Search Results (64)

Forest canopy near-infrared reflectance and mass-based canopy nitrogen concentration (canopy %N) have been shown to be positively correlated. While the mechanisms underpinning this relationship remain unresolved, the broad range of wavelengths involved points to structural properties that influence scattering and covary with %N. Despite this, efforts that have focused on commonly measured structural properties such as leaf area index (LAI) have failed to identify a causal mechanism. Here, we sought to understand how lidar-derived canopy traits related to additional properties of foliar arrangement and structural complexity modulate the effects of leaf spectra and leaf area index (LAI) on canopy reflectance. We developed a leaf layer spectra model to explore how canopy reflectance would change if complex foliage arrangements were removed, compressing the canopy into optically dense, uniform stacked layers while maintaining the same leaf area index. Model results showed that LAI-weighted leaf reflectance saturates at a leaf area index of approximately two for needleleaf species and four for broadleaf species. When upscaled to estimate plot-level canopy reflectance in the absence of structural complexity (NIRr_LAI), results showed a strong positive relationship with canopy %N (r² = 0.86), despite a negative relationship for individual leaves or “big-leaf” canopies with an LAI of one (NIRrL, r² = 0.78). This result implies that the relationship between canopy near-infrared reflectance and canopy %N results from the integrated effects of canopy complexity acting on differences in leaf-level optical properties. We introduced an index of relative reflectance (IRr) that shows that the relative contribution of structural complexity to canopy near-infrared reflectance (NIRr_C) is related to canopy %N (r² = 0.55), with a three-fold reduction from potential canopy near-infrared reflectance observed in stands with low %N compared to a two-fold reduction in stands with high %N. These findings support the hypothesis that the correlation between canopy %N and canopy reflectance is the result of interactions between leaf traits and canopy structural complexity. Full article

(1) Forest fragmentation and associated edge effects can strongly modify the diversity and distribution of soil invertebrates, yet their responses in temperate floodplain forests remain poorly understood. We investigated myriapod (centipede and millipede) assemblages in a fragmented forest mosaic in the protected landscape area Litovelské Pomoraví (Czech Republic), focusing on the role of stand age, ecotones and key microhabitat variables. (2) Myriapods were sampled continuously during two years using pitfall traps arranged along transects crossing four neighboring patches (clear-cut with seedlings, 10-year-old stand, 87-year-old and 127-year-old Querco–Ulmetum forests). Species diversity was quantified using the Shannon–Wiener index, and patterns were analyzed by t-tests, canonical correspondence analysis and generalized additive models. (3) We collected over six thousand individuals (10 centipede and 10 millipede species). Diversity peaked in old-growth stands and adjacent ecotones, and two of the three ecotones supported particularly high species abundances. Litter cover and thickness, stand age, and the structure of the herb and shrub layers were the most important predictors of species distributions. Dominant species (e.g., Glomeris tetrasticha Brandt, 1833, Lithobius mutabilis L. Koch, 1862, L. forficatus (Linnaeus, 1758)) showed contrasting habitat preferences, reflecting niche differentiation along microhabitat and stand-age gradients. (4) Our findings indicate that conserving a fine-grained mosaic of stand ages, together with structurally complex forest interiors and ecotones, is essential for maintaining myriapod diversity and the ecosystem functions they provide in Central European forests. Full article

The ecological strategies of understory shrubs are critical for maintaining the structure and function of forest understory vegetation. Understanding the assembly mechanisms of these shrub communities is a central issue in modern ecology. To address this, our study was conducted in the typical red soil regions of Jiangxi, China, focusing on secondary forests (including both broadleaved and coniferous types) of similar stand age. We aimed to assess the effects of various environmental factors—such as soil pH, total nitrogen content, bulk density, and understory temperature—along with tree-layer characteristics—including canopy closure, tree species richness, and diameter at breast height (DBH)—on the species composition, functional traits, and phylogenetic structure of the shrub layer. Results showed: One-way ANOVA revealed significant differences in functional traits between the two forest types. Specifically, leaf thickness, specific leaf area, and chlorophyll content were significantly higher in the coniferous forest, whereas leaf dry matter content was significantly lower compared to the broadleaved forest (p < 0.05). These results suggest that understory shrubs in the coniferous forest primarily adopt a resource-conservative strategy, while those in the broadleaved forest exhibit a resource-acquisitive strategy. Phylogenetic analysis further revealed that the phylogenetic diversity (PD) of coniferous forests was significantly lower than that of broadleaved forests (p < 0.05). The phylogenetic structure in coniferous forests showed a more clustered pattern (NTI > 0, NRI > 0), suggesting stronger environmental filtering. Diversity index analysis showed that the Chao1 index indicated a richer potential species pool in broadleaved forests (p < 0.05), while species distribution was more even in coniferous forests (p < 0.05). Random Forest model analysis identified the diameter at breast height (DBH) of trees as the most critical negative driver, while soil pH was the primary positive driver. Redundancy Analysis (RDA) confirmed that the community structure in coniferous forests was mainly driven by biotic competition pressure represented by DBH, whereas the structure in broadleaved forests was more closely associated with abiotic factors like soil total nitrogen and pH (R² = 0.29, p < 0.05). These environmental drivers, through strong environmental filtering, collectively resulted in a phylogenetically clustered pattern of shrub communities in both forest types. This study demonstrates that the assembly of understory shrub communities is a complex, multi-level process co-regulated by multiple factors, shaped by both the biotic pressure from the overstory structure and abiotic filtering from the soil environment. This finding deepens our understanding of the rules governing community assembly in forest ecosystems. Full article

Crown width (CW) is a critical metric for characterizing tree-canopy dimensions; however, its direct measurement remains labor-intensive and is often impractical in inaccessible crowns. Consequently, CW is frequently derived from projections, which are susceptible to multiple sources of imprecision, including canopy density, crown irregularity, terrain heterogeneity, and the observer’s vantage point, especially in structurally complex natural forests. While deep neural network (DNN) models show substantial potential for CW prediction, their performance in heterogeneous forests remains uncertain. We developed DNN models integrated with a Height Threshold Method (HTM) to predict individual-tree CW in the natural mixed forests of Northern China, dominated by Larix principis-rupprechtii and Picea asperata. Our study further compared the relative importance of feature engineering versus model architectural complexity in predictive accuracy and identified the key ecological variables governing CW. The model performance was evaluated through the coefficient of determination (R²), mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Field surveys of 34 representative sample plots produced 1884 individual-tree records. The main results were as follows: (1) all DNNs avoided overfitting, and were statistical stable under ten-fold cross-validation; (2) the optimized DNN3-2 model (tuned hidden layer count, neurons/hidden layer, L₂ regularization, and dropout) achieved peak performance, explaining 69% of CW variance with residuals with stable variance and excellent coverage properties; (3) tree size, neighborhood competition, species identity, and site quality were the most important predictors; and (4) stand parameters calculated from competitive neighborhoods defined by the HTM, particularly mean stand crowding, Simpson’s index (1-D), and Shannon’s index (H′), significantly improved prediction accuracy. By integrating DNN with the HTM, our approach allows for accurate prediction of individual-tree CW in natural mixed forests of Northern China, dominated by Larix principis-rupprechtii and Picea asperata. Full article

The aim of this study is the estimation of the biodiversity of pure oak stands within the jurisdiction of the Forest Service of Xanthi in northeastern Greece. Using a published graded biodiversity index that operates on management-plan description sheets, we scored five stand-level attributes (total wood stock, age of trees, canopy density, presence of regeneration, and stand aspect/orientation) for every eligible stand and classified biodiversity as low, moderate, or high. These data were sourced from the description sheets of pure oak stands found in the management plans of public forest complexes. Moderate biodiversity predominates (63.4% of stands), followed by low (33.5%), while high biodiversity is scarce (3.1%). Forest practice can influence all the factors which were used for the assessment of the biodiversity characterization of the stands except the aspect of the stand. From these factors the total amount of wood stock and the canopy density were the main factors which determined the low percentage of high-biodiversity stands. On the other hand, the age structure and the regeneration existence were the main factors which counterbalanced the negative influence of the total amount of wood stock and of the canopy density and thus led to the dominance of the stands characterized as having moderate biodiversity score. Full article

Cryptography stands out as a scientific methodology for safeguarding communication against unauthorized access. This article proposes a newly formulated graph termed the Index Difference Graph (IDG). The proposed graph model serves as the secret key in the encryption process. Furthermore, we present a new graph-based algorithm, the Index Difference Modular Cryptographic (IDMC) Algorithm, and analyze it using centipede and path graphs. The goal of this graph-based approach is to increase the encryption rate while maintaining computational efficiency. This research investigates different types of index difference graphs and analyzes the time and space complexity of the algorithm. IDMC exhibits a lower collision probability, thereby enhancing encryption security. When employing a graph that admits an Index Difference Graph structure in the cryptographic algorithm, both the sender and receiver must be aware of the graph’s precise structure, as this strengthens the robustness of the cryptographic key. The application of the index difference centipede graph $P_n\odot 2k_1$ in cryptography, examined through the IDMC algorithm, demonstrates exceptionally high brute-force resistance estimated at approximately $2.6\times {10}^{39}$ for smaller instances with $n\le 7$ and escalating to $6.93\times {10}^{163}$ for larger graphs with $n\ge 20$. This resistance underscores the algorithm’s efficiency and cryptographic resilience. Full article

This study proposes a hybrid approach to analyze and forecast non-stationary financial time series by combining statistical models with deep neural networks. A model is introduced that integrates three key components: the Generalized Autoregressive Score (GAS) model, which captures volatility dynamics; an attention mechanism (ATT), which identifies the most relevant features within the sequence; and a Long Short-Term Memory (LSTM) neural network, which receives the outputs of the previous modules to generate price forecasts. This architecture is referred to as GAS-ATT-LSTM. Both unidirectional and bidirectional variants were evaluated using real financial data from the Nasdaq Composite Index, Invesco QQQ Trust, ProShares UltraPro QQQ, Bitcoin, and gold and silver futures. The proposed model’s performance was compared against five benchmark architectures: LSTM Bidirectional, GARCH-LSTM Bidirectional, ATT-LSTM, GAS-LSTM, and GAS-LSTM Bidirectional, under sliding windows of 3, 5, and 7 days. The results show that GAS-ATT-LSTM, particularly in its bidirectional form, consistently outperforms the benchmark models across most assets and forecasting horizons. It stands out for its adaptability to varying volatility levels and temporal structures, achieving significant improvements in both accuracy and stability. These findings confirm the effectiveness of the proposed hybrid model as a robust tool for forecasting complex financial time series. Full article

The Amazon rainforest hosts a diverse array of forest types, including those where Brazil nut (Bertholletia excelsa) occurs, which plays a crucial ecological and economic role. The Brazil nut is the second most important non-timber forest product in the Amazon, a symbol of development and sustainable use in the region, promoting the conservation of the standing forest. Understanding the vertical structure of these forests is essential to assess their ecological complexity and inform sustainable management strategies. We used terrestrial laser scanning (TLS) to assess the vertical structure of Amazonian forests with the occurrence of Brazil nut (Bertholletia excelsa) at regional (Amazonas, Mato Grosso, Pará, and Amapá) and local scales (forest typologies in Amapá). TLS allowed high-resolution three-dimensional characterization of canopy layers, enabling the extraction of structural metrics such as canopy height, rugosity, and leaf area index (LAI). These metrics were analyzed to quantify the forest vertical complexity and compare structural variability across spatial scales. These findings demonstrate the utility of TLS as a precise tool for quantifying forest structure and highlight the importance of integrating structural data in conservation planning and forest monitoring initiatives involving B. excelsa. Full article

Forest aboveground biomass (AGB) serves as a crucial indicator of productivity and carbon storage capacity. While the impact of stand structure on AGB is well-documented for pure moso bamboo stands, the specific structural factors influencing AGB and the mechanisms driving these effects in mixed moso bamboo forests, characterized by species diversity and structural complexity, require further elucidation. This study analyzed 9453 bamboos and arbor trees within the TianBao MetaPlot, which were tessellated into 108 standard plots in Tianbaoyan National Nature Reserve, Fujian, China. Using a multi-method voting approach, we identified the key structural factors influencing stand AGB and employed Partial Least Squares Path Modeling (PLS-PM) to assess their direct and indirect effects. We found that the stand density, moso bamboo mixing ratio, Shannon’s index, Simpson’s index, mean tree height, openness, and tree size variation coefficient were the key structural factors influencing the stand AGB. The PLS-PM analysis showed that stand density had a negative effect on stand AGB, which can be explicitly decomposed through a direct negative effect and an indirect negative effect. Tree diversity showed a strong positive effect, supporting the niche complementarity theory. The stand mean tree height and stand tree size variation had positive effects on stand AGB, while stand openness had a negative effect. The direct effects of tree diversity, stand mean tree height, and stand openness were stronger than the indirect effects on stand AGB, while the indirect effect of stand density was greater than the aforementioned effects. These results highlight the complex interactions between stand structure and stand AGB in mixed moso bamboo forests. The negative effect of stand density on stand AGB is in contrast with previous findings on arbor forests, wherein a higher stand density often promotes AGB, highlighting the unique structural characteristics of mixed moso bamboo forests. To promote biomass accumulation and enhance carbon sequestration in mixed moso bamboo stands, it is recommended to increase the tree size variability, enhance the tree species diversity, and apply rational thinning of moso bamboo, based on site-specific conditions. Full article

Wildfire is a critical driver of ecological processes in western U.S. forests, but recent shifts in climate, land use, and fire suppression have altered forest structure and disturbance regimes. Understanding post-fire recovery is essential for land management, particularly across complex montane landscapes like the southern Rocky Mountains. We assessed forest recovery in montane conifer forests, ranging from ponderosa pine to spruce-fir, following a large mixed-severity fire using field-based forest stand data and remotely sensed Leaf Area Index (LAI) measurements. Our objectives were to determine whether LAI is a meaningful proxy for post-fire vegetative recovery and how recovery patterns vary by forest type, burn severity, and abiotic factors. Stand characteristics predicted crown burn severity inconsistently and did not predict soil burn severity. LAI correlated strongly with live overstory tree density and shrub cover (R² = 0.70). Recovery trajectories varied by forest type, with lower-severity burns generally recovering four years post-fire, while high-severity burns showed delayed recovery. Regeneration patterns were strongly influenced by climate, with higher seedling densities occurring at wetter sites. Our findings highlight the utility of LAI as a proxy for vegetative recovery and underscore the importance of forest type, fire severity, and climatic factors when assessing post-fire resilience. Full article

Breast cancer is a prevalent type of cancer worldwide, leading to both high incidence and mortality, and hence, effective and safe drugs are needed. Because of this, the use of natural products and their derivatives has become a popular strategy for developing new chemotherapeutic agents. In this study, 17 new sesquiterpene-aryl derivatives were synthesized using (−)-drimenol as the starting material. The cytotoxicity of these semi-synthetic derivatives was determined in MCF-7 cells, a breast cancer model, and in a non-tumor cell line, MCF-10, to evaluate selectivity. The results show that five of these sesquiterpene derivatives had IC₅₀ values between 9.0 and 25 µM. Of these, compound 14c stands out for its higher cytotoxicity in MCF-7 cells but lower cytotoxicity in MCF-10 cells, being more selective than daunorubicin (selective index values of 44 and 28, respectively). In addition, compound 14c induced oxidative stress in MCF-7 cells, activated caspases-3/7, and selectively inhibited topoisomerase II (TOP2) versus topoisomerase I (TOP1) in MCF-7 cells. In silico studies allowed us to propose a binding mode for 14c to the TOP2 DNA complex to validate the experimental results. Therefore, this study demonstrated the importance of aryl-sesquiterpene structures and their promising profiles in the search for new bioinspired antitumor drugs in natural products. Full article

In the management of forests, the boundaries of individual units of land containing similar forest resources (e.g., stands) are delineated and used to guide the implementation of management activities. Traditionally, stand boundaries are drawn or digitized by hand; however, work recently has been conducted to automate the process using aerial imagery or airborne light detection and ranging (LiDAR) data as supporting resources. The work described here applies an object-based image analysis (OBIA) process to aerial imagery and to a landform index database. The size and shape of stands in the outcomes of these applications are then adjusted to conform to the desired product of land managers. These products are then intersected as they each contain information of value in the stand delineation process. The intersected database is then adjusted once again to conform to the desired product of land managers. Conformity of the size and shape of the resulting stand boundaries to a reference database drawn subjectively by hand was low to moderate. Specifically, the overall agreement for spatial and thematic (class names) accuracies was 43.0% and 56.8%, respectively. Nevertheless, the process of automating the stand delineation effort remains promising for achieving an efficient and non-subjective characterization of a structurally complex forested environment. Full article

Traditional image denoising algorithms often struggle with real-world complexities such as spatially correlated noise, varying illumination conditions, sensor-specific noise patterns, motion blur, and structural distortions. This paper presents an enhanced residual denoising network, R-REDNet, which stands for Reinforced Residual Encoder–Decoder Network. The proposed architecture incorporates deeper convolutional layers in the encoder and replaces additive skip connections with averaging operations to improve feature extraction and noise suppression. Additionally, the method leverages an iterative refinement approach, further enhancing its denoising performance. Experiments conducted on two real-world noisy image datasets demonstrate that R-REDNet outperforms current state-of-the-art approaches. Specifically, it attained a peak signal-to-noise ratio of 44.01 dB and a structural similarity index of 0.9931 on Dataset 1, and it obtained a peak signal-to-noise ratio of 46.15 dB with a structural similarity index of 0.9955 on Dataset 2. These findings confirm the efficiency of our method in delivering high-quality image restoration while preserving fine details. Full article

The European Green Deal (EGD), a complex policy program meant to address climate change and ensure a “just, fair and inclusive” transition into a more sustainable and greener Europe, was launched by the EU in 2019. It was clear from the very beginning that the opportunities and costs of the ambitious green transition would be very uneven geographically, not only within the EU but also among its regions and locations. Regions with higher environmental stress and/or less technological and economic capacities will inevitably be disadvantaged. The EGD requires large-scale public acceptance, which comes with democratic innovations and participative practices, which are less embedded in many regions. The former socialist bloc still struggles today with establishing decent levels of public participation, as well as with adopting and implementing democratic community principles in practice. The main aim of this paper is twofold. On the one hand, we intend to give an assessment of where Central and Eastern European (CEE) countries stand in the process of the green transition and what the major focus areas affecting them are, based on the existing academic literature. On the other hand, we intend to give a clear overview of the causes for and aspects of the weakness of civil society in the post-communist bloc and reasons why the adaptation of democratic innovations is lagging in this region. For this purpose, a systematic literature search and bibliometric analysis was performed based on articles indexed in the Scopus and Web of Science databases. Altogether, 172 articles published in the region were filtered and systematically analyzed according to the main themes of the papers related to the EGD. Research findings show that the interests of researchers in CEE countries largely differ from those in the mainstream academic discourse. Research topics, high on the agenda in Western countries, are hardly present in the academic discourse in CEE countries. On the other hand, issues like energy efficiency, urbanization’s impacts on green growth and renewable energy development, and innovations towards a circular economy dominate the research agenda. This region started the green transition process with major handicaps compared to the West, connected to path dependency and the legacy of socialist structures. The paper analyses the bibliometric aspects of articles published on these topics and highlights the highly sectoral and country-focused approaches taken with regard to the EGD. In our paper, we highlight the importance of the region on a geographic scale, which goes beyond the initial framework and offers a different approach to addressing the issue. The paper proves that the presence of EGD-related participation processes is significantly lacking in academic literature in the CEE region. However, the most important finding of our paper is the identification of an academic gap regarding democratic innovations and deliberation, as well as regarding active involvement and participation of people in EGD-related programs. This gives an even more important base for the assessment of the region in terms of the EGD, which faces growing populism and advancing authoritarian regimes, such that public participation and citizen control have become vitally important for the implementation of the green transition. Full article

This study examines the provision of ecosystem services in natural mixed broadleaf forests located in the Hyrcanian region of Iran. These services include habitat conservation, soil preservation, timber production, and carbon storage (C-stock). The forests are managed under three different silvicultural methods: shelterwood, selection cutting, and protection, allowing for a comparative analysis of their impact on these critical services. The time since the last cutting operation varied among the forest stands. In the shelterwood stand, 25 years had passed since the previous operation, while in the selection cutting stand, it had been 13 years. In contrast, the protected stand had remained untouched by logging for the past 40 years. This presents a valuable opportunity to assess the effects of the recovery period and evaluate the extent of ecosystem service restoration. Additionally, it allows for determining whether these services have reached the levels observed in a protected forest. The results show that habitat conservation, soil preservation, and carbon stock (C-stock) values ranked as follows: protection > selection cutting > shelterwood. In contrast, timber production values were highest under selection cutting, followed by shelterwood, and lowest in protected areas. Furthermore, the Stand Structural Complexity Index (SCI) was greatest in protected stands, with selection cutting and shelterwood-managed stands ranking second and third, respectively. Similarly, species diversity indices, the abundance of large-diameter trees, and the volume of deadwood followed this same trend. These findings highlight a trade-off in forest management practices. While selection cutting and shelterwood management simplify stand structure to enhance timber production and maximize economic returns, they also lead to a significant reduction in other critical forest ecosystem services. Our findings further revealed that, even decades after the cessation of forest operations, the ecological value of previously managed forests remains substantially lower than that of protected forests. Moreover, the results demonstrate that a single silvicultural rotation period is insufficient to fully restore the ecological value of managed forests, regardless of whether they were subjected to selection cutting or shelterwood management practices. Full article