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

Abstract

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 (Quercus 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.

Temporal Dynamics of Root Reinforcement in European Spruce Forests

“…The use of a linear model is considered in general to give a good approximation of an initial growing phase or for a very slow growth process. Vice versa, "logistic" or "Gompertz" models are expected to fit better for more complex growing behaviours [29,44]. The very large dataset considered in the analysis supports, within the aim of this study, the linear growth model approximation to perform best, allowing us to reduce the model complexity by however achieving a suited prediction accuracy.…”

Application of Random Effects to Explore the Gulf of Mexico Coastal Forest Dynamics in Relation to Meteorological Factors

“…Comparatively, conditional R 2 values generally appear to be higher than the marginal R 2 values, which indicates a significant improvement in explaining variations in NDVI by introducing both random-effects and fixed-effects variables. This finding is consistent with previous studies showing that mixed effects models fitted data better than fixed effects models in terms of R 2 values [30], [33]. The results obtained for three forest types were then compared by the amount of NDVI variance explained by each random intercept and random meteorology-slope model.…”

“…Additionally, to deal with spatial variabilities LMM can relax the assumption of independence. For instance, LMM considers spatial dependence and spatial heterogeneity in modeling processes to deal with spatial effects of forests with different spatial patterns of tree locations [28], [31], [32], [33]. Previous studies have demonstrated that the LMM is capable of characterizing the variance parameters of random effects in model residuals, and of obtaining more accurate predictions than those derived from general fixed effects models.…”

Pollen Production of Quercus in the North-Western Iberian Peninsula and Airborne Pollen Concentration Trends during the Last 27 Years

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