also obtained, with mean values of 0.29 (branches/stem), 0.10 (leaves/stem), and 0.25 (roots/AGB), a wood density of 0.63 g·cm⁻³, and a biomass expansion factor of 1.37. Independently derived NPP estimates based on stem analysis were comparable (9.02 kg tree⁻¹ yr⁻¹ and 2.43 t C ha⁻¹ yr⁻¹ at individual and stand levels, respectively), supporting the robustness of the approach. These findings provide robust, site-calibrated allometric models for S. chaenomeloides, supporting accurate biomass estimation, carbon accounting, and the evaluation of riparian ecosystems in climate change mitigation and restoration contexts. From a sustainability perspective, these results highlight the development of tools for evaluating the carbon budget of riparian vegetation, which are not yet incorporated into the Korean national IPCC report. They also demonstrate progress in carbon budget assessment by integrating both allometry and stem analysis. Full article

Ungulate browsing is a major driver of forest regeneration dynamics and habitat structure in managed temperate forests, influencing species composition, regeneration success, and long-term stand development. Traditional assessments of browsing impacts often rely on field-based indicators such as regeneration density or visual cover, but these metrics provide limited insight into three-dimensional habitat structure. Mobile handheld LiDAR offers highly detailed measurements of forest structure, enabling objective and reproducible quantification of structural complexity that complements and extends conventional field-based methods. In this study, we applied handheld LiDAR as an innovative indicator for habitat structure within the ungulate browsing zone (<2 m height) to evaluate structural development across sites differing in management context. Paired fenced and unfenced plots (12 × 12 m) were surveyed within the WiWaldI project framework in 2019 and 2023 and compared across three hunting regimes representing different degrees of ungulate population management. Structural complexity was quantified by deriving box-counting dimensions from LiDAR point clouds, providing a measure of spatial arrangement and density relevant to ungulate–vegetation interactions. To support interpretation and ecological context, we complemented LiDAR indicators with streamlined field assessments. Based on this framework, we assessed whether forest structural complexity and visual cover differ among regions and over time, and whether ungulate browsing induces detectable structural differences between fenced whether structural differences between fenced and unfenced plots are detectable. We further examined the relative importance of tree species composition, plant architecture, and hunting regime as drivers of three-dimensional habitat structure. A simplified octant method characterized the spatial distribution of woody regeneration, while a silhouette-based approach quantified visual cover from the perspective of a standard ungulate profile. These auxiliary measures contextualize visual and spatial aspects of structure that LiDAR metrics capture with minimal observer bias. LiDAR studies have previously demonstrated potential for linking high-resolution structural data to ungulate habitat use, and our approach extends this by focusing on structural complexity as a habitat indicator. Results show a consistent increase in LiDAR-derived structural complexity between 2019 and 2023 across all regions. This increase occurred across management contexts and was not consistently explained by fencing or hunting regime effects, suggesting that site conditions, forest composition, and successional processes were dominant drivers during the observation period. Hunting regime showed no statistically significant and no consistent effect on structural complexity across regions or years. Visual cover metrics varied strongly among regions and species and declined over time. These findings suggest that three-dimensional habitat structure information has the potential to enhance the evaluation of ungulate impacts and may support evidence-based forest and wildlife management, particularly when interpreted in the context of site conditions and successional dynamics. Beyond ungulate impact assessment, the presented handheld LiDAR approach provides a scalable remote sensing framework for precision forestry by capturing three-dimensional structural attributes that are directly linked to forest stability, resilience, growth dynamics, and stand-level species mixing, thereby supporting evidence-based forest management recommendations. Full article

Illegal logging is a major driver of tropical deforestation, accounting for the majority of timber harvested in many tropical countries and degrading many protected areas, due to both weak law enforcement capacity and corruption. Commercial logging is illegal in Peru’s Allpahuayo-Mishana National Reserve, a state protected area, but clandestine logging operations persist and affect its biodiversity, including the endemic bird species associated with its rare Amazonian white-sand forests. We examined the effects of illegal logging operations on white-sand forest understory bird communities as sentinels of biodiversity. We sampled birds with mist nets at 12 study sites in unlogged forest and forest regenerating between 1 and 10 years after timber harvest, capturing and releasing 348 birds representing 54 species in 16 families. Forest structure differed significantly between forest treatments, with canopy cover in logged forest significantly lower than in unlogged forest. All avian foraging guilds tested (including ant followers, other insectivores, frugivores, granivores, and nectarivores) responded significantly to changes in one or more forest structure characteristics we measured. The abundance of ant followers and other insectivores was positively correlated with canopy cover, while granivore abundance was positively correlated with subcanopy cover, and both frugivore and nectarivore abundance was negatively correlated with the numbers of trees in white-forest stands. We also took a rare opportunity to compare avian foraging guilds and relative abundance using capture data collected at the same white-sand forest sites in both 2005 and 2023. Over this 18-year period, the total number of understory birds and ant followers in particular declined, whereas other insectivores increased with time since logging. Our results demonstrate that logging has significant influences on white-sand forest habitat structure and bird community dynamics for decades after logging events. Illegal logging threatens forests and wildlife in many tropical protected areas, and we recommend their managers prioritize both preventing illegal logging and mitigating its negative effects to effectively conserve biodiversity. Full article

The eastern Baltic region is rich in hemiboreal forests, which are both commercially important and provide habitats for rare and/or endangered forest-dwelling species, which are sensitive to accelerating climatic changes. Under the intensifying climatic disturbances that are stressing forests worldwide, sanitary logging is a widely used harvesting technique for the mitigation of commercial losses. The effects of salvage logging on the biodiversity of forests remain ambiguous due to the larger scale and higher intensity of timber harvesting, which can alter the recovery of stands and succession of their vegetation. Furthermore, EU legislation is increasingly emphasizing conservation/restoration and mandating its implementation. The recovery of ecosystems, and hence the biodiversity of disturbed managed forests, can take several decades to centuries, depending on the site conditions. Long-term (~60 years, four remeasurements) changes in the composition and structure of vegetation, as an indicator of overall health and nutrient cycling, were studied in conventionally managed hemiboreal forests. Potential forest transformation (paludification) risks associated with large-scale logging were assessed in mixed coniferous stands in the Baltics, Latvia. Following logging, the stands were conventionally managed, including artificial regeneration. According to ground cover vegetation, 50 years was the period for the disturbance effects to start subsiding, as a dynamic equilibrium was reached and the canopies of regenerating trees were closing. A gradual decrease in moisture levels in the middle parts of salvage-logged areas, and later at their edges, indicated that the stands have escaped paludification, likely as the climate has been warming. Distance from the edge of the salvage-logged areas had a secondary effect on ground cover vegetation recovery after storms, alleviating concerns about the explicit negative impact of the scale of harvesting. Thus, in managed seminatural forest landscapes with a historically small to moderate scale of anthropogenic disturbance, salvage logging at a scale locally deemed as large had a transient effect in the Baltics. This indicates successful regeneration of the forest ecosystem over a timeframe shorter than the conventional rotation period, suggesting overall conservation efficiency of conventionally managed forests. Accordingly, salvage logging can be sustainable in terms of biodiversity and forest continuity in the long run under traditional management, as environmental changes accelerate. Full article

Urban forests are highly valued for the multiple benefits they provide to city dwellers. The strategic provision of ecosystem services by these forests is threatened by climate change, warming conditions being responsible for heat waves and chronic droughts that inflict stress and mortality on trees. A three-year study (2011–2013) conducted at Parco Nord Milano (PNM) (Milano, Italy) assessed the impact of thinning interventions on the dynamics of fungal pathogens in declining forest plots. Symptomatic trees of the genera Alnus, Acer, Fraxinus, Platanus, Quercus and Ulmus, exhibited in thinned subplot pronounced decline/dieback, exhibiting symptoms like microphyllia, leaf yellowing, leaf shedding, sunken cankers, shoot wilting and branch dieback. Comparative analyses between the thinned and unthinned subplots revealed a significantly higher incidence of pathogens in the thinned one. Five species of Botryosphaeriaceae, namely Botryosphaeria dothidea, Diplodia corticola, Diplodia seriata, Dothiorella omnivora and Neofusicoccum parvum, were consistently isolated from tissues of declining hosts. There is evidence that thinning altered plot-level microclimate conditions and microbial equilibrium, favoring the proliferation of latent, pathogenic Botryosphaeriaceae. In fact, during the study period, the presence of N. parvum increased tenfold and that of B. dothidea fivefold in thinned subplot. Conversely, in unthinned subplot, the same pathogenic taxa maintained stable proportions. These results demonstrate that thinning altered ecological balances increasing tree susceptibility to harmful, cosmopolitan botryosphaeriaceous fungi. Our findings challenge assumptions about thinning as a universally beneficial practice, emphasizing the need for silvicultural strategies that take into account host and pathogen ecology and the microclimatic resilience of forest stands. This study emphasizes the importance of adaptive management in urban forestry to mitigate the unintended ecological consequences of climate change. Full article

This article describes a field- and laboratory-based framework that can be used to monitor the C balance in Mediterranean pine forest ecosystems under different management practices that determine their structure and function. By jointly monitoring stand structure, gas exchange, litter, and decomposition dynamics, this protocol enables the assessment of how management-driven changes regulate carbon uptake, turnover, and losses, thereby affecting carbon sequestration potential. As an example, we suggest the implementation of the protocol at ten (10) permanent monitoring plots across three study areas located in Greece. The first group of plots represents a post-fire chronosequence in pine stands with no management interventions. The second group includes pine stands that exhibit variation in overstory and understory density driven by differences in microclimate and management history. The third group consists of peri-urban pine stands subjected to thinning of varying intensity. The monitoring protocol is implemented across all plots and the collected data can be classified into three analytical domains: (a) demography, encompassing measurements of tree growth and mortality; (b) litter and decomposition dynamics, involving the quantification of litterfall and its seasonality and the estimation of its decomposition rates; and (c) gas exchange, focusing on measurements of leaf photosynthesis and respiration (including relevant leaf functional traits) and monitoring of soil respiration. These three data domains can be used to comparatively consider the effect of forest management on key ecosystem processes and to constrain local-scale vegetation dynamics models. Full article

Rainfall is an essential component of boreal forest ecosystems. Aerotechnogenic pollution significantly affects the composition of rainfall. To predict the dynamics of biogeochemical cycles and develop strategies to enhance forest resilience in the Arctic zone, it is necessary to study the composition and characteristics of rainfall. The objective of this study is to evaluate the variation in the chemical composition of stemflow in the most typical pine and spruce forests of Fennoscandia under conditions of aerotechnogenic pollution based on long-term monitoring data from 1999 to 2022. The research was carried out in forests exposed to atmospheric industrial pollution from the largest copper–nickel smelter in northern Europe (Murmansk Region, Russia). The study of rainwater composition was conducted in four microsites: open areas (OA), between crowns (BWC), below crowns (BC) and stemflow (SF). A significant influence of the tree canopy on the rainfall composition was noted. Stemflow was found to have the highest concentration of pollutants, indicating a significant biochemical role of this type of precipitation. The results showed an increase in the concentrations of heavy metals and sulfates in rainwater as we moved closer to the pollution source. Below crowns and in the stemflow of spruce forests, element concentrations are higher compared to pine forests. The highest concentrations of major pollutants in stemflow (Ni, Cu and SO₄²⁻) are observed in June—at the beginning of the growing season. Long-term dynamics reveal a decrease in the concentrations of Cu, Cd and Cr in defoliated forests and technogenic sparse forests. Stemflow volume rises from background to technogenic sparse forests due to deteriorating tree-crown conditions. This is associated with the deteriorating condition of tree stands, as manifested by reductions in tree height, diameter and needle cover. It has been established that under pollution conditions, trees’ assimilating organs actively accumulate heavy metals, thereby altering the composition of precipitation passing through the canopy. Full article

This investigation examines diffusion processes for predicting whole-stand volume, incorporating the variability and uncertainty inherent in regional, operational, and environmental factors. The distribution and spatial organization of trees within a specified forest region, alongside dynamic fluctuations and intricate uncertainties, are modeled by a set of nonsymmetric stochastic differential equations of a sigmoidal nature. The study introduces a three-dimensional system of stochastic differential equations (SDEs) with mixed-effect parameters, designed to quantify the dynamics of the three-dimensional distribution of tree-size components—namely diameter (diameter at breast height), potentially occupied area, and height—with respect to the age of a tree. This research significantly contributes by translating the analysis of tree size variables, specifically height, occupied area, and diameter, into stochastic processes. This transformation facilitates the representation of stand volume changes over time. Crucially, the estimation of model parameters is based exclusively on measurements of tree diameter, occupied area, and height, avoiding the need for direct tree volume assessments. The newly developed model has proven capable of accurately predicting, tracking, and elucidating the dynamics of stand volume yield and growth as trees mature. An empirical dataset composed of mixed-species, uneven-aged permanent experimental plots in Lithuania serves to substantiate the theoretical findings. According to the dataset under examination, the model-based estimates of stand volume per hectare in this region exhibited satisfactory goodness-of-fit statistics. Specifically, the root mean square error (and corresponding relative root mean square error) for the living trees of mixed, pine, spruce, and birch tree species were 68.814 m³ (20.4%), 20.778 m³ (7.8%), 32.776 m³ (37.3%), and 4.825 m³ (26.3%), respectively. The model is executed within Maple, a symbolic algebra system. Full article

The abundant satellite data have enabled the study of the dynamics of forest logging and its corresponding carbon balance with remote sensing. Change detection techniques with moderate-resolution imagery have been widely developed. Yet the signal processing or machine learning methods are sample-dependent, lacking an understanding of spectral signals of forest growth and logging cycles, which is necessary to distinguish logging from other types of disturbance, and mechanism models addressing post-logging tree changes are too complex for parameter inversion. We therefore proposed an efficient physical-based model for spectral simulation of annual forest logging by coupling forest dynamic model ZELIG and the stochastic radiative transfer (SRT) model. The forest logging simulation was conducted and validated by Abies forest field data before and after logging in Wangqing County, Northeastern China (R² = 0.85, RMSE = 10.82 t/ha). The spectral changes in Abies forest stands with annual growth and varying logging intensities were simulated by the novel model. The annual Landsat-8 and Gaofen-1 fusion multispectral imagery of the study area from 2013 to 2016 was furtherly used to extract annual sequence spectral data of 350 forest plots and perform inversion of the annual difference in above-ground biomass (dAGB). With the inversion method combining the look-up table of the ZELIG-SRT model and the random forest regression, the retrieved dAGB of the 350 plots indicated consistency with the measured data on the whole (R² = 0.71, RMSE = 13.32 t/ha). The novel physical-based approach for AGB monitoring is more efficient than previous 3D computer models and less dependent on field samples than data-driven models. This study provides a theoretical basis for understanding the remote sensing response mechanism of forest logging and a methodological basis for improving forest logging monitoring algorithms. Full article

Reliable estimation of oil palm carbon stock is essential for climate mitigation, concession management, and sustainability certification. While satellite-based approaches offer scalable solutions, redundancy among spectral indices and inter-sensor variability complicate model development. This study evaluates machine learning regressors for predicting oil palm carbon stock at tree (CO_tree, kg C tree⁻¹) and hectare (CO_ha, Mg C ha⁻¹) scales using spectral indices derived from Landsat-8, Landsat-9, and Sentinel-2. Fourteen vegetation indices were screened for multicollinearity, resulting in a lean feature set dominated by NDMI, EVI, MSI, NDWI, and sensor-specific indices such as NBR2 and ARVI. Ten regression algorithms were benchmarked through cross-validation. Ensemble models, particularly Random Forest, Gradient Boosting, and XGBoost, outperformed linear and kernel methods, achieving R² values of 0.86–0.88 and RMSE of 59–64 kg tree⁻¹ or 8–9 Mg ha⁻¹. Feature importance analysis consistently identified NDMI as the strongest predictor of standing carbon. Spatial predictions showed stable carbon patterns across sensors, with CO_tree ranging from 200–500 kg C tree⁻¹ and CO_ha from 20–70 Mg C ha⁻¹, consistent with published values for mature plantations. The study demonstrates that ensemble learning with sensor-specific index sets provides accurate, dual-scale carbon monitoring for oil palm. Limitations include geographic scope, dependence on allometric equations, and omission of belowground carbon. Future work should integrate age dynamics, multi-year composites, and deep learning approaches for operational carbon accounting. Full article

Understanding the ecological processes that shape spatial patterns across different growth stages is crucial for revealing the mechanisms of species coexistence and community dynamics. This study investigates the spatial patterns and associations between the regeneration layer and the overstory layer in Quercus variabilis forests in northern China. Using spatial point pattern analysis, we analyzed the distribution of 2761 seedlings and 449 adult trees across twelve 20 × 20 m plots. Our results revealed a consistent pattern where seedlings exhibited significant spatial aggregation, best fitted by a simple Thomas process with an average cluster radius of 3.89 m calculated across all plots, while adult trees displayed a complete spatial random distribution. A marked reduction in local density from seedlings to adults, indicated by a self-thinning index greater than 1 in most plots, provided evidence for density-dependent mortality during stand development. However, bivariate analysis detected no significant spatial association or mark correlation between adult trees and seedlings in most plots, suggesting limited interaction between these layers after initial seedling establishment. These findings demonstrate a clear transition from clustered regeneration to randomly distributed adults, which is consistent with the potential roles of dispersal limitation, habitat filtering and competition processes, with implications for the management and conservation of temperate Quercus forest ecosystems. Full article

Chinese fir (Cunninghamia lanceolata) is a cornerstone timber species in southern China. However, yet its plantation productivity frequently declines under successive rotations, threatening long-term sustainability. While belowground processes are suspected drivers, the mechanisms—particularly plant–soil–microbe interactions—remain poorly resolved. To address this, we examined a chronosequence of C. lanceolata plantations (5, 15, 20, and 30 years) in Jingdezhen, Jiangxi Province, integrating soil physicochemical assays, high-throughput sequencing, and extracellular enzyme activity profiling. We found that near-mature stands (20 years) exhibited a 60.7% decline in mean annual volume increment relative to mid-aged stands (15 years), despite continued increases in individual tree volume—suggesting a strategic shift from resource-acquisitive to nutrient-conservative growth. Peak values of soil organic carbon (32.87 g·kg⁻¹), total nitrogen (2.51 g·kg⁻¹), microbial biomass carbon (487.33 mg·kg⁻¹), and phosphorus (25.65 mg·kg⁻¹) coincided with this stage, reflecting accelerated nutrient turnover and intensified plant–microbe competition. Microbial communities shifted markedly over time: Basidiomycota and Acidobacteria became dominant in mature stands, replacing earlier Ascomycota and Proteobacteria. Random Forest and Partial Least Squares Path Modeling (PLS-SEM) identified total nitrogen, ammonium nitrogen, and total phosphorus as key predictors of productivity. PLS-SEM further revealed that stand age directly enhanced productivity (β = 0.869) via improved soil properties, but also indirectly suppressed it by stimulating microbial biomass (β = 0.845)—a “dual-effect” that intensified nutrient competition. Fungal and bacterial functional profiles were complementary: under phosphorus limitation, fungi upregulated acid phosphatase to enhance P acquisition, while bacteria predominately mediated nitrogen mineralization. Our results demonstrate a coordinated “soil–microbe–enzyme” feedback mechanism regulating productivity dynamics in C. lanceolata plantations. These insights advance a mechanistic understanding of rotation-associated decline and underscore the potential for targeted nutrient and microbial management to sustain long-term plantation yields. Full article

The sustainability of apiculture within Mediterranean forest ecosystems is contingent upon the extent and health of melliferous tree habitats. This study outlines a five-year initiative (2020–2024) aimed at mapping and monitoring four principal honey-producing tree species—pine (Pinus halepensis and Pinus nigra), Greek fir (Abies cephalonica), oak (Quercus ithaburensis subsp. macrolepis), and chestnut (Castanea sativa)—across Evia, Greece. This is achieved through the utilization of high-resolution Sentinel-2 satellite imagery in conjunction with a hierarchical deep learning framework. Distinct from prior vegetation mapping endeavors, this research introduces an innovative application of a hierarchical framework for species-level semantic segmentation of apicultural flora, employing a U-Net convolutional neural network to capture fine-scale spatial and temporal dynamics. The proposed framework first stratifies forests into broadleaf and coniferous types using Copernicus DLT data, and subsequently applies two specialized U-Net models trained on Sentinel-2 NDVI time series and DEM-derived topographic variables to (i) discriminate pine from fir within coniferous forests and (ii) distinguish oak from chestnut within broadleaf stands. This hierarchical decomposition reduces spectral confusion among structurally similar species and enables fine-scale semantic segmentation of apicultural flora. Our hierarchical framework achieves 92.1% overall accuracy, significantly outperforming traditional multiclass approaches (89.5%) and classical ML methods (76.9%). The results demonstrate the framework’s efficacy in accurately delineating species distributions, quantifying the ecological and economic impacts of the catastrophic 2021 forest fires, and projecting long-term habitat recovery trajectories. The integration of a novel hierarchical approach with Deep Learning-driven monitoring of climate- and disturbance-driven changes in honey-producing habitats marks a significant step towards more effective assessment and management of four major beekeeping tree species. These findings highlight the significance of such methodologies in guiding conservation, restoration, and adaptive management strategies, ultimately supporting resilient apiculture and safeguarding ecosystem services in fire-prone Mediterranean landscapes. Full article