Zhou Xiao, Zhou Yang, Zhang Xuan, Sharma Ram P, Guan Fengying, Fan Shaohui, Liu Guanglu
International Center for Bamboo and Rattan, Key Laboratory of National Forestry and Grassland Administration, Beijing, China.
National Location Observation and Research Station of the Bamboo Forest Ecosystem in Yixing, National Forestry and Grassland Administration, Yixing, China.
Front Plant Sci. 2023 Mar 30;14:1095126. doi: 10.3389/fpls.2023.1095126. eCollection 2023.
Height to crown base (HCB) is an important predictor variable for forest growth and yield models and is of great significance for bamboo stem utilization. However, existing HCB models built so far on the hierarchically structured data are for arbor forests, and not applied to bamboo forests. Based on the fitting of data acquired from 38 temporary sample plots of forests in Yixing, Jiangsu Province, we selected the best HCB model (logistic model) from among six basic models and extended it by integrating predictor variables, which involved evaluating the impact of 13 variables on HCB. Block- and sample plot-level random effects were introduced to the extended model to account for nested data structures through mixed-effects modeling. The results showed that bamboo height, diameter at breast height, total basal area of all bamboo individuals with a diameter larger than that of the subject bamboo, and canopy density contributed significantly more to variation in HCB than other variables did. Introducing two-level random effects resulted in a significant improvement in the accuracy of the model. Different sampling strategies were evaluated for response calibration (model localization), and the optimal strategy was identified. The prediction accuracy of the HCB model was substantially improved, with an increase in the number of bamboo samples in the calibration. Based on our findings, we recommend the use of four randomly selected bamboo individuals per sample to provide a compromise between measurement cost, model use efficiency, and prediction accuracy.
树高至树冠基部高度（HCB）是森林生长和产量模型的重要预测变量，对竹杆利用具有重要意义。然而，目前基于分层结构数据构建的现有HCB模型是针对乔木林的，尚未应用于竹林。基于对江苏省宜兴市38个临时森林样地采集的数据进行拟合，我们从六个基本模型中选择了最佳的HCB模型（逻辑模型），并通过整合预测变量对其进行扩展，其中涉及评估13个变量对HCB的影响。将样地和样方水平的随机效应引入扩展模型，通过混合效应建模来考虑嵌套数据结构。结果表明，与其他变量相比，竹高、胸径、所有直径大于目标竹的竹个体的总断面积以及郁闭度对HCB变异的贡献显著更大。引入两级随机效应显著提高了模型的准确性。评估了不同的抽样策略用于响应校准（模型本地化），并确定了最优策略。随着校准中竹样本数量的增加，HCB模型的预测准确性大幅提高。基于我们的研究结果，我们建议每个样本使用四个随机选择的竹个体，以在测量成本、模型使用效率和预测准确性之间取得折衷。
