Camellia osmantha is an economically valuable woody oil plant with increasing cultivation in southern China, yet its potential distribution under climate change remains poorly understood. Here, we used an optimized MaxEnt model with 27 occurrence points and 11 environmental variables (retained from 27 candidate predictors via correlation filtering and MaxEnt-based contribution and permutation importance) to predict its potential distribution in Guangxi under current and future climate scenarios (2050s, 2090s; SSP126, SSP370, SSP585). The model demonstrated acceptable predictive performance (training AUC = 0.7784, test AUC = 0.6935) with the test AUC falling within the acceptable/fair range (0.6–0.7) according to standard evaluation criteria. The dominant drivers were available water capacity (24.3%), altitude (24.1%), and temperature annual range (21.9%), with suitability declining when AWC exceeded 3.0, altitude exceeded 543 m, or bio7 fell below 23.06 °C. Current suitable habitats cover 18.53 × 10⁴ km² (89.7% of Guangxi), with high suitability areas concentrated in the northeast and southwest. Under future scenarios, total suitable area remained stable (−1.2% to +3.2%), while high suitability areas expanded under SSP370/SSP585 (+39.0%/+41.1%) and contracted under SSP126 (−30.3%) by 2090s; centroid shifts were limited (2.95–14.93 km), with core habitats remaining in central Guangxi. These findings provide a quantitative basis for conservation and sustainable cultivation strategies for this species under climate change, and highlight the importance of soil water availability alongside topographic and climatic factors in shaping its distribution.

Understanding the structural characteristics of Pinus taiwanensis plantations in climatically transitional regions is essential for developing science-based management strategies under global change. This study investigated 23 plots in Huangbai Mountain Forest Farm, Henan Province, China, classified into low-, medium-, and high-density stands (n = 9, 9, and 5, respectively). Diameter distributions were fitted using six probability functions, and four spatial structure parameters—mixing degree (Mc), size ratio (U), uniform angle index (W), and forest layer index (S)—were quantified. In addition, five comprehensive spatial structure indices—average superiority coefficient index (SPV), spatial structure comprehensive index (Q), stand spatial structure distance index (FSI), Comprehensive Distance Evaluation (CDEV), and Comprehensive Assessment of Proximity Vector (CAPV)—were constructed using a combined analytic hierarchy process and entropy weight method. Given the unbalanced sample sizes, non-parametric Kruskal–Wallis tests were employed for comparisons, and bootstrap resampling (1000 iterations) was performed to assess the reliability of mean estimates. The results showed that both the Gamma and Weibull distributions were equally suitable for describing diameter distribution under different stand densities, as their AIC differences were below 2 for all density classes. Correlation analysis indicated that the relative importance of spatial parameters followed the order S > U > Mc > W. Medium-density stands exhibited the most optimal spatial structure, whereas low-density stands showed the poorest performance. These findings suggest that both overly dense and sparse stands negatively affect spatial organization. Appropriate management practices, such as thinning or enrichment planting, are recommended to optimize stand structure and enhance ecological resilience.

Seedling growth is an essential stage for the future growth of trees, and Paulownia is treated as a rapidly growing hardwood tree species with wide industrial application. There is a knowledge gap in the modeling and analysis of the seedling growth pattern of Paulownia hybrid clones under field nursery conditions. A total of 300 saplings belonging to 25 Paulownia hybrid clones (named PT1–PT25, 12 saplings per clone) were observed 16 times after flat stubble treatment, yielding 4800 records. After accounting for mortality under field nursery conditions, 118 saplings survived, resulting in 1888 valid records for model construction. Seven theoretical growth equations with clone-specific dummy variables were fitted using nonlinear mixed-effects models (NLMM) with an AR(1) correlation structure to account for the hierarchical structure of the repeated measurements. Among the seven candidate models, Hossfeld IV was selected as the optimal model for both ground diameter (AIC = 8250.62, RMSE = 1.47 mm, conditional R² = 0.9892) and tree height (AIC = 15586.26, RMSE = 10.48 cm, conditional R² = 0.9916). However, because the Hossfeld IV model lacks an analytical third derivative, the standard logistic model was adopted to calculate the biologically meaningful fast-growing period boundaries. PT24 exhibited the highest growth potential among all clones, with asymptotic maximum values of 86.76 mm for ground diameter and 490.00 cm for tree height. However, its low survival rate (25%, n = 3) limits its practical recommendation. In addition, height-to-diameter (H/D) ratio analysis revealed considerable variation among clones, ranging from 3.89 cm∙mm⁻¹ (PT22) to 8.06 cm∙mm⁻¹ (PT3). The H/D ratio showed no significant correlation with ground diameter A (r = 0.286, p = 0.166), but a significant positive correlation with tree height A (r = 0.660, p < 0.001). PT24 maintained a moderate H/D ratio (6.92) despite its superior growth, indicating favorable stem stability. PT6 (91.67% survival, moderate growth) represents a more robust alternative for breeding programs prioritizing establishment success. This study demonstrates that a dual-model strategy provides a robust framework for evaluating clone-specific growth potential and supports informed breeding decisions in Paulownia improvement programs.