Abstract
Accurately quantifying the size–density relationships is important to predict stand development, estimate stand carrying capacity and prescribe silvicultural treatments. Parametric methods, such as segmented regression, were proposed to estimate the complicated trajectory of size–density relationships. However, applying nonparametric methods to assess stand development has not been explicitly examined. In this study, we compared parametric and nonparametric methods for estimating size–density relationships for Japanese cedar plantations in Taiwan. Specifically, we compared the efficacy of two segmented regression models with the penalized spline and random forest for regression methods. We also examined various stages in stand development for old-growth Japanese cedar stands. Data collected from 237 Japanese cedar permanent plots were used in model fitting and validation. Results indicated that the parametric and nonparametric methods used in this study can provide reliable estimates of the size–density relationship for Japanese cedar. Higher accuracy was achieved before the stands diverged from the self-thinning line. The penalized spline approach behaved consistently well regardless of datasets or stages in stand development, while the predictability of the random forest algorithm slightly decreased when the validation data was fitted. The results of this study provide insights on the use of methods to quantify the size–density relationships as well as enhance the understanding of long-term stand development.
Highlights
Quantifying size–density relationships is important in forest management to predict stand development, estimate stand carrying capacity and prescribe silvicultural treatments [1,2].The relationship between number of trees per unit area and the quadratic mean diameter (Dq ) of a stand on a log-log scale is commonly used to describe the size–density relationship of a stand
Fifteen long-term experimental plots for the growth study of Japanese cedar maintained by Fifteen long-term experimental plots for the growth study of Japanese cedar maintained by the the National Taiwan University Experimental Forest (NTUEF) were used
For the validation dataset, the size–density relationships were better predicted by the segmented regression models, which implied that the segmented models could be relatively more robust than the penalized spline and random forest algorithm
Summary
Quantifying size–density relationships is important in forest management to predict stand development, estimate stand carrying capacity and prescribe silvicultural treatments [1,2]. The number of trees per unit area and the average tree size in the stand will reach and stay on a relatively stable equilibrium for a period of time This period is known as maximum size–density relationship, or self-thinning [1]. Yoda et al [12] indicated the mean mass (or weight) and number of plants per unit area on the log-log scale follow the -3/2 rule of self-thinning when the stands are in the competition-induced mortality stage. We compared parametric and nonparametric methods for estimating the size–density relationship for Japanese cedar plantations in Taiwan. With rising concerns about climate change and global warming, this study conducted in the subtropical island (Taiwan) will be beneficial for long-term Japanese cedar plantation management in the temperate zone, like Japan
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