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**Chapter 11** 

© 2012 Chahouki, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Chahouki, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Classification and Ordination Methods as a Tool** 

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. Hence for this purpose, it

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

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 &

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

This study aims to explain these methods as tool for analyzing of plant Communities. The use of multivariate analysis has been extended much more widely over the past 20 years. 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). It is a main objective in data analysis to distinguish random from deterministic components. Therefore spatial and temporal interactions add to the complexity of

**for Analyzing of Plant Communities** 

Mohammad Ali Zare Chahouki

analysis (RDA) will be explained briefly.

methods (Legendre & Legendre, 1998).

vegetation systems (Wildi, 2010).

http://dx.doi.org/10.5772/54101

**1. Introduction** 

Legendre, 1998).

Additional information is available at the end of the chapter

is necessary to imply best statistical methods (Causton, 1988)

