Assessing arthropod biodiversity with DNA barcoding in Jinnah Garden, Lahore, Pakistan.

Abstract

Previous difficulties in arthropod taxonomy (such as limitations in conventional morphological approaches, the possibility of cryptic species and a shortage of knowledgeable taxonomists) has been overcome by the powerful tool of DNA barcoding. This study presents a thorough analysis of DNA barcoding in regards to Pakistani arthropods, which were collected from Lahore's Jinnah Garden. The 88 % (9,451) of the 10,792 specimens that were examined were able to generate DNA barcodes and 83% (8,974) of specimens were assigned 1,361 barcode index numbers (BINs). However, the success rate differed significantly between the orders of arthropods, from 77% for Thysanoptera to an astounding 93% for Diptera. Through morphological exams, DNA barcoding, and cross-referencing with the Barcode of Life Data system (BOLD), the Barcode Index Numbers (BINs) were assigned with a high degree of accuracy, both at the order (100%) and family (98%) levels. Though, identifications at the genus (37%) and species (15%) levels showed room for improvement. This underscores the ongoing need for enhancing and expanding the DNA barcode reference library. This study identified 324 genera and 191 species, underscoring the advantages of DNA barcoding over traditional morphological identification methods. Among the 17 arthropod orders identified, Coleoptera, Diptera, Hemiptera, Hymenoptera, and Lepidoptera from the class Insecta dominated, collectively constituting 94% of BINs. Expected malaise trap Arthropod fauna in Jinnah Garden could contain approximately 2,785 BINs according to Preston log-normal species distribution, yet the Chao-1 Index predicts 2,389.74 BINs. The Simpson Index of Diversity (1-D) is 0.989, signaling high species diversity, while the Shannon Index is 5.77, indicating significant species richness and evenness. These results demonstrated that in Pakistani arthropods, DNA barcoding and BOLD are an invaluable tool for improving taxonomic understanding and biodiversity assessment, opening the door for further eDNA and metabarcoding research.