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首页> 外文期刊>Revista rvore >Utiliza??o de métodos estatísticos multivariados na caracteriza??o do mosaico sucessional em floresta semidecídual
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Utiliza??o de métodos estatísticos multivariados na caracteriza??o do mosaico sucessional em floresta semidecídual

机译：多变量统计方法在半近代森林中连续马赛克表征中的用途

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摘要

The aim of this paper was to verify the feasibility of using multivariate statistical methods through structural variables for successional mosaic characterization in a semideciduous forest section. Plots measuring 10m x 10m were allocated for structural analysis (phytosociological survey plus the variables Coverage Percentage (CP), Canopy Height (CH) and Liana Cover (LC). The following statistical methods have been used: Principal Components Analysis and Cluster Analysis - more specifically, Ascending Hierarchical Classification. 43.96% of the total variance has been explained by the first principal component and 25.66% by the second one. The variables Basal Area (BA), Average Diameter (AD) and Average Dominance (ADOM) presented positive correlations (higher than 0.75) among themselves. Therefore, AD and ADOM may be considered as variable groups. The variables Number of Individuals (NI) and Number of Species (NE) showed a 0.60 correlation. The variables CH, LC and CP presented lower correlations. These findings show that their inclusion in this analysis was important. The hierarchical classification and the division of the groups in four parts have been performed considering two first factorial axes. Results showed two different types of behavior: 1) low values for CH and BA - Group 1 with low values also for NI, NE and CP (Gap Phase) and Group 2 with high values for NI and LC and low values for ADOM and AD (Building Phase); 2) high values for CH and BA - Group 3 with high values also for NI, NE and CP and low value for LC (Mature Phase) and Group 4 with high values for ADOM and AD, and lower for LC (Degradation Phase). The multivariate statistical methods allowed the forest mosaic developing phase characterization through structural variables. The estimative of CH, LC and CP variables must be improved. Other variables should be included in order to better differentiate the phases.
机译：本文的目的是验证通过结构变量使用多变量统计方法的可行性，用于在半婚外林段中的连续马赛克表征。分配了10m×10M的图来分配了结构分析（植物病毒调查加上变量覆盖百分比（CP），冠层高度（CH）和LC）。已经使用了以下统计方法：主要成分分析和聚类分析 - 更多具体而言，上升分层分类。第一个主成分的43.96％的总方差由第二个主成分解释，第二个主要组成部分解释了25.66％。变量基础区域（BA），平均直径（AD）和平均优势（ADOM）呈现正相关（高于0.75）之间。因此，广告和ADOM可以被认为是可变组。变量（Ni）和物种数量（NE）的变量显示为0.60相关性。变量CH，LC和CP呈现较低的相关性。这些调查结果表明，它们在这种分析中的包容性很重要。考虑到两个部分进行分层分类和组的分组分组第一因子轴。结果显示了两种不同类型的行为：1）CH和BA - 组1的低值，其中Ni，NE和Cp（间隙阶段）和组2的基团2，适用于NI和LC的高值，以及ADOM和AD的低值（建设阶段）; 2）CH和BA - 组3的高值，具有高值，Ni，Ne和Cp和LC（成熟相）的低值（具有高值的LC（成熟阶段）和ADOM值的低值，并且LC（降解相）降低。多变量统计方法使森林马赛克通过结构变量开发相位表征。必须提高CH，LC和CP变量的估计值。应包括其他变量，以便更好地区分阶段。

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