In the past, the phylogenetic tree method was used to represent the historical relationships of groups of organisms, usually species, and each group was called a taxon (plural taxa). Charles Darwin introduced the idea of a “phylogenetic tree” of phylogeny in his influential book, Origin of Species (1859). Until the 70s and 20s these relationships were based solely on morphological features from existing taxa and fossil sites. With the advent of molecular sequences, new data grew to incredible levels creating a whole new picture in taxonomy.
We can simply understand that the phylogenetic tree consists of two elements, nodes and branches. A branch is a line connecting 2 nodes. Nodules can be external or internal. As shown in Figure 2: the notes F42nagami, F43 quat, F44hanhMK and M37duong are external notes. The note Mk8Camsan, M11Waly Tangerine…M4ClemenD is the inner note.
Bootstrap index: is the frequency of occurrence of a group (cluster) on the number of times the schema is set up. The unit of measure is % (percent). According to Felsenstein (1985) bootstrap is a support tool for building phylogenetic trees. The bootstrap index represents the reliability of the group membership of the phylogenetic tree, .
Consistency Index (CI): is a measure of the compatibility between any tree out of the total number of analyzed trees with the least total number of branches. The CI value ranges from 1.0 (maximum compatible) to 0 (least compatible). The larger the CI value, the more reliable the results are.
The CI index is calculated by the formula: CI = M/S
M: smallest possible number of trait changes (rank) in any phylogenetic tree.
S: the actual number of trait changes (ranks) in the phylogenetic tree in question (the phylogenetic tree already explains all the trait distributions of the cultivar to be classified).
RI (Retention Index): the index showing the number of similar traits of 2 or more varieties of the same ancestor on the taxonomic tree.
There are three groups of methods commonly used to draw phylogenetic tree structures from a matrix.
– Distance methods – group of distance methods: Distance is the evolutionary distance between pairs of objects being compared.
– Maximum parsimony method – the method of maximum parsimony. This method will select the evolutionary tree that meets the condition that the number of modified features must be the lowest to explain the observed data. The Maximum parsimony method assumes that the best evolutionary tree that best describes the evolutionary process is the tree that describes the species that are least variable, i.e., have the least mutations. has the lowest score (reckless) according to a predefined criterion (Hall, 2001).
Maximum Likelihood methods – This group of methods is based on a mathematical function that calculates the probability that an evolutionary tree is made up of observed data. This function allows the integration of the evolutionary processes of the feature into a probabilistic model. The maximal rationality method selects the maximum evolutionary tree that, when observing the data under a certain model, has the maximum probability (Hall, 2001).