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    吉林蛟河不同树种储水量分配特征与预测模型

    Allocation characteristics and prediction models of water storage capacity among different tree species in Jiaohe, Jilin Province of northeastern China

    • 摘要:
      目的 分析吉林蛟河12个乔木树种整株及各组分含水率和储水量分配特征,构建并筛选各树种最优储水量预测模型,探讨不同树种储水量随森林发育阶段的变化,为该地区森林树种储水量估算提供模型参考。
      方法 采用单因素方差分析,对比12个树种不同器官含水率和储水量占比的差异,并通过多重比较法进行显著性检验。应用肯德尔秩相关分析法,以胸径(D)、树高(H)、D2H为模型自变量,整株及各器官储水量为因变量,构建多种形式的储水量预测模型,并通过模型决定系数、参数显著性以及赤池信息准则筛选最优模型。结合林地信息,计算不同发育阶段树种的储水量。
      结果 (1)12个树种器官平均含水率顺序为树叶 > 树根 > 树枝 > 树干。除千金榆外,其余树种在各器官储水量分配上普遍呈现树干 > 树根 > 树枝 > 树叶的趋势。随胸径增大,树枝储水量占比增大,而树干与树叶的储水量占比减小,树根储水量变化不显著。(2)12个树种的储水量预测模型均为对数函数形式,不同树种器官的最优模型自变量各异。(3)随着森林演替,单位面积乔木的储水量和生物量均增加。
      结论 本研究揭示了储水量占比和含水率在器官间与物种间存在显著差异,其中储水量与树高、胸径之间存在种间特异性,且不同器官储水量占比随胸径增长呈现不同变化趋势。所筛选的储水量最优模型均为对数函数形式,其中单树种储水量预测模型具有较高的拟合精度,而全树种模型更适用于估算区域性储水量。本文阐明了吉林蛟河树木水分状况在不同时空尺度上的变化规律,有助于加深对生态系统动态过程的理解,并为该地区森林树种储水量的精确估算提供了可靠的模型参考。

       

      Abstract:
      Objective This paper analyzed the distribution characteristics of moisture contents of 12 tree species in northeastern China and species-specific allometric equations of 12 tree species were established to explore the differences in water storage capacity characteristics among different tree species with forest developing, as well as providing model reference for the estimation of water storage capacity in this area.
      Method One-way ANOVA and multiple comparison methods were used to contrast differences in moisture content and water storage capacity proportion among various organs across the 12 tree species. Utilizing Kendall’s rank correlation analysis to identify DBH (D), tree height (H), and D2H as predictor variables in water storage capacity prediction models with whole-tree and organ-specific water storage capacity serving as response variables. Different forms of water storage capacity prediction models were constructed based on these relationships. Optimal models were selected through evaluation using the coefficient of determination, parameter significance level, and Akaike’s information criterion. Integrating stand information, this approach was employed to calculate the water storage capacity of trees across varied developing stages.
      Result (1) Overall, average moisture content was highest in leaves, followed by roots, branches, and stems. Except for Carpinus cordata, all other species showed a consistent pattern in water allocation across organs: stem > root > branch > leaf. As D increased, the proportion of branch water storage capacity increased, while the proportion of stem and leaf water storage capacity decreased, with no significant changes in root water storage capacity. (2) The water storage capacity prediction models for all 12 tree species were best represented by logarithmic functions. The optimal independent variables for organ moisture content models of different tree species were different. (3) With forest succession, both water storage capacity and biomass per unit area increased.
      Conclusion The study highlights significant differences in water storage capacity and distribution among organs and tree species, with species-specific relationship between water storage capacity and D, as well as H. The percentage of water storage capacity of different organs shows different trends with the increase of breast diameter. The water content prediction models for all 12 tree species were best represented by logarithmic functions. The single-species models have higher fitting accuracy, while the multi-species model has broader application. This research elucidates the spatiotemporal dynamics of water status in temperate-boreal tree species, contributing to a deeper understanding of ecosystem dynamics. It provides a scientific basis for accurate estimation of tree water storage capacity in the forest region of Jiaohe, Jilin Province of northeastern China.

       

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