International Journal of Insect and Animal Diversity Research

Background: Insects constitute the most species-rich animal class on Earth, yet conservative estimates suggest 60–80% of species remain formally undescribed. Morphological taxonomy alone is insufficient to uncover this hidden diversity, particularly in cryptic species complexes where phenotypic differences are minimal or absent.
Objective: This study evaluates the efficacy of an integrated multimodal framework combining high-throughput DNA barcoding, whole-genome sequencing, micro-computed tomography (micro-CT), and AI-assisted image classification for accelerating the discovery and formal description of cryptic insect species in tropical forest ecosystems.
Methods: A collection of 1,240 insect specimens from three biogeographic zones in South and Southeast Asia was processed using DNA barcoding (COI gene), metabarcoding, micro-CT scanning, and automated morphological imaging. Species delineation was performed using the GMYC, bPTP, and ABGD algorithms in conjunction with geometric morphometric analyses.
Results: The multimodal pipeline identified 41 putative new species, compared to 23 via genomics alone and 14 via imaging alone. Species identification accuracy reached 98.4% with the integrated approach, versus 94.2% and 88.6% for single-modality methods. Discovery efficiency scores improved by 31% over the best single-modality approach.
Conclusion: Multimodal integration of genomic sequencing and advanced imaging technologies substantially increases the rate and reliability of cryptic insect species discovery. Standardization of these pipelines is urgently needed to address the global taxonomic impediment.