Two-level mixed-effects height to crown base model for moso bamboo (Phyllostachys edulis) in Eastern China

Xiao Zhou; Yang Zhou; Xuan Zhang; Ram P Sharma; Fengying Guan; Shaohui Fan; Guanglu Liu

doi:10.3389/fpls.2023.1095126

Two-level mixed-effects height to crown base model for moso bamboo (Phyllostachys edulis) in Eastern China

Front Plant Sci. 2023 Mar 30:14:1095126. doi: 10.3389/fpls.2023.1095126. eCollection 2023.

Authors

Xiao Zhou^{1

2}, Yang Zhou^{1

2}, Xuan Zhang^{1

2}, Ram P Sharma³, Fengying Guan^{1

2}, Shaohui Fan¹, Guanglu Liu¹

Affiliations

¹ International Center for Bamboo and Rattan, Key Laboratory of National Forestry and Grassland Administration, Beijing, China.
² National Location Observation and Research Station of the Bamboo Forest Ecosystem in Yixing, National Forestry and Grassland Administration, Yixing, China.
³ Institute of Forestry, Tribhuwan University, Kritipur, Kathmandu, Nepal.

Abstract

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 Phyllostachys edulis 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.

Keywords: BAL; crown density; logistic function; mean response; random effects prediction; response calibration; sample selection strategy.