Short Communication: COI-based molecular identification of bagworms (Psychidae) from an oil palm plantation in Muara Enim, South Sumatra, Indonesia
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Abstract. Anggraini E, Zuhri S, Herlinda S, Lau WH, Muslim A, Irsan C, Suwandi S, Ramayudha R. 2026. Short Communication: COI-based molecular identification of bagworms (Psychidae) from an oil palm plantation in Muara Enim, South Sumatra, Indonesia. Biodiversitas 27 (2): d270212. https://doi.org/10.13057/biodiv/d270212. Bagworms (Lepidoptera: Psychidae) are major defoliating pests of oil palm in Southeast Asia, yet accurate species identification is often hindered by morphological similarity and phenotypic plasticity of larval cases. This study aimed to confirm the identity of bagworm species collected from an oil palm plantation in Muara Enim, South Sumatra, Indonesia, using an integrated approach combining mitochondrial Cytochrome Oxidase I (COI) barcoding and morphological assessment. A total of 12 larvae representing four field morphotypes were collected, of which 10 high-quality COI sequences were obtained. Nucleotide sequences were translated into amino acids, and phylogenetic reconstruction was performed using 211 aligned amino acid positions under the best-fit substitution model (MTVER+G4) selected by Bayesian Information Criterion. Maximum likelihood analysis recovered four distinct lineages corresponding to Mahasena corbetti, Pteroma pendula, a distinct Pteroma lineage, and Metisa plana. Specimens initially identified in the field as Clania tertia clustered within the genus Pteroma, indicating morphology-based misidentification. Despite moderate nucleotide similarity (~89-90%), amino acid-based phylogenetic reconstruction and larval morphology consistently supported its identification as M. plana. By integrating amino acid-based phylogenetic inference with morphological evidence, this study provides preliminary molecular confirmation of bagworm taxa from a single plantation site in South Sumatra and highlights the importance of combining molecular and morphological approaches for accurate pest identification.
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