By investigating the characteristics and spatial patterns of plant alpha diversity in the Kangnan forestry general farm, this study aims to provide a scientific basis for differentiated ecological conservation and forestry management in this forest area.

Using data from the second national census of forest and grassland genetic resources, the importance values of the tree layer, shrub layer and herb layer, as well as the plant α diversity index were calculated through statistical aggregation. The diversity characteristics were summarized, and combined with relevant analysis and spatial autocorrelation analysis, the spatial pattern of plant diversity in the forest area was revealed.

Key plot data indicate that the tree layer consists of a community dominated by species from the Fagaceae, Betulaceae, Cornaceae, and Pinaceae families. In the shrub layer, the Bambusoideae subfamily constitutes a single-dominant community accounting for 15.6% of the total, while in the herbaceous layer, communities dominated by the genus Carex accounted for 6.4%. Regarding the alpha diversity indices of forest vegetation, the Patrick richness index, Shannon-Wiener diversity index, Simpson diversity index, and Pielou evenness index generally showed a pattern of tree layer < herbaceous layer < shrub layer, indicating the presence of highly stable and less degraded plant communities. The alpha diversity indices of the tree layer in the forest area showed a strong and significant positive correlation with precipitation and a significant positive correlation with aspect; the alpha diversity indices of the shrub layer showed a significant negative correlation with elevation; the alpha diversity indices of the herb layer showed a strong and significant negative correlation with slope and a significant negative correlation with elevation. The spatial autocorrelation of the plant α-diversity indices in the sample plots showed a confidence level of over 99% and exhibited significant positive spatial correlation, with the strength of spatial autocorrelation being highest in the tree layer, followed by the herb layer and then the shrub layer. Except for the evenness index of the shrub layer, which showed no high/low clustering, the plant α-diversity indices of the other layers all exhibited varying degrees of high-high and low-low clustering, as well as low-high and high-low clustering anomalies.

Overall, plant diversity in the forest area remains stable, but some communities are at risk of degradation; tree layer diversity is significantly influenced by precipitation and aspect, shrub layer diversity is markedly influenced by elevation, and herb layer diversity is influenced by both slope and elevation; spatial autocorrelation of plant diversity in the forest area is pronounced, with both highly stable high-high clusters and degraded low-low clusters, as well as anomalous low-high and high-low clusters. These characteristics provide important theoretical foundations and technical references for forest management across the entire region.

To explore and evaluate the nonlinear response relationship and driving mechanisms between forest landscape patterns and carbon storage in Hunan Province, and to optimize forest management and enhance regional carbon sequestration capacity.

Based on land use data from 2000 to 2020 in Hunan Province, forest landscape patterns were assessed using the Fragstats and moving window method. The Invest model was used to evaluate the spatiotemporal variation characteristics of carbon storage. On this basis, the random forest machine learning method was applied to clarify the nonlinear impact of forest landscape patterns on regional carbon storage. Finally, the XGBoost-SHAP model was employed to analyze the driving intensity of natural and socio-economic factors on carbon storage.

1) From 2000 to 2020, forest patch density in Hunan Province increased from 1.2 to 1.4, while the interspersion and juxtaposition index increased from 55.7 to 59.5. This indicates intensified forest landscape fragmentation and more pronounced spatial interspersion among different landscape types; 2) The total carbon storage in Hunan Province decreased from 2.097 billion tons to 2.086 billion tonnes, with the spatial distribution of carbon storage being extremely uneven, concentrated mainly in the southern and western regions of Hunan. The total carbon storage in the Changsha-Zhuzhou-Xiangtan urban agglomeration continued to decline; 3) Shannon's diversity index, patch density, and contagion index positive correlations with total carbon, aboveground, and soil carbon storage; 4) GDP, temperature, and population density are the main factors driving the changes in unit carbon storage, with GDP having the most significant positive driving effect (SHAP = 0.61).

This study emphasizes that optimizing forest landscape structure and increasing economic investment can sustainably enhance regional carbon storage capacity. The findings provide a scientific basis for understanding the dynamic relationship between forest landscape pattern evolution and carbon storage, as well as for forest management practices.

To explore the effects of the decomposition of Salix psammophila mechanical sand barriers on the composition of soil bacterial and fungal communities.

Salix psammophila sand barriers deployed for 1, 3, 5, and 7 years were selected as research objects, with bare sandy land as the control (CK). Field in-situ sampling, indoor indicator measurements, and Illumina MiSeq sequencing technology were employed to reveal the response relationships between bacterial and fungal diversity, soil properties, and enzyme activity during the decay process of Salix sand barriers.

1) Soil organic carbon (SOC) and soil moisture content (SMC) increased significantly with the duration of deployment (SOC increased by 71.56% after 7 years compared to 1 year, and SMC increased by 79.90%); 2) Richness (Sobs and Ace indices) of bacterial communities peaked at 3 years of deployment, while diversity (Shannon index) was highest at 7; 3) Fungal diversity and richness generally showed an increasing trend, with Talaromyces dominating at 5 years, accounting for up to 83.25%, and the Shannon index of fungal communities significantly increased by 7 years; 4) Redundancy analysis indicated that bacterial communities were driven by SOC and alkaline phosphatase (APh), while fungal communities were primarily regulated by APh, SMC, and urease (Ure).

The decomposition of Salix psammophila mechanical sand barriers significantly influenced the structural and functional succession of microbial communities by altering soil physicochemical properties and enzyme activity, providing a scientific basis for optimizing sand barrier materials and evaluating ecological functions in desertification control.

This study aims to develop an efficient tissue culture propagation system for Eucalyptus maidenii and identify high- performance genotypes, thereby establishing a theoretical and technical foundation for large-scale seedling production.

Seeds from elite E. maidenii families were used as explants. Using full factorial and orthogonal experimental designs, the optimal culture conditions were determined for primary culture, subculture proliferation, adventitious root induction, and hardening. The selected optimal proliferation and rooting media were then applied to axenic seedlings of different genotypes to screen genotypes that exhibited both high proliferation efficiency and robust rooting competence.

The optimal conditions for the primary culture and subculture of sterile stem segments of E. maidenii are MS medium supplemented with 0.5 mg/L 6-BA, 0.6 mg/L NAA, 20 g/L sucrose, and 4.5 g/L agar, resulting in a 100% axillary bud induction rate, a 10.40-fold monthly proliferation rate, a plantlet height of 1.14 cm, and robust shoot clusters. Adventitious root induction is optimally achieved on 1/2 DCR medium containing 0.6 mg/L IBA, 0.3 mg/L NAA, 1.0 mg/L ABT1, 20 g/L sucrose, 5 g/L agar, and 0.16 g/L activated charcoal, with a 86.67% rooting rate after 20 days. A 1:1:1 (v/v/v) mixture of humus soil, vermiculite, and peat soil provides the highest transplantation success, with a 76.67% survival rate. A total of 118 E. maidenii genotypes were evaluated for proliferation and rooting capacity, from which one superior genotype (G116) with both high proliferation efficiency and strong rooting ability was selected. After 60 days of culture, G116 exhibits a 11.76-fold proliferation rate, reaches 1.65 cm in height, and develops robust shoot clusters. This genotype also demonstrates a 93.33% rooting rate within 20 days of culture.

The research successfully developed an efficient in vitro propagation system for E. maidenii and selected one genotype exhibiting superior propagation traits. This methodology enhanced seedling production efficiency.

To systematically identify the MAPK gene family members based on the whole-genome data of Prunus sibirica, analyze their bioinformatics characteristics and expression patterns under low-temperature stress, and to reveal the potential functions of these family members in response to low-temperature stress.

Based on the genomic and transcriptomic data of P. sibirica, methods such as genome-wide identification, conserved motif analysis, phylogenetic tree construction, and syntenic analysis were used to determine the basic information, physicochemical properties, gene structures, phylogenetic relationships, and response mechanisms under low-temperature stress of MAPK family genes.

A total of 84 MAPK genes were identified in P. sibirica, including 11 PsMAPK, 10 PsMAPKK and 63 PsMAPKKK genes, which were widely distributed across eight chromosomes. Phylogenetic analysis classified all MAPK family members into three subfamilies: MAPK, MAPKK, and MAPKKK (including MEKK, ZIK, and Raf). Conserved motif analysis revealed that motifs within each subfamily were conserved. Promoter cis-element prediction showed that the promoter regions of 44 genes were enriched in low-temperature-responsive elements. Transcriptome data indicated that 14 MAPK genes were differentially expressed under low-temperature stress, and the expression levels of genes such as PsMAPK1 and PsMAPKKK2 were significantly upregulated with decreasing temperature.

Through comprehensive analysis of the gene structure, chromosomal distribution, and phylogenetic relationships of the MAPK gene family members in P. sibirica, the physicochemical properties and molecular characteristics of the MAPK family members were clarified. Further expression analysis indicated that 14 genes, including PsMAPK1 and PsMAPKK7, are involved in important signaling pathways responding to low-temperature stress.

To reveal the differences in growth rhythms and ecological strategies among three dominant Castanopsis species in the subtropical broadleaved secondary forests of Chongyi County, Jiangxi Province, and to provide a theoretical basis for classified management, structural regulation, and sustainable management of this region and similar subtropical secondary forests, as well as methodological references for multidimensional tree growth simulation under small-sample conditions.

Three dominant Castanopsis species-C. fargesii, C. faberi, and C. carlesii-in the subtropical secondary forests of Chongyi County were selected for analysis. Sample trees were subjected to stem analysis to obtain precise time-series data of annual radial growth. Five classic growth functions (Logistic, Gompertz, Hossfeld IV, Korf, and Richards) were applied to fit and compare the growth processes of DBH, height, and volume for each species. The Richards model, identified as the best-fitting function in most cases, was used to construct a system of nonlinear compatible growth equations. These equations were then estimated using the seemingly unrelated regression (SUR) method. Additionally, logarithmic transformation was introduced to correct heteroscedasticity in allometric growth models, thereby improving model stability. Based on the modeling results, growth rate and maturation patterns of the three species were further compared and analyzed.

Among the five tested models, the Richards function provided the best fit across multiple growth dimensions. The SUR-based compatible model revealed significant residual correlations between DBH and volume (r=0.875 1), and between DBH and height (r=0.682 0), validating the necessity of joint modeling. After logarithmic transformation, the allometric models exhibited a substantial reduction in mean systematic error (18.7% to 56.7%), while maintaining high R² values and stable fitting performance. Growth pattern analysis showed that C. faberi and C. carlesii had stronger early growth potential and faster maturation, making them suitable for timber-oriented management. In contrast, C. fargesii exhibited a slower growth rate but demonstrated strong ecological adaptability, making it ideal for deployment in ecological conservation forests.

Under limited sample conditions, this study successfully developed nonlinear compatible models for DBH, height, and volume of three dominant Castanopsis species, and effectively identified interrelationships among growth dimensions using the SUR method. The introduction of logarithmic transformation significantly improved the performance of the allometric models by reducing heteroscedasticity. Furthermore, through a comprehensive analysis of current annual increment and mean annual increment, the study further reveals significant differences among the three Castanopsis species in growth rates of diameter at breast height, tree height, and stem volume, as well as in maturation patterns. C. faberi and C. carlesii exhibit a fast-growing strategy, whereas C. fargesii shows a stable and conservative strategy, reflecting interspecific differences in resource utilization and environmental adaptation.

To clarify the patterns of provenance variation in mineral elements in the stems of Cunninghamia lanceolata, analyze their driving effects on growth and carbon sequestration function, and to provide a theoretical basis for breeding high-carbon-sink germplasm and implementing precise nutrient management.

In 2017, a provenance trial plantation of 36-year-old C. lanceolata at Dagang mountain, Jiangxi Province, was studied. Fifteen geographical provenances were selected to determine the contents of ten elements in stem wood, including carbon (C), nitrogen (N), phosphorus (P), sulfur (S), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), and zinc (Zn), and the stoichiometric ratios of C, N, and P were calculated. By integrating geographic and climatic factors from the provenance origins, as well as growth and carbon sequestration traits, analysis of variance (ANOVA), redundancy analysis (RDA), and structural equation modeling (SEM) were employed to elucidate the associations and driving pathways among these variables.

Obvious provenance variations were observed in stem growth, biomass, carbon storage, and mineral element content. Carbon storage showed the highest variation (the coefficient of variation is 50.73%), indicating substantial differences in carbon sink potential among provenances. Stem P and K contents were the core drivers of increases in DBH, volume, biomass, and carbon storage (For example, the direct path coefficient from biomass to carbon storage was 0.916, indicating an extremely strong positive effect).), while Mn content significantly inhibited biomass accumulation. Geographic and climatic factors of the provenance origin (July mean temperature, annual mean precipitation) indirectly influence growth and carbon sequestration function primarily by regulating the stoichiometric balance of C, N, and P, rather than through the absolute contents of P and K. Cluster analysis identified four superior provenances, including Shunchang (Fujian) and Zhijiang (Hunan), characterized by high P, high K, and high carbon sink potential.

Phosphorus and potassium are the core nutrient elements driving biomass accumulation and carbon sequestration enhancement. Provenance selection and plantation management should prioritize stem P and K contents and their utilization efficiency.

To investigate the applicability of unmanned aerial vehicle (UAV) LiDAR, handheld LiDAR, and their fused point cloud data for individual tree parameter extraction and aboveground biomass estimation, and to improve the accuracy of single-tree scale modeling in complex forest stands.

The study was conducted in five plots of Pinus kesiya at the Puwen experimental forest of the Yunnan Academy of Forestry and Grassland. UAV LiDAR point cloud data and handheld LiDAR point cloud data were collected separately as two independent data sources, followed by data fusion of the two datasets. The watershed algorithm was applied to segment individual trees from the UAV LiDAR point cloud, whereas a distance-based clustering algorithm was used for the handheld and fused point clouds. Then, a total of 71 variables were extracted from individual tree point clouds, including 4 structural features, 40 height-related variables, 10 density variables, and 17 voxel-based features. The Boruta algorithm and random forest feature importance ranking were used for variable selection, and key variables were then used to build random forest models for AGB estimation. The modeling accuracy of different data sources was compared.

1) The overall segmentation F-scores for UAV, handheld, and fused point cloud data were 0.90, 0.95 and 0.97, respectively; 2) The R² values of tree height estimation from UAV, handheld, and fused data were 0.93, 0.94 and 0.94, respectively, with RMSEs of 1.43 m, 1.74 m, and 1.68 m. The fused data achieved a DBH estimation accuracy of R²=0.95 and RMSE is 2.13 cm; 3) For the three types of point cloud data, 71 variables were extracted and independently selected using the Boruta algorithm and Random forest. For each dataset, eight key variables were identified separately and used for single-tree aboveground biomass modeling; 4) The estimation accuracy of individual tree above-ground biomass based on UAV point cloud data was R²=0.83, RMSE is 85.69 kg/plant and rRMSE is 44.53%; based on handheld point cloud data, R²=0.87, RMSE is 72.02 kg/plant and rRMSE is 36.34%; and based on fused point cloud data, R²=0.86, RMSE is 70.65 kg/plant and rRMSE is 36.72%.

The individual tree biomass model constructed based on the random forest method demonstrated good predictive performance across different data sources, with the fused point cloud data from UAV and handheld LiDAR showing superior accuracy and stability.

To clarify the radial growth dynamics of Abies nephrolepis across different diameter classes in the Changbai mountains, reveal the response patterns and differences in growth to climatic factors among trees of various diameter classes, and to provide a scientific basis for forest management under climate change.

Three different diameter classes of [15, 25), [25, 35) and [35, 45) cm were continuously monitored by radial growth measuring instruments. The growth season was determined by the zero growth method, and the radial growth sequence was extracted. The dynamic relationship between the radial increment of different diameter classes and climate factors was explored by sliding correlation analysis.

1) There was a significant difference in the daily radial increment sequence among [25, 35) cm, [15, 25) cm and [35, 45) cm in the growing season of 2022 (P<0.001). The total radial growth and average growth rate increased with the increase of diameter class, and the maximum growth rate of the [25, 35) cm diameter class was the fastest. The maximum growth rates of [25, 35) cm and [35, 45) cm were at the beginning of the growing season, while the maximum growth rate of [15, 25) cm was at the middle of the growing season. 2) In the growing season of the year, there was a strong dynamic consistency between the radial growth of different diameter classes of A. nephrolepis and the response of climatic factors, but there were differences in the strength of the relationship with climatic factors at different stages, and the climatic factors with significant positive and negative correlations were different. As the growing season progresses, the daily radial increment of each diameter class of A. nephrolepis is similar to the dynamic response trend of average temperature and soil temperature. The radial growth of A. nephrolepis [15, 25) cm was promoted by mean air temperature and 10 cm soil temperature, and was more inhibited by soil water content and air relative humidity. The negative correlation between radial growth and photosynthetically active radiation at [25, 35) cm was stronger. The radial growth of A. nephrolepis [35, 45) cm was more strongly inhibited by average temperature and 10 cm soil temperature, and also showed a stronger negative correlation with saturated water vapor pressure deficit, indicating that this diameter class tree was more vulnerable to temperature and drought stress, but it showed stronger adaptability and regulation ability to photosynthetically active radiation.

The radial growth of A. nephrolepis is affected by a variety of climatic factors. The physiological processes and competition of different diameter A. nephrolepis are different, which makes their radial growth dynamics and the response relationship with climatic factors different. Through the study, the response dynamics of various climatic factors and the radial growth of different diameter classes of A. nephrolepis were obtained. Differentiated conservation measures such as monitoring temperature and humidity dynamics, paying attention to the health status of diseased and weak plants, and preventing and controlling high temperature and drought stress should be taken for [15, 25), [25, 35), and [35, 45) cm Abies nephrolepis to maintain growth stability and carbon sequestration.