Search Results (278)

Soil preferential flow plays a crucial role in governing hydrological cycles and soil moisture distribution in mountain forests. This makes it essential for understanding subsurface water movement and for guiding hillslope hydrological management. In this study, soil preferential flow, soil properties, and root characteristics across three slope positions on a Larix gmelinii var. principis-rupprechtii (Mayr) Pilger (larch) plantation hillslope in the Liupan Mountains were systematically observed to reveal the spatial patterns and formation mechanisms of hillslope soil preferential flow. The results showed that soil preferential flow development followed a distinct spatial pattern across the slope positions, with the mid-slope exhibiting the most developed preferential flow characteristics. The comprehensive preferential flow index further quantified this spatial variation, ranking the slope positions as mid-slope > upper slope > lower slope. Different soil structural properties exerted varying influences on preferential flow. Macropore-related properties (low bulk density and high porosity and saturated conductivity) promoted most preferential flow, whereas aggregate-related properties (high organic matter and water-stable aggregates) suppressed it. The influence of root characteristics on preferential flow was also dual. Root length density generally promoted preferential flow (e.g., DC, LI, and UniFr), whereas root surface area density primarily exerted an inhibitory effect (e.g., LI, UniFr, and total stained area TotStAr). This study clarifies how slope position modulates preferential flow through soil and root characteristics, offering insights for slope-specific hydrological understanding and targeted soil and water conservation practices. Full article

The collection of high-quality seeds to produce forest seedlings is closely linked with the time of harvesting. Climate warming is already having visible effects in all life stages of forest tree species, including the timing of seed maturation. The purpose of this study was to update the knowledge on seed collection timing and to identify the indicators of physiological maturity for three key Eastern European tree species—silver fir (Abies alba), European larch (Larix decidua), and small-leaved lime (Tilia cordata). Seeds and cones were collected from Romanian clonal seed orchards and evaluated at several stages of seed maturation using germination tests for European larch and tetrazolium viability tests for silver fir and small-leaved lime. The results revealed species-specific differences in seed maturation timing: in silver fir seed viability increased slightly from late August to early September, in European larch germination remained low (≈20%) regardless of harvest time, while small-leaved lime viability declined significantly after late August. These findings suggest that the harvest period observed during the study years occurred earlier than the traditionally recommended intervals and could be linked to recent warming trends. This study highlights the relevance of re-evaluating seed collection schedules under changing climatic conditions, while further multi-year studies are required to confirm these patterns and refine practical recommendations. Full article

Global climate change is reshaping Central European conifer forests, affecting growth and ecosystem dynamics. At the same time, tree species differ in their productivity and responses to climatic conditions. Across mid-elevation monocultures of European yew (Taxus baccata L.), Norway spruce (Picea abies [L.] Karst.), Scots pine (Pinus sylvestris L.), silver fir (Abies alba Mill.), and European larch (Larix decidua Mill.), we quantified stand structure, volume, biomass carbon sequestration, and growth–climate responses (1971–2023). Silver fir reached the highest stand volume (711 m³ ha⁻¹), with lower productivity in pine (−17.0%), larch (−22.9%), spruce (−26.0%), and yew (−70.6%). In contrast, larch maximised biomass carbon sequestration (267.7 t ha⁻¹), whereas yew had the lowest value (87.7 t ha⁻¹), but the greatest stand diversity (except high differentiation), while pine showed the lowest diversity. Radial growth was most constrained by warm Junes and dry Julys; an early-season multi-month drought compounded by heat further suppressed radial increments, and severe winter frosts added stress. Among the studied species, spruce was the most climate-sensitive, whereas fir and pine showed comparatively more resilience. From a practical forestry perspective, promoting structurally diverse stands with high production potential and prioritising climate-resilient tree species, especially fir, can help sustain production and stability at mid elevations under climate warming. Our results provide species-specific benchmarks for adaptive silviculture and identify the seasonal windows when growth is most vulnerable. Full article

Wildfires alter multiple soil functions in forest ecosystems, but how they reconfigure the linkages between these functions is not fully understood. We evaluated the 1-year-postfire and 11-year-postfire effects of wildfire on carbon sequestration, nutrient cycling, fertility maintenance, and erosion regulation, as well as their relationships, in a Chinese boreal larch forest. We further identified the environmental drivers regulating these associations. One year postfire, the soil fertility index transiently increased by 85%, whereas the carbon sequestration and nutrient cycling declined by 58% and 54%, respectively. Principal component analysis showed that wildfire decoupled the multivariate relationships between four soil functions. While these functions were closely clustered in unburned controls, they became dispersed one year postfire, indicating functional dissociation. After eleven years of recovery, a partial reassembly occurred, but with a reconfigured functional structure distinct from the pre-fire state. For the functional pairs, the impact of wildfire was limited to shifting the relationship between the soil fertility and nutrient cycling from a non-significant negative correlation to a significant positive correlation. Redundancy analysis showed that the soil water content remained the primary environmental driver of soil functional relationships before and after the fire, but its role reversed from negative in unburned stands to positive during the postfire recovery, suggesting a shift toward water-mediated functional coupling. Wildfires in boreal forests have far-reaching effects on soil ecosystems, including impacts on the relationships between various soil functions. Our results indicate that wildfire reconfigures the network of soil function linkages in boreal forests, with implications for the recovery of boreal soil ecosystems. Full article

The dead fine fuel moisture content (DFFMC) directly affects forest fire occurrence and spread. Accurate DFFMC prediction is key to estimating forest fire risk and behavior. The well-fitting machine learning (ML)-based meteorological factor regression models are a focus of DFFMC prediction modeling. Nevertheless, this method’s reliance on a considerable amount of training data and limited extrapolation hinders its potential for extensive implementation in practice. To improve the prediction accuracy of the model in the context of limited training data volumes and interspecies and spatial extrapolated predictions, this study proposed a novel DFFMC prediction method based on a knowledge-embedded neural network (KENN). By integrating the partial differential equation (PDE) of the meteorological response of forest fuel moisture content into a multilayer perceptron (MLP), the KENN utilizes prior physical knowledge and posterior observational data to determine the relationship between meteorology and moisture content. Data from Mongolian oak, white birch, and larch were collected to evaluate model performance. Compared with three representative ML algorithms for DFFMC prediction—random forest (RF), long short-term memory networks (LSTM), and MLP—the KENN can efficiently reduce training data volume requirements and improve extrapolation prediction accuracy within the investigated fire season, thereby enhancing the usability of ML-based DFFMC prediction methods. Full article

Broad-scale assessments often track forest productivity, yet they rarely quantify how soil conditions determine whether these gains persist as long-lived carbon and generate measurable economic value. This study focused on Northeast China, where forests include boreal coniferous stands dominated by Dahurian larch, temperate conifer–broadleaf mixed forests with Korean pine, and temperate deciduous broadleaf forests dominated by Mongolian oak. We combined GLASS net primary productivity and ESA CCI Land Cover to delineate forest pixels, used 2000 to 2005 as the baseline, and converted productivity anomalies into pixel level carbon economic value using a consistent pricing rule. Forest NPP increased significantly during 2000 to 2018 (slope = 1.57, p = 0.019), and carbon economic value also increased over time during 2006 to 2018 (slope = 2.24, p = 0.002), with the highest values in core mountain forests and lower values in the western forest–grassland transition zone. Correlation analysis, explainable random forests, and variance partitioning characterized spatial and temporal dynamics from 2000 to 2018 and identified environmental controls. Carbon value increased over time and showed marked spatial heterogeneity that mirrored productivity patterns in core mountain forests. Climate was the dominant predictor of value, while higher soil pH and clay content were negatively associated with value. The random forest model explained about 70% of the variance in carbon value (R² = 0.695), and variance partitioning indicated substantial unique and joint contributions from climate and soil alongside secondary topographic effects. The automatable framework enables periodic updates with new satellite composites, supports ecological compensation zoning, and informs soil-oriented interventions that enhance the monetized value of forest carbon sinks in data-limited regions. Full article

Woody debris decomposition is a key process in forest ecosystem material cycles, with invertebrate communities playing a vital role. Distinct physicochemical properties of woody debris types lead to varying effects on these communities. Taking woody debris in Saihanba’s Larix principis-rupprechtii plantations, Betula platyphylla natural secondary forests, and larch–birch mixed forests (northern China) as objects, we collected woody debris-inhabiting invertebrates via hand-sorting. We studied how tree species (larch/birch), forest types (pure/mixed), and decay stages (I–V) collectively regulate invertebrate community assembly. Results showed significant differences in woody debris physicochemical properties across these factors. Phytophagous groups dominated early decay stages (I–III) and decreased significantly (p < 0.05) with reduced wood density. In contrast, saprophagous and predatory groups increased with decay, correlated with higher TN and were more abundant in mixed than pure forests. NMDS indicated significant community differences among tree species/forest types in early decay, converging later. PLS-PM further confirmed functional groups’ response pathways to woody debris characteristics. Thus, preserving woody debris integrity and diversity in plantations is crucial for maintaining invertebrate diversity, promoting nutrient cycling, and enhancing forest ecosystem functions. Full article

The pine wood nematode (PWN) Bursaphelenchus xylophilus is a highly destructive forest quarantine pest and causal agent of pine wilt disease. The molecular response mechanism of Larix kaempferi (Japanese larch) to B. xylophilus infection remains unclear. This study aims to reveal the dynamic patterns of its defense response and screen key genes through time series transcriptomics. We found larch trees can proactively adjust their defense strategies to deal with the invasion of B. xylophilus. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, plant hormone signal transduction, MAPK signal pathway, and genes related to phenylpropane biosynthesis were more important. Through weighted gene coexpression network analysis (WGCNA), we identified two core modules that were rich in terpenoids, genes related to phenylpropane metabolism and cell wall strengthening, hormone signaling and defense regulation, and cytoskeleton and transport. Ultimately, we identified 20 core genes that were associated with several resistance-related processes, including the biosynthesis of resistance metabolites, post-translational regulation of protein homeostasis and defense signals, and transcriptional and translational reprogramming of gene expression. This study systematically depicted for the first time the continuous transcriptional regulatory network of L. kaempferi in response to pine wood nematodes. The key genes discovered provide important targets for subsequent functional verification and resistance breeding. Full article

With the large-scale invasion of alien species, forest ecosystems are facing severe challenges, and the health of trees is increasingly threatened. Accurately detecting and counting trees affected by such invasive species has become a critical issue in forest conservation and resource management. Traditional detection methods usually rely only on the information of a single modality of an image, lack linguistic or semantic guidance, and often can only model a specific diseased tree situation during training, making it difficult to achieve effective differentiation and generalization of multiple diseased tree types, which limits their practicality. To address the above challenges, we propose an end-to-end multimodal diseased tree detection model. In the visual encoder of the model, we introduce rotational positional encoding to enhance the model’s ability to perceive detailed structures of trees in images. This design enables more accurate extraction of features related to diseased trees, especially when processing images with complex environments. At the same time, we further introduce a cross-attention mechanism between image and text modalities, so that the model can realize the deep fusion of visual and verbal information, thus improving the detection accuracy based on understanding and recognizing the semantics of the disease. Additionally, this method possesses strong generalization capabilities, enabling effective recognition based on textual descriptions even when samples are not available. Our model achieves optimal results on the Larch Casebearer dataset and the Pests and Diseases Tree dataset, verifying the effectiveness and generalizability of the method. Full article

Accurate estimation of individual tree aboveground biomass (AGB) is essential for understanding forest carbon dynamics, optimizing resource management, and addressing climate change. Conventional methods rely on destructive sampling, whereas unmanned aerial vehicle (UAV) remote sensing provides a non-destructive alternative. In this study, spectral indices, textural features, and canopy height attributes were extracted from high-resolution UAV optical imagery and Light Detection And Ranging (LiDAR) point clouds. We developed an improved YOLOv8 model (NB-YOLOv8), incorporating Neural Architecture Manipulation (NAM) attention and a Bidirectional Feature Pyramid Network (BiFPN), for individual tree detection. Combined with a random forest algorithm, this hybrid framework enabled accurate biomass estimation of Chinese fir, Chinese pine, and larch plantations. NB-YOLOv8 achieved superior detection performance, with 92.3% precision and 90.6% recall, outperforming the original YOLOv8 by 4.8% and 4.2%, and the watershed algorithm by 12.4% and 11.7%, respectively. The integrated model produced reliable tree-level AGB predictions (R² = 0.65–0.76). SHapley Additive exPlanation (SHAP) analysis further revealed that local feature contributions often diverged from global rankings, underscoring the importance of interpretable modeling. These results demonstrate the effectiveness of combining deep learning and machine learning for tree-level AGB estimation, and highlight the potential of multi-source UAV remote sensing to support large-scale, fine-resolution forest carbon monitoring and management. Full article

The ecological restoration process of larch plantations to mixed forests contributes to enhancing the stability and functionality of forest ecosystems, with soil microbes playing a crucial role in this process. To elucidate the changes in soil microbial communities during this transition and their relationships with soil and litter properties, the study used 16S/ITS rRNA high-throughput sequencing to investigate the diversity and composition of soil bacterial and fungal communities at two soil depths across four restoration stages, and further quantified the relative contributions of soil and litter properties to variations in microbial community structure. The results indicated that bacterial and fungal $\mathsf{\alpha }$-diversity remained relatively stable in the topsoil but varied significantly across restoration stages in the subsoil ($p<0.05$), with the highest levels observed during the broadleaf species invasion stage. Fungal community structure demonstrated greater sensitivity to the restoration process, whereas bacterial communities showed stronger spatial dependency. Variance partitioning analysis revealed that soil properties were the main contributors to the variations of bacterial and fungal communities, accounting for 41% and 28% of the total variance, respectively. Fungal communities were more closely associated with litter properties than bacterial communities. Redundancy analysis combined with hierarchical partitioning further revealed that soil available phosphorus (AP) and total nitrogen (TN) were key factors explaining the variation in both bacterial and fungal communities. Additionally, litter total nitrogen (LTN) also emerged as an important factor affecting soil fungal communities. These findings provide critical microbiological evidence for accelerating the forest restoration in Northeast China through soil fertility management and regulation of litter inputs. Full article

Forest density is a key parameter in forestry research, and its variation can significantly impact ecosystems. Saihanba, as a focal site for afforestation and restoration, offers an ideal case for monitoring these dynamics. In this study, we compared three machine learning algorithms—Random Forest, Support Vector Regression, and XGBoost—using Landsat surface reflectance data together with the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI), and reference tree densities derived from LiDAR individual tree segmentation. The best-performing algorithm, XGBoost (R² = 0.65, RMSE = 174 trees ha⁻¹), was then applied to generate a long-term forest density dataset for Saihanba at five-year intervals, covering the period from 1988 to 2023. Results revealed distinct differences among tree species, with larch achieving the highest accuracy (R² = 0.65, RMSE = 161 trees ha⁻¹), whereas spruce had larger prediction errors (RMSE = 201 trees ha⁻¹) despite a relatively high R² (0.70). Incorporating 30 m slope data revealed that moderate slopes (5–30°) favored faster forest recovery. From 1988 to 2023, average forest density rose from 521 to 628 trees ha⁻¹—a 20.6% increase—demonstrating the effectiveness of restoration and providing a transferable framework for large-scale ecological monitoring. Full article

The aim of this paper is to investigate forest litter yield and decomposition rate estimation methods to provide a basic theory for litter production and decomposition studies and a scientific foundation for forest management practices. The Greater Khingan Dahurian larch (Larix gmelinii) forest in China was taken as the study subject. Forest litter was defined as the cumulative product of annual litterfall. The Olson exponential decay model, which is widely recognized in ecological studies, was employed to develop a system of equations representing the dynamic equilibrium among litter production, decomposition, and accumulation. Litter yield and decomposition rate estimation models were formulated based on this system. Model parameters were analyzed using multiple linear regression techniques. The proposed estimation methods were verified through field survey data and one-sample t-tests. The relative error for litter production estimation ranged from 0.01 to 0.25, with an average of 0.13, and the t-test yielded a p-value of 0.108. The relative error of the decomposition rate estimation was 0.00–0.35, with an average of 0.12, and the corresponding t-test yielded a p-value of 0.151. A litter yield and decomposition rate model with easily obtained predictor variables was constructed in this study. The model can rapidly estimate the litter yield and decomposition rate of survey sites and has important application value for litter yield- and decomposition-related studies. Full article

Forests, as one of the most vital ecosystems on Earth, play essential roles in climate regulation, water conservation, and resource provision. However, forest health is threatened by pests, among which the larch caterpillar (Dendrolimus superans) is one of the most destructive defoliators of coniferous forests in northern China. Previous studies have mostly relied on single data sources for pest detection, which are limited by insufficient spectral information or inappropriate selection of sensitive bands, making it difficult to achieve high detection accuracy. Therefore, this study integrates hyperspectral imagery from Zhuhai-1 and multispectral imagery from Sentinel-2, leveraging their high spectral resolution and broad spectral range, thus enhancing discrimination capability. Genetic algorithm (GA) was employed to select optimal features from spectral indices, texture features, and fractional-order derivatives (FOD). Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) were compared, and model interpretability was further analyzed using Shapley additive explanations (SHAP). The results showed that XGBoost achieved the highest performance, with an overall accuracy and Kappa coefficient of 93.47% and 89.81%, demonstrating superior adaptability. Moreover, the integration of hyperspectral and multispectral data significantly improved detection accuracy compared to using either data source alone. Among the GA-selected features, Band 15 of Zhuhai-1 hyperspectral imagery exhibited strong sensitivity to pest infestation. This study provides a novel and practical approach for forest pest monitoring based on the synergistic use of hyperspectral and multispectral remote sensing data. Full article

Climate change has a profound impact on the spatio-temporal patterns and successional dynamics of forest ecosystems, particularly at forest edges. The Altay Mountains are located at the junction of China, Russia, Kazakhstan and Mongolia, and the southern edge of the boreal forest in interior Eurasia. It is highly necessary to compare the differences in the responses of forest ecosystems in large transnational mountain ranges to climate change under the background of climate change. This study analyzed 558 tree cores collected from 20 sample sites dominated by Siberian larch (Larix sibirica Ledeb.) in the high-elevation of Altay Mountains. Using tree-ring width data and meteorological observations from Altay Mountains both in China and Russia, we investigated how climate influences the radial growth of L. sibirica across these regions. The results indicate that temperature is the primary factor driving radial growth, with early summer temperatures acting as the main growth-limiting factor on both China and Russia. Notably, the radial growth-climate response is stronger in Russia than China. Despite ongoing climate change, the dominant climatic drivers of radial growth in the Altay Mountains have remained stable, with temperature continuing to exert a significant and consistent influence on L. sibirica growth in the high-elevation of Altay Mountains. This study enhances our understanding of the climate change impacts on boreal forest ecosystems and highlights potential risks to forest health in the Altay Mountains. Full article