The development in electronic computer science led to the possibility of using the Principle referred to by (Stace, 1989) in determining the genetic homogeneity of organisms.  Most researchers in taxonomy try to consider some characteristics or traits more important than others in classifying certain groups of organisms and then classification under these tests is more art than science and is influenced by the personal opinions of researchers. That is, all adjectives are given equal weight, that is, they have the same importance and weight, which is difficult to accept by many traditional classifiers (Stace, 1980). Using this principle, the classification is based on a mathematical method, which is currently called numerical classification or computer taxonomy, which was known by Sneath and Sokal (1973), Esma et al., (2020).
       
The genus comprises approximately 180-190 species in the world, most of which are perennial herbaceous plants. It originates in the Ancient Mediterranean. The Scorzonera species mainly inhibits temperate and arid regions of Central Europe Central Asia and Northern Africa, with the significant center of diversity in the arid and Irano-Turanian regions.  It is the compilation of taxonomic units by using numerical methods in taxa classifications based on their characteristics. The initialization of numerical classification is by obtaining units that fit the type of traits studied and the way they are arranged. The term OTUS (Operational Taxonomic Units) was suggested by Jones and Sachin (1980) and Sivakumar et al. (2024) for the organisms to be classified numerically. Sneath (1995) has stated that numerical classification in the broad sense is the greatest advance in taxonomy since Darwin or perhaps since Linnaeus. It has stimulated many new fields of growth, including numerical phylogenetics, molecular taxonomy, morphometrics and numerical identification.  The numerical classification uses binary variables, which are known as one of the two cases, the absence of the trait and the condition of the presence of the trait, or divided into several layers with one classification weight such as (3.2.1......... etc.) and numerical classification methods have been used In Iraq, in the study of many genus and from different families, such as Moter (2000), Sosa (2004), AL-Jowary et al. (2018) and Kumar et al., (2023). The current research dealt with 16 species belonging to the genus Scorzonera L. of the family Asteraceae (Compositae).

The 16 species of Scorzonera L. growing in Iraq, including two species, were treated as practical taxonomic units (OTUs) and 14 traits were collected for the order of Polygonal Graphs, as a number of morphological and anatomical traits were selected that characterize the species and the drawings were carried out according to Radford et al., (1974). As for the Dendrogram, 42 random traits were taken and treated with one weight and those traits were compared between species according to Sneath and Sokal (1973 ) in order to obtain similarities between the species under study and the process was conducted accurately to obtain the best interrelationships between species only The following steps:-
1 - Choice of Operational Taxonomic Units (OTUs).   
2 - Choose attributes to organize the data.
3 - Coding traits, which is about giving a value to the trait within a certain range so that it occupies all kinds of traits and this is necessary to convert the raw information into a specific form of scales that fit the work of taxonomic calculations and that are used for the work of the classification system.
4 - Create a numerical classification matrix by distributing the characteristics according to the code given to them in Step (1) on the OTUs selected in Step (1).
5 - The similarity ratios between OTUs were extracted according to Eq:-
 

 

S = Similarity coefficient
     a = Both elements of practical taxonomic units exhibit the same trait
     b = The first element shows only the adjective.
     c = The second element shows only the adjective
     n = sum of all traits.
     After extracting the similarity ratios between the studied species, the Similarity Matrix was obtained.
6 -   The dendritic shape was drawn between the operational taxonomic units (OTUs) by using the Agglomerative Method where the clustering process begins by finding the pair of OTUs with the highest similarity that represent the nucleus of the cluster or group that appeared in step (5). Then the similarity between this group formed and any of the remaining (OTUs) is calculated by extracting the arithmetic average of the similarity values between them in step (5) and the process is repeated a second time to form new groups depending on the higher similarity coefficient and this may be between two (OTUs) or Between (OTUs) and a group formed during the first aggregation cycle. Thus, the large cluster or dendritic shape that includes all species is obtained. OTUs can be linked in addition to the dendritic shape using other shapes such as regular circles or columns, depending on the similarity ratios.

The results of the current study showed that the polygonal shapes of the species of the genus under study, shown in (Fig 1), show convergence between certain species and divergence and difference between other species. These shapes were drawn from selected and prominent characters (Table 1). A matrix was made for these characters (Table 2).






       
The two varieties S. cana var. jacquiniana and S. cana var. radicosa showed a great similarity, which is obvious since they belong to the same species. The two species S. cinerea and S. incisa also showed little similarity to them. S. phaeopappa and S. semicana showed similarity in their polygonal shapes. The species S. cinerea, S. divisii and S. mollis showed great similarity to it in polygonal shapes, S. lanata and S. pseudolanata also showed great similarity to each other. S. latifolia, S. ramosissima and S. veratrifolia also showed great similarity in their polygonal shapes. Also, 42 selected characteristics were used for the species under study to create a numerical classification matrix and to calculate the degree of similarity between them, (Table 3) and the tree diagram that connects them was drawn, (Fig 2). (Table 5) and the tree diagram show that the highest similarity percentage between the two varieties was 89.95%, followed by 78.57% and the two species S. lanata and S. pseudolana were similar in this way, as were the two species S. phaeopappa and S. semicana, as they showed the same similarity percentage.




       
The two species S. mollis and S. schweinfurthii were related by a similarity of 76.19%, while the species S. mucida, S. phaeopappa and S. semicana were related by a similarity of 73.80%. The species S. ramosissima met with them with a similarity percentage of 69.04%. The species S. papposa met with the four previously mentioned species with a similarity percentage of 64.28% (Table 4). The similarity percentage of S. latifolia and S. veratrifolia was 61.90%. The two varieties S. cana and S. incisa met at the same percentage of similarity and S. cinerea was associated with them at a percentage of similarity of 57.14%.  At 55.65% similarity, S. mollis, S. mucida, S. papposa, S. phaeopappa, S. ramosissima, S. semicana and S. schweinfurthii met, while S. divisii and S. tortuosissima met at 52.38% similarity. These two species met with the previous seven species with a similarity rate of 50.74%. All these species were associated with S. latifolia and S. veratrifolia with a similarity rate of 48.21%.  These species were linked to the species S. cinerea, S. incisa and S. pseudolanata with a similarity percentage of 42.27% and these species in turn were linked as a single unit with the rest of the species at a similarity percentage of 36.90%. Thus, the study showed that the species of the genus Scorzonera converge at this ratio.


 
OTUs
 
Table 5 shows that the lowest similarity percentage was between S. latifolia and S. papposa, reaching 20.47%, while the highest similarity percentage was between the two varieties of the S. cana species, reaching 80.95%.


       
The polygonal shapes of the species of the genus Scorzonera showed great similarity between some species and divergence and difference between others.  The two S. cana species showed a remarkable similarity in polygonal shapes, which is obvious since they belong to the same species. This was also confirmed by the similarity percentage between them shown by the dendrogram, which reached 89.95%, which is the highest percentage among species. These two species met with S. incisa species with a similarity percentage of 61.90% and their polygonal shapes were similar, which confirmed the similarity in most of the characteristics of these species.  These species met with the species S. cinerea with a similarity rate of 57.14%, as they are species that show a fair similarity in many features, while the polygonal shapes showed a clear difference between them and this may be due to the selected features in drawing the shapes.
       
The species S. phaeopappa and S. semicana also showed great similarity in polygonal shapes and they are similar species in many characteristics. The similarity percentage was high, reaching 78.57%. They were placed by Boissior (1975) within one sector, Foliosae, which divided it into two groups. The first group included the two aforementioned species in addition to the species S. incise, which showed little similarity with them in the similarity percentage and in polygonal shapes, despite the fact that they belong to the same group. The second group in this sector, Lasiopra, included the species S. ramosissima and S. veratrifolia, which showed partial similarity in polygonal shapes and the similarity percentage was 48.21%. Although S. lanata and S. pseudolanata are very similar in most of the features, the polygonal shapes showed differences between them and the reason may be that the selected characters in drawing the polygonal shapes were limited and showed differences between the two species, while the percentage of similarity between them shown by the dendrogram is 78.57%, due to the use of the largest possible number of vegetative, reproductive and anatomical features regardless of their taxonomic importance. Although these two species were similar to S. veratrifolia in many features, the polygonal shapes were far from them. The percentage of similarity with them in the general percentage that all species met was 36.90% and the reason may be in the type of selected features for them.  Thus, the process of clustering continued for the pairs of species with the highest similarity, as shown in (Table 5b) and according to the gradation of similarity percentages, until all species met as one unit at a similarity percentage of 36.90%. It is noted from the tree diagram that 87% of the species met at a similarity percentage of 39.58%, which confirms the connection of these species and supports the unity of each of them as an independent species. Although the results obtained in the current study through numerical classification are more objective and clear, they cannot completely replace the traditional classification followed in morphological and anatomical studies by diagnosing species and isolating them from each other.

The study concludes that the two S. cana species showed a remarkable similarity in polygonal shapes, which is obvious since they belong to the same species. This was also confirmed by the similarity percentage between them shown by the dendrogram, which reached 89.95%, which is the highest percentage among species. These two species met with S. incisa species with a similarity percentage of 61.90%.

The present study was supported by University of Al-Qadisiyah, Education College, Biology Department.
 
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