To accurately predict the growth dynamics of Chinese fir and construct a dominant tree diameter at breast height (DBH) growth model that integrates site and climatic conditions, and to provide guidance for sustainable management and carbon sequestration assessment of Chinese fir plantations.

Utilizing 1 121 DBH-age datasets from 129 Chinese fir plantation plots, topographic, edaphic and climatic factors were incorporated. Significant environmental drivers affecting DBH growth were identified through Quantification Theory I. The optimal theoretical growth equation was selected from six candidate models. Site types and climate types were clustered into site type groups and climate type groups using the k-means algorithm. Nonlinear mixed-effects regression models were developed by incorporating site type, climate type, and their clustered groups as random effects, either individually or in combination.

Seven leading factors influencing DBH growth of dominant trees, including altitude, aspect, Climatic moisture deficit and extreme minimum temperature, were screened. Among the six basic models, the Korf model had the best fitting effect, with a determination coefficient R² of 0.782 0, 75 site types and 173 climate types clustered into 5 site type groups and 7 climate type groups, respectively, and the random effect model R² with site factor groups and climate factor groups only was 0.822 9 and 0.839 1, respectively, and the random effect model with site and climate factors was R²= 0.854 3 which was significantly better than the other models.

The nonlinear mixed-effects model developed in this study, which integrates site- and climate-effects, comprehensively incorporates environmental factors influencing the DBH growth of dominant Chinese fir trees. This model not only provides scientific guidance for predicting the DBH growth of dominant Chinese fir trees, but also establishes a generalizable modeling framework for analyzing and forecasting environmental impacts on the growth of subtropical Chinese fir plantations.

Cunninghamia lanceolata is an important plantation tree species in subtropical China, and the decline of stand productivity caused by its intensive management mode needs to be solved urgently. The objectives of this study were to explore the effect of Cunninghamia lanceolata crown structure on standing tree volume under different stand densities, so as to provide a theoretical basis for increasing the yield of Cunninghamia lanceolata plantation forests.

123 Cunninghamia lanceolata plantations in six provinces and autonomous regions (Yunnan, Guizhou, Guangxi, Hunan, Jiangxi and Fujian) were selected as the research objects. Firstly, Spearman's correlation analysis was used to explore the relationship between crown structural parameters, standing density and standing tree volume. The density was divided into three grades (0-1 200 trees/hm², 1 201-2 400 trees/hm², 2 401-3 600 trees/hm²), and then the relationship between crown characteristic parameters and standing tree volume under different densities was studied by difference analysis. Finally, subgroup analysis was used to analyze the interaction between density and crown structural parameters on standing tree volume.

The results showed that the crown volume, crown width, crown length, crown shape ratio and standing volume showed a very significant positive correlation (P<0.01), with the correlations ranging from strongest to weakest: crown volume > crown length > crown width > crown shape radio > crown radio; while the density showed a very significant negative correlation with the crown structure and standing tree volume; At different density levels, there were significant differences in standing volume, crown volume, crown width, crown length and crown shape radio (P<0.01); The interaction between density and crown volume, crown length, crown ratio, has a significant positive impact on standing tree volume (P<0.05).

Stand density has a significant negative impact on the standing volume and crown structure, and the accumulation of standing volume is affected by the interaction between density and crown structure, which provides scientific guidance for the adjustment of crown structure of Cunninghamia lanceolata plantations under different densities.

To study the effects of competition and climatic factors on the growth of subtropical Chinese fir plantation, and construct a mixed-effect model with competition and climatic effects, so as to provide reference for the harvesting and management of subtropical Chinese fir plantation.

103 pieces of Chinese fir plantation in 6 subtropical provinces of China were used as research objects, and the random forest method was used to screen the competition and climatic factors with significant effects; the screened factors were graded and combined into competition and climatic types according to the standard, and then clustered into the competition and climate groups by K-means clustering; the optimal base model was screened out from five base models; the significance factors, significance factor, competitive type group and climatic type group were added as random effects to each parameter of the optimal base model to construct a mixed-effects model.

1) The competition factors screened for significant effects on stand break area were the ratio of the sum of the diameter at breast height of other trees not equal to the DBH of the target tree to the DBH of the target tree (CI1) and the Alemdag competition index (CI6); and he climatic factors are Hargreaves Climate Moisture Deficit (CMD), Mean Warmest Monthly Temperature (MWMT), Mean Summer Temperature (Tave_sm), Mean Minimum Summer Temperature (Tmin_sm); 2) The optimal base model obtained by screening was the Schumaker model M1 (R²=0.938 2); 3) The evaluation results of the constructed mixed-effects model showed that the coefficients of determination (R²) of the model increased to 0.954 3 and 0.955 8 when the significant competition and climatic factors were considered as random effects, and the coefficients of determination (R²) of the mixed-effects model further increased to 0.961 3 and 0.967 1 when competition and climate groups were considered as random effects. Considering that competition and climate have a common effect on stand break area, the coefficient of determination (R²) reaches 0.979 9 when the competition and climate groups are used as random effects.

Competition and climate have a significant effect on the growth of stand breaks, and the subtropical Chinese fir plantation stand break area model with competition and climatic factors has a better fitting effect and prediction accuracy, and provides a theoretical basis for the management of stand growth and management of subtropical Chinese fir plantation. It provides a theoretical basis for the growth and management of subtropical Chinese fir plantation.

A mixed-effect model of mean tree height was constructed with site and climate factors, provide an effective method for scientific management decision-making and site quality evaluation of fir plantations.

The average tree height and age of 406 groups of Chinese fir plantations in 95 Chinese fir plantation plots were used as the study subjects, using quantitative method I to screen the site factors and climatic factors that significantly affect the average tree height growth, the k-means clustering method was used to cluster into climate type group and site type group according to the standard. Based on the classification and screening results of climate factors and site factors, six commonly used tree height growth models were selected. The optimal combination after clustering was added to the optimal basic model as a random effect, the mixed effect method was used to explore the effects of climatic factors and site factors on the average tree height growth model.

The significance order of site factors was slope aspect > soil type > altitude > slope position, the significance order of climate factors was the average temperature differences > average annual temperature > annual average precipitation, the optimalbasic model is Schumark, and R² is 0.699 9, the tree height growth equations R² of Cunninghamia lanceolata plantations with climatic factors and site factors were 0.823 0 and 0.934 7, respectively; the tree height growth equation R² of Cunninghamia lanceolata plantation with site and climate factors was 0.936 9, which was 33.86% higher than that of the basic model. The results indicated that the average tree height model of Chinese fir plantation with climate and site factors had good fitting effect and prediction accuracy.

Therefore, the tree height growth model constructed provides reasonable support for regional site quality evaluation and for considering climate and site factors in the tree height growth model. At the same time, it is also of great significance for the prediction and management of forest stands.

To investigate the response of Cunninghamia lanceolata radial growth to climate change and provide scientific basis for the management of Cunninghamia lanceolata plantations.

The study was conducted on Cunninghamia lanceolata plantations in four locations (Chengbu County, Hunan Province; Liping County, Guizhou Province; Maguan County, Yunnan Province; and Nandan County, Guangxi Province). Tree trunk analysis was used to obtain data from breast height disks. A tree-ring width standard chronology was established, and its correlation with climatic factors was analyzed. The Mann-Kendall test was used to detect climate abrupt changes. The relationship between radial growth and temperature and precipitation before and after the temperature abrupt changes was analyzed and verified.

1) The statistical parameters of the tree-ring width standard chronologies of Cunninghamia lanceolata from the four study locations contained rich environmental information, making them suitable for climate correlation analysis; 2) The radial growth of Cunninghamia lanceolata in all four study locations was closely related to the hydrothermal conditions during the growing season (March to October), but exhibited a significant “lag effect”; 3) Temperature abrupt changes were observed in all four study locations. Before the temperature abrupt changes, the chronologies of all study locations showed a significant positive correlation with temperature, indicating that temperature promoted the radial growth of Cunninghamia lanceolata. Summer precipitation was positively correlated with the chronologies, whereas winter precipitation was negatively correlated. After the temperature abrupt changes, Cunninghamia lanceolata growth was limited by temperature, with the temperature's influence shifting from a positive to a negative correlation over several months, and the response to precipitation also changed to a significant positive correlation.

The radial growth of Cunninghamia lanceolata is influenced by both temperature and precipitation. With the intensification of global climate warming in the future, the limitation of temperature on the radial growth of Cunninghamia lanceolata will further increase, thereby affecting the productivity and carbon sequestration capacity of Cunninghamia lanceolata forests.

The spatial structure of stands is one of the important factors affecting the species diversity of saplings. Studying the influence of stand spatial structure on the species diversity of undergrowth saplings can provide a theoretical basis for improving the stability of ecosystem structure and reasonable forest management.

In this study, 49 typical 20 m×30 m plots of oak natural secondary forest were set up and investigated in Qingyanghu forest farm in Ningxiang County, Wugaishan forest farm in Chenzhou City, Lutou forest farm in Pingjiang County, Longhushan forest farm in Yiyang City and Badagongshan nature reserve in Sangzhi County. The stand spatial structure was described by the mingling index, crown index, opening degree index and aggregation index, and the species diversity of saplings was described by the Margalef index, Auclair & Goff index and Pielou's index. Pearson's correlation analysis, step-wise multiple linear regression models and canonical correlation analysis (CCA) were used to explore the influence of spatial structure of tree layers on the species diversity of saplings under the forest.

1) Pearson's correlation analysis showed that the influence of mingling degree on the Margalef's evenness index was extremely significant (P < 0.01). The aggregation index had a significant effect(P < 0.05), and mingling degree and the crown index had an extremely significant effect (P < 0.01) on the Pielou's richness index. The crown index had a significant influence (P < 0.05), and the mingling degree had an extremely significant influence (P < 0.01) on the Auclair & Goff's diversity index; 2) Step-wise multiple linear regression models showed that the dominant factors affecting the three species diversity indices were the mingling degree index and crown index; 3) The results of canonical correlation analysis showed that the number of canonical correlation variables in the first group was 0.647 1 (P < 0.01), indicating that the overall correlation between stand spatial structure and species diversity of saplings was extremely significant. The results of canonical load analysis indicated that mingling degree and the crown index were the key factors for the species diversity of saplings. 4) According to the analysis results of the three statistical methods above, mingling degree and the crown index were the main driving factors affecting the species diversity of saplings in the natural secondary oak forests in Hunan.

The mingling index and crown index are the key stand spatial structure factors affecting the species diversity of saplings in the natural secondary oak forest in Hunan. Therefore, the mingling index and crown index of stand structure can be changed by regulating the mingling degree and canopy structure of tree species, so as to promote their natural regeneration and achieve the purpose of protecting and enriching the species diversity of saplings.

This study was carried out to analyze the effects of site factors and competition factors on the crown width growth, construct a crown width model of Hunan Quercus natural forests with the mixed effects of site types and competition factor. It provides a theoretical basis for scientific management decision of Quercus natural forests.

With 1 429 Quercus trees in 51 natural forest plots of Hunan province as the research object, the factors that had significant influence on crown extent were screened, and the factors were classified and combined according to the standard to form site types and competition types. The optimal basic model was selected from 10 basic models. k-means clustering was used to cluster the initial competition types into competition type groups. The competition type group and the site type group were added into the optimal basic model as random effects, and the mixed effects model including the site type and the competition type group was constructed.

The factors that significantly affected crown width included altitude, slope, slope position, slope direction, soil type, relative fault area (R_S), simple competition index (C_LH), and greater than the sum of the object wood fault area (B_AL). The significance order of site factors was altitude > slope > slope direction > slope position > soil type, and Rs was positively correlated with crown width among competition factors. C_LH and B_AL were negatively correlated with crown width. The optimal basic model was allometric growth model (with intercept), R² was 0.534 8. The three competition factors were classified and combined according to the standard to form the competition type, and the mixed effect model with competition type was added to the basic model, and the R² increased to 0.583 5. k-means clustering was applied to cluster the initial competition types into 17 competition type groups, which were added to the basic model as random effects, and R² increased to 0.749 2. The combined site types were added to the model as random effects, and the crown width model based on the mixed effects of competition and site was constructed. The R² of the model increased to 0.841 6, which was 57.36% higher than that of the basic model.

The natural forest canopy model of Hunan Quercus with competition factor and site factor has better fitting effect and prediction accuracy. Therefore, the canopy width model constructed in this study can well predict the canopy width of natural Quercus forest in Hunan, and provide support for the study of the growth and management of natural Quercus forest and the update of forest resource survey database.

This study aims to investigate the impact of site factors on the diameter at average breast height dominant Chinese fir trees in Hunan province and develop a site quality evaluation model with site random effects to address the issue of site quality assessment.

Based on data collected from 509 plots of Chinese fir plantations in Hunan province, significant site factors influencing the DBH growth of average dominant fir trees were identified using quantitative method I in statistical forest software. Initial site types were then established by classifying and combining these influential factors according to standard criteria. The correlation between DBH and age of average dominant trees was fitted using R language, and the optimal basic growth model was selected from four candidate models. K-means clustering was employed to group similar parameter values into distinct site type groups, which were subsequently added as additional random effects into the optimal basic model, resulting in a mixed-effects evaluation model for assessing site quality.

1) Elevation, slope position, soil type, and soil thickness were found to influence the DBH of average dominant Chinese fir trees, with slope position exerting the greatest impact followed by soil type, soil thickness, and elevation. 2) Amongst the four candidate basic growth models tested, their fitting accuracy was relatively low (R²=0.641 5-0.642 0). In this study, we selected Mitscherlich molecular formula (R²=0.642 0) as the basic model for evaluating site quality. 3) The influence of site effects on our model predictions, nonlinear mixed-effect simulations were conducted incorporating different combinations of individual site factors and their effects; this resulted in an improved determination coefficient ranging from 0.664 2-0.825 8 compared to previous values ranging from 0.641 5-0.642 0. The fitting accuracy was closely associated with the significance of the dominant site factor. The simulation accuracy of the mixed model incorporating site type exhibited the highest value (R²=0.825 8). 4) Site types were categorized into 10 groups based on a clustering accuracy standard with a determination coefficient ≥ 0.95. The mixed model utilizing site type groups proved to be convenient for practical application and enhanced modeling accuracy (R²=840 9).

Site factors exerted significant effects on the diameter at breast height growth of average dominant Chinese fir trees. Additionally, incorporating site factors as random effects into the hybrid model improved prediction precision, rendering it more suitable for evaluating site quality across diverse site types.