Predicting crown width using nonlinear mixed-effects models accounting for competition in multi-species secondary forests.

ABSTRACT: Crown width (CW) is an important tree variable and is often used as a covariate predictor in forest growth models. The precise measurement and prediction of CW is therefore critical for forest management. In this study, we introduced tree species as a random effect to develop nonlinear mixed-effects CW models for individual trees in multi-species secondary forests, accounting for the effects of competition. We identified a simple power function for the basic CW model. In addition to diameter at breast height (DBH), other significant predictor variables including height to crown base (HCB), tree height (TH), and competition indices (CI) were selected for the mixed-effects CW model. The sum of relative DBH (SRD) was identified the optimal distance-independent CI and as a covariate predictor

SUBMITTER: Hou R 

PROVIDER: S-EPMC9055997 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature