Objective: This study investigates the relationship and mechanisms among the digital economy development, the value realization of forest ecological products, and the urban-rural income gap. The aim is to provide a scientific foundation for promoting equitable urban-rural income through the integrated development of the digital ecological. Method: Based on panel data from 11 prefecture-level cities in Zhejiang Province between 2011 and 2020, this study employs mediation effect models and interaction effect models to investigate the impact and mechanisms of the effects of the digital economic development, value realization of forest ecological products, and their interaction terms on the urban-rural income gap. Result: 1) Both the digital economic development and the value realization of forest ecological products can significantly reduce the urban-rural income gap. 2) Results from the analysis of mediation and interaction effects indicate that the level of digital economic development can diminish the urban-rural income gap by enhancing the value realization efficiency of forest ecological products. The mediating effect of reducing the urban-rural income gap reached 11.76% of the total effect. Moreover, under their interaction, the combined digital ecological demonstrates an intensified effect in narrowing the urban-rural income gap. 3) Analysis of the working mechanism reveals that the value realization efficiency of forest ecological products contributes to narrowing the urban-rural income gap by increasing non-agricultural employment. 4) Heterogeneity analysis indicates that the three dimensions of digital economic development ( digital talent, digital industry, and digital application) effectively reduce the urban-rural income gap. However, only the digital industry dimension can reduce the urban-rural income gap by enhancing the value realization efficiency of forest ecological products. The impact of the level of digital economic development, the value realization efficiency of forest ecological products, and their interaction is greater in areas with larger urban-rural income gaps, particularly in the southwestern Zhejiang. Conclusion: During the comprehensive advancement of rural digitization, there should be continuous efforts to strengthen the deep integration between the digital economy and forestry production processes. This aims to achieve synchronous and coordinated development between the value realization efficiency of forest ecological products and the narrowing of the urban-rural income gap. The strategy involves nurturing and attracting digital talent, promoting research and application of digital technologies, and enhancing the efficiency of digital technology in supporting rural residents’ forestry production. This approach maximizes the role of digital economic development in facilitating the value realization of forest ecological products and reducing the urban-rural income gap. Implementing dynamic and diversified strategies for digital economic development, particularly in mountainous and rural areas with significant urban-rural income gap, involves strengthening their digital infrastructure. This strategy enables them to leverage the “digital dividend” to drive efficient realization of forest ecological products value and sustainable, coordinated growth in rural residents’ forestry income, acting as a subsequent force for development.

Objective: By studying the level of integrated development of digital economy and forestry industry in 11 prefectural-level cities in Zhejiang Province, its spatial distribution characteristics, components and their external influencing factors, the integration mechanism of digital economy and forestry industry could be clarified to provide scientific basis for promoting the high-quality development of forestry industry in China. Method: The evaluation index system of development level of digital economy is constructed from three dimensions of digital infrastructure, digital business scale, and digital technology innovation, and the index system of forestry industry development level is constructed from three dimensions of scale, structure, and efficiency. The panel data of 11 prefectural-level cities in Zhejiang Province from 2015 to 2021 is collected, and the entropy method, the coupled coordination model, the spatial analysis of the Moran index are applied to measure the integrated development level of the digital economy and the forestry industry, time evolution, regional differences and spatial distribution, and the panel data model is constructed to explore the main external factors affecting the development level. Result: 1) The integrated development level of digital economy and forestry industry in 11 prefectural level cities in Zhejiang Province is relatively low, and there is an annual declining trend. 2) The spatial distribution of the integrated development level in most prefectural level cities in Zhejiang Province varies significantly, and there are negative or positive correlation characteristics in neighboring prefectural level cities. 3) The number of computers per 100 households of rural residents and the number of owned mobile telephones per 100 rural residents have the strongest correlation with the integrated development level. 4) Regional per capita GDP and traffic convenience have a significant negative effect on promoting the integrated development of digital economy and forestry industry, and the digital economy index has a significant positive effect on the integrated development level. Conclusion: The integrated development level of digital economy and forestry industry in 11 prefectural level cities in Zhejiang Province from 2015 to 2021 shows a downward trend with no obvious spatial variations characteristics, and the various components of the digital economy and external factors affect the integration development level of digital economy and forestry industry in different directions and degrees. Therefore, Zhejiang Province needs to attach great importance to the integrated development of digital economy and forestry industry, and take the lead in creating a demonstration highland for high-quality development of the forestry industry empowered by digitization.

Objective: In the process of realizing a Chinese-style modernization with shared prosperity for all citizens, this study examines the impact and mechanisms of rural e-commerce development on income gap and shared prosperity in forested areas. It aims to provide valuable insights for reducing income gap within rural communities, achieving an organic unity of efficiency and equity, and promoting shared prosperity. Method: The study treats the“comprehensive demonstration policy of e-commerce into rural areas”as a quasi-natural experiment for the development of rural e-commerce and utilizes five waves of panel data from the China family panel studies (CFPS) spanning from 2012 to 2020. It employs a supervised double machine learning method to analyze the impact and mechanisms of rural e-commerce development on both the macro-level village-level intra-regional income gap among all forest farmer households within each forested village and the micro-level intra-regional income gap among individual forest farmer households within each village, which can help to answer the question of how rural e-commerce development contributes to shared prosperity. Result: 1) Rural e-commerce development significantly reduces the gini coefficient of annual income for all forest farmer households within each forested villages and the relative deprivation index among individual forest farmer households within each forested village, implying that rural e-commerce can effectively lower income gap within forested areas. These results remain robust after a series of sensitivity tests. 2) The reduction of income gap within forested areas due to rural e-commerce development is primarily achieved by substantially increasing the income of low-income forest farmer households that below the 40th percentile and below the mean income, thereby promoting shared prosperity. 3) The promotion of non-agricultural employment among forest farmer households, the enhancement of entrepreneurial activities among forest farmer households, and the increase in the value of agricultural products are the primary pathways through which rural e-commerce development reduces income gap within forested areas. 4) Rural e-commerce development has a greater income-increasing effect on forest farmer households with low skill levels, low social capital, and below the poverty line, indicating its inclusive characteristic. Conclusion: Through an empirical study with a large micro-database, the balancing effect between equity and efficiency demonstrated by rural e-commerce as an important manifestation of the digital economy in rural forest areas is verified. Under the wave of digital economy development, this study not only provides new empirical evidence of rural e-commerce in narrowing the internal income gap in forest areas, but also provides important policy insights for promoting common prosperity.

Objective: This study aims to investigate the mechanisms through which rural digital development improves forestry economic resilience, providing a scientific basis for promoting the formation of a new pattern of sustainable forestry development. Method: Drawing upon panel data from 30 provinces (autonomous regions, municipalities) in China spanning the period 2011 to 2020, we constructed quantified measures of rural digital development and an evaluation index system for forestry economic resilience. Employing various statistical models including fixed effect, mechanism testing, panel threshold effect and spatial Dubin models, we analyzed the relationship between rural digital development and forestry economic resilience. Result: 1) Rural digital development is found to positively influence forestry economic resilience by enhancing productivity in both labor and land factors. These results remains robust even after a series of robustness tests and endogenous treatments. Significant regional differences exist in the impact of rural digital development, with stronger effects observed in the eastern region of China. Moreover, the enhancing effect of rural digital development on forestry economic resilience strengthens as the resilience level increases. 2) The relationship between rural digital development and forestry economic resilience exhibits nonlinear characteristics, characterized by an initial strong“digital dividend”, followed by a“digital bottleneck”in the middle stage, and ultimately yielding increasing benefits in the later stage. 3) There is a threshold effect of rural digital development on forestry economic resilience. When the level of digital development is below the first threshold, the impact on forestry economic resilience is significantly positive. However, as the level of digital development increases beyond the first threshold, the marginal returns diminish. Significant spatial spillover effects between rural digital development and forestry economic resilience are identified, with both direct and indirect effects being positive. 4) In addition to the level of rural digital development, economic development, industrial structure, and precipitation also wield significant positive effects on forestry economic resilience. Conclusion: Rural digital development stands as a potent means to effectively enhance forestry economic resilience. Accordingly, we advocate for leveraging digital technology to optimize labor and land resource allocation, fostering robust growth in the rural digital economy, and enhancing forestry economic resilience through phased, dynamic, and differentiated strategies. Concurrently, efforts to fortify economic support, streamline industrial structures, and leverage natural endowments should be intensified to further bolster forestry economic resilience.

Objective: Under the background of accelerating the promotion of the deep integration of the digital economy and the real economy to empower high-quality development of the modern forestry industry, studying the influence mechanism of digital rural construction on the development and upgrading of forestry industry can provide a scientific basis for formulating policies and strategies for high-quality development of forestry by digital empowerment in areas rich in forest resources. Method: Based on the panel data of 26 mountainous counties in Zhejiang Province from 2001 to 2021, we built the evaluation index system of county digital rural construction level to measure the level of digital rural construction, and the two-way fixed effect model, intermediary effect model, threshold effect model, and spatial Durbin model were used to quantitatively analyze the impact of digital rural construction on the development and upgrading of forestry industry and its mechanism. Result: 1) From 2001 to 2021, the level of digital rural construction in 26 mountainous counties in Zhejiang Province showed an increasing trend, among which the level of digital rural construction in cross-development counties was higher than that in ecological development counties. From 2001 to 2021, the scale of forestry industry development in 26 mountainous counties of Zhejiang Province continued to expand, but there were obvious regional differences and serious polarization. From 2001 to 2021, the rationalization of forestry industry structure in 26 mountainous counties of Zhejiang Province increased slightly, and the degree of advanced forestry industry structure volatility rose. 2) Digital rural construction can promote the development and upgrading of forestry industry, which means digital rural construction has a significant positive impact on the development scale of forestry industry, rationalization of forestry industry structure, and advanced forestry industry. There is a threshold effect on the impact of digital rural construction on the rationalization of forestry industry structure and the advanced forestry industry structure. 3) Digital rural construction can promote the development and upgrading of the forestry industry through technology empowerment, labor empowerment, and governance empowerment. 4) There is a positive spatial spillover effect of digital rural construction on the development and upgrading of forestry industry. The digital rural construction can significantly promote the development scale of forestry industry, rationalization of local forestry industry structure, and advanced forestry industry structure, at the same time, it has a significant positive impact on the development scale, rationalization of forestry industry structure and advanced forestry industry structure in neighboring areas. Conclusion: From 2001 to 2021, the level of digital rural construction in 26 mountainous counties of Zhejiang Province continued to improve, the development and upgrading of forestry industry were improving, and factors such as technology application level, human capital level, and value realization efficiency of forest ecological products affected the digital rural construction to promote the development of forestry industry to varying degrees. Therefore, all county units need to accelerate the digital rural construction and make efforts in terms of the application of innovative technologies, vigorously introducing talents, improving the ecological governance capabilities of the forestry sector and the operation and management level of forestry enterprises, and cultivating leading forestry enterprises and giving play to the role of radiation and driving force, adapt to local conditions and carry out categorized policies. Thus, it will promote the development and upgrading of forestry industry in mountainous counties, and create a demonstration model for the construction of high-quality development of forestry with digital empowerment.

Objective: This study aims to lay a foundation for further understanding the process of subtropical vegetation reconstruction and restoration by analyzing the population dynamics and driving mechanisms of Pinus massoniana, a typical pioneer species in subtropical ecological restoration in China, in coniferous and broad-leaved mixed forests with different mixing ratios. Method: Based on the survey data of P. massoniana population in four coniferous and broad-leaved mixed forests with different mixing ratios in an ecological restoration area of Changting County, Fujian Province, an integral projection model (IPM) was constructed. Elastic analysis and life table response experiment (LTRE) were used to explore the population dynamics and driving mechanisms of P. massoniana in the mixed forests. Result: The overall performance of the P. massoniana populations in four coniferous and broad-leaved mixed forests was in a continuous decline state (population growth rate λ<1). However, with the increase of the relative abundance of broad-leaved tree species in the stand, the average survival rate, average reproduction rate and λ of P. massoniana individuals increased first and then decreased. Elastic analysis showed that the survival of individuals was the most important for the growth of P. massoniana population in the mixed forest. The positive growth of small-diameter individuals was beneficial to the development of the population, while the effect of reproduction on population growth was relatively limited. With the increase of the relative abundance of broad-leaved tree species, the most important individuals for the growth of P. massoniana population gradually changed from seedlings to adult trees, and the risk of population maintenance gradually increased. Life table response experiments showed that the difference in individual growth was the main source of the difference in population growth of P. massoniana among different mixed forests. Conclusion: With the increase in the proportion of mixed broad-leaved trees, the proportion and importance of seedlings and young trees in P. massoniana population continue to decrease, while the proportion and importance of adult trees continue to increase. The differential response of individual life rate at different life history stages drives changes of P. massoniana population dynamics.

Objective: This study aims to investigate the structural differences of the leaves of the main Bambusa and Phyllostachys species in Nanjing, to understand the three-dimensional images of the internal anatomical structures of the leaves of those bamboo plants, and construct the three-dimensional morphological structure maps of the leaves of 52 bamboo species. Method: Six species in Bambusa and 46 species in Phyllostachys collected from Nanjing area were used as test materials, and three-dimensional sections of the leaves were prepared by paraffin section method. The anatomical structures of each section of the leaves of 52 bamboo species were observed and measured by an optical microscope imaging system, and the three-dimensional structure diagram of the leaves of 52 bamboo species were reconstructed by Photoshop. Result: 1) The results showed that the thickness of leaf cuticle and the number of lower epidermal papillae were the markers to distinguish Bambusa from Phyllostachys. The leaf thickness and cuticle thickness of Phyllostachys were significantly greater than those of Bambusa, and the number of lower epidermal papillae was significantly greater than that of Bambusa. 2) Usually, 2 to 6 vesicular cells made up a group, and the morphology and number of vesicular cells were not fixed between species, and there were obvious differences in the number of vesicular cells in a group between genera. The vesicular cells in each group in the genus Bambusa were arranged in a fan-shaped (obtuse) pattern, while those in the genus Phyllostachys were arranged at a right angle or an acute angle pattern. 3) Each vascular system consisted of main vein, parallel lateral vein, secondary parallel lateral vein and minor transverse vein. The gap of spindle cells on both sides of the bundle of Bambusa was developed, while the spindle cells and gaps on both sides of the bundle of Phyllostachys were generally undeveloped and discontinuous. 4) There were 3–4 mesophyll cell layers between the parallel lateral veins of Bambusa, while there were 4–6 mesophyll cell layers between the parallel lateral veins of Phyllostachys. The mesophyll cells of Phyllostachys were more compact than those of Bambusa. Conclusion: The leaf thickness and cuticle thickness of Phyllostachys are greater than those of Bambusa, and the finger-like arm cells and plum cells of Phyllostachys are denser than those of Bambusa, while the cellular space of Phyllostachys is smaller than that of Bambusa, and the number of papillae in the lower epidermis of Phyllostachys is obviously greater than that of Bambusa. Therefore, the cold resistance of Phyllostachys may be stronger than that of Bambusa. This study is an important supplement to establish and improve the observation method of bamboo leaf structure, and provides a basis and reference for the classification of bamboo with the characteristics of plant anatomy and morphology.

Objective: The Central and Eastern European Countries (CEEC) which are the Belt and Road initiative are important forces in Europe and link Asia and Europe. Forest resources monitoring helps understand the forest development in CEEC, and is the basis for forestry cooperation between China and CEEC. Method: Taking CEEC as the study area, the cloudless images of CEEC in the growing season in 2020 are obtained based on the Google Earth Engine platform. The images are classified by random forest algorithm. The forest change information is obtained by the continue change detection and classification (CCDC) algorithm, and updated classification results by this information. Finally, the land cover results of CEEC are obtained. The results are evaluated and verified through the land use/cover area frame survey (LUCAS) data, visual interpretation data, and Food and Agricultural Organization of the United Nations statistical data (FAOSTAT). Finally, the mapping results are analyzed through Google high-resolution images. Result: Results show that our map obtained the users’ accuracy for forest type is 0.930 and 0.911 which validated by the LUCAS and visual interpretation data, the producer’s accuracy for forest type is 0.860 and 0.956, respectively, overall accuracy is 0.810 and 0.881, respectively. The accuracy of our map is better than the GlobeLand30 2020 products, in which the user’s accuracy for forest type is 0.920 and 0.900, while the producer’s accuracy for forest type is 0.690 and 0.840, respectively, and the overall accuracy is 0.700 and 0.832. According to the research results, the overall forest coverage rate of CEEC is 39.6%, this result is closer to the FAOSTAT result (40.0%) than the GlobeLand30 2020 result (34.4%). The final mapping results are richer in detail than the GlobeLand30 2020 product and can more accurately reflect the distribution characteristics of forests. In 2020, the forest cover of CEEC is rich in the north, southeast, and southwest, and less in the south. Estonia, Latvia, Slovenia, Montenegro, and other countries have rich forest distribution, while Poland, Hungary, and other countries have relatively rare forest distribution. Conclusion: This study has developed a forest cover mapping method based on change detection and update according to the application requirements of forest cover in CEEC and obtain a 2020 LC map. The product has high precision and provides a new reference for regional forest cover mapping research. This result is helpful in understanding the forest cover situation in CEEC.

Objective: The aim of this study was to study develop the rapid, accurate and macroscopic methods for obtaining effective leaf area index (LAI_e) of different forest types, and to explore their spatial distribution, so as to provide new ideas for the development of medium and small-scale forest LAI_e remote sensing products, and offer scientific and reliable technological means for precision forestry monitoring and simulation of forest ecosystem carbon and water cycles. Method: Using Changbai Mountain as the research area, this study extracts the spatial distribution of four coniferous forest types (Larix olgensis, Pinus sylvestris var. mongolica, Pinus koraiensis and Picea koraiensis ) based on Sentinel-2A multispectral images through a three-dimensional convolutional neural network. It adopts 2 schemes, differentiated forest types and full sample, to analyze the correlation between field-measured LAI_e and 7 vegetation indices [enhanced vegetation index (EVI), inverted red-edge chlorophyll index (IRECI), modified simple ratio (MSR), normalized difference water index (NDWI), normalized difference vegetation index(NDVI), soil adjusted vegetation index (SAVI), simple ratio (SR)]. The optimal vegetation index corresponding to each tree species was used to construct regression models for LAI_e and vegetation index of differentiated forest type and full sample, and the accuracy performance of differentiated forest type model, full sample model and PROSAIL model in LAI_e inversion was compared based on validation sample data. Subsequently, spatial pattern and change rule of LAI_e of 4 tree species were analyzed by combining geographical factors. Result: 7 vegetation indices in all sample groups were significantly correlated with relative LAI_e values (P<0.01), except for EVI and LAI_e of Pinus koraiensis , SR and LAI of Picea koraiensis, the correlation coefficients were all greater than 0.6, while the correlation between LAI_e and different vegetation indexes was significantly different among groups. LAI_e of Pinus koraiensis, Larix olgensis and Pinus sylvestris var. mongholica had the highest correlation with IRECI, while LAI_e of Picea koraiensis and Pinus sylvestris var. mongholica had the highest correlation with EVI and MSR, respectively. Compared with the full-sample model, the R² value of the 4 different forest types model increased by more than 12.7%, and RMSE decreased by 34.5%. The LAI_e of the 4 forest types in the study area ranged from 0.37 to 5.86, and the average LAI_e from high to low was Pinus koraiensis, Larix olgensis, Pinus sylvestris var. mongholica. and Picea koraiensis. Pinus koraiensis was the most sensitive to changes in altitude, slope and aspect, followed by Picea koraiensis and Pinus sylvestris var. mongholica, while Larix olgensis was the least affected. Conclusion: There were significant differences in the correlation between LAI_e and remote sensing vegetation index among different forest types. The inversion accuracy of LAI_e can be improved by constructing specific regression models for different forest types. The accuracy of the fitted linear model after distinguishing forest types was higher than that of the PROSAIL model and the full-sample model, but in the area with high LAI_e value performed unstable compared with the PROSAIL model. LAI_e of the 4 tree species varied greatly in reflecting the changes of geographical factors. This study can provide a scientific reference for the selection of LAI remote sensing inversion models for coniferous forests in small and medium scales under tree species differentiation.

Objective: This work aims to explore the traits and influence factors of soil nirK-type denitrifying microbial community in different forests in Songshan, Beijing, providing novel insights into the nitrogen cycling process and environmental change of forest soil ecosystem in the warm temperate forest soil ecosystem of North China. Method: Three typical forest types of Pinus tabuliformis, Populus davidiana and Quercus mongolica were selected in the Songshan Nature Reserve of Beijing. Three standard sample plots (20 m×20 m) were set up in each forest type for investigation. The 0?20 cm surface mixed soil samples were collected to analyze soil properties, and extract total soil microbial DNA, PCR was used to amplify nirK gene coding the key enzyme, nitrate reductase, in the denitrification process. The second generation high-throughput sequencing was used to analyze the composition and diversity of soil denitrifying microbial communities in the different forest types, and to explore the effects of soil properties on the characteristics of denitrifying microbial communities. Pearson’s correlation analysis and redundancy analysis (RDA) were used to examine the soil properties factors influencing the trait of denitrifying microbial community. Result: 1) A total of 993 401 effective sequences with 770 328 high-quality sequences of nirK gene were obtained by high-throughput sequencing from 9 soil samples in P. tabuliformis forest, P. davidiana forest and Q. mongolica forest. There were 7 phyla and 78 genera of nirK gene detected in the soil of three different forest types. Among the identified microorganisms, the dominant bacteria of soil nirK denitrification microorganisms in the three forests were Proteobacteria, with the relative abundance of 50% in the three forest types, and the highest of 58.2% in the P. tabuliformis forest. The dominant genera were Bradyrhizobium, Mesorhizobium and Rhodopseudomonas, with a total relative abundance of more than 50%, among which the core genus was Bradyrhizobium. 2) The analysis of alpha diversity of nirK denitrifying microorganisms showed that the Shannon, Simpson and Chao1 diversity index of nirK-type denitrifying microorganisms in P. tabuliformis forest (7.59±0.56, 0.98±0.01, 2 164.24±214.08) were significantly higher than those in P. davidiana forest (5.23±0.26, 0.89±0.02, 1 650.56±136.69) and Q. mongolica forest (5.76±0.38, 0.93±0.02, 1 621.36±156.70) (P<0.05). Based on Bray-Curtis distance algorithm and PCoA analysis, the difference among soil denitrifying microbial communities in different forest types was greater than that within the community. 3) Redundancy analysis showed that soil alkali hydrolyzable nitrogen, nitrate nitrogen and soil organic matter were the key factors that significantly affected the composition of the nirK-type denitrifying microbial community (P<0.05). Conclusion: There are obvious differences in the composition and diversity of soil nirK-type denitrifying microbial communities among P. tabuliformis forest, P. davidiana forest and Q. mongolica forest in the warm temperate zone of North China. Forest types and soil properties such as alkali hydrolyzable nitrogen, nitrate nitrogen and soil organic matter are important factors affecting the traits of soil nirK-type denitrifying microbial communities.

Objective: This study isolated and identified the pathogen causing canker disease of Toona sinensis in Zhejiang Province, to reveal the etiology of the disease and provide a theoretical basis for controlling Chinese toon canker disease. Method: The symptoms of T. sinensis canker disease under natural conditions were observed. The conventional tissue isolation approach was used to isolate the causal pathogen from infected tissues. The morphological traits and molecular methods were used to identify the pathogen species. The pathogenicity of the isolates was verified according to Koch’s postulates. Result: 1) Two strains of pathogenic fungi were isolated from the infected tissues of T. sinensis, XCKY1 and XCKY2. The culture and morphological characteristics of the teleomorph and anamorph of the pathogen were consistent with those of Nectria pseudotrichia. 2) The LSU rDNA, ITS rDNA, and tef1-α gene fragments of the XCKY2 strain were all 100.00% homologous to those of N. pseudotrichia. The XCKY1 and XCKY2 strains clustered with 11 N. pseudotrichia strains with a Bayesian posterior probability of 0.94. 3) The pathogenicity test result showed that both fungal agar plugs and conidial suspensions of N. pseudotrichia caused necrotic lesions on healthy T. sinensis seedlings, and N. pseudotrichia was re-isolated from the necrotic tissue. Thus, it was confirmed that N. pseudotrichia is the causal agent of T. sinensis canker disease. Conclusion: Two strains of N. pseudotrichia isolated from infected tissues of T. sinensis in Zhejiang Province have been identified as the pathogen causing Chinese toon canker disease. This is the first report of N. pseudotrichia causing canker disease in T. sinensis.

Objective: The moisture content of the surface fine dead combustibles (SFDC, including dead leaves, thin branches, dead grass, needles, etc.) significantly influences forest fire ignition and behavior. Understanding of the SFDC is essential for early warning of forest fire in a region. This study focuses on predicting SFDC in Masson pine forests in southern Jiangxi Province. Method: We conducted long-term field observations of SFDC in Masson pine, a prevalent vegetation type in southern Jiangxi. The study involved a comparative analysis of various predictive models, considering meteorological factors' random forest relative importance and Pearson correlation in different terrains and times. Result: SFDC in Masson pine forests shows a notable variability, with higher moisture content on shady slopes compared to sunny slopes, especially at the early time of fire prevention periods. A strong correlation (P<0.001) exists between SFDC and meteorological factors (temperature, humidity, wind speed, sunlight). The random forest model outperformed the meteorological factor regression model in accuracy, particularly on shady slopes. Sunlight, with a lag effect, and air humidity on sunny slopes and wind speed on shady slopes were the most influential factors. Conclusion: Meteorological factors with time lag critically affect SFDC in Masson pine forests. Improved consideration of these factors enhances the prediction accuracy of the moisture content of the SFDC, offering a reliable basis for early warning of fire risk.

Objective: Based on quantitative wood anatomy (QWA) method, the wood anatomical features of three Swietenia species were analyzed to reveal the patterns of their interspecific and intraspecific variation, and provide a scientific basis for accurate wood identification at the level of “species”. Method: The microscopic images of S. macrophylla, S. mahagoni and S. humilis were collected from transverse, radial and tangential section respectively using a microscope, and the quantitative data of seven anatomical features of wood, i.e. the vessel element length (VEL), tangential diameter of vessel lumina (TVD), vessels per square milimeter (FOV), fiber length (FL), ray width (RW), ray height (RH) and rays per millimeter (RPMM) were measured by image analysis software to investigate the interspecific and intraspecific variation of wood anatomical features. Additionally, the random forest algorithm was used to discriminate three Swietenia species at the species level, and the contributions of different quantitative anatomical features to wood identification were comparatively analyzed. Result: The wood anatomical features of the three Swietenia species were highly closed in vessel, axial parenchyma and wood rays under microscope, therefore it is difficult to distinguish them artificially. However there were significant difference among three Swietenia species in six quantitative wood anatomical features except for RW. The random forests algorithm has a discrimination accuracy of 86.67% for Swietenia mahagoni. VEL showed the highest value of the mean decrease accuracy (3.956) and the mean decrease Gini (6.311), followed by the TVD. RW exhibited the lowlest value of the mean decrease accuracy (0.797) and the mean decrease Gini (2.175). Among the seven wood anatomical features, VEL exhibit the most contribution to the wood identification accuracy, while the RW had the least contribution. Conclusion: This study reveals the interspecific and intraspecific variation of three selected Swietenia species and the key quantitative anatomical features in wood identification based on quantitative wood anatomy, which provided a scientific basis of the accurate wood identification at “species” level.

Objective: By constructing the Yangtze River Delta urban forest construction level index, we reveal the spatial correlation of urban forest construction in the Yangtze River Delta region and the evolutionary characteristics of its spatial correlation, break through the previous barriers of inter-regional elements being independent of each other, and analyze the direct and spillover effects of each influencing factor on the long-term and short-term urban forest construction in the Yangtze River Delta in a spatial perspective. To provide reference and decision-making basis for the construction of differentiated urban forests, the integrated construction of the Yangtze River Delta forest city cluster and the integrated ecological green development of the Yangtze River Delta. Method: The entropy-weighted TOPSIS method is used to construct a comprehensive urban forest construction index, which incorporates the carbon sequestration benefits of urban vegetation and measures the urban forest construction level of 41 cities in the Yangtze River Delta from 2006 to 2019, and the dynamic spatial Durbin model is used to analyze the spatial spillover effects of urban forest construction and the short- and long-term indirect and direct effects of factors such as urban economic and social development and the creation of forest cities on the urban forest construction level. Result: 1) The spatial weight matrix based on the inverse of spatial distance is consistent with the extensive spatial relationship of urban forest construction in the Yangtze River Delta, and the global Moran index of urban forest construction in the Yangtze River Delta shows a fluctuating upward trend above 0.244 3. 2) Urban forest construction in 41 cities in the Yangtze River Delta exhibits stable spatially blocky cluster heterogeneity in space. “High-high”(H-H) value clusters, all concentrated in Shanghai, Jiangsu and Zhejiang, forming a development trend of coastal to inland diffusion; the“low-low”(L-L) value clusters are mostly concentrated in Anhui Province, and show a trend of diffusion first and then contraction. 3) The spillover effect of urban forest construction in the Yangtze River Delta is around 0.172, and the direct effects of per capita GDP on the construction level of urban forest in the short and long term are ?0.014 2 and 0.091 4, respectively. The direct and spillover effects of creating forest cities are significantly positive in the short term, but negative in the long term. There are differences in the short-term decrease and long-term increase of the effects of population density, tourism, and tertiary industry share on the construction level of urban forest. Conclusion: There is a significant spatial correlation in the construction of urban forests in the Yangtze River Delta region, economic development puts pressure on the level of urban forest construction in the Yangtze River Delta region in the short term, while economic development drives the development of urban forests in the long term. The increase in population density and tourism and the share of tertiary sector in the long term may force managers to focus on ecological conservation in the hope of obtaining greater ecological benefits. The increase in road area per capita improves the level of forest construction in the city in the short term and radiates to neighboring cities, but in the long term it limits the space for further development of urban forests; after the success of creating a forest city for a period of time managers may further improve the level of urban forest construction and have a positive impact on the forest construction in surrounding cities, but lacks sustainability in the long term.

Phosphorus (P) is one of the key elements that affecting plant growth, and below-ground foraging plasticity of fine roots is an important way for plants to improve the absorption of soil P. Under natural environmental conditions, heterogeneous or patchy distributions of soil nutrients have been well documented, especially for relatively immobile nutrients such as P. It is unclear how plants adjust the plastic responses of different below-ground foraging traits to nutrient patches, and the prediction of plastic responses of specific fine root traits to below-ground foraging traits is more uncertain. In this paper, the influencing factors of plant below-ground foraging were summarized, and the plastic responses of fine roots (morphology, architecture, proliferation, chemistry, and physiology) and mycorrhizal fungi to P-rich patches were expounded. The differences of below-ground foraging traits and plastic responses between arbuscular mycorrhizal and ectomycorrhizal tree species were analyzed from the aspects of fungal colonization mode, root morphology structure and nutrient acquisition strategies. Based on the carbon cost hypothesis of below-ground foraging, it was considered that morphological plasticity and physiological plasticity of fine roots were the results of resource competition in many cases. The proliferation plasticity of fungal mycelia seemed to be more economically significant for plants, but when both root and mycorrhizal fungi were present in nutrient patches, root proliferation was more responsive than fungal response. The prediction of nutrient foraging plasticity by fine root traits was also discussed, and it was pointed out that fine root diameter was a good predictor of changes in below-ground foraging traits. Finally, focusing on the shortcomings of the current research on the below-ground foraging plasticity of plants, the future research directions were proposed from the aspects of below-ground foraging foraging plasticity framework, below-ground foraging mechanism, prediction of fine root traits on the plasticity of plant nutrient acquisition, and the relationship between fine root foraging plasticity and defense plasticity, which would be helpful to understand the strategies of plant below-ground P acquisition and its adaptation mechanisms to environmental changes.