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林业科学 ›› 2021, Vol. 57 ›› Issue (3): 39-50.doi: 10.11707/j.1001-7488.20210305

• 论文与研究报告 • 上一篇    下一篇

竞争和气候及其交互作用对杉木人工林胸径生长的影响

臧颢1,刘洪生2,黄锦程2,张祖栋2,欧阳勋志1,宁金魁1,*   

  1. 1. 江西农业大学林学院 南昌 330045
    2. 崇义县林业局 赣州 341300
  • 收稿日期:2019-03-11 出版日期:2021-03-25 发布日期:2021-04-07
  • 通讯作者: 宁金魁
  • 基金资助:
    国家自然科学基金项目“气候和竞争对亚热带杉木人工林单木生长的交互作用”(31700563);江西省教育厅科技计划项目“气候敏感的杉木人工林林分断面积生长模型研究”(GJJ160397)

Effects of Competition, Climate Factors and Their Interactions on Diameter Growth for Chinese Fir Plantations

Hao Zang1,Hongsheng Liu2,Jincheng Huang2,Zudong Zhang2,Xunzhi Ouyang1,Jinkui Ning1,*   

  1. 1. College of Forestry, Jiangxi Agricultural University Nanchang 330045
    2. Forestry Bureau of Chongyi Ganzhou 341300
  • Received:2019-03-11 Online:2021-03-25 Published:2021-04-07
  • Contact: Jinkui Ning

摘要:

目的: 构建包含竞争指标和气候因子的胸径生长模型, 分析竞争和气候及其交互作用对杉木人工林胸径生长的影响, 为气候变化背景下模拟抚育间伐、择伐后保留木的生长变化奠定基础, 为森林适应性经营中科学合理地对杉木人工林进行间伐、择伐提供依据。方法: 基于江西省赣州市南康区、崇义县和上犹县杉木人工林固定样地数据, 采用潜在生长量修正法构建胸径生长模型。利用分位数回归模拟潜在生长量, 运用7个环境因子(5个气候因子: 调查间隔期的平均温度、最高温度、最低温度、降水量和大于5℃的积温; 2个地形因子: 海拔和坡度)反映立地质量对潜在生长量的影响, 依据参数显著性和方差膨胀因子确定可作为自变量的环境因子。采用指数函数形式构建修正函数, 修正函数的竞争因子包括3个林分密度指标和3个单木竞争指标(2个与距离无关的竞争指标和1个与距离有关的竞争指标), 筛选出最优竞争因子后考虑其与5个气候因子间的交互作用对估计精度的影响。通过模型评价, 选出估计精度最高的模型添加样地水平随机效应参数, 用于分析竞争和气候及其交互作用对杉木人工林胸径生长的影响。使用平均绝对误差(MAE)、平均相对误差绝对值(RMAE)和平均预估误差(MPE)评价模型估计精度。结果: 海拔、调查间隔期的最低温度和降水量对杉木人工林胸径潜在生长量具有显著影响。胸径20 cm时, 潜在生长量达到最大值, 调查间隔期的最低温度和降水量对潜在生长量最大值呈正向影响, 而海拔则呈负向影响。对比各竞争指标构建的胸径生长模型估计精度发现, 含与距离无关的竞争指标的生长模型估计精度最高, 其次是含与距离有关的竞争指标的生长模型。鉴于气候和竞争的交互作用可提升模型估计精度, 对比只在修正函数中考虑竞争因子的模型, 考虑交互作用的生长模型MAE降低0.60%~18.69%, MPE降低0.12%~9.72%。竞争因子选用大于对象木的断面积之和, 对应的气候因子选用平均温度、最高温度和最低温度进行交互作用的模型估计精度最好, 以其作为基础模型并添加样地水平随机效应参数构建最终胸径生长模型, 模型估计精度较好, MAE为0.071 1 cm、RMAE为20.37%、MPE为4.90%。基于最终模型发现, 除承受竞争压力很小和竞争压力较大的林木外, 温度变化可加剧竞争对胸径潜在生长量的修正效应。结论: 分位数回归对胸径潜在生长量的模拟效果较好, 气候和竞争的交互作用也可提升模型估计精度, 如果建模区域气候变化较大, 构建单木生长模型时, 建议在模型中考虑气候和竞争的交互作用。

关键词: 胸径生长, 竞争, 交互作用, 杉木, 降水, 温度

Abstract:

Objective: This study aimed to construct a growth model of diameter at breast height(DBH) for Chinese fir plantations based on competition and climate factors, and analyze effects of competition, climate factors and their interactions on DBH growth. The results were expected to provide a reference for the growth of reserved trees after thinning and selective cutting under climatic changes. Method: Based on the permanent sample plots in the south of Jiangxi Province, the potential increment equation with multiplicative modifiers was used to construct DBH growth model. The quantile regression technique was used to model the potential DBH increment, and seven environmental factors(mean temperature, maximum temperature, minimum temperature, precipitation, accumulated temperature greater than 5℃, elevation, slope) were used to describe the effects of site quality on potential increment. Parameter significance and variance inflation factor were chosen to determine environmental factors, which could be the independent variables. Exponential function form was used to construct modifier functions. Competition variables included in the modifier functions were three stand-level and three tree-level competition indices(2 distance-independent indices and 1 distance-dependent index). The interactions between the selected best competition index and five climate factors on good-of-fit of growth models were also considered. According to model evaluation, the model with the best good-of-fit was selected to add plot-level random effect parameters, and then used to analyze the effects of competition, climate and their interactions on DBH growth of Chinese fir plantations. Model evaluation criteria included mean absolute error(MAE), relative mean absolute error (RMAE), and mean predicted error(MPE). Result: Elevation, minimum temperature and precipitation during investigation intervals showed significant effects on the potential DBH increment for Chinese fir. The potential increment would reach the maximum when DBH was 20 cm. Minimum temperature and precipitation during investigation intervals showed positive effects on the maximum of potential DBH increment, but elevation showed a negative effect. Compared with DBH growth models based on the other competition indices, the models with distance-independent indices showed the best good-of-fit, and followed by the models with distance-dependent indices. After containing the interactions between climate and competition, the good-of-fit of models was improved. Compared with the models which only included competition indices in modifier functions, MAE of growth models with interactions decreased 0.60%-18.69%, MPE decreased 0.12%-9.72%. The model included the interactions between basal area in larger trees as the competition index and mean temperature, maximum temperature and minimum temperature as climate factors showed the best good-of-fit, thus the model was selected to add plot-level random effect parameters to construct the final model. The final model had a good good-of-fit, and MAE was 0.071 1 cm, RMAE was 20.37%, MPE was 4.90%. The final model showed that temperature could increase the modifying effects of competition on potential DBH increment, except for the trees with a little competitive pressure or with a large competitive pressure. Conclusion: The quantile regression could present a good effect when used to model the potential increment. The interactions between climate and competition could improve the good-of-fit of DBH growth model. Therefore, if climate changed dramatically, we recommended that the interactions between competition and climate should be considered into individual tree growth model.

Key words: DBH growth, competition, interaction, Chinese fir, precipitation, temperature

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