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应用生态学报 ›› 2023, Vol. 34 ›› Issue (9): 2345-2354.doi: 10.13287/j.1001-9332.202309.001

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兴安落叶松天然林更新数量相容性预测模型

肖晨, 田栋元, 马榕, 董灵波*   

  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:2023-05-10 修回日期:2023-07-22 出版日期:2023-09-15 发布日期:2024-03-16
  • 通讯作者: *E-mail: farrell0503@126.com
  • 作者简介:肖 晨, 女, 1999年生, 硕士研究生。主要从事森林可持续经营研究。E-mail: 980590318@qq.com
  • 基金资助:
    “十四五”国家重点研发计划项目(2022YFD2200502)和黑龙江省头雁创新团队计划项目(森林资源高效培育技术研发团队)

Compatibility predictive model for regeneration quantities of Larix gmelinii natural forest in Daxing’anling Mountains, China

XIAO Chen, TIAN Dongyuan, MA Rong, DONG Lingbo*   

  1. Ministry of Education Key Laboratory of Sustainable Forest Ecosystem Management, School of Forestry, Northeast Forestry University, Harbin 150040, China
  • Received:2023-05-10 Revised:2023-07-22 Online:2023-09-15 Published:2024-03-16

摘要: 天然更新等级是制定森林经营措施的重要依据,传统研究仅考虑了林分整体或部分优势树种的更新数量预测模型,并未解决不同树种和林分整体更新数量预测结果间的相容性,即林分整体预测结果不等于各个树种预测结果之和。为此,本研究在传统计数模型的基础上,构建林分中不同树种更新数量的相容性预测模型,为天然林的合理经营和决策提供理论依据。以大兴安岭地区翠岗林场、新林林场和壮志林场96块标准样地调查数据为基础,从立地因子、土壤因子、林分因子、林木多样性以及林分空间结构5个方面选取30个指标,以泊松模型、负二项模型为基础模型,分别构建兴安落叶松、白桦和其他树种的更新数量预测模型,对比2种传统计数模型的精度和拟合效果之后选择最优模型,采用似乎不相关方法构建不同树种的更新数量相容性预测模型。兴安落叶松、白桦和其他树种更新数量的最优基础模型均为泊松模型,以其为基础构建的兴安落叶松、白桦、其他树种以及全林分更新数量相容性预测模型的均方根误差分别为388、413、504和871株·hm-2,调整决定系数分别为0.389、0.421、0.488和0.407。影响兴安落叶松、白桦、其他树种更新数量重要性的变量大小依次为胸径Pielou均匀度指数(25.2%)、草本植物盖度(34.6%)以及土壤B层有机质含量(23.2%)。本研究构建的更新数量相容性预测模型系统,满足兴安落叶松、白桦、其他树种和全林分之间的可加性逻辑,为促进天然更新的准确估测提供了基础。

关键词: 更新, 数量预测模型, 相容性模型, 似乎不相关回归

Abstract: The natural regeneration grade is an important foundation for formulating forest management measures. Traditional studies have only considered the regeneration quantities predictive model of the total stand or dominant tree species, but the consistency among the prediction results of different tree species and the total regeneration quantities of stand is not solved. That is, the regeneration prediction results at the stand level are not equal to the sum of the predicted results of all tree species. To address this, on the basis of the traditional counting model, we attempted to construct a compatibility predictive model for regeneration quantities of different tree species within the stand, which would provide a theoretical basis for the rational management and decision-making of natural forest. Based on the survey data from 96 standard plots of Cuigang Forest Farm, Xinlin Forest Farm, and Zhuangzhi Forest Farm in Daxing’an Mountains, we selected 30 basic indices from five aspects of site factor, soil factor, stand factor, tree diversity and stand spatial structure, and used Poisson model and negative binomial model as the basic models to construct the regeneration prediction models of Larix gmelinii, Betula platyphylla and other tree species. By comparing the accuracy and fitting effect of the two traditional counting models, we selected the optimal model and used the seemingly unrelated regressions to further construct the compatibility predictive model for regeneration quantities of different tree species. Poisson model was the best one for the regeneration of L. gmelinii, B. platyphylla, and other tree species. The test index RMSE of the compatibility predictive model for regeneration quantities of L. gmelinii, B. platyphylla, other tree species and total stand regeneration quantities were 388, 413, 504, and 871 trees·hm-2, respectively. The adjusted R2 was 0.389, 0.421, 0.488, and 0.407, respectively. The most influential variables for regeneration quantities of L. gmelinii, B. platyphylla and other tree species were Pielou evenness index of DBH (25.2%), herbal coverage (34.6%) and organic matter in B layer (23.2%). In this study, the compatibility predictive model system for regeneration quantities satisfied the additive logic among L. gmelinii, B. platyphylla, other tree species, and total stands, and provided a basis for accurately estimating natural regeneration.

Key words: regeneration, quantity prediction model, compatibility model, seemingly unrelated regression