Explain Operational Taxonomic Units - Numerical Taxonomy
1) Then the characters to be used for studying the OTUs are selected. Usually a large number of characters are taken as it is presumed that the greater the number of characteristics the more valid the classification.
2. The characters chosen include any observable attribute or trait of the OTUs: morphological, behavioural, ecological embryological etc. All such traits have to be observable phenotypic characters and so it is for this reason that the resultant grouping, classification or key obtained from taximetric is said Lo be phenetic. In other words, it is not based on evolutionary or phylogenetic or phyletic relationships. All the selected characters are given equal weight age. Thus each character is of the same value as the other.
3. Once the characters are selected they have to be recorded in a suitable form. This process is called coding. Quantitative attributes like the number or lips in the nematodes, or measurements of parts of the body can be recorded directly. Two state or multistate characters need to be coded. In the case of two state characters the presence is usually coded as 1 or + and absence as 0 or -.
4) When a character exists in more than one qualitative state, these may be broken down into a series of two state characters. For example, the cuticle of an insect may be white, yellow, brown or black, the character can be coded + or 1 when! present or -,or 0 when absent for each state in turn. Or each of the possible states can be indicated by a number and the disagreement or agreements (usually I I termed mismatch or match) can be recorded as + or - for each comparison i between OTUs.
5) An alternative method would be to use multistate coding where a single trait can be coded in a number of states, each being represented by a numerical symbol or &ode e. g, 1, 2, 3 etc. depending on the range of variation. Thus if we again look at the colour of the cuticle of an insect, we can assign different code to different colours such as white = 1, brown = 2, black = 3, and so on, Besides qualitative characters such as colour of hair, cuticle etc, multi state coding is also useful in quantitative characters such as number of lips, setae, length of body, width of body, number of markings and other characters involving measurements. A code is prepared for the range of variation as for example, body length ma9 be coded as 5-10 mm = 1,11-15 mm = 2, 16-20 mm = 3,21-25 mm = 4 etc.
Occasionally due to some reason or another no comparison is possible or data may be missing for one of the traits of the OTUs. Such situations are coded as NC (no comparison).
6) Now the next step after the OTUs have been selected and the character states and their subsequent coding has been determined is the presentation of data in the form of a primary data matrix or tax matrix where 't' represents the OTUYs and 'n' the characters Fig. 8.28a and 8.28b.
If we have studied 50 OTUs and scored 100 characters from each, then we will obtain 50 x 100 = 50,000 Units of information. Thus the large amount of information obtained makes the use of computers usually absolutely necessary in numerical taxonomy.
7) Now, in the next stage, each OTU is compared in turn with all the others with respect to their character states . In order to accomplish this there must be some means of. comparing the degree of similarity. This is achieved by calculating the coefficient of similarity (S) S = m/n. Here, m is the number of matches of character states between pairs of OTUs, and n is the total number of characters (NC entries are excluded). The coefficient of similarity is presented.
The coefficients of similarity for OTUs are indicated as fractions more usually, however, in decimal fractions or percentages.
8) Let us see how the coefficient of similarity is calculated in percentage. You will observe that one out of a total of 4 of its characters match so the similarity between them will be 114 x 100 = 25 per cent. Obviously 100 per cent would mean that the two groups are identical with respect to the characteristic chosen and 0 per cent means they are totally different. The type of numerical taxonomic analysis described here is the simplest of a large number of possible techniques and is known as 'single linkage cluster analysis' in which the measure of similarity is based on match/ mismatch.
worked not in percentage of 10. OTUs (A-J) and their corresponding observable characters 1-10'(t) in order to explain to you how the measure of similarity is further worked out by the single linkage cluster analysis.
9) You will observe that the figures on the upper and lower sides of the diagional line are mirror images. So it is customery to illustrate only one part as we have done.
10) Now, the next step involve the rearrangement of the similarity matrix so that the groups of OTUs which show closest similarity are clustered together. Again several techniques are available for doing this. However, with the small numbers of OTUs and their correspondingly small number of characters, chosen by us, it is possible by just looking at Fig. 8.29 to observe immediately a high degree of similarity between ACF and H, between D E and J and again between B, G, and I. So, the matrix can be organised to form blocks of high similarity. Usually however the date of the similarity matrix to very large and so blocks of high similarity can only be calculated with the help of computers.
11) The blocks of similarity are based on the percentage of shared character states and can be represented graphically in the form of a tree or dendogram known as 'phenogram'. In this type of data presentation, the phenetically similar OTUs can be linked together by horizontal lines drawn at appropriate distances in relation to a vertical scale, which represents degrees of similarity and which, as you can see, is expressed in percentage. The clearly evident clusters of similar OTUs are termed as 'phenon' and the levels which indicate the degree of similarity on the vertical scale are known as phenon lines.
In the hypothetical dendogram the OTUs A, C, F and H form a phenon which have a similarity of 80-87% D and J have 96% similarity end both are joined to E at about 70% level. G, I and B are joined at 85-87% level. The group formed by J, D and E is linked to A, C, F and H at 60% phenon line. More distant from all of these seven are G I and B to which they are linked with only 50-57%, similarity.
It is quite tempting though not justifiable to regard such dendograms as phylogenetic trees or at least approximations to them. It is mainly because of this and because of the subjective nature of categories, such as genus, subfamily, family etc, that levels of phenon lines have been used to delimit tax above the species level. For instance, . phenon with about 70% similarity may be regarded as of generic rank and those of say 50% of family rank. 'This provides an obvious though arbitrary method of standardizing the level of various categories in the taxonomic hierarchy. Whether or not genera and family should be delimited in this manner by more or less constant levels of phenon lines is a, debatable points.