Modeling the number of the first- and second-order branches within the live tree crown of Korean larch plantations in Northeast China

Abstract

Modeling the number of branches in a tree is fundamental for simulating other branch characteristics and crown structure. In this study, a total of 77 Korean larch (Larix olgensis Henry) trees were destructively sampled from plantations in Northeast China. The number of first- and second-order branches was modeled using seven count data models, namely Poisson, negative binomial (i.e., NB, including NB-1, NB-2, and NB-P), and generalized Poisson (i.e., GP, including GP-1, GP-2, and GP-P) regression models. Generalized linear mixed models (GLMMs) were then applied to those models using the sampled trees as the random effects. The results showed that (i) the Poisson regression was preferred for modeling the number of first-order branches; (ii) the GP-1 regression was considered the optimal model for the number of second-order branches; (iii) the significant predictor variables included tree height increment, branch position, relative tree size, mean dominant height, and tree age; (iv) the GLMMs significantly improved both model fit and prediction performance; (v) the prediction accuracy of the GLMMs increased gradually with increasing sample size; and (vi) a relatively small sample size with an appropriate sampling strategy would be adequate to provide a good estimation at a specific crown section.