Journal of Forestry Research最新文献

Abstract

The role of the temperate mixed broadleaf-Korean pine forest (BKF) in global biogeochemical cycles will depend on how the tree species community responds to climate; however, species-specific responses and vulnerabilities of common trees in BKF to extreme climates are poorly understood. Here we used dendrochronological methods to assess radial growth of seven main tree species (Pinus koraiensis, Picea jezoensis, Abies nephrolepis, Fraxinus mandshurica, Phellodendron amurense, Quercus mongolica, and Ulmus davidiana) in an old-growth BKF in response to climate changes in the Xiaoxing’an Mountains and to improve predictions of changes in the tree species composition. Temperature in most months and winter precipitation significantly negatively affected growth of P. jezoensis and A. nephrolepis, but positively impacted growth of P. koraiensis and the broadleaf species, especially F. mandshurica and U. davidiana. Precipitation and relative humidity in June significantly positively impacted the growth of most tree species. The positive effect of the temperature during the previous non-growing season (PNG) on growth of F. mandshurica and Q. mongolica strengthened significantly with rapid warming around 1981, while the impact of PNG temperature on the growth of P. jezoensis and A. nephrolepis changed from significantly negative to weakly negative or positive at this time. The negative response of radial growth of P. jezoensis and A. nephrolepis to precipitation during the growing season gradually weakened, and the negative response to PNG precipitation was enhanced. Among the studied species, P. koraiensis was the most resistant to drought, and U. davidiana recovered the best after extreme drought. Ulmus davidiana, P. jezoensis and A. nephrolepis were more resistant to extreme cold than the other species. Climate warming generally exacerbated the opposite growth patterns of conifer (decline) and broadleaf (increase) species. Deciduous broadleaf tree species in the old-growth BKF probably will gradually become dominant as warming continues. Species-specific growth-climate relationships should be considered in future models of biogeochemical cycles and in forestry management practices.

Quantifying the biomass of saplings in the regeneration component is critical for understanding biogeochemical processes of forest ecosystems. However, accurate allometric equations have yet to be developed in sufficient detail. To develop species-specific and generalized allometric equations, 154 saplings of eight Fagaceae tree species in subtropical China’s evergreen broadleaved forests were collected. Three dendrometric variables, root collar diameter (d), height (h), and crown area (ca) were applied in the model by the weighted nonlinear seemingly unrelated regression method. Using only d as an input variable, the species-specific and generalized allometric equations estimated the aboveground biomass reasonably, with ({R}_{adj}^{2}) values generally > 0.85. Adding h and/or ca improved the fitting of some biomass components to a certain extent. Generalized equations showed a relatively large coefficient of variation but comparable bias to species-specific equations. Only in the absence of species-specific equations at a given location are generalized equations for mixed species recommended. The developed regression equations can be used to accurately calculate the aboveground biomass of understory Fagaceae regeneration trees in China’s subtropical evergreen broadleaved forests.

Prediction, prevention, and control of forest fires are crucial on at all scales. Developing effective fire detection systems can aid in their control. This study proposes a novel CNN (convolutional neural network) using an attention blocks module which combines an attention module with numerous input layers to enhance the performance of neural networks. The suggested model focuses on predicting the damage affected/burned areas due to possible wildfires and evaluating the multilateral interactions between the pertinent factors. The results show the impacts of CNN using attention blocks for feature extraction and to better understand how ecosystems are affected by meteorological factors. For selected meteorological data, RMSE 12.08 and MAE 7.45 values provide higher predictive power for selecting relevant and necessary features to provide optimal performance with less operational and computational costs. These findings show that the suggested strategy is reliable and effective for planning and managing fire-prone regions as well as for predicting forest fire damage.

Episodes of drought-induced decline in tree growth and mortality are becoming more frequent as a result of climate warming and enhanced water stress in semi-arid areas. However, the ecophysiological mechanisms underlying the impact of drought on tree growth remains unresolved. In this study, earlywood and latewood tree-ring growth, δ¹³C, and δ¹⁸O chronologies of Picea mongolica from 1900 to 2013 were developed to clarify the intra- and inter-annual tree-ring growth responses to increasingly frequent droughts. The results indicate that annual basal area increment residuals (BAI_res), which removed tree age and size effects, have significantly decreased since 1960. However, the decreasing trend of earlywood BAI_res was higher than that of latewood. Climate response analysis suggests that the dominant parameters for earlywood and latewood proxies (BAI_res, δ¹³C and δ¹⁸O) were drought-related climate variables (Palmer drought severity index, temperature, relative humidity, and vapor pressure deficit). The most significant period of earlywood and latewood proxies’ responses to climate variables were focused on June–July and July–August, respectively. BAI_res, and δ¹³C were significantly affected by temperature and moisture conditions, whereas δ¹⁸O was slightly affected. Decreasing stomatal conductance due to drought outweighed the influence of increasing CO₂ on intrinsic water use efficiency (iWUE), and ultimately led to a decline in BAI_res. Compared to latewood, the faster decreasing BAI_res and smaller increasing iWUE of earlywood suggested trees were more vulnerable to water stress in the early growing season. Our study provides insights into the inter- and intra-annual mechanisms of tree-ring growth in semi-arid regions under rising CO₂ and climate change.

Disturbances such as forest fires, intense winds, and insect damage exert strong impacts on forest ecosystems by shaping their structure and growth dynamics, with contributions from climate change. Consequently, there is a need for reliable and operational methods to monitor and map these disturbances for the development of suitable management strategies. While susceptibility assessment using machine learning methods has increased, most studies have focused on a single disturbance. Moreover, there has been limited exploration of the use of “Automated Machine Learning (AutoML)” in the literature. In this study, susceptibility assessment for multiple forest disturbances (fires, insect damage, and wind damage) was conducted using the PyCaret AutoML framework in the Izmir Regional Forest Directorate (RFD) in Turkey. The AutoML framework compared 14 machine learning algorithms and ranked the best models based on AUC (area under the curve) values. The extra tree classifier (ET) algorithm was selected for modeling the susceptibility of each disturbance due to its good performance (AUC values > 0.98). The study evaluated susceptibilities for both individual and multiple disturbances, creating a total of four susceptibility maps using fifteen driving factors in the assessment. According to the results, 82.5% of forested areas in the Izmir RFD are susceptible to multiple disturbances at high and very high levels. Additionally, a potential forest disturbances map was created, revealing that 15.6% of forested areas in the Izmir RFD may experience no damage from the disturbances considered, while 54.2% could face damage from all three disturbances. The SHAP (Shapley Additive exPlanations) methodology was applied to evaluate the importance of features on prediction and the nonlinear relationship between explanatory features and susceptibility to disturbance.

Populus alba ‘Berolinensis’ is a fast-growing, high-yielding species with strong biotic and abiotic stress resistance, and widely planted for timber, shelter belts and aesthetic purposes. In this study, molecular development is explored and the important genes regulating xylem formation in P. alba ‘Berolinensis’ under artificial bending treatments was identified. Anatomical investigation indicated that tension wood (TW) was characterized by eccentric growth of xylem and was enriched in cellulose; the degree of lignification was lower than for normal wood (NW) and opposite wood (OW). RNA-Seq-based transcriptome analysis was performed using developing xylem from three wood types (TW, OW and NW). A large number of differentially expressed genes (DEGs) were screened and 4889 counted. In GO and KEGG enrichment results, genes involved in plant hormone signal transduction, phenylpropanoid biosynthesis, and cell wall and secondary cell wall biogenesis play major roles in xylem development under artificial bending. Eight expansin (PalEXP) genes were identified from the RNA-seq data; four were differentially expressed during tension wood formation. Phylogenetic analysis indicated that PalEXLB1 belongs to the EXPB subfamily and that the other PalEXPs are members of the EXPA subfamily. A transcriptional regulatory network construction showed 10 transcription factors located in the first and second layers upstream of EXP, including WRKY, ERF and bHLH. RT‒qPCR analysis in leaves, stems and roots combined with transcriptome analysis suggests that PalEXPA2, PalEXPA4 and PalEXPA15 play significant regulatory roles in cell wall formation during tension wood development. The candidate genes involved in xylem cell wall development during tension wood formation marks an important step toward identifying the molecular regulatory mechanism of xylem development and wood property improvement in P. alba ‘Berolinensis’.

Abstract

Increasing human activity is altering the structure of forests, which affects the composition of communities, including birds. However, little is known about the key forest structure variables that determine the richness of bird communities in European temperate oak forests. We, therefore, aimed to identify key variables in these habitats that could contribute to the design of management strategies for forest conservation by surveying 11 oak-dominated forest sites throughout the mid-mountain range of Hungary at 86 survey points to reveal the role of different compositional and structural variables for forest stands that influence the breeding bird assemblages in the forests at the functional group and individual species levels. Based on decision tree modelling, our results showed that the density of trees larger than 30 cm DBH was an overall important variable, indicating that large-diameter trees were essential to provide diverse bird communities. The total abundance of birds, the foliage-gleaners, primary and secondary cavity nesters, residents, and five specific bird species were related to the density of high trunk diameter trees. The abundance of shrub nesters was negatively influenced by a high density of trees over 10 cm DBH. The density of the shrub layer positively affected total bird abundance and the abundance of foliage gleaners, secondary cavity nesters and residents. Analysis of the co-dominant tree species showed that the presence of linden, beech, and hornbeam was important in influencing the abundance of various bird species, e.g., Eurasian Treecreeper (Certhia familiaris), Marsh Tit (Poecile palustris) and Wood Warbler (Phylloscopus sibilatrix). Our results indicated that large trees, high tree diversity, and dense shrub layer were essential for forest bird communities and are critical targets for protection to maintain diverse and abundant bird communities in oak-dominated forest habitats.

As one of the regions most affected by global climate warming, the Tianshan mountains has experienced several ecological crises, including retreating glaciers and water deficits. Climate warming in these mountains is considered mainly to be caused by increases in minimum temperatures and winter temperatures, while the influence of maximum temperatures is unclear. In this study, a 300-year tree-ring chronology developed from the Western Tianshan Mountains was used to reconstruct the summer (June–August) maximum temperature (T_max6–8) variations from 1718 to 2017. The reconstruction explained 53.1% of the variance in the observed T_max6–8. Over the past 300 years, the T_max6–8 reconstruction showed clear interannual and decadal variabilities. There was a significant warming trend (0.18 °C/decade) after the 1950s, which was close to the increasing rates of the minimum and mean temperatures. The increase in maximum temperature was also present over the whole Tianshan mountains and its impact on climate warming has increased. The T_max6-8 variations in the Western Tianshan mountains were influenced by frequent volcanic eruptions combined with the influence of solar activity and the summer North Atlantic Oscillation. This study reveals that climate warming is significantly influenced by the increase in maximum temperatures and clarifies possible driving mechanisms of temperature variations in the Western Tianshan mountains which should aid climate predictions.

Native grasslands in the Pampas of South America are increasingly being replaced by Eucalyptus and Pinus stands. The short rotation regimes used for the stands require high nutrient levels, with litterfall being a major source of nutrient return. To model the litterfall production using climatic variables and assess the nutrient return in 14-year-old Eucalyptus grandis and Pinus taeda stands, we measured litter production over 2 years, using conical litter traps, and monitored climatic variables. Mean temperature, accumulated precipitation, and mean maximum vapor pressure deficit at the seasonal level influenced litterfall production by E. grandis; seasonal accumulated precipitation and mean maximum temperature affected litterfall by P. taeda. The regression tree modeling based on these climatic variables had great accuracy and predictive power for E. grandis (N = 33; MAE (mean absolute error) = 0.65; RMSE (root mean square error) = 0.91; R² = 0.71) and P. taeda (N = 108; MAE = 1.50; RMSE = 1.59; R² = 0.72). The nutrient return followed a similar pattern to litterfall deposition, as well as the order of importance of macronutrients (E. grandis: Ca > N > K > Mg > P; P. taeda: N > Ca > K > Mg > P) and micronutrients (E. grandis and P. taeda: Mn > Fe > Zn > Cu) in both species. This study constitutes a first approximation of factors that affect litterfall and nutrient return in these systems.

Tree radial growth can have significantly different responses to climate change depending on the environment. To elucidate the effects of climate on radial growth and stable carbon isotope (δ¹³C) fractionation of Qinghai spruce (Picea crassifolia), a widely distributed native conifer in northwestern China in different environments, we developed chronologies for tree-ring widths and δ¹³C in trees on the southern and northern slopes of the Qilian Mountains, and analysed the relationship between these tree-ring variables and major climatic factors. Tree-ring widths were strongly influenced by climatic factors early in the growing season, and the radial growth in trees on the northern slopes was more sensitive to climate than in trees on the southern. Tree-ring δ¹³C was more sensitive to climate than radial growth. δ¹³C fractionation was mainly influenced by summer temperature and precipitation early in the growing season. Stomatal conductance more strongly limited stable carbon isotope fractionation in tree rings than photosynthetic rate did. The response between tree rings and climate in mountains gradually weakened as climate warmed. Changes in radial growth and stable carbon isotope fractionation of P. crassifolia in response to climate in the Qilian Mountains may be further complicated by continued climate change.