Journal of Forestry Research最新文献

Plant carbon (C) concentration is a fundamental trait for estimating C storage and nutrient utilization. However, the mechanisms of C concentration variations among different tree tissues and across species remains poorly understood. In this study, we explored the variations and determinants of C concentration of nine tissues from 216 individuals of 32 tree species, with particular attention on the effect of wood porosity (i.e., non-porous wood, diffuse-porous wood, and ring-porous wood). The inter-tissue pattern of C concentration diverged across the three porosity types; metabolically active tissues (foliage and fine roots, except for the foliage of ring-porous species) generally had higher C levels compared with inactive wood. The poor inter-correlations between tissue C concentrations indicated a necessity of measuring tissue- and specific-C concentrations. Carbon concentration for almost all tissues generally decreased from non-porous, to diffuse-porous and to ring-porous. Tissue C was often positively correlated with tissue (foliage and wood) density and tree size, while negatively correlated with growth rate, depending on wood porosity. Our results highlight the mediating effect of type of wood porosity on the variation in tissue C among temperate species. The variations among tissues were more important than that among species. These findings provided insights on tissue C concentration variability of temperate forest species.

Effective breeding requires multiplying desired genotypes, keeping them at a convenient location to perform crosses more efficiently, and building orchards to generate material for reforestation. While some of these aims can be achieved by conventional grafting involving only rootstock and scion, topgrafting is known to deliver all in a shorter time span. In this study, Scots pine scions were grafted onto the upper and lower tree crowns in two clonal archives with the aim of inducing early female and male strobili production, respectively. Their survival rates and strobili production were analyzed with generalized linear mixed models. Survival was low (14%) to moderate (41%), and mainly affected by the topgraft genotype, interstock genotype, crown position and weather conditions in connection with the grafting procedure. Survival was not affected by the cardinal position in the crown (south or north). Male flowering was ample three years after grafting and reached 56% in the first year among live scions, increasing to 62 and 59% in consecutive years. Female flowering was scarce and was 9% at first, later increasing to 26 and 20% of living scions but was strongly affected by the topgraft genotype. In one subset of scions, female flowering was observed 1 year after grafting. Overall, flowering success was mainly affected by the topgraft and interstock genotypes, and secondary growth of scions. This is one of few reports on topgrafting in functional Scots pine clonal archives.

Typhoons are becoming frequent and intense with ongoing climate change, threatening ecological security and healthy forest development in coastal areas. Eucalyptus of a predominant introduced species in southern China, faces significant growth challenges because of typhoon. Therefore, it is vital to investigate the variation of related traits and select superior breeding materials for genetic improvement. Variance, genetic parameter, and correlation analyses were carried out on wind damage indices and eight wood properties in 88 families from 11 provenances of 10-year-old Eucalyptus camaldulensis. The selection index equation was used for evaluating multiple traits and selecting superior provenances and family lines as future breeding material. The results show that all traits were highly significantly different at provenance and family levels, with the wind damage index having the highest coefficient of genetic variation. The heritability of each trait ranged from 0.48 to 0.87, with the wind damage index, lignin and hemicellulose contents, and microfibril angle having the highest heritabilities. The wind damage index had a positive genetic correlation with wood density, a negative correlation with lignin content, a negative phenotypic correlation and a negative genetic correlation with microfibril angle. Wind damage index and genetic progress in the selection of eight wood traits varied from 7.2% to 614.8%. Three provenances and 12 superior families were selected. The genetic gains of the wind damage index were 10.2% and 33.9% for provenances and families, and these may be starting material for genetic modification for wind resistance in eucalyptus and for their dissemination to typhoon-prone coastal areas of southern China.

Pine wood nematode infection is a devastating disease. Unmanned aerial vehicle (UAV) remote sensing enables timely and precise monitoring. However, UAV aerial images are challenged by small target size and complex surface backgrounds which hinder their effectiveness in monitoring. To address these challenges, based on the analysis and optimization of UAV remote sensing images, this study developed a spatio-temporal multi-scale fusion algorithm for disease detection. The multi-head, self-attention mechanism is incorporated to address the issue of excessive features generated by complex surface backgrounds in UAV images. This enables adaptive feature control to suppress redundant information and boost the model’s feature extraction capabilities. The SPD-Conv module was introduced to address the problem of loss of small target feature information during feature extraction, enhancing the preservation of key features. Additionally, the gather-and-distribute mechanism was implemented to augment the model’s multi-scale feature fusion capacity, preventing the loss of local details during fusion and enriching small target feature information. This study established a dataset of pine wood nematode disease in the Huangshan area using DJI (DJ-Innovations) UAVs. The results show that the accuracy of the proposed model with spatio-temporal multi-scale fusion reached 78.5%, 6.6% higher than that of the benchmark model. Building upon the timeliness and flexibility of UAV remote sensing, the proposed model effectively addressed the challenges of detecting small and medium-size targets in complex backgrounds, thereby enhancing the detection efficiency for pine wood nematode disease. This facilitates early preemptive preservation of diseased trees, augments the overall monitoring proficiency of pine wood nematode diseases, and supplies technical aid for proficient monitoring.

The rate of fire spread is a key indicator for assessing forest fire risk and developing fire management plans. The Rothermel model is the most widely used fire spread model, established through laboratory experiments on homogeneous fuels but has not been validated for conifer-deciduous mixed fuel. In this study, Pinus koraiensis and Quercus mongolica litter was used in a laboratory burning experiment to simulate surface fire spread in the field. The effects of fuel moisture content, mixed fuel ratio and slope on spread rate were analyzed. The optimum packing ratio, moisture-damping coefficient and slope parameters in the Rothermel model were modified using the measured spread rate which was positively correlated with slope and negatively with fuel moisture content. As the Q. mongolica load increased, the spread rate increased and was highest at a fuel ratio of 4:6. The model with modified optimal packing ratio and slope parameters has a significantly lower spread rate prediction error than the unmodified model. The spread rate prediction accuracy was significantly improved after modifying the model parameters based on spread rates from laboratory burning simulations.

Annual tree rings are widely recognized as valuable tools for quantifying and reconstructing historical forest disturbances. However, the influence of climate can complicate the detection of disturbance signals, leading to limited accuracy in existing methods. In this study, we propose a random under-sampling boosting (RUB) classifier that integrates both tree-ring and climate variables to enhance the detection of forest insect outbreaks. The study focused on 32 sites in Alberta, Canada, which documented insect outbreaks from 1939 to 2010. Through thorough feature engineering, model development, and tenfold cross-validation, multiple machine learning (ML) models were constructed. These models used ring width indices (RWIs) and climate variables within an 11-year window as input features, with outbreak and non-outbreak occurrences as the corresponding output variables. Our results reveal that the RUB model consistently demonstrated superior overall performance and stability, with an accuracy of 88.1%, which surpassed that of the other ML models. In addition, the relative importance of the feature variables followed the order RWIs > mean maximum temperature (T_max) from May to July > mean total precipitation (P_mean) in July > mean minimum temperature (T_min) in October. More importantly, the dfoliatR (an R package for detecting insect defoliation) and curve intervention detection methods were inferior to the RUB model. Our findings underscore that integrating tree-ring width and climate variables as predictors in machine learning offers a promising avenue for enhancing the accuracy of detecting forest insect outbreaks.

The reverse J-shaped diameter distribution is considered an inherent attribute of natural forests, crucial for forest resource utilization and community stability. However, in karst regions, intense habitat heterogeneity might alter species composition, spatial distribution, growth, biomass allocation, and mortality processes, yet its impact on diameter structure remains unclear. A fixed plot of 200 m × 110 m was established in the Nanpan River Basin, Southwest China, within an old-growth oak forest (> 300 years old), and the influence of site substrates (i.e., rock and soil), topographic factors, sample area, and orientation on diameter distribution was analyzed. Trees on both rock and soil exhibited a reverse-J shape, quantifiable through the Weibull function. The substrates had a similar density, approximately 2100 plants/ha. However, the average and range of diameter of trees on rock were smaller than those on soil, suggesting that rock constrains tree growth. The diameter distribution of trees across microtopography also displayed a reverse-J shape. Yet, higher elevations and sunny slopes showed a greater curvature of diameter classes compared to lower elevations and shady slopes, indicating habitat preferences in karst trees. Sample area and orientation had minimal effects on diameter class curve that reached stability when the plot size was 6000 m². These results suggest that the reverse J-shaped diameter distribution prevails at small scales in karst old-growth forests, encompassing multiple curvatures and spanning forest ecosystems.

Plant stoichiometry and nutrient allocation may reflect adaptation strategies to environmental nutrient changes. Fire, as a major disturbance in forests, mediates soil nutrient availability that may influence plant nutrient dynamics. However, plant–soil stoichiometric allocation strategies during different post-fire periods and the effects of soil, enzymes, and microbial biomass on plant stoichiometry are largely unknown. The pioneer tree species Betula platyphylla in burnt forests of northern China was the object of this study, and severely burned areas selected with different fire years. Nearby unburned areas acted as a control. Carbon (C), nitrogen (N), and phosphorus (P) contents in leaves, branches, and fine roots and rhizosphere soil, C-, N- and P-acquiring enzyme activities were examined. Microbial biomass C, N, and P were measured, and factors influencing C:N:P stoichiometry of plants during the burned area restoration were explored. Our results show that C and N contents in leaves increased with time since fire, while C and P in branches and C, N and P in fine roots decreased. Activities of C-, N-, and P-acquiring enzymes and microbial biomass N increased with time since fire. Redundancy analysis showed that changes in soil N-acquiring enzyme activity, microbial biomass C, and N had significant effects on plant ecological stoichiometry. These results show a significant flexibility in plant nutrient element allocation strategies and C:N:P stoichiometric characteristics. Soil extracellular enzyme activity drives the changes in stoichiometry during the process of post-fire restoration.

In northern China, light and temperature are major limiting factors for plant growth, particularly during seed production and seedling establishment. While previous studies suggested a possible role for the MYB97 gene in cold-stress, confirmation through documented evidence was lacking. In this study, we transformed the MYB97 gene from Iris laevigata into tobacco, and discovered that the gene boosted photosynthesis, photoprotection and resilience to cold. The transgenic tobacco seeds exhibited enhanced germination and accelerated seedling growth. Moreover, these plants had decreased levels of MDA (Malondialdehyde) and relative conductance, coupled with elevated concentrations of proline and soluble sugars. This response was accompanied by heightened activity of antioxidant enzymes during periods of cold stress (4 and − 2 °C). Exposure to low temperatures (0–15 °C) also reduced heights but accentuated primary root growth in transgenic tobacco plants. Additionally, tobacco leaves showed an increased growth along with higher chlorophyll levels, net photosynthetic rates, stomatal conductance, transpiration rates and non-photochemical quenching coefficient. This study shows that IlMYB97 (The MYB97 genes in I. laevigata) improves cold-resistance, and enhances photosynthesis and photoprotective ability, and thus overall growth and development. These findings would offer the genetic resources to further study cold resistance and photosynthesis.

Ecoregion-based height-diameter models were developed in the present study for Scots pine (Pinus sylvestris L.) stands in Turkiye and included several ecological factors derived from a pre-existing ecoregional classification system. The data were obtained from 2831 sample trees in 292 sample plots. Ten generalized height–diameter models were developed, and the best model (HD10) was selected according to statistical criteria. Then, nonlinear mixed-effects modeling was applied to the best model. The R² for the generalized height‒diameter model (Richards function) modified by Sharma and Parton is 0.951, and the final model included number of trees, dominant height, and diameter at breast height, with a random parameter associated with each ecoregion attached to the inverse of the mean basal area. The full model predictions using the nonlinear mixed-effects model and the reduced model (HD10) predictions were compared using the nonlinear sum of extra squares test, which revealed significant differences between ecoregions; ecoregion-based height–diameter models were thus found to be suitable to use. In addition, using these models in appropriate ecoregions was very important for achieving reliable predictions with low prediction errors.