Malaria vector surveillance in Indonesia: COX1 phylogenetic reveals monophyletic clades and cryptic diversity in Anopheles mosquitoes

BACKGROUND OBJECTIVES: Anopheles mosquitoes are key malaria vectors, their high diversity influences transmission competence. Accurate species identification is crucial for understanding malaria epidemiology and implementing effective vector control strategies. The COX1 gene is a widely used DNA barcoding marker for Anopheles due to its high mutation rate and species-specific variations. This study evaluates the consistency of morphological and molecular identification using COX1, analyzes phylogenetic relationships, and explores the implications of these findings for malaria vector control strategies. METHODS: Anopheles mosquitoes were collected from Bangsring, Banyuwangi, and Hargowilis, Kulonprogo, Indonesia, two geographically distinct sites with a history of malaria outbreaks. Mosquitoes were collected using human landing catches. Identification was performed morphologically and confirmed by molecular analysis based on COX1 sequences. Phylogenetic tree and genetic distances were analyzed in MEGA11 using the Neighbor-Joining method with the Kimura-2 Parameter model. RESULTS: Morphological and COX1-based identification were mostly consistent; however, specimens identified as Anopheles aconitus and An. minimus from Hargowilis were molecularly confirmed as An. flavirostris. Phylogenetic analysis revealed eight monophyletic clades with strong bootstrap support (≥99% for six), confirming species groupings. Genetic distance analysis showed An. minimus from Hargowilis clustering more closely with An. flavirostris than with An. minimus from other Asian regions. The dominance of An. sundaicus (68%) in Bangsring and An. flavirostris (13%) in Hargowilis highlights the need for targeted vector control strategies. INTERPRETATION CONCLUSION: Misidentification of cryptic Anopheles species may lead to ineffective vector control in specific epidemiological settings. Integrating molecular tools into malaria surveillance can support more accurate species identification and contribute to informed disease prevention strategies.