Sanjeet Kumar
Numerical
Taxonomy
The classification of Taxonomic
units into various groups by numerical methods is called “Numerical Taxonomy”.
The publication of pioneer work of Sokal and Sneath (1963) “Principle of
Numerical Taxonomy” was the first comprehensive exposition in this regard.
Since then, there has been a very rapid increase in the development of methods
dealing with numerical taxonomy as well as their outstanding applications for
the cause of this new aid to plant taxonomy.
It is a classification system in
biological systematic which deals with the grouping by numerical methods of
taxonomic units based on their character states. It aims to create a taxonomy
using numeric algorithms like cluster analysis rather than using subjective
evaluation of their properties. Robert R. Sokal and Peter H. A. Sneath in 1963,
they divided the field into phenetics in which classifications are formed based
on the patterns of overall similarities and cladistics in which classifications
based on the branching patterns of the estimated evolutionary history of the
taxa. Note: in recent years many authors treat numerical taxonomy and phenetics
as synonyms despite the distinctions made by those authors. Although intended
as an objective classification method, in practice the choice and implicit
weighing of characteristics is, of course, influenced by available data and
research interests of the investigator.
What was made objective was the
introduction of explicit steps to be used to create phenograms and cladograms
using numerical methods rather than subjective synthesis of data As a matter of
fact, numerical taxonomy is primarily based on phenotypic evidence rather than
on supposed phylogeny. Then, it is divided into a number of repeatable logical
steps. The lowest ranking taxa in any particular study are called as
operational taxonomic units (OTU’s). These basic units could be treated as an
individual or supra-individual category such as species, genus or even higher
ranked taxa.
A sufficiently large number of suitable characters of OUT’s are
selected in a regular manner. It is usually recommended that not less than 50
characters should be used and where feasible considerably more characters
should be employed. The value of similarity coefficient becomes more stable
with the increase in the number of characters sampled. It is suggested that
taxonomic characters should be selected from all parts and all stages of the
life cycles and all characters varying within the group studied should be used.
Characters capable of expression as binary, qualitative multi state or
quantitative multistate data are generally used while studying this numerical
taxonomy. Binary characters are to possess two contrasting states, such as the
presence or absence of some specific features ego presence or absence of spine
or any other contrasting alleles of single character i.e. fruits being
dehiscent or indehiscent.
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