Comparing and classifying communities

35

because both sites were unsuitable.

7.3

Cluster analysis

When numbers of sites or habitats are to be compared the 

similarity measures

82

offered by CAP can form the basis of cluster analysis, which seeks to identify

groups of sites, or stations that are similar in their species composition.

Classification methods comprise two principal types, hierarchical, where objects

are assigned to groups that are themselves arranged into groups as in a

dendrogram, and non hierarchical, where the objects are simply assigned to

groups. The methods are further classified as either 

agglomerative

75

 where the

analysis proceeds from the objects by sequentially uniting them or 

divisive

80

,

where all the objects start as members of a single group which is repeatedly

divided. For computational and presentational reasons hierarchical agglomerative

methods are the most popular.

The basic computational scheme used in cluster analysis can be illustrated using

single linkage cluster analysis as an example. This is the simplest procedure and

consists of the following steps.

1.

 Start with n groups each containing a single object (sites or

species).

2.

 Calculate, using the similarity measure of choice, the array of

between object similarities.

3.

 Find the two objects with the greatest similarity, and group them

into a single object.

4.

 Assign similarities between this group and each of the other objects

using the rule that the new similarity will be the greater of the two

similarities prior to the join.

5.

 Continue steps 3 and 4 until only one object is left.

The results from a cluster analysis are usually presented in the form of a

dendrogram.

The problem with all classification methods is that there can be no objective

criteria of the best classification; indeed even randomly generated data can

produce a pleasing and convincing dendrogram. Always consider carefully

whether the groupings identified seem to make sense and reflect some feature of

the natural world.

7.4

Multivariate analysis

Multivariate analysis is used when the objective is to search for relationships

between or classify objects (sites or species) that are defined by a number of

attributes. Generally, we seek to show the relationship between sites (or samples)

using the species as the attributes. Data sets can be large, for example marine

benthic or forest beetle faunal studies can easily require analysis of a matrix of

100 samples (stations) by 350 species and thus multivariate analysis requires a

computer. If the objective is to assign objects to a number of discrete groups then

cluster analysis should be considered. If there is no 

a priori

 reason to believe the

objects will or could naturally fall into groups then an ordination technique may be

more suitable. Ordination assumes the objects form a continuum of variation and

the objective is often to generate hypotheses about the environmental factor(s)

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