Abstract

Height-to-diameter at breast height (DBH) ratio (HDR) is an important tree and stand stability measure. Several factors such as stand dynamics, natural and anthropogenic disturbances, and silvicultural tending significantly affect HDR, and, therefore, in-depth investigation of HDR is essential for better understanding of ecological processes in a forest. A nonlinear mixed-effects HDR model applicable to several tree species was developed using the Czech national forest inventory data comprising 13,875 sample plots and 348,980 trees. The predictive performance of this model was evaluated using the independent dataset which was originated from 25,146 trees on 220 research sample plots. Among various tree- and stand-level variables describing tree size, site quality, stand development stage, stand density, inter-tree spacing, and competition evaluated, dominant height (HDOM), dominant diameter (DDOM), relative spacing index (RS), and DBH-to-quadratic mean DBH ratio (dq) were identified as the most important predictors of HDR variations. A random component describing sample plot-specific HDR variations was included through mixed-effects modelling, and dummy variables describing species-specific HDR variations and canopy layer-specific HDR variations were also included into the HDR model through dummy variable modelling. The mixed-effects HDR model explained 79% of HDR variations without any significant trends in the residuals. Simulation results showed that HDR for each canopy layer increased with increasing site quality and stand development stage (increased HDOM) and increasing competition (increased RS, decreased DDOM and dq). Testing the HDR model on the independent data revealed that more than 85% of HDR variations were described for each individual species (Norway spruce, Scots pine, European larch, and European beech) and group of species (fir species, oak species, birch and alder species) without significant trends in the prediction errors. The HDR can be predicted with a higher accuracy using the calibrated mixed-effects HDR model from measurements of its predictors that can be obtained from routine forest inventories. To improve the prediction accuracy, a model needs to be calibrated with the random effects estimated using one to four randomly selected trees of a particular species or group of species depending on the availability of their numbers per sample plot. The HDR model can be applied for stand stability assessment and stand density regulation. The HDR information is also useful for designing a stand density management diagram. Brief implications of the HDR model for designing silviculture strategies and forest management planning are presented in the article.

Highlights

  • Height-to-diameter ratio (HDR) is total height divided either by diameter at breast height or by diameter at the root collar of a tree

  • We evaluated the relative spacing index (RS), which is defined by RS = 10000/N/HDOM [55,76,77] for its potential contribution index (RS), which is defined by RS

  • With four covariate predictors (HDOM, DDOM, RS, and dq) and one sample plot‐specific effect parameter included into the HDR model, the fitting improvement achieved was the highest random effect parameter included into the HDR model, the fitting improvement achieved was the relative to that of the HDR model fitted with diameter at breast height (DBH) as a single predictor

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Summary

Introduction

Height-to-diameter ratio (HDR) is total height divided either by diameter at breast height or by diameter at the root collar of a tree. Large values of HDR indicate that either trees have grown in a crowded stand with mutual support from neighboring trees or they have grown in an extremely open stand where no significant competition exists [1,2,3,4]. Smaller values of HDR indicate longer crown length, higher crown projection area, better developed root system, lower position of the center of gravity, and higher stability of the trees. HDR for characterizing tree and stand stability and their vulnerability to natural disasters [3,5,6,7,8,9]. Trees with large HDR may be more vulnerable because their stems may not be adapted to a condition of higher mechanical perturbation [3,4,14,15]

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