Numerical Taxonomy & Biometrics.pdf

Numerical Taxonomy & Biometrics.pdf

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  PLANTTAXONOMY NUMERICAL TAXONOMY & BIOMETRICS
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  WELCOME TO THISDOMAIN Biometrics, Numerical Taxonomy and Cladistics, Characters; Variations; OTUs, Character Weighting and Coding; Cluster Analysis; Phenograms, Cladograms – A Bird’s eye View of the issue A Presentation By Dr. N. Sannigrahi, Associate Professor, Department of Botany, Nistarini College, Purulia, D.B. Road, Purulia (W.B) India 723101
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  BIOMETRICS  Increasingly, quantitativeapproaches to plant morphology are changing the landscape of research for plant ecology, physiology, and evolution. Advances in microscopy, imaging, and computational analyses potentially allow more detailed investigations than have previously been possible. This has increased the variety and quantity of data available for phenotypic analysis, and is stimulating new directions and applications in the study of plant morphology. The increased speed and detail that can be captured by these new technologies also means that the information represented by shape and form can potentially be as rich a bio informatics data source as genetic data. The object of the science is to bring together plant morphologists, systematists, and paleobotanists, alongside computer scientists, applied mathematicians, and informaticians, who were united in their interest in developing or applying novel biometric or bio-informatics methods to the form and function of plants. Biometrics thus an approach to pay light on the unit characters which likely to be two states that can not be subdivided logically.
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  BIOMETRICS  Verification systemseither reject or accept the submitted claim of identity by comparing it with the pre-stored biometrics of the same individual while identification systems recognize an individual by searching the matching queried biometric in the entire database. Biometric identification is a pattern recognition based classification system that recognizes an individual by determining its authenticity using a specific physiological or behavioral characteristic. Biometric authentication systems generally use a pattern recognition approach that verifies or identifies an individual’s authenticity of its uniquely identifiable physiological or behavioral characteristic, which is called a biometric.  There are two methods used to establishing the relationships between the organisms- Phenetic method & Phylogenetic or Cladistics method.  PHENETIC- Here overall similarity of taxa based on the overall similarity of taxa regardless whether their similarities are symplesiomorphies ( Shared primitive characters) or Synapomorphies ( shared derived characters) in a pohylogenetic sense.  CLADISTIC-Strictly phylogenetic and only synapomorphies are used to assess the phylogenetic relationship while shared symplesiomorphies have no use. A phylogenetic tree or Cladogram is developed and the approach is called Cladistics.
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  NUMERICAL TAXONOMY  Numericaltaxonomy or taximetrics, nowadays frequently and perhaps more appropriately referred to as phenetics, refers to the application of various mathematical procedures to numerically encoded character state data for organisms under study.  Thus, it is the analysis of various types of taxonomic data by mathematical or computerized methods and numerical evaluation of the similarities or affinities between taxonomic units, which are then arranged into taxa on the basis of their affinities.  According to Heywood the numerical taxonomy may be defined as the numerical evaluation of the similarity between groups of organisms and the ordering of these groups into higher ranking taxa on the basis of these similarities.  Numerical taxonomy involves two aspects-  i. Construction of taxonomic groups by involving the larger number of characters and the resemblance on the basis of cluster analysis ,  Ii. Discrimination of the taxonomic groups to show the overlapping of characters and the discrimination is used to select them.
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  WHAT IS NUMERICALTAXONOMY?  The period from 1957 to 1961 saw the development of first methods and of theory of numerical taxonomy. Plants as we all know are classified based on their characters. It was Michel Adanson, a French botanist, who for the first time put forward a plan for assigning numerical values to the similarity between organisms and proposed that equal weightage should be given to all the characters while classifying plants.  He used as many characters as possible for the classification, and such classifications came to be known as Adansonian classifications. Numerical taxonomy was however largely developed and popularized by Sneath and Sokal.  The application of Adansonian principles and use of modern methods and electronic data-processing techniques, have helped in the evolution of several new classifications of plants during the past few decades.  Phylogenetic conclusions can be drawn from the taxonomic structure of a group and from character correlations assuming some evolutionary mechanisms and pathways.  The science of taxonomy is viewed and practiced as an empirical science
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  PRINCIPLES OF NUMERICALTAXONOMY  PRINCIPLES:  i. The more the selection of characters, the more the clarity & authentic the classification,  ii. Every Characters should be given equal weightage,  iii.The entire classification basically should be done on their basis of phenetic approach.  (a) Construction of Taxonomic Groups:  i. Any sample, item, individual is selected to analyze OUT which is the basic unit of Numerical Taxonomy  ii. In numerical taxonomy, first, individuals are selected and their characters spotted out. There is no limitation to the number of characters to be considered. However, the larger the number of characters, better is the approach for generalization of the taxa.  ii. The resemblances among the individuals are then established on the basis of character analysis, which can often be worked out with the help of computers, the accuracy of which depends on the appropriateness in character. The best way to delimitate taxa is, to utilize maximum number of characters, with similar weightage given to all of them.
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  PRINCIPLES OF NUMERICALTAXONOMY  (b) Discrimination of the Taxonomic Groups:  When the taxonomic groups chosen for the study show overlapping of characters, discrimination should be used to select them. Discrimination analysis can be done by various techniques, specially devised for such purposes. Numerical taxonomy is thus, based on certain principles, also called neo Adansonian principles.  Principles of numerical taxonomy have been enumerated by Sneath and Sokal:  (i) The greater the content of information in the taxa, and more the characters taken into consideration, the better a given classification system will be.  (ii) Every character should be given equal weightage in creating new taxa.  (iii) The overall similarity between any two entities is a function of the individual similarities in each of the many characters, which are considered for comparison.
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  PRINCIPLES OF NUMERICALTAXONOMY  (iv) Correlation of characters differ in the groups of organisms under study. Thus distinct taxa can be recognized.  (v) Phylogenetic conclusions can be drawn from the taxonomic structure of a group and from character correlations, assuming some evolutionary mechanisms and pathways.  (vi) The science of taxonomy is viewed and practiced as an empirical science.  (vii) Phenetic similarity is the base of classifications.  SLECTION OF CHARACTERS:  The unit characters may be two or more states depending upon the nature of the characters taken into account successive weightage should be employed for homoplasy which arte basically unique in all respect.  A. Binary Characters: This characters basically denote only two states- present or absent denoted by +/- or 1 /0, if missing denoted with NC  B. Multistate Characters: More different states of characters are explored either qualitative or quantitative. It is denoted with 0,1,2,3,4,5,6,7 etc . For example color of the flower to be denoted by different assigned values for each color.
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  PRINCIPLES OF NUMERICALTAXONOMY  RULES:  There are number of rules are followed in this regard as stated below.  i. The characters must to be chosen almost all parts of the organism irrespective of the occurrence,  Ii. Not less than 50 characters are to be considered in this analysis,  The characters must comprise almost all the stages of the life cycle of the plants,  Attention to be given in all the respects with special reference to morphology, physiology, ecology etc.  CHARACTERS WEIGHTING:  1. Equal weightage – All the characters should be given equal weightage as the numerical taxonomy deserves equal importance of all the measurable units,  Ii. Successive Weightage- Homoplasy characters which are unique in real sense like thorns, spines etc should be given more weightage for consideration
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  CLUSTER ANALYSIS  Itis a statistical tool in which more similar elements are placed in one group rather different from objects belonging to other groups used for the exploratory data analysis. Here, mostly computer driven software sort out the OTUs according to their overall similarity and produces a phenogram , a kind of dendogram which shows the taxonomic relationship between the two organisms.  It can be done by two categories methods-  A. Hierarchical cluster analysis method- two types. Agglomerative and divisive method. In the agglomerative method, all objects start in separate clusters till slowly the similar objects are combined and the optimum number is chosen from among all options.  In divisive method, all objects start in the same cluster and it is reverse of agglomerative method  B. Non- hierarchical cluster analysis method.
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  CLUSTERING METHODS  Clusteringcan be done by two methods-  Monolethic system- this system employs the attributes one at a time. This method leads to the artificial clustering.  Considering all their attributes simultaneously, this method gives a natural grouping. One cluster group is separated from the other by dividing line which indicates a distinct gap between the two.  Phenetic Clustering methods:  It is mostly phenetic approaches and is now rarely employed in taxonomy as they often lead to substantially different clusters when applied to real data. The following method is mostly used in this case.  Nearest neighbor clustering method: It is called a single linkage clustering . Here phenograms are constructed by joining OTUs and groups on the basis of their similar members i.e almost shortest distance. Whether an OUT will join an existing cluster depends on its maximum similarity ( or minimum distance from) any member of that cluster. A Phenogram is initiated by joining the two most similar OTUs. The following example can impregnate the concept.
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  SIMILARITY MATRIX C BA A 0.40 0.63 1.0 B 0.53 1.0 C 1.0
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  Sequence Procedure involvedin the Phenetic Clustering of the aforesaid three taxa: STEP ACTION RESULT ACTION TREE 1. Find greatest similarity less than unity 0.63( A-B) Join A to B at level 0.63 See on the next page 2. Find the next highest similarity 0.53 ( B-C) Join C to A+B Cluster at level 0.53 See on the next page
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  Sequence Procedure involvedin the Phenetic Clustering of the aforesaid three taxa.
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  I- Cladograms, II-Phylogram, III- Dendrogram
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  ADVANTAGES  a. Thedata of conventional taxonomy is improved by numerical taxonomy as it utilizes better and more number of described characters. The data are collected from a variety of sources, such as morphology, chemistry, physiology, etc.  b. As numerical methods are more sensitive in delimiting taxa, the data obtained can be efficiently used in the construction of better keys and classification systems, creation of maps, descriptions, catalogues, etc. with the help of electronic data processing systems. Numerical taxonomy has in fact suggested several fundamental changes in the conventional classification systems. c. The number of existing biological concepts have been reinterpreted in the light of numerical taxonomy.  C.  d. Numerical taxonomy allows more taxonomic work to be done by less highly skilled workers.
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  DISADVANTAGES  a. Thenumerical methods are useful in phenetic classifications and not phylogenetic classifications.  b. The proponents of “biological” species concept, may not accept the specific limits bound by these methods.  c. Character selection is the greatest disadvantage in this approach. If characters chosen for comparison are inadequate, the statistical methods may give less satisfactory solution.  d. According to Steam, different taxonometric procedures may yield different results. A major difficulty is to choose a procedure for the purpose and the number of characters needed in order to obtain satisfactory results by these mechanical aids. It is necessary to ascertain whether a large number of characters would really give satisfactory results than those using a smaller number.
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  APPLICATIONS OF NT a. Study of similarities and differences in bacteria, other micro- organisms and several animal groups.  b. Delimitation of several angiospermic genera like Oryza, Sarcostemma Solarium, and other groups including Farinosae of Engler and a few others.  c. In the study of several other angiosperm genera including Apocynum, Chenopodium, Crotalaria, Cucurbita, Oenothera, Salix, Zinnia, wheat cultivars, Maize cultivars, etc.  d. Phyto-chemical data from seed protein and mitochondrial DNA RELP studies has been numerically analyzed by Mondal et al. to study the inter-specific variations among eight species of cassia L .Based on the results of electrophoresis patterns, the degree of pairing affinity (PA) or similarity index was calculated by the following formula, according to the method of Sokal & Sneth and Romero Lopes et al.:
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  PHENOGRAM  Phenograms atype of dendrogram which is based on phenetic data . Lines called phenon lines, drawn at right angles to the dichotomously branching dendrogram, represent lines of percentage similarity of phenetic features between organisms. A Phenogram is mainly built up on the data of the numerical analysis of phenetic data. During the construction of diagram, results of large number of characters data from all available field are taken. It includes calculating the similarity between taxa and constructing a diagram through cluster analysis. Phenogram is very useful as it involves a large number of characters. Further, hierarchical classification can be achieved by deciding upon the similarity between taxa assigned to various rank.
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  PHENOGRAM
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  CLADOGRAM A Cladogram isa diagram used in cladistics to show relationships among organisms. A cladogram is not, however, an evolutionary tree because it does not show how ancestors are related to the descendents, nor does it show how much they have changed; many evolutionary trees can be inferred from a single cladogram. A Cladogram uses lines that branch off in different directions ending at clade, a group of organisms derived from a last common ancestor. There are many shapes of cladograms but all have lines that branch off from others. The lines can be traced back to where they branch off. These branching point represents a hypothetical ancestor which can be inferred to exhibit the traits shared among the terminal taxa above it. The hypothetical ancestor might then provide clues about the order of evolution of various features, adaptation and other evolutionary narratives about the ancestor. Primarily, the morphological data are mostly used but with the passage of time, DNA, RNA sequencing data are now commonly used in the generation of cladogram in addition to the conventional data.
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  CLADOGRAM
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  DIFFERENCE BETWEEN CLADISTICS& PHENETICS Cladistics Phenetics It is the method of the classifying organisms on the basis of their ancestry & evolutionary relationships. It is the method of the classifying organisms based on the structural & morphological characters that are observable. Mostly evolutionary relationships, genetic similarity & ancestry related characters are taken into account Structure , morphology and ecological characters are taken into account . Cladograms of phylogenetic tree is considered to describe relationship. Phenograms are used to describe relationship High accuracy & high reproducibility Low accuracy & low reproducibility. Taxa may be grouped together by shared group character states (Synapomorphies) Only Taxa may be grouped together by shared ancestral features (Symplesiomorphies) as well shared derived character states ( Synapomorphies)
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  HOPE, YOU HAVE ENJOYEDTHE JOURNEY THANKYOUTO EXPLORE THE ROAD
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  ACKNOWLEDGEMENT  Google forimages  College Botany- B.P. Pandey  Advanced Plant Taxonomy by A. K. Mondal  A Text book of Botany- Hait, Bhattacharya & Ghosh  Plant Taxonomy –O. P. Sharma  Text Book of Plant Systematics- Chittaranjan Mohanty  Taxonomy of Vascular plants- H. G. Lawrence.  Disclaimer:  This presentation has been developed for the enrichment of online learning without any financial interest. The author is grateful for all for having the pleasure of uses of the content used in this presentation.