欢迎访问《应用生态学报》官方网站,今天是 分享到:

应用生态学报 ›› 2024, Vol. 35 ›› Issue (7): 1735-1743.doi: 10.13287/j.1001-9332.202407.006

• • 上一篇    下一篇

帽儿山不同种源人工红松的生长差异性

范迎新, 贾炜玮*, 李凤日, 李丹丹, 张聪   

  1. 东北林业大学林学院/森林生态系统可持续经营教育部重点实验室, 哈尔滨 150040
  • 收稿日期:2024-04-14 修回日期:2024-05-25 出版日期:2024-07-18 发布日期:2025-01-18
  • 通讯作者: *E-mail: jiaww2002@163.com
  • 作者简介:范迎新, 女, 2000年生, 硕士研究生。主要从事林分生长与收获模型研究。E-mail: 2116154438@qq.com
  • 基金资助:
    国家自然科学基金区域创新发展联合基金重点项目(U21A20244)和中央高校基本科研业务费专项资金项目(2572019CP08)

Growth difference of planted Pinus koraiensis from different provenances in Maoer Mountain, China

FAN Yingxin, JIA Weiwei*, LI Fengri, LI Dandan, ZHANG Cong   

  1. College of Forestry, Northeast Forestry University/Key Laboratory of Sustainable Forest Ecosystem Management, Ministry of Education, Harbin 150040, China
  • Received:2024-04-14 Revised:2024-05-25 Online:2024-07-18 Published:2025-01-18

摘要: 为分析红松树高的生长规律,筛选生长优良种源,本研究利用帽儿山实验林场的26个种源234株人工红松树高、胸径和材积的差异对种源进行分组,结合Gompertz、Korf、Richards、Logistic、Schumacher基础模型构建树高生长方程,对比选出最优基础模型,将种源分组作为哑变量引入基础模型,根据确定系数(R2)、均方根误差(RMSE)、赤池信息准则(AIC)、模型预估精度(FP)对模型进行综合评价,构建基于帽儿山林场红松生长的最优树高生长方程。结果表明: 26个种源的生长性状值在区组间具有显著差异,而在种源间树高和胸径表现为差异显著。综合考虑不同生长性状指标所划分的4组种源生长量分别为A组(五营、鹤北、临江、东方红、桦南、露水河、方正)>B组(爱辉三站、凉水、铁力、清河)>C组(乌伊岭、沾河、亮子河、白河、柴河、草河口、八家子)>D组(桶子沟、大石头、汪清、和龙、延寿、大海林、小北湖、穆棱)。4组的最优基础树高生长模型为Gompertz模型,引入哑变量后模型的拟合精度(R2=0.9353)高于基础模型(R2=0.9303),模型预测精度也有一定的提升。各组种源树高生长曲线均符合“S”形变化规律,但各组存在明显差异,以A组种源表现最好。不同种源的红松生长量在一定程度上具有差异,含种源分组哑变量的人工红松树高生长模型能有效提高模型的预测精度,反映不同种源红松的树高生长差异,可以为红松人工林的选种培育提供科学依据。

关键词: 红松, 种源, 生长差异, 种源分组, 哑变量

Abstract: In order to analyze the growth pattern of tree height of planted Pinus koraiensis and screen the provenances with fastest growth, we grouped the provenances using the differences in tree height, diameter at breast height (DBH) and volume of timber of 234 individuals of planted P. koraiensis from 26 provenances in Maoershan Experimental Forest Farm. We constructed the growth equation for tree height by combining the base models of Gompertz, Korf, Richards, Logistic, and Schumacher, and then selected the optimal one. We introduced the prove-nance grouping as a dummy variable into the base model, and evaluated the optimal tree height growth equation by a comprehensive evaluation of the model according to the coefficient of determination (R2), the root-mean-square error (RMSE), the Akaikei Information Criterion (AIC), and the model's predictive precision (FP). The results showed that the growth traits of the 26 provenances had significant difference among the groups, and that tree height and DBH showed significant differences among the provenances. According to the comprehensive consideration of different growth traits, the four groups of provenance growth were divided into group A (Wuying, Hebei, Linjiang, Dongfanghong, Huanan, Lushuihe, Fangzheng) >group B (Aihuisanzhan, Liangshui, Tieli, Qinghe) > group C (Wuyiling, Zhanhe, Liangzihe, Baihe, Chaihe, Caohekou, Bajiazi) >group D (Tongzigou, Dashitou, Wangqing, Helong, Yanshou, Dahailin, Xiaobeihu, Muling). The optimal base tree height growth model of the four groups was the Gompertz model, and the fitting accuracy of the model after the introduction of dummy variables (R2=0.9353) was higher than that of the base model (R2=0.9303), and the model prediction accuracy was also improved. The tree height growth curves of each provenance group conformed to the “S”-shaped rule of change. There were obvious differences among the groups, with the best performance of the provenances in group A. The growth of P. koraiensis from different provenances was different, and the tree height growth model with dummy variables of provenance groups could effectively improve the prediction accuracy of the model, reflect the differences in height growth of P. koraiensis of different provenances, which could provide the scientific basis for the selection and cultivation of P. koraiensis plantations.

Key words: Pinus koraiensis, provenance, growth difference, provenance grouping, dumb variable