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<p>Dominant height (DH) is commonly used for assessing forest site and stand productivity. This study developed a mixed-effects DH model through inclusion of the province- and county-level random effects. The study used data from 259 sample plots located in moso bamboo (<i>Phyllostachys pubescens</i>) forests across the eight provinces (Jiangsu, Hunan, Hubei, Jiangxi, Zhejiang, Fujian, Sichuan, Guangxi) in China. Among several climate and dendrometric factors analyzed, quadratic mean DBH (QMD), dominant DBH (DD), canopy density (CD), soil organic carbon (SOC) and mean warmest month temperature (MWMT) were selected as predictor variables because of their significant contribution to the DH model. The results showed the elevation and interaction of slope and elevation were significant effects on DH. DH reached its maximum value at elevation 600–700&#xa0;m. DH increased with increasing DD and decreasing CD, MWMT and SOC. The precision of the DH model significantly increased when the province- and county-level random effects were included in the model. The proposed mixed-effects DH model will have an important application in assessing moso bamboo forest productivity across the eight provinces in China.</p>

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Climate sensitive mixed-effects dominant height model for moso bamboo in China

  • 期刊论文

  • 发表日期：

    2025年5月2日

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  • Xiao Zhou,
  • Xuan Zhang,
  • Zhen Li,
  • Liyang Liu,
  • Ram P. Sharma,
  • Fengying Guan

摘要

Dominant height (DH) is commonly used for assessing forest site and stand productivity. This study developed a mixed-effects DH model through inclusion of the province- and county-level random effects. The study used data from 259 sample plots located in moso bamboo (Phyllostachys pubescens) forests across the eight provinces (Jiangsu, Hunan, Hubei, Jiangxi, Zhejiang, Fujian, Sichuan, Guangxi) in China. Among several climate and dendrometric factors analyzed, quadratic mean DBH (QMD), dominant DBH (DD), canopy density (CD), soil organic carbon (SOC) and mean warmest month temperature (MWMT) were selected as predictor variables because of their significant contribution to the DH model. The results showed the elevation and interaction of slope and elevation were significant effects on DH. DH reached its maximum value at elevation 600–700 m. DH increased with increasing DD and decreasing CD, MWMT and SOC. The precision of the DH model significantly increased when the province- and county-level random effects were included in the model. The proposed mixed-effects DH model will have an important application in assessing moso bamboo forest productivity across the eight provinces in China.