Spider Species Identification: Bridging Traditions with Molecular and Deep Learning Approaches

Aakriti Shrivastava *

Department of Zoology, Govt. Holkar Science College, Indore, India.

V. K. Sharma

Department of Zoology, Govt. Holkar Science College, Indore, India.

*Author to whom correspondence should be addressed.


This article provides an in-depth review of various methods employed in the identification and sequencing of spiders, highlighting the advancements and challenges in the field. With the increasing importance of spiders in ecological studies, medical research, and biodiversity conservation, accurate identification and genetic analysis have become crucial. This review discusses traditional and modern techniques, shedding light on their applications, limitations, and future prospects.

The exploration begins with an analysis of taxonomists' etymological choices, examining patterns in naming conventions across continents and centuries. Traditional morphological identification, anchored in backbone taxonomy, dichotomous keys, and statistical analyses, highlights the advantages and challenges of relying on observable features. The study transitions to molecular techniques, elucidating the applications and challenges of DNA barcoding, Next-Generation Sequencing (NGS), and metabarcoding in spider identification. The integration of deep learning models, exemplified by the YOLOv7-based Spider Identification APP, represents a landmark in computer vision for efficient and user-friendly spider species recognition. The study's multifaceted approach provides a nuanced understanding of spider taxonomy, bridging historical practices with state-of-the-art technologies, and lays the groundwork for future advancements in the field.

Keywords: Spider identification, morphological taxonomy, DNA barcoding, next-generation sequencing, metabarcoding, deep learning models, biodiversity analysis

How to Cite

Shrivastava, Aakriti, and V. K. Sharma. 2024. “Spider Species Identification: Bridging Traditions With Molecular and Deep Learning Approaches”. Annual Research & Review in Biology 39 (7):40-45. https://doi.org/10.9734/arrb/2024/v39i72097.


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Bjerge K, Mann HM, Høye TT. Real‐time insect tracking and monitoring with computer vision and deep learning. Remote Sensing in Ecology and Conservation. 2022;8(3):315-27.

Vendetti J, Garland R. Species name formation for zoologists: A pragmatic approach. Journal of Natural History. 2019;53(47-48), 2999-3018.

Kuntner M, Elgar MA. Evolution and maintenance of sexual size dimorphism: Aligning phylogenetic and experimental evidence. Frontiers in Ecology and Evolution. 2014;2:26.

Kuntner M, Coddington JA, Schneider J. Intersexual arms race? Genital coevolution in nephilid spiders (Araneae, Nephilidae). Evolution. 2009;63(6):1451–1463.

Bonnet P. Bibliographia Araneorum. Methodical analysis of all araneological literature until 1939. Volume III. Toulouse: Douladoure Printing Company; 1961.

Nilsson AN. All diving beetle specific and subspecific names explained. Skörvnöpparn, Umeå Supplement. 2010;1:1–42.

Zuur AF, Ieno EN. A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution. 2016;7(10):1121–1131.

Cardoso P, Pekár S. wscmap: A world spider catalog map. Biodiversity Data Journal. 2022;10:e79648.

Arel-Bundock V, Enevoldsen N, Yetman CJ. Country code: Convert country names and country codes. Journal of Open Source Software. 2018;3(28):1025.

Levi HW. The spider genera Enoplognatha, Theridion, and Paidisca in America north of Mexico. American Museum Novitates. 1957;1832:1-39.

Hebert PD, Ratnasingham S, deWaard JR. Biological identifications through DNA barcodes. Proceedings of the Royal Society of London. Series B: Biological Sciences. 2003;270(1512): 313-321.

Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. Journal of Molecular Biology. 1990;215(3):403-410.

Elbrecht V, Leese F. PrimerMiner: An R package for development and in silico validation of DNA metabarcoding primers. Methods in Ecology and Evolution. 2017;8(5):622-626.

Deagle BE, Thomas AC, Shaffer SA, Trites, AW, Jarman SN. DNA metabarcoding and the cytochrome c oxidase subunit I marker: Not a perfect match. Biological Letters. 2014;10(9): 20140562.