The objective of this research was to apply multivariete techniques analysis to separate ecological groups. Data of 37 tree species, in area without intervention, obtained in ten years of survey by the Experiment of Sustainable Production in Secondary Forest of Transition, established in 1986, at Rio Vermelho and Serra Azul de Minas, Minas Gerais State, Brazil were used. The species were separated in pioneers, early secondary and old secondary. The considered variables were: number of trees per hectare, number of ingrowth, mortality, basal area, volume, mean diameter, increment in diameter, increment in basal area, increment in volume, index of value of importance and natural regeneration. Principal components analysis (PCA); cluster analysis (CA) and the discriminant analysis (DA) were used. By PCA it was possible to reduce the dimension to three-dimensional with variance explanation above 79%. In the CA, seeking a classification at posteriori, it was observed that group formation did not correspond to the classification at priori. With the DA, 92.86 and 57.14% of classification at posteriori and at priori respectively were correct. In conclusion: the use of the principal components analysis, cluster analysis and discriminant analysis allowed the identification of tree species that should be classified in a larger number of ecological groups; and the application of PCA, CA and DA in the evaluation of at priori classification confirms most researchers' subjectivity in classifying ecological groups of tree species.
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机译：本研究的目的是将多态技术分析应用于分离生态群体。 37种树种的数据在没有干预的情况下，在1986年在Rio Vermelho和Serra Azul de Minas的1986年在1986年成立的次级过渡的可持续生产中获得了十年的调查中，使用了米纳斯吉拉斯州。这些物种在先驱，早期和旧的中学中分离。所考虑的变量是：每公顷的树木数量，成长数，死亡率，基础面积，体积，平均直径，增量，基础面积增量，体积增量，重点值和自然再生的索引。主成分分析（PCA）;使用聚类分析（CA）和判别分析（DA）。通过PCA，可以将维度降低到三维，方差解释79％。在CA中，在后验中寻求分类，观察到群体形成与先验的分类没有对应。随着DA，92.86和57.14％的分类分别是正确的。总之：使用主成分分析，聚类分析和判别分析允许鉴定应在更大数量的生态群体中分类的树种;在PriaI级分类评估中PCA，CA和DA的应用证实了大多数研究人员在分类树种生态群体方面的主观性。
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