![]() Therefore spatial and temporal interactions add to the complexity of vegetation systems ( Wildi, 2010). It is a main objective in data analysis to distinguish random from deterministic components. Much more is included on techniques such as Canonical Correspondence Analysis (CCA) and Non-metric Multidimensional Scaling (NMS), Principal component analysis (PCA) and another technique to include plant communication and plant-environment relationships ( Kent, 2006). The use of multivariate analysis has been extended much more widely over the past 20 years. This study aims to explain these methods astool for analyzing of plant Communities. The choice of the mathematical method of analysis is mainly determined by availability rather than an accurate knowledge of the properties and limitations of the possible different methods ( Legendre & Legendre, 1998). Ordination (or inertia) methods, like principal component and correspondence analysis,and clustering and classification methods are currently used in many ecological studies (Anderson, 1971 Gauch et aL, I982a Orloci, 1978 Whittaker et al, 1967 Legendre & Legendre, 1998). In this study, some important classification and ordination methods such as cluster analysis (CA), Two way Indicator Species Analysis (TWINSPAN), Polar Ordination (PO), Nonmetric Multidimensional Scaling (NMS), Principal component analysis (PCA), Detrended Correspondence Analysis (DCA), Canonical correspondence analysis (CCA), Redundancy analysis (RDA) will be explained briefly. ![]() Hence for this purpose, it is necessary to imply best statistical methods ( Causton, 1988) ![]() Community ecologists aim at understanding the occurrence and abundance of taxa (usully species) in space and time and the goal of all studies in plant ecology, is finding spatial and temporal interactions add to the complexity of vegetation systems. ![]()
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