**1. Introduction**

220 Multivariate Analysis in Management, Engineering and the Sciences

Stem Cells, 2008; 26(1) 108-118.

Optics 2011; 16(5) 057005.

1993; 36(1) 59-74.

2009; 37(15) 5197-5207.

Research, 2004; 105(2-4) 215-21.

Reviews, 1972; 41(3) 258 -280.

173.

2551.

British Journal of Cancer, 2011; 104(5) 790-797.

spectroscopy. Langmuir, 2005; 21(20) 9091-9097.

Class Discrimination Segregates Markers Responsible for Inter- and Intra-Category

Variance in Exfoliative Cervical Cytology. Biomarker Insights, 2008; 3 179–189. [70] Kelly JG, Singh MN, Stringfellow HF, Walsh MJ, Nicholson JM, Bahrami F, Ashton KM, Pitt MA, Martin-Hirsch PL, Martin FL. Derivation of a subtype-specific biochemical signature of endometrial carcinoma using synchrotron-based Fourier-transform

[71] Walsh MJ, Fellous TG, Hammiche A, Lin WR, Fullwood NJ, Grude O, Bahrami F, Nicholson JM, Cotte M, Susini J, Pollock HM, Brittan M, Martin-Hirsch PL, Alison MR, Martin FL. Fourier transform infrared microspectroscopy identifies symmetric PO(2)(-) modifications as a marker of the putative stem cell region of human intestinal crypts.

[72] Taylor SE, Cheung KT, Patel II, Trevisan J, Stringfellow HF, Ashton KM, Wood NJ, Keating PJ, Martin-Hirsch PL, Martin FL. Infrared spectroscopy with multivariate analysis to interrogate endometrial tissue: a novel and objective diagnostic approach.

[73] Chonanant C, Jearanaikoon N, Leelayuwat C, Limpaiboon T, Tobin MJ, Jearanaikoon P, Heraud P. Characterisation of chondrogenic differentiation of human mesenchymal stem cells using synchrotron FTIR microspectroscopy. The Analyst, 2011; 136(12) 2542-

[74] Thumanu K, Tanthanuch W, Danna Y, Anawat S, Chanchao L, Rangsun P, Philip H. Spectroscopic signature of mouse embryonic stem cell-derived hepatocytes using synchrotron Fourier transform infrared microspectroscopy. Journal of Biomedical

[75] Debey P, Szöllösi MS, Szöllösi D, Vautier D, Girousse A, Besombes D. Competent mouse oocytes isolated from antral follicles exhibit different chromatin organization and follow different maturation dynamics. Molecular Reproduction and Development,

[76] Marty R, N'soukpoé-Kossi CN, Charbonneau DM, Kreplak L, Tajmir-Riahi HA. Structural characterization of cationic lipid-tRNA complexes. Nucleic Acid Research,

[77] Liu J, Conboy JC. Structure of a gel phase lipid bilayer prepared by the Langmuir-Blodgett/Langmuir-Schaefer method characterized by sum-frequency vibrational

[78] Gentile L, Monti M, Sebastiano V, Merico V, Nicolai R, Calvani M, Garagna S, Redi CA, Zuccotti M. Single-cell quantitative RT-PCR analysis of Cpt1b and Cpt2 gene expression in mouse antral oocytes and in preimplantation embryos. Cytogenetic and Genome

[79] Zhizhina GP, Oleinik EF. Infrared spectroscopy of nucleic acids. Russian Chemical

[80] Ten GN, Baranov VI. Manifestation of intramolecular proton transfer in imidazole in the electronic-vibrational spectrum. Journal of Applied Spectroscopy, 2008; 75(2) 168–

infrared microspectroscopy. Cancer Letters, 2009; 274(2) 208-217.

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

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.

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).

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).

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 vegetation systems (Wildi, 2010).

© 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.

Some basic knowledge of Classification and Ordination methods that influence vegetation ecology might be needed to understand the examples presented in this study.

Classification and Ordination Methods as a Tool for Analyzing of Plant Communities 223

**Figure 1.** Location of study area and the distribution of the vegetation types.

interpret.

The below is a relatively simple data set. However, it is easy to imagine that a true data set may encounter dozens of species over 270 of samples. Complex sample by species matrices represent dozens to 270 of dimensions which are impossible to visualize or interpret. Even graphed, species response curves of large community data sets can be nearly impossible to

Studying the vegetation distribution pattern is a basic aspect of the design and management (Zhang et al., 2006). Quantitative separation was studied by previous scholars to investigate the contribution of environmental factors to the whole or different layers of plant community distribution pattern. (Zhang et al., 2004). Actually, natural plant communities are distributed continuously, and they are composed of plant communities at different succession stages which response to environmental factors differently.
