**2. Data**

Commonly, data interpreted using Classification and ordination, are collected in a species by sample data matrix, similar to the matrixes presented below.

Species abundances as main data matrix will also use the standardized set of no redundant environmental variables for use with clustering and indicator species analysis. Will be not need a second matrix, although Cluster analysis will produce one for use during this exercise. For explaining the issue, using data from Study area that is located in the North-East of the Semnan province in center of Iran (35º 53´ N, 54º 24´ E to 35º50´ N, 53º43´ E) (Fig 1).


**Table 1.** Data matrix using in Classification (using ordinal scale of Van-der-Marrel)

**2. Data** 

(Fig 1).

270 plots 9 Species

Some basic knowledge of Classification and Ordination methods that influence vegetation

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

Commonly, data interpreted using Classification and ordination, are collected in a species

Species abundances as main data matrix will also use the standardized set of no redundant environmental variables for use with clustering and indicator species analysis. Will be not need a second matrix, although Cluster analysis will produce one for use during this exercise. For explaining the issue, using data from Study area that is located in the North-East of the Semnan province in center of Iran (35º 53´ N, 54º 24´ E to 35º50´ N, 53º43´ E)

 Q Q Q Q Q Q Ar.si Se.ro Eu.ce St.ba Zy.er ... 1 10 0.5 0.5 0.5 0.5 ... 2 1.75 3.75 0.5 0.5 0.5 ... 3 1 0.5 3.75 1 0.5 ... 4 3.75 0.5 3.75 0.5 0.5 ... 5 6.25 0.5 0.5 1.75 0.5 ... 6 1 0.5 3.75 1 0.5 ... 7 10 0.5 1 1 0.5 ... 8 3.75 0.5 1 0.5 0.5 ... 9 3.75 0.5 1 0.5 0.5 ... 10 6.25 0.5 1.75 0.5 0.5 ... 11 6.25 1.75 3.75 0.5 0.5 ... 12 3.75 0.5 0.5 0.5 1 ... 13 10 0.5 15 1 0.5 ... 14 3.75 0.5 1.75 0.5 0.5 ...

**Table 1.** Data matrix using in Classification (using ordinal scale of Van-der-Marrel)

ecology might be needed to understand the examples presented in this study.

succession stages which response to environmental factors differently.

by sample data matrix, similar to the matrixes presented below.

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

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 interpret.

A quantitative survey of the vegetation is carried out during 2009-2010. In each of the studied types, soil and vegetative attributes were described within quadrates located along three 150m transverse transects. Quadrate size was determined for each vegetation type using the minimal area method. Considering variation of vegetation and environmental factors, forty five quadrates with a distance of 50m from each other were established in each vegetation type. Sampling method was randomized systematic. Floristic list, density and canopy cover percentage were determined in each quadrate. Vegetation cover data were recorded using ordinal scale of Van-der-Marrel (1979).

Classification and Ordination Methods as a Tool for Analyzing of Plant Communities 225

groups them into larger and larger clusters, until the entire data set is sampled (Pielou,

Cluster analysis, on the other hand, seeks to divide the n quadrates into groups of high internal similarity with respect to species or characters used. In the classical approach of Williams & Lambert (1959), the so-called Association-Analysis, communities are defined by the presence or absence of single species. This is highly dependent on the vagaries of sampling; many workers have felt the method may result in botanical over simplification, so

From the above discution, it can be seen that ordination and cluster analysis are not competing approaches and provided the ecologist is cautious in making inferences, both can reasonably

In classification of species the basic idea is that a characteristic species combination (or at least a group of differentiated species) should gather samples containing these species into

In fact, Classification assumes from the outset that the species assemblages fall into discontinuous group, whereas ordination starts from the idea that such assemblages very

Clustering, sometimes simply a synonym of classification, but more usually referring to

Clustering is a straightforward method to show association data, however, the confidence of the nodes are highly dependent on data quality, and levels of similarity for cluster nodes is dependent on the similarity index used. Krebs (1999) shows that mean linkage is superior to single and complete linkage methods for ecological purposes because the other two are extremes, either producing long or tight, compact clusters respectively. There are, however,

The objective of Cluster Analysis is to graphically show the relationship between cluster

The resulting graph makes it easy to see similarities and differences between rows in the same group, rows in different groups, columns in the same group, and columns in different groups. Groups of rows and columns relate to each other, could be seen graphically. Twoway clustering refers to doing a cluster analysis on both the rows and columns of your matrix, followed by graphing the two dendrograms simultaneously, adjacent to a representation of your main matrix. Rows and columns of your main matrix are re-ordered

Fig 1 showed dendrogram of Cluster analysis (study area: North East of Semnan rangelands, Iran). Grouping was performed using Euclidean distance and the Ward method.

no guidelines as to which mean-linkage method is the best (Swan, 1970).

to match the order of items in your dendrogram (Mucina, 1997).

Species with less than 2 entries in the matrix were deleted from the analysis.

be applied in the examination of multivariate samples (Pritchard & Anderson, 1971).

that nowadays polythetic methods are more usually applied.

clusters of similar samples (Tavili & Jafari, 2009).

1984).

gradually

**3.1. Cluster analysis** 

agglomerative classification.

analyses and your individual data points.

In fact, the cover data transformed using an eight-point scale ((0–1=0.5, 1–2.5=1.75, 2.5– 5=3.75, 5–7.5=6.25, 7.5–12.5=10, 12.5–17.5=15, 17.5–22.5=20, 22.5–27.5=25, >27.5=30)

Sample data may include measures of density, biomass, frequency, importance values, presence/absence, or any number of abundance measures.

Ordination can help us find structure in these complicated data sets. By using various mathematical calculations, ordination techniques will identify similarity between species and samples. Results are then projected onto two dimensions in such a way that species and samples most similar to one another will be close together, and species and samples most dissimilar from one another will appear farther apart (as shown at this study).


**Table 2.** Data matrix using in Ordination

Data analysis was performed on the species, averaging all plots per site. All numerical analyses were done with the PC-ORD, V. 4 package (McCune and Mefford, 1999).
