Using Functional Traits to Improve Estimates of Height–Diameter Allometry in a Temperate Mixed Forest

Abstract

Accurate estimates of tree height (H) are critical for forest productivity and carbon stock assessments. Based on an extensive dataset, we developed a set of generalized mixed-effects height–DBH (H–D) models in a typical natural mixed forest in Northeastern China, adding species functional traits to the H–D base model. Functional traits encompass diverse leaf economic spectrum features as well as maximum tree height and wood density, which characterize the ability of a plant to acquire resources and resist external disturbances. Beyond this, we defined expanded variables at different levels and combined them to form a new model, which provided satisfactory estimates. The results show that functional traits can significantly affect the H–D ratio and improve estimations of allometric relationships. Generalized mixed-effects models with multilevel combinations of expanded variables could improve the prediction accuracy of tree height. There was an 82.42% improvement in the accuracy of carbon stock estimates for the studied zone using our model predictions. This study introduces commonly used functional traits into the H–D model, providing an important reference for forest growth and harvest models.