生物多样性 ›› 2021, Vol. 29 ›› Issue (4): 456-466.DOI: 10.17520/biods.2020369

• 研究报告: 植物多样性 • 上一篇    下一篇

基于枝条木材密度分级的鼎湖山南亚热带常绿阔叶林树高曲线模型

张剑坛1,2,3, 李艳朋4, 张入匀5, 倪云龙1,2,3, 周文莹1,2,3, 练琚愉1,2,6,*(), 叶万辉1,2,6   

  1. 1 中国科学院华南植物园退化生态系统植被恢复与管理重点实验室, 广州 510650
    2 中国科学院华南植物园广东省应用植物学重点实验室, 广州 510650
    3 中国科学院大学, 北京 100049
    4 中国林业科学研究院热带林业研究所森林生态研究中心, 广州 510520
    5 华东师范大学生态与环境科学学院, 上海 200241
    6 南方海洋科学与工程广东省实验室(广州), 广州 511458
  • 收稿日期:2020-09-19 接受日期:2020-11-05 出版日期:2021-04-20 发布日期:2021-04-20
  • 通讯作者: 练琚愉
  • 基金资助:
    中国科学院战略性先导科技专项(XDB31030000);国家重点研发计划(2017YFC0505802);南方海洋科学与工程广东省实验室(广州)人才团队引进重大专项(GML2019ZD0408);中国森林生物多样性监测网络建设项目

Height-diameter models based on branch wood density classification for the south subtropical evergreen broad-leaved forest of Dinghushan

Jiantan Zhang1,2,3, Yanpeng Li4, Ruyun Zhang5, Yunlong Ni1,2,3, Wenying Zhou1,2,3, Juyu Lian1,2,6,*(), Wanhui Ye1,2,6   

  1. 1 Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650
    2 Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650
    3 University of Chinese Academy of Sciences, Beijing 100049
    4 Forest Ecology Research Center, Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou 510520
    5 School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241
    6 Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458
  • Received:2020-09-19 Accepted:2020-11-05 Online:2021-04-20 Published:2021-04-20
  • Contact: Juyu Lian
  • About author:* E-mail: lianjy@scbg.ac.cn

摘要:

如何便捷准确地测量树高一直是林学及群落生态学所关心的问题。由于木材密度与树木生长密切相关, 因此基于木材密度建立树高曲线模型能够为测量树高提供新的方法。本文以鼎湖山南亚热带常绿阔叶林1.44 ha塔吊样地内119个物种的4,032个个体为研究对象, 利用树高、胸径和木材密度数据来探究基于枝条木材密度分级的树高曲线模型。首先, 对个体进行随机抽样, 将其划分为建模样本(占总样本量的70%)和检验样本(占总样本量的30%), 并通过聚类分析将所有个体的木材密度划分为4级。其次, 基于建模样本利用常见的5种理论生长方程(Richards、Korf、Logistic、Gompertz和Weibull方程)对不同分级建立树高-胸径模型; 基于检验样本检验模型精度, 并确定各分级的最适模型。最后, 构建基于物种分类的树高曲线模型, 并比较其与木材密度分级模型的差异。结果表明: 基于木材密度分级的模型, 各分级小组检验样本的平均绝对误差(MAE)和均方根误差(RMSE)最小值所对应的模型类型与建模样本结果一致, 确定Gompertz模型和Weibull模型为鼎湖山南亚热带常绿阔叶林最适树高模型类型。比较基于木材密度分级的模型与基于物种分类的模型, 发现二者的MAE、RMSE指数差异小。综上, 基于木材密度分级的树高曲线模型对树高估测精度高, 使用方便, 为树高预测提供了新方法, 可以更好服务森林调查等生产实践。

关键词: 植物功能性状, 木材密度分级, 理论生长方程, 非线性回归, 树高-胸径模型

Abstract

Aims: Knowing how to measure tree height conveniently and accurately has always been a concern for the fields of forestry and community ecology. Since wood density is closely related with tree growth, building a tree height curve model based on wood density could provide a new method for measuring tree height. This method would provide data support for vegetation investigation of forest dynamics plots and exploration of spatial differences in the radial and vertical distribution of community species resources.

Methods: Here, we explored tree height using a curve model based on branch wood density classification using tree height data, diameter at breast height (DBH), and wood density of 4,032 individuals belonging to 119 species in a 1.44 ha plot in a south subtropical evergreen broad-leaved forest in Dinghushan (DHS). First, we randomly sampled individuals, and divided them into model development (70% of the total sample size) and model validation (30% of the total sample size). We then classified wood density of all individuals into one of several categories using a cluster analysis. Second, we built a tree height-DBH model for different classifications based on modeling samples using five common theoretical growth equations (Richards, Korf, Logistic, Gompertz and Weibull equations). We estimated the fitting accuracy using the root mean squared error (RMSE) and Akaike information criterion (AIC). A smaller RMSE index and AIC index indicated the best fitting effect. Third, we determined the most optimal models based on the one model with the smallest mean average absolute error (MAE) and RMSE index. Finally, we established tree height curve models using species classification and compared the differences between models based on wood density and species classifications using the MAE index and RMSE index.

Result: Results suggest that when the classification order of cluster analysis was 4, the SSI (simple structure index) value was the largest, so the individual wood density of the plot was unevenly divided into four categories: [0.06, 0.31), [0.31, 0.45), [0.45, 0.57), and [0.57, 0.82]. There was little difference when fitting the five equations and all the parameter values were extremely significant. Models based on wood density classification corresponding to the MAE index and RMSE index were consistent with the results of the modeling samples. The Gompertz equation and Weibull equation were selected as the optimal tree height models and the Weibull equation had the highest frequency equation for the DHS plot. Moreover, when comparing models based on wood density classification with species classification, the MAE and RMSE indices of the two models in 17 species were less different. In addition, since the estimation accuracy of models based on wood density classification and species classification was low, the tree height of Caryota maxima, Schima superbaand Castanea henryi was hardly to estimated.

Conclusions: The tree height curve model based on wood density classification has a well-fitting effect and high estimation accuracy. It is also more convenient and generally used than the species classification model, which can realize the establishment of tree height curve models for many species easily. What’s more, models based on wood density classification directly reflect plant response to the environment from a mechanistic perspective, and represents the ecological trade-off among individuals with different wood densities in the vertical growth of trees. In summary, this model based on wood density classification provides a new method for tree height prediction and can better serve production practices such as forest surveys and help with understanding scientific issues.

Key words: plant functional traits, wood density classification, theoretical growth equations, nonlinear regression analysis, height-diameter model