Merits and Demerits of Numerical Taxonomy

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 Merits and Demerits of Numerical Taxonomy 

The process of categorizing organisms using numerical techniques is known as numerical taxonomy. This method firmly clarifies as well as displays the degree of link among the species. Organisms are classified into taxa according to their similarities and differences. 


Numerical Taxonomy is at present a very essential part of current systematics. Numerical Taxonomy is also known as Taximetrics; however, presently it is more commonly referred to as Phenetics. It is the Numerical evaluation of the similarities or affinities of taxonomic units, which are typically classified into taxa based on their affinities. It is also known as Adansonian taxonomy or phenetics. Robert Sokal and Peter Sneath defined numerical taxonomy. It is a method of classifying organisms with the help of numerical methods. In this type of taxonomy, each character is given equal weightage in creating new taxa. The phenetic similarity is the basis of this classification.

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The following are some benefits of numerical taxonomy as stated by Sneath and Sokal:

Because Numerical Taxonomy employs more numbers and more thoroughly stated characters than conventional taxonomy, it improves the data.

  • A range of sources, including morphology, chemistry, physiology, etc., are used to get the data.
  • Better classification systems and keys can be constructed more effectively using the data obtained, since numerical approaches are more sensitive in delimiting taxa.
  • In actuality, numerical taxonomy has proposed a number of significant modifications to the traditional classification schemes.
  • Numerous biological notions that have been around for a while have been reinterpreted in the context of numerical taxonomy.
  • More taxonomic work can be completed by less skilled people thanks to numerical taxonomy.
  • Because numerical taxonomy uses better and more detailed characters than conventional taxonomy, it improves the data. A range of sources, including morphology, chemistry, physiology, etc., are used to get the data
  • With the use of electronic data processing systems, the data collected can be effectively utilized in the development of improved keys and classification systems, as well as in the production of maps, descriptions, catalogues, and other materials, as numerical approaches are more sensitive in defining taxa. In actuality, numerical taxonomy has proposed a number of significant modifications to the traditional classification schemes.

Demerits of Numerical Taxonomy

  • Instead of phylogenetic classifications, the numerical approaches accompany phenetic classifications.
  • The advocates of the "biological" species notion might not agree with the limits that these techniques have identified.
  • The biggest drawback of this approach is character selection. The statistical techniques could not provide the anticipated result if there are insufficient characters chosen for comparison.
  • According to Steam, various taxometric procedures may produce different outcomes. Selecting a technique that meets the requirements and counts the number of features required to acquire the desired outcomes from these mechanical aids is a critical challenge.
  • Determining whether a larger set of qualities would actually lead to more anticipated outcomes than a smaller set of traits is also crucial. 

Sneath and Sokal named the following seven numerical taxonomy principles:

(i) A classification system will perform better the more characters it takes into account and the more information it has in the taxonomy.

(ii) When establishing new taxa, each character ought to be given the same weight.

(iii) The specific similarities in each of the numerous characters that are taken into consideration for comparison determine the overall similarity between any two entities.

(iv) Character correlation varies among the studied organism groupings. Different taxa can therefore be identified.

(v) If certain evolutionary processes and routes are assumed, phylogenetic inferences can be made from character correlations and the taxonomic organization of a group.

(vi) Taxonomy is an empirical science that is studied and applied.

(vii) The foundation of categories is phenotypic similarity.

Applications of Numerical Taxonomy

Numerous Angiosperms, including Apocynum, Oenothera, Salix, Zinnia, Chenopodium, Crotalaria, Cucurbita, wheat cultivars, and maize cultivars, have been successfully studied using numerical taxonomy.
Numerical Taxonomy similarities and affinities in bacteria can be used to support research on a variety of different microorganisms.
To find the interspecific differences, metabolic DNA RFLP and phytochemical data from seed protein investigations have been quantitatively analyzed.
Many Angiosperms have reached delimitation, including Oryza, Sarcostemma Solarium, and other groupings that make up Engler's Farinosae family.
The following formula, which is derived using the Sokal & Sneath and Romero Lopes et al. technique, is used to calculate the degree of pairing affinity, or PA or similarity index. This is mostly predicated on the outcomes.

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