Forests

Timber architectures have arisen as sustainable solutions for high-rise and long-span buildings, assisting in implementing a circular economy. The creep strain dissipation of laminated veneer lumber (LVL) was investigated in this work to understand the inherent creep behaviors of LVL derived from natural wood. The results demonstrated a significant loading regime dependency of the creep behaviors of LVL. Coupled creep strain dissipation that transits/is parallel to the wood–adhesive interface was proven in the creep deformation of flat-wise and edge-wise bent LVL. In contrast, the creep strain dissipated considerably along the wood–adhesive interface when the LVL was subjected to axial compression creep. Further investigation into the morphologies of LVL after creep revealed that direct contact between the loading plane and wood–adhesive interface could be a plausible trigger for the accelerated deformation and the resultant plastic deformation of the LVL after creep. We believe that this work provides essential insights into the creep strain dissipation of LVL. It is thus beneficial for improving creep resistance and assisting in the long-term safe application of LVL-based engineered wood products in timber architectures. Full article

An accurate simulation model is crucial for the analysis of the correct modal information of the ginkgo seed–stem system (ginkgo subsystem). This underpins the provision of technical rationale for efficient and low-damage precision vibrational harvesting operations in ginkgo cultivation. In this study, based on the modal parameters of the ginkgo subsystem, a finite element model updating method is proposed to correct the elastic modulus of the stem with the natural frequency of the first bending mode. The large difference in the modal results calculated before and after model updating reveals that model updating is a critical step in the finite element analysis of crop subsystems. Then, an uncertainty parameter modeling method is proposed to investigate the modal variability of the ginkgo subsystem by finite element analysis. The results show that the stem length is a key parameter affecting the variability of natural frequencies, and the seed weight is a minor parameter. The variability of the ginkgo seed’s gravity center offset has a negligible effect on the natural frequencies of the system. The first natural frequency of the ginkgo subsystem can be utilized for vibrational harvesting. In addition, since the difference between the upper and lower limits of the first natural frequency of the ginkgo subsystem does not exceed 1 Hz, a specific excitation frequency can cause most ginkgo subsystems to resonate. This result facilitates the determination of precise excitation frequencies for efficient and low-damage ginkgo vibrational harvesting, ensuring both economic and ecological benefits in the management of ginkgo plantations. Full article

Due to the inherent metals (Cu, As and Cr) in preserved wood waste (CCA-treated wood waste) that pose a risk to both the environment and human health, it is crucial to dispose of CCA-treated wood properly. Carbon materials have received widespread attention for their high porosity, renewability and simplicity of fabrication. This work presents a simple and effective process for producing carbon materials from leftover CCA-treated wood (chromated copper arsenate). Utilizing CCA-treated wood derived carbon (CCA-BC) and activating it with KOH (CCA-AC), electrode materials for supercapacitor applications were created and its electrochemical characteristics were investigated. The resulting material combines the conductivity of the metal in preserved wood with the good porosity provided by carbon materials. Compared with common wood biomass, carbon (W-BC) and common wood activated carbon (W-AC), CCA-BC and CCA-AC have better electrochemical properties. After being pyrolyzed at 600 °C for two hours, CCA-AC performed optimally electrochemically in 1 M Na₂SO₄ electrolyte, demonstrating a 72% capacity retention rate after 2000 charge and discharge cycles and a specific capacity of 76.7 F/g. This study provides a novel approach for the manufacture of supercapacitor electrodes, which also allows preserved wood waste an environmentally nondestructive form of elimination. Full article

Windthrow is a phenomenon that causes major changes to tree stand evolution by blowing down or breaking either isolated trees or entire tree stands, with a strong ecological, social and economic impact. Both scattered and large-scale windthrow occurred in spruce (Picea abies (L.) Karst.) tree stands of Romania. They affected surfaces of various dimensions from harvestable forests. Such a phenomenon took place in the Curvature Carpathians in February 2020. Large-scale windthrow occurred in this area in 1995 as well, in the upper watershed of Bâsca river. Using the climate data from February 2020, this paper aims to identify the manner in which factors such as climate and site conditions together with tree stand characteristic and the anthropogenic factor impacted and influenced the occurrence of windthrow. The results showed that the intensity of this phenomenon had maximum effects when the wind coming from north/northeast reached the maximum speed of 32 m·s⁻¹. Pure spruce tree stands situated on slopes with an inclination between 16 and 30° were mainly affected. Their position was counter to the wind direction, at an altitude between 1300 and 1500 m, on cambisols and spodosols. The analysis and statistical interpretation of data in the case of scattered and large-scale windthrow from the two management units showed that the same factors studied influence the variation of windthrow intensity in a different manner, or sometimes they do not influence it at all or they can only account for a small part of this variation. Full article

To achieve a micro-destructive and rapid measurement of the wood density of standing trees, this study investigated the possibility of the unified modeling of multiple tree species, the reliability of the micro drilling resistance method for measuring wood density, the relationship between drilling needle resistance and wood density, and whether moisture content has a significant impact on the model. First, 231 tree cores and drill resistance data were sampled from Pinus massoniana, Cunninghamia lanceolate, and Cryptomeria fortunei. The basic density and moisture content of each core were measured, and the average value of each resistance data record was calculated. Second, the average drill resistance, the natural logarithm of average drill resistance, and absolute moisture content were used as independent variables, while the basic wood density was used as the dependent variable. Third, the total model of the three tree species and sub-model for each tree species were established through a stepwise regression method. Finally, the accuracy of each model was compared and analyzed with that of using the average basic density of each tree species as an estimated density. The estimated accuracy of the total model, sub model, and average wood density modeling data were 90.070%, 93.865%, and 92.195%, respectively. The results revealed that the estimation accuracy of the sub-model was 1.670 percentage points higher than that of the average wood density modeling data, while the estimation accuracy of the total model was 2.125 percentage points lower than that of the average wood density modeling data. Additionally, except for Cryptomeria fortunei, the natural logarithm of drill resistance significantly influenced the wood density model at a significance level of 0.05. Moreover, moisture content significantly affected the total model and sub-models of Pinus massoniana at a significance level of 0.05. The results indicated the feasibility of using the micro-drilling resistance method to measure the wood density of standing trees. Moreover, the relationship between wood density and drill resistance did not follow a linear pattern, and moisture content slightly influenced the drill needle resistance. Furthermore, the establishment of a mathematical model for each tree species was deemed essential. This study provides valuable guidance for measuring the wood density of standing trees through the micro-drilling resistance method. Full article

Knots in wood have a substantial impact on both the physical and mechanical properties of derived products. It is necessary to study their effect on the mechanical properties of wood and understand the mechanisms behind the effect. The modulus of elasticity (MOE) and modulus of rupture (MOR) of specimens without knots and with knots are measured using the three-point bending test. The size and position of knots are recorded. The specimens with knots are analyzed according to failure not at knots and failure at knots. For specimens with failure at knots, they are further divided into two sub-groups, i.e., failure around knots (FK-A) or failure in knots (FK-I). Full article

Soil carbon and nitrogen pools are crucial for maintaining the balance of carbon and nitrogen cycling in ecosystems and also for reducing the impacts of global climate change. However, current research lacks an understanding of the effects of long-term vegetation restoration on soil carbon and nitrogen pools and their storage in vulnerable ecosystems. Therefore, we studied the characteristics of soil carbon (soil organic carbon, microbial biomass carbon, dissolved organic carbon) and nitrogen pools (total nitrogen, ammonium nitrogen, nitrate nitrogen) and their storage under four types of vegetation restoration (Stipa bungeana Trin., SB; Caragana korshinskii Kom., CK; Xanthoceras sorbifolia Bunge., XS; Picea asperata Mast., PA) in the Longzhong Loess Plateau area. We found that the carbon and nitrogen pools in the 0–40 cm soil layer under the XS and PA vegetation restoration types were higher compared to those under the SB and CK vegetation, and the values of soil ammonium–nitrogen ratios ranged from 0.72 to 0.83 under different vegetation types. Carbon and nitrogen interactions were stronger in the 0–40 cm soil under PA vegetation, which had significantly higher soil carbon (49.06 t·ha⁻¹) and nitrogen (1.78 t·ha⁻¹) storage than did the other vegetation types. We also found that soil carbon and nitrogen stores differed among different types of vegetation restoration. These elements were mainly distributed in soils from 0 to 20 cm depth, where the carbon and nitrogen pools in soils from 0 to 10 cm exceeded those in the lower layers. Furthermore, we discovered that redundancy analysis (RDA) supported by soil enzyme activity and physical properties significantly explained the variation in soil carbon and nitrogen triggered by vegetation restoration. According to this research, the stability and transformation of soil carbon and nitrogen pools in the region can be influenced by various forms of vegetation restoration. Additionally, the findings highlight that forest vegetation restoration can be a successful strategy for effectively sequestering soil carbon and nitrogen within the Longzhong Loess Plateau area. Full article

The Amazonian native tree species Parkia multijuga has potential silvicultural characteristics that can be utilized to productive plantations. Understanding its mating system is necessary to delineate the methods for the breeding of the species, the collection of seeds for conservation, and the use of seedlings for production plantations. The aim of this study is to evaluate the mating system and population genetic diversity of P. multijuga, using molecular markers. The DNA of 221 plants was extracted and genotyped with nine microsatellite loci using capillary electrophoresis in an automated DNA sequencer. The estimates for single and multilocus crossing rates were 0.998 and 1.0, respectively. The paternity correlation was low (${\widehat{r}}_{p\left(m\right)}$ = 0.307). The fixation index (f) showed values below zero, indicating an excess of heterozygotes. The cluster number K = 2 shows a better grouping among families for genetic structure. P. multijuga families consist mainly of half-sibs, and the reproductive strategy of the species is allogamy. Full article

Pine wilt disease (PWD) is a highly contagious and devastating forest disease. The timely detection of pine trees infected with PWD in the early stage is of great significance to effectively control the spread of PWD and protect forest resources. However, in the spatial domain, the features of early-stage PWD are not distinctly evident, leading to numerous missed detections and false positives when directly using spatial-domain images. However, we found that frequency domain information can more clearly express the characteristics of early-stage PWD. In this paper, we propose a detection method based on deep learning for early-stage PWD by comprehensively utilizing the features in the frequency domain and the spatial domain. An attention mechanism is introduced to further enhance the frequency domain features. Employing two deformable convolutions to fuse the features in both domains, we aim to fully capture semantic and spatial information. To substantiate the proposed method, this study employs UAVs to capture images of early-stage pine trees infected with PWD at Dahuofang Experimental Forest in Fushun, Liaoning Province. A dataset of early infected pine trees affected by PWD is curated to facilitate future research on the detection of early-stage infestations in pine trees. The results on the early-stage PWD dataset indicate that, compared to Faster R-CNN, DETR and YOLOv5, the best-performing method improves the average precision (AP) by 17.7%, 6.2% and 6.0%, and the F1 scores by 14.6%, 3.9% and 5.0%, respectively. The study provides technical support for early-stage PWD tree counting and localization in the field in forest areas and lays the foundation for the early control of pine wood nematode disease. Full article

Wildfires are a significant problem in Irkutsk Oblast. They are caused by climate change, thunderstorms, and human factors. In this study, we use the Random Forest machine learning method to map the wildfire susceptibility of Irkutsk Oblast based on data from remote sensing, meteorology, government forestry authorities, and emergency situations. The main contributions of the paper are the following: an improved domain model that describes information about weather conditions, vegetation type, and infrastructure of the region in the context of the possible risk of wildfires; a database of wildfires in Irkutsk Oblast from 2017 to 2020; the results of an analysis of factors that cause wildfires and risk assessment based on Random Forest in the form of fire hazard mapping. In this paper, we collected and visualized data on wildfires and factors influencing their occurrence: meteorological, topographic, characteristics of vegetation, and human activity (social factors). Data sets describing two classes, “fire” and “no fire”, were generated. We introduced a classification according to which the probability of a wildfire in each specific cell of the territory can be determined and a wildfire risk map built. The use of the Random Forest method allowed us to achieve the following risk assessment accuracy indicators: accuracy—0.89, F1-score—0.88, and AUC—0.96. The comparison of the results with earlier ones obtained using case-based reasoning revealed that the application of the case-based approach can be considered the initial stage for deeper investigations with the use of Random Forest for more accurate forecasting. Full article

Proteins containing the homologous E6-AP carboxyl-terminal (HECT) domain are a class of E3 ubiquitin ligases involved in the ubiquitin–proteasome pathway, which plays an irreplaceable role in plant growth, development, and stress resistance. However, a phylogenetic analysis and expression profile of the HECT gene (PtrHECT) in the model plant Populus trichocarpa (Torr. & Gray) have not been reported. In this study, we identified 13 PtrHECT genes using genome-wide analysis, and then these were divided into four groups. The protein interaction networks showed that the PtrHECT protein may interact with PTR6 and participate in ABA signal regulation. Abiotic stress is the main environmental factor limiting plant growth and development. The qRT-PCR results showed that PtrHECT1, 4, 7, 8, and 9 were significantly up-regulated in leaves at each time point under drought stress, and most PtrHECT genes responded to both drought and high salt stress, consistent with their promoter sequence analysis, revealing the presence of an important number of phytohormone-responsive and stress-related cis-regulatory elements. This study provides useful information for further analysis of the functions of the HECT gene family in P. trichocarpa. Full article

As wild Aquilaria sinensis resources are exhausted and protected, China has established a huge number of plantations of Aquilaria trees and developed artificial induction techniques. However, the current output and quality of artificial induction technology have not yet met the expected results. It has been found that high-oil-containing agarwood may contain particular fungal stains associated with agarwood production. To enhance the quality of agarwood, we recovered and characterized three highly active fungi from high-oil-containing agarwood and inoculated them onto A. sinensis trees using two traditional physical methods. The results showed that fungi extracted from high-oil-containing agarwood can effectively increase the yield and quality of agarwood. During the agarwood formation process, parenchyma cells, xylem rays, and axial parenchyma cells in the xylem gradually undergo apoptosis, thereby promoting the expansion of the color range of agarwood. Nine months after the treatment, the alcohol-soluble extract content in agarwood reached the standard specified in the Chinese Pharmacopoeia (10%), and the proportions of sesquiterpenes and chromones in each treatment were 55.82%, 58.31%, 62.65%, 70.97%, and 13.71%, respectively. These results indicate that fungal induction has a positive impact on the quality of agarwood. In addition, compared to drilling and fungus combined induction, “burning holes and fungi” combined induction demonstrates better results and can further improve the yield and quality of agarwood. Full article

Siberian pine (Pinus sibirica Du Tour) is a widespread and long-lived species in the northern hemisphere, which makes it a good potential proxy for climatic data. However, the tree-ring growth of this species weakly correlates with climatic conditions, which prevents its use in dendroclimatic reconstruction. It was proposed to use the measurements of tracheid characteristics as model predictors to reconstruct the smoothed temperature of the key periods in tree growth. In this study, algorithms for preprocessing tracheids and temperature data, as well as for model cross-validation, were developed to produce reliable high-resolution (weekly-based) temperature reconstructions. Due to the developed algorithms, the key time periods of Siberian pine growth were identified during the growing season—early June (most active cell development) and mid-July (setting new buds for the next growing season). For these time periods, reliable long-term temperature reconstructions (R² > 0.6, p < 10⁻⁸) were obtained over 1653–2018. The temperature reconstructions significantly correlated (p < 10⁻⁸) with independent reanalysis data for the 19th century. The developed approach, based on preprocessing tracheid and temperature data, shows new potential for Siberian pine in high-resolution climate reconstructions and can be applied to other tree species that weakly respond to climate forcing. Full article

Forest expansion in Mediterranean mountain areas is a widespread phenomenon resulting from the abandonment of agricultural and pastoral activities during the last century. Therefore, knowledge of the long-term storage capacity of soil organic carbon (SOC) in Mediterranean forests is of great interest in the context of global change. However, the effects of these land uses and covers (natural secondary forest, afforestation with conifers and silvo-pastoral ecosystems (dehesas)) on SOC dynamics are still uncertain. The main objectives of this study were to evaluate physico-chemical soil properties, SOC and nitrogen stocks, and SOC fractions in Mediterranean forests and to assess the effects of tree species, the soil environment (acidic and alkaline), and land management. We selected five land uses and land covers: managed and non-managed afforestation and dehesa (except for alkaline dehesa) and a stage of succession when tree species begin to become established after abandonment. This study concludes that although total SOC stocks are higher in afforested systems with conifers, SOC is stored in less stable carbon pools than in broadleaf forests. In addition, this study confirms that there are marked differences in the results between acidic and alkaline environments. Finally, the management system is also a significant factor, particularly for afforested sites. Full article

The study explores the use of convolutional neural networks (CNNs) and satellite remote sensing imagery for walnut analysis in Ganquan Township, Alar City, Xinjiang. The recent growth of walnut cultivation in Xinjiang presents challenges for manual data collection, making satellite imagery and computer vision algorithms a practical solution. Landsat-8 satellite images from Google Earth Engine underwent preprocessing, and experiments were conducted to enhance the ResNet model, resulting in improved accuracy and efficiency. Experiments were conducted to evaluate multiple CNN models and traditional methods, and the best detection method was chosen through comparisons. A comparison was drawn between traditional algorithms and convolutional neural network algorithms based on metrics such as precision, recall, f1-score, accuracy, and total time. The results indicated that although traditional methods were more efficient compared to CNN, they exhibited lower accuracy. In the context of this research, prioritizing efficiency at the cost of accuracy was deemed undesirable. Among the traditional algorithms employed in this study, k-NN produced the most favorable outcomes, with precision, recall, f1-score, and accuracy reaching 75.78%, 92.43%, 83.28%, and 84.46%, respectively, although these values were relatively lower than those of the CNN algorithm models. Within the CNN models, the ResNet model demonstrated superior performance, yielding corresponding results of 92.47%, 94.29%, 93.37%, and 93.27%. The EfficientNetV2 model also displayed commendable results, with precision, recall, and f1-score achieving 96.35%, 91.44%, and 93.83%. Nevertheless, it is worth noting that the classification efficiency of EfficientNetV2 fell significantly short of that of ResNet. Consequently, in this study, the ResNet model proved to be relatively more effective. Once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. Through our studies, we discovered that once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. In this study, empirical evidence demonstrates that integrating CNN-based methods with satellite remote sensing technology can effectively enhance the statistical efficiency of agriculture and forestry sectors, thus leading to substantial reductions in operational costs. These findings lay a solid foundation for further research in this field and offer valuable insights for other agricultural and forestry-related studies. Full article

Large, mapped forest research plots are important sources of data to understand spatial and temporal changes in forest communities in the context of global change. Here, we describe the data from the first three censuses of the 16-ha UC Santa Cruz Forest Ecology Research Plot, located in the Mediterranean-climate forest on the central coast of California, USA. The forest includes both mixed-evergreen forest and redwood-dominated forest and is recovering from significant logging disturbances in the early 20th century. Each woody stem with a diameter ≥ 1 cm at 1.3 m was mapped, tagged, identified, and measured, with censuses performed at ~5-year intervals. The first census included just 6 ha (previously described), and the area was then expanded to 16 ha in the second census. We describe the temporal dynamics of the forest in the original 6 ha, as well as the structure and temporal dynamics of the full 16 ha. The community includes 34 woody species, including 4 gymnosperm and 9 angiosperm tree species, 18 species of shrubs, and 3 species of lianas. The community includes eight non-native species, representing less than 0.5% of the stems. More than half the species show greater rates of mortality than recruitments, reflective of a dynamic forest community. Over a decade, the number of living woody stems has declined, but the basal area has increased, reflecting a self-thinning process. Full article

The genetic relationships between Coffea arabica resources were analyzed via specific length amplified fragment sequencing (SLAF-seq) and transcriptome sequencing to provide the theoretical basis for breeding new varieties. Twenty C. arabica accessions were used to analyze genetic diversity on the basis of SNPs identified in SLAFs and the transcriptome data. For the SLAF-seq analysis of 20 C. arabica accessions, two Coffea canephora accessions, one Coffea liberica accession, and one Coffea racemosa accession, the number of reads ranged from 2,665,424 to 7,210,310, with a GC content of 38.49%–40.91% and a Q30 value of 94.99%–96.36%. A total of 3,347,069 SLAF tags were obtained, with an average sequencing depth of 13.90×. Moreover, the 1,048,575 SNPs identified in the polymorphic SLAFs were filtered, then the remaining 198,955 SNPs were used to construct a phylogenetic tree, perform a principal component analysis, and characterize the population structure. For the transcriptome analysis, 128.50 Gb clean reads were generated for the 20 C. arabica accessions, with a GC content of 44.36%–51.09% and a Q30 value of 94.55%–95.40%. Furthermore, 25,872 genes’ expression levels were used for the correlation analysis. The phylogenetic relationships as well as the results of the principal component analysis, population structure analysis, and correlation analysis clearly distinguished C. arabica Typica-type accessions from the C. arabica Bourbon-type accessions. Notably, several C. arabica local selections with unknown genetic backgrounds were classified according to all four clustering results. Full article

The Dendrolimus houi, a phytophagous pest, displays a broad range of adaptations and often inflicts localized damage to its hosts. Cupressus funebris, an indigenous timber species in China, is significantly impacted by D. houi. Investigating the suitable habitat distribution and changes in D. houi and its host plant, C. funebris, within the context of climate warming, is essential for understanding D. houi’s development and providing novel insights for managing D. houi and conserving C. funebris resources. In this study, MaxEnt was employed to simulate the distribution of D. houi and its host plant, C. funebris, in their suitable habitats, evaluating the influence of environmental factors on their distribution and determining changes under a warming scenario. MaxEnt model parameters were adjusted using the Kuenm data package based on available distribution and climatic data. The minimum temperature of the coldest month emerged as the primary environmental factor influencing the distribution of a suitable habitat for D. houi and C. funebris, with a percentage contribution of environmental factors over 60%. There was a substantial similarity in the suitable habitat distributions of D. houi and C. funebris, with varying degrees of expansion in the total habitat area under different temporal and climatic scenarios. Intersection analysis results indicated that the 2041–2060 period, especially under low (SSP1-2.6) and high (SSP5-8.5) emission scenarios, is a critical phase for D. houi control. The habitat expansion of D. houi and C. funebris due to climate change was observed, with the distribution center of D. houi shifting northeast and that of C. funebris shifting northwest. Full article

Soil CO₂ efflux (F_s) plays an important role in forest carbon cycling yet estimates of F_s can remain unconstrained in many systems due to the difficulty in measuring F_s over long time scales in natural systems. It is important to quantify seasonal patterns in F_s through long-term datasets because individual years may show patterns that are not reflective of long-term averages. Additionally, determining predictability of net patterns in soil carbon flux based on environmental factors, such as moisture and temperature, is critical for appropriately modeling forest carbon flux. Ecosystems in moderate climates may have strong CO₂ efflux even during winter, and so continuous quantification of annual variability is especially important. Here, we used a 2008–2023 dataset in a lowland temperate forest ecosystem to address two main questions: (1) What are the seasonal patterns in F_s in a highly productive temperate rainforest? (2) How is average F_s across our study area predicted by average coincident temperature, soil moisture and precipitation totals? Data showed clear seasonality where F_s values are higher in summer. We also find F_s across our measurement network was predicted by variation in abiotic factors, but the interaction between precipitation/moisture and temperature resulted in greater complexity. Specifically, in spring a relatively strong relationship between air temperature and F_s was present, while in summer the relationship between temperature and F_s was flat. Winter and autumn seasons showed weak positive relationships. Meanwhile, a negative relationship between precipitation and Fs was present in only some seasons because most precipitation falls outside the normal growing season in our study system. Our data help constrain estimates of soil CO₂ fluxes in a temperate rainforest ecosystem at ~14–20 kg C ha⁻¹ day⁻¹ in summer and autumn, and 6.5–10.5 kg C ha⁻¹ day⁻¹ in winter and spring seasons. Together, estimates suggest this highly productive temperate rainforest has annual soil-to-atmosphere fluxes of CO₂ that amount to greater than 4.5 Mg C ha⁻¹ year⁻¹. Sensitivity of such fluxes to regional climate change will depend on the balance of F_s determined by autotrophic phenological responses versus heterotrophic temperature and moisture sensitivity. Relatively strong seasonal variation coupled with comparatively weak responses to abiotic variables suggest F_s may be driven largely by seasonal trends in autotrophic respiration. Accordingly, plant and tree responses to climate may have a stronger effect on F_s in the context of climate change than temperature or moisture changes alone. Full article