[Maximum density-size line for Larix olgensis plantations based on quantile regression.]

Area of Science:

• Forestry science
• Quantitative ecology
• Silviculture

Background:

• Stand density and tree size are critical factors in forest management.
• Understanding the maximum density-size relationship is essential for predicting forest dynamics and optimizing resource allocation.
• Previous methods for determining maximum density-size lines often relied on arbitrary data selection.

Purpose of the Study:

• To develop and validate a robust model for the maximum density-size line in Larix olgensis plantations.
• To compare the performance of linear quantile regression with traditional methods like Ordinary Least Squares (OLS) and Maximum Likelihood (ML) regression.
• To establish a limited density-size line for enhanced forest management predictions.

Main Methods:

• Utilized data from 378 permanent and 415 temporary plots in Northeast China.
• Employed linear quantile regression (τ=0.90, 0.95, 0.99) to model the maximum density-size relationship.
• Applied Generalized Pareto distribution from extreme value theory to derive the limited density-size line.
• Compared results with OLS and ML regression models.

Main Results:

• Linear quantile regression with a 0.99 quantile provided stable and accurate estimations, comparable to ML regression.
• Selecting 5 data points within the diameter class yielded a satisfying prediction model for the maximum density-size line.
• The developed limited density-size line was slightly above the maximum density-size line.
• Models demonstrated suitability for predicting density-size lines in existing forest stands.

Conclusions:

• Linear quantile regression (0.99 quantile) is the optimal method for constructing maximum and limited density-size lines for Larix olgensis plantations.
• The established density-size lines offer a scientific basis for the sustainable management of these plantations.
• The findings contribute to a better understanding of stand density dynamics and their impact on tree growth.