Mixed-effects modeling of vertical distribution branch characteristics for Korean pine: Integrating climate-competition interactions for precision plantation management

Branching pattern forms the structural foundation of tree crowns and plays an important role in determining wood quality. This study investigates the vertical distribution pattern of branching characteristics in Korean pine and explores their relationships with stand density, competition pressure, and climate factors, providing scientific insights for forest management. Using data from 184 Korean pine trees in Northeast China, we developed a comprehensive vertical distribution prediction model with three approaches: Poisson regression (branch number), modified Weibull distribution (branch size), and nonlinear regression (branch insertion angle). The models integrate hierarchical variables spanning individual tree traits, stand structure, competition intensity, and climatic conditions. Tree growth vigor (height increment) and crown morphology primarily influence branch quantity; DBH and competition intensity significantly influence branch insertion angle, with branch age showing negative effects; branch size responds positively to tree dimension variables but negatively to competition pressure. It is noteworthy that competition-climate interactions exhibit distinct regulatory patterns compared to single factors, with drought indices modulating the effects of competition on branch characteristics. The model demonstrated robust performance, with mixed-effects models yielding low prediction errors: a mean absolute error ( MAE ) of 2.34 branches per whorl for quantity, a root mean square error ( RMSE ) of 8.7° for insertion angle, and an RMSE of 3.2 mm for branch diameter, as well as an RMSE of 0.7 cm for branch length. These models facilitate the quantitative prediction of knot size distribution and crown space requirements, offering a valuable tool for optimizing stand management and enhancing timber quality in Korean pine plantations. • Develop comprehensive models of branch patterns. • Models integrated tree traits, stand structure, competition, and climate factors. • Sensitivity analysis identified key drivers of branch characteristics. • Provide implications for forest management and timber quality.