To clarify the influence of individual-tree competition and spatial structure on the growth of Cunninghamia lanceolata diameter at breast height (DBH), and to provide theoretical basis and practical recommendations for the fine management and sustainable development of Cunninghamia lanceolata forests.

Taking 22 fixed sample plots of Cunninghamia lanceolata trees in Kaihua County as the research object, based on the 2014-2019 forest resources inventory data, we calculated breast diameter growth indexes (breast diameter growth, breast diameter squared growth and its logarithmic conversion form), 10 competition indexes (7 distance-independent and 3 distance-related) and 4 spatial structure indexes (breast diameter size ratio, breast diameter size differentiation, mixing degree, and angular scale). Variables were screened by optimal subset regression, Pearson correlation analysis was used to investigate the correlation between each factor and the indicators of breast diameter growth, severely collinear independent variables were eliminated, and the relative contribution of each factor to breast diameter growth was quantified by the linear mixed-effects model and variance decomposition method.

The optimal subset regression identified key factors influencing various DBH growth indicators. Spatial structure indices and competition indices (C2, C5, C6, and C17) jointly affected multiple growth indicators, while initial DBH (D₁) and competition index C15 also played important roles. Pearson correlation analysis showed that D₁ was significantly correlated with several DBH growth indicators, while the spatial structure indicator U_D was negatively correlated with all DBH growth indicators. Competition indices reflecting competitive ability were significantly positively correlated with DBH growth, whereas most indices representing competitive pressure showed significant negative correlations. The linear mixed-effects model outperformed the ordinary linear regression model, indicating that differences in site conditions among plots had a significant impact on DBH growth. The relative importance of driving factors varied among different DBH growth indicators, with competition indices generally having the greatest influence, followed by D₁, while the contribution of U_D was relatively small. Interaction effects in the mixed-effects model revealed that competition and spatial structure exhibited synergistic effects. In stands with higher diameter differentiation (higher U_D), the interaction terms C6 × U_D, C2 × U_D and C5 × U_D had significant positive effects on DBH growth, whereas C17 × U_D showed a significant negative interaction effect. These results indicate that increased spatial heterogeneity enhances the positive effects of competitive ability indices on growth while weakening the inhibitory effects of certain competition pressure indices (e.g., Alemdag index).

DBH growth of Cunninghamia lanceolata is jointly influenced by initial DBH, competition indices, and spatial structure, among which competition plays a dominant role. This complex multi-factor interaction mechanism provides a scientific basis for the precision management and sustainable management of Cunninghamia lanceolata stands.

The oak species within the natural arboreal forests of Jingning She Autonomous County, Zhejiang Province, were selected as the research subjects to investigate the appropriateness of incorporating forest stand spatial structure into individual tree basal area growth models of oak forests and its potential to enhance the accuracy of growth prediction.

Based on the continuous forest resource inventory data from Zhejiang Province in 2014 and 2019, integrating forest stand spatial structure factors such as the Hegyi competition index, complete mingling, and aggregation index. By employing the entropy method, a comprehensive spatial structure Index (S) is constructed to fully reflect the spatial distribution and competitive relationships of trees. Utilizing the upper exclusion method, the S is categorized into three distinct levels and introduced as a dummy variable into four commonly applied theoretical growth equations, namely Schumacher, Johnson-Schumacher, Gompertz and Logistic. This integration establishes individual tree basal area growth models inclusive of the S dummy variable. Subsequently, a comparative analysis is conducted with the fundamental growth models that do not incorporate the S dummy variable to assess the influence of spatial structure on growth prediction accuracy.

1) S exerts a significantly positive influence on the growth of breast-height basal area; 2) incorporating S as a dummy variable into the four foundational growth models enhances the models’ fitting accuracy and predictive precision; 3) among all the models tested, the basal area growth model that integrates S as a dummy variable, based on the Johnson-Schumacher model, demonstrates the highest predictive accuracy.

Incorporating stand spatial structure into individual tree basal area growth models is not only suitable for application but also improves the accuracy of tree growth predictions. Holding substantial theoretical and practical significance for forest management and ecological conservation.