This document discusses numerical taxonomy, which is the use of mathematical methods to group taxonomic units based on their characteristics. It defines key terms like phenetics and provides details on principles of numerical taxonomy like neo-Adansonian methods. The document outlines steps in classification, types of data used, and methods for numerical taxonomy including classic and clustering methods. It also discusses aims and applications of numerical taxonomy as well as merits and limitations.

1. BIOSYSTEMATICS AND
CLASSIFICATION
CUST ISLAMABAD
RIMSHA FAROOQ
(BBS153009)
FAIZA NAZIR AHMAD
(BBS153010)

2. TOPIC OF
PRESENTATION
CUST Islamabad

3. DEFINITION
CUST ISLAMABAD
Grouping by
numerical methods
of taxonomic units
into taxa on the basis
of their states.
Or the use of
mathematical
methods

4. Principles of
Numerical Taxonomy
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 Neo-Adansonian
• Content of information
• Weight of character
• Similarity between characters
• Recognition of distinct taxa

5. Principles of
Numerical Taxonomy
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• Phylogenetic inference
• Empirical science
• Phenetic similrity

6. The Estimation
of Resemblance
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• Collection of information
• Discovery or collection of
information
• Assignment of weight
• Measuring the resemblance
•

7. • Matrix of resemblance
• Computational methods
Construction of
Taxa
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8. • Collection of data
• Data must be coded
• Similarity or resemblance
• Taxonomic structure
• Properties of the phenons can be
tabulated
Steps in
Classification
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9. • Organisms
• Characters
Data for
Numerical Taxonomy
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A hydrozoan medusa

10. • Operational Taxonomic Units
Organisms
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11. • Character states
• Determination of ho
-mology
• Unit character
• Character complexes
Characters
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12. • Strains of species
• Similarity values
Number of
Characters
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13. • Replicate test
• Reduplication of data
• Similarity and experimental
variation
Quality of
Data
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14. • Delimitation of taxonomic group
• Boundaries for taxonomic group
Common Aspects
of all Methods
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15. Stability of
Numerical Taxonomy
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• More information
• New taxonomic entities

16. • Classic Methods
• Clustering Method
or Method B
Methods for
Numerical Taxonomy
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17. • Similarity coefficient
• hierarchical taxa clustering
method
Classic Methods
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18. • Single clustering stage
• Based on entropy
B Method
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19. • Use of overall information
• uses intra-cluster information
• does not distorts this inform-
ation in any way
Improvements
of B Method
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20. • Defining homogeneous clusters
• Integrating data of different
kinds
Aims of
Numerical Taxonomy
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21. Attributes that an organism
possesses today
Similarity on
Observed Properties
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22. Phylogeny of organisms, and not
necessarily to their present
attributes.
Relationship by
Ancestry
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23. • Taxospecies (a cluster of strains of
high mutual phenetic similarity)
• Genospecies (a group of strains
capable of gene exchange)
Taxospecies
& Genospecies
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24. • Nomenspecies (a group bearing
a binomial name whatever its status
in other respects)
• Genomospecies, a group of strains
that have high DNA-DNA
relatedness
Nomospecies
& Genomospecies
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25. • Coding of reaction in to positive
negative form.
• give each character equal weight
• Adansonian
• Similarity and Matches
Coding of
Result
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26. • Utilizes better and more number of
• described characters
• Sensitivity
• Reinterpretation
• Allows more taxonomic work
Merits of
Numerical Taxonomy
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27. • Not for phylogenetic classification
• proponents of “biological” species
• Character selection
• Choose a procedure for
satisfactory results
Demerits of
Numerical Taxonomy
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28. • Study of similarity and differences
in bacteria
• Delimitation of several angiospermic
genera
• Study of several other angiospermic
genera
Importance of
Numerical Taxonomy
CUST ISLAMABAD