Development of a Mixed-Effects Individual-Tree Basal Area Increment Model for Oaks (<i>Quercus</i> spp.) Considering Forest Structural Diversity

• Main Authors: Wenwen Wang, Xinyun Chen, Weisheng Zeng, Jianjun Wang, Jinghui Meng
• Format: Article
• Language: English
• Published: MDPI AG 2019-05-01
• Series: Forests
• Subjects: individual-tree basal area increment model; oaks; forest structural diversity; longitudinal and nested structure; a linear mixed-effects approach; heteroscedasticity; autocorrelation
• Online Access: https://www.mdpi.com/1999-4907/10/6/474

In the context of uneven-aged mixed-species forest management, an individual-tree basal area increment model considering forest structural diversity was developed for oaks (<i>Quercus</i> spp.) using data collected from 11,860 observations in 845 sample plots from the 7th (2004), 8th (2009), and 9th (2014) Chinese National Forest Inventory in Hunan Province, south-central China. Since the data was longitudinal and had a nested structure, we used a linear mixed-effects approach to construct the model. We also used the variance function and an autocorrelation structure to describe within-plot heteroscedasticity and autocorrelation. Finally, the optimal mixed-effects model was determined based on the Akaike information criterion (AIC), Bayesian information criterion (BIC), log-likelihood (Loglik) and the likelihood ratio test (LRT). The results indicate that the reciprocal transformation of initial diameter at breast height (1/DBH), relative density index (RD), number of trees per hectare (NT), elevation (EL) and Gini coefficient (GC) had a significant impact on the individual-tree basal area increment. In comparison to the basic model developed using least absolute shrinkage and selection operator (LASSO) regression, the mixed-effects model performance was greatly improved. In addition, we observed that the heteroscedasticity was successfully removed by the exponent function and autocorrelation was significantly corrected by AR(1). Our final model also indicated that forest structural diversity significantly affected tree growth and hence should not be neglected. We hope that our final model will contribute to the scientific management of oak-dominated forests.