This article investigates an in-silico experiment to evaluate the genetic diversity of fish species found within the Daya Riv-er, specifically concerning the values of nucleotide diversity π and other parameters important within population genetics. Nucleotide diversity π is a molecular marker that measures the genetic difference at the nucleotide base level by estimating the number of pair wise differences between randomly sampled sequences in a population. It can be said that high values for π are reflective of larger population sizes with high genetic variability, whereas lower values may hint at bottleneck effects, inbreeding, or fragmented populations. The study employs publicly available DNA sequences, including mito-chondrial genes cytochrome oxidase I (COI), NADH dehydrogenase subunit 2 (ND2), and cytochrome b (cytb), and appropriate nuclear markers whenever available. These DNA sequences are obtained from credible molecular data re-sources like NCBI GenBank and Barcode of Life Data System (BOLD), so as to allow only high-quality data. Every sequence submitted is strictly screened in terms of duplicates, ambiguous nucleotides, and sequences with mislabelled information. The sequences are then aligned with high-quality tools such as MAFFT or MUSCLE, focusing on accurate comparison of homologous sequences. The analysis part uses several bioinformatics packages such as MEGA for phylo-genetic analysis, DnaSP for calculation of nucleotide diversity, haplotype diversity, and number of segregating sites, while Arlequin is used for population structure analysis such as FST, AMOVA. Python, BioPython, scripts are used. In addition, neutrality tests including Tajima's D and Fu's Fs are also employed to detect the evolutionary forces under which the population has evolved, either by means of selection, bottleneck events, or population expansion. Haplotype networks and phylogenetic trees are graphical representations of genetic data, while ecological and conservation inferences are used to identify endangered populations. In conclusion, the in- silico approach presented here is an effective and inexpensive way of exploring genetic diversity and population structures and evolutionary history of fish species in Daya River.

Nucleotide Diversity, Mitochondrial DNA (mtDNA), Haplotype Diversity, Population Structure, Fish Population