Clustering of forest species by univariate and multivariate analysis of anatomical, physical and chemical characteristics of wood.
In the grouping of native and exotic species it is important to examine several wood features. In this respect, the multivariate analysis has advantages; since it provides the application of statistical methods that allow describing and analyzing the anatomical, physical and chemical wood characteristics. This work aimed at grouping and identifying the potential use of 12 forest species by their anatomical, physical and chemical wood properties and then applying the Scott-Knott test to interpret of results. Twelve native and introduced wood species were analyzed; for which physical characteristics (density, volumetric shrinkage), anatomical (length, width and fiber wall thickness, diameter, frequency and area occupied by vessels) as well as chemical (extractives total, holocellulose and lignin) were determied. The univariate and multivariate statistical analysis indicated that wood characteristics are effective for grouping and identifying for potential uses. The most important characteristics of wood in separating groups of forest species by Principal Component Analysis were: holocellulose, basic density and fiber wall thickness in Component 1 and volumetric shrinkage, vessel diameter and total extractives in Component 2.