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

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 for spatially non-explicit CW models, whereas the sum of the Hegyi index for fixed number competitors (SHGN) was the optimal distance-dependent CI for spatially explicit CW models, with significant linear correlation ( = 0.943, < 0.001). Both spatially non-explicit and spatially explicit mixed-effects CW models were developed for studied secondary forests. We found that these models can describe more than 50% of the variation in CW without significant residual trends. Spatially explicit models exhibited a significantly larger effect on CW than spatially non-explicit ones; however, spatially explicit models are computationally complex and difficult and can be replaced by corresponding spatially non-explicit models due to the small differences in the fit statistics. The models we present may be useful for forestry inventory practices and have the potential to aid the evaluation and management of secondary forests in the region.