Individual tree diameter growth modelling for natural secondary forest in Changbai Mountains, Northeast China

Individual modelling is a foundational approach to study the natural forest growth and in this paper, we develop a serial distance-depended individual tree model for some species in natural forest which would provide prediction and characteristics for natural species. The data used to develop individual model for natural mixed forests were collected from 712 remeasured 10-year periodic permanent sample plots of in Baihe Forest Bureau of Changbai Mountains, northeast China. Based on analyzing relationship between diameter increment of individual trees with tree size, competitive status, and site condition and finding out the major independent variables, the growth models for individual trees of 15 species in the natural mixed forests, that have good predicting precision, and easily applicable, were developed using stepwise regression method. The individual growth model developed in this study can reflect the tree increment of 15 species and be generally well suited for simulating tree and stand growth for natural mixed forests in Changbai Mountains. The research results for individual trees growth model of each species showed that main variable to influence on diameter increment of individual trees for natural mixed forests were tree size (D) and then competition index. The site condition was not related with diameter increment. The natural logarithm of DBH (lnD) and square diameter (D²) were included in the predicting models of diameter increment for all 15 species. The diameter increment was directly proportional to tree diameter for each species. For the competitive indexes in growth model, the relative diameter (RD), canopy closure (P), and the ratio of diameter of subject tree with maximum diameter (DDM) were related to diameter increment and the stand density measures were not significantly influenced on diameter increment. As canopy closure increase, tree increment decreases. The site conditions were performance less of factors in increment predictions in the model.