An integrative approach to DNA barcoding, geometric morphometrics, and machine learning for field identification of Culex mosquitoes (Diptera: Culicidae), with implications for vector-borne disease surveillance
Culex mosquitoes are of considerable medical and veterinary importance, acting as vectors of arboviruses such as Japanese encephalitis, Rift Valley fever, and West Nile virus, as well as the filarial parasite Wuchereria bancrofti. Accurate identification of Culex species, however, remains challengin...
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| Veröffentlicht in: | Acta tropica Jg. 271; S. 107885 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.11.2025
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| ISSN: | 0001-706X, 1873-6254, 1873-6254 |
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| Abstract | Culex mosquitoes are of considerable medical and veterinary importance, acting as vectors of arboviruses such as Japanese encephalitis, Rift Valley fever, and West Nile virus, as well as the filarial parasite Wuchereria bancrofti. Accurate identification of Culex species, however, remains challenging due to their close morphological similarity, frequent damage to field-collected specimens, and the limited availability of trained taxonomists. To address these challenges, this study employed an integrative framework combining DNA barcoding, wing geometric morphometrics (GM), and Random Forest (RF) to improve the identification of 12 Culex species (Cx. bicornutus, Cx. bitaeniorhynchus, Cx. brevipalpis, Cx. fuscocephala, Cx. gelidus, Cx. hutchinsoni, Cx. nigropunctatus, Cx. pseudovishnui, Cx. quinquefasciatus, Cx. sinensis, Cx. sitiens, and Cx. tritaeniorhynchus) in Thailand. DNA barcoding successfully validated the morphological identifications, with nucleotide sequences from representative specimens showing strong concordance with the GenBank and Barcode of Life Data Systems (BOLD) databases (≥96 %), confirming the reliability of morphological diagnoses. Complementarily, wing GM demonstrated stronger discriminatory power: Mahalanobis distance analysis revealed all species to be significantly different (p < 0.05), and a cross-validated reclassification test achieved 82.18 % performance with an adjusted total accuracy of 80 %. For field identification of unknown specimens, both Mahalanobis distance and RF produced comparable results, yielding very high accuracy (80 %–100 %) for eight species. Overall, the integration of DNA barcoding, wing GM, and machine learning offers a robust and practical framework for strengthening mosquito-borne disease surveillance. Nonetheless, as each method has distinct strengths and limitations, their application should be carefully adapted to specific epidemiological and operational contexts. |
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| AbstractList | Culex mosquitoes are of considerable medical and veterinary importance, acting as vectors of arboviruses such as Japanese encephalitis, Rift Valley fever, and West Nile virus, as well as the filarial parasite Wuchereria bancrofti. Accurate identification of Culex species, however, remains challenging due to their close morphological similarity, frequent damage to field-collected specimens, and the limited availability of trained taxonomists. To address these challenges, this study employed an integrative framework combining DNA barcoding, wing geometric morphometrics (GM), and Random Forest (RF) to improve the identification of 12 Culex species (Cx. bicornutus, Cx. bitaeniorhynchus, Cx. brevipalpis, Cx. fuscocephala, Cx. gelidus, Cx. hutchinsoni, Cx. nigropunctatus, Cx. pseudovishnui, Cx. quinquefasciatus, Cx. sinensis, Cx. sitiens, and Cx. tritaeniorhynchus) in Thailand. DNA barcoding successfully validated the morphological identifications, with nucleotide sequences from representative specimens showing strong concordance with the GenBank and Barcode of Life Data Systems (BOLD) databases (≥96 %), confirming the reliability of morphological diagnoses. Complementarily, wing GM demonstrated stronger discriminatory power: Mahalanobis distance analysis revealed all species to be significantly different (p < 0.05), and a cross-validated reclassification test achieved 82.18 % performance with an adjusted total accuracy of 80 %. For field identification of unknown specimens, both Mahalanobis distance and RF produced comparable results, yielding very high accuracy (80 %-100 %) for eight species. Overall, the integration of DNA barcoding, wing GM, and machine learning offers a robust and practical framework for strengthening mosquito-borne disease surveillance. Nonetheless, as each method has distinct strengths and limitations, their application should be carefully adapted to specific epidemiological and operational contexts. Culex mosquitoes are of considerable medical and veterinary importance, acting as vectors of arboviruses such as Japanese encephalitis, Rift Valley fever, and West Nile virus, as well as the filarial parasite Wuchereria bancrofti. Accurate identification of Culex species, however, remains challenging due to their close morphological similarity, frequent damage to field-collected specimens, and the limited availability of trained taxonomists. To address these challenges, this study employed an integrative framework combining DNA barcoding, wing geometric morphometrics (GM), and Random Forest (RF) to improve the identification of 12 Culex species (Cx. bicornutus, Cx. bitaeniorhynchus, Cx. brevipalpis, Cx. fuscocephala, Cx. gelidus, Cx. hutchinsoni, Cx. nigropunctatus, Cx. pseudovishnui, Cx. quinquefasciatus, Cx. sinensis, Cx. sitiens, and Cx. tritaeniorhynchus) in Thailand. DNA barcoding successfully validated the morphological identifications, with nucleotide sequences from representative specimens showing strong concordance with the GenBank and Barcode of Life Data Systems (BOLD) databases (≥97-100%), confirming the reliability of morphological diagnoses. Complementarily, wing GM demonstrated stronger discriminatory power: Mahalanobis distance analysis revealed all species to be significantly different (p < 0.05), and a cross-validated reclassification test achieved 82.18% performance with an adjusted total accuracy of 80%. For field identification of unknown specimens, both Mahalanobis distance and RF produced comparable results, yielding very high accuracy (80%-100%) for eight species. Overall, the integration of DNA barcoding, wing GM, and machine learning offers a robust and practical framework for strengthening mosquito-borne disease surveillance. Nonetheless, as each method has distinct strengths and limitations, their application should be carefully adapted to specific epidemiological and operational contexts.Culex mosquitoes are of considerable medical and veterinary importance, acting as vectors of arboviruses such as Japanese encephalitis, Rift Valley fever, and West Nile virus, as well as the filarial parasite Wuchereria bancrofti. Accurate identification of Culex species, however, remains challenging due to their close morphological similarity, frequent damage to field-collected specimens, and the limited availability of trained taxonomists. To address these challenges, this study employed an integrative framework combining DNA barcoding, wing geometric morphometrics (GM), and Random Forest (RF) to improve the identification of 12 Culex species (Cx. bicornutus, Cx. bitaeniorhynchus, Cx. brevipalpis, Cx. fuscocephala, Cx. gelidus, Cx. hutchinsoni, Cx. nigropunctatus, Cx. pseudovishnui, Cx. quinquefasciatus, Cx. sinensis, Cx. sitiens, and Cx. tritaeniorhynchus) in Thailand. DNA barcoding successfully validated the morphological identifications, with nucleotide sequences from representative specimens showing strong concordance with the GenBank and Barcode of Life Data Systems (BOLD) databases (≥97-100%), confirming the reliability of morphological diagnoses. Complementarily, wing GM demonstrated stronger discriminatory power: Mahalanobis distance analysis revealed all species to be significantly different (p < 0.05), and a cross-validated reclassification test achieved 82.18% performance with an adjusted total accuracy of 80%. For field identification of unknown specimens, both Mahalanobis distance and RF produced comparable results, yielding very high accuracy (80%-100%) for eight species. Overall, the integration of DNA barcoding, wing GM, and machine learning offers a robust and practical framework for strengthening mosquito-borne disease surveillance. Nonetheless, as each method has distinct strengths and limitations, their application should be carefully adapted to specific epidemiological and operational contexts. |
| ArticleNumber | 107885 |
| Author | Laojun, Sedthapong Changbunjong, Tanasak Kamoltham, Thavatchai Chaiphongpachara, Tanawat |
| Author_xml | – sequence: 1 givenname: Sedthapong surname: Laojun fullname: Laojun, Sedthapong organization: Department of Public Health and Health Promotion, College of Allied Health Sciences, Suan Sunandha Rajabhat University, Samut Songkhram, Thailand – sequence: 2 givenname: Tanasak orcidid: 0000-0002-3526-9961 surname: Changbunjong fullname: Changbunjong, Tanasak organization: Department of Pre-Clinic and Applied Animal Science, Faculty of Veterinary Science, Mahidol University, Nakhon Pathom, Thailand – sequence: 3 givenname: Thavatchai surname: Kamoltham fullname: Kamoltham, Thavatchai organization: Department of Cannabis Health Sciences, College of Allied Health Sciences, Suan Sunandha Rajabhat University, Samut Songkhram, Thailand – sequence: 4 givenname: Tanawat orcidid: 0000-0001-9585-4241 surname: Chaiphongpachara fullname: Chaiphongpachara, Tanawat email: tanawat.ch@ssru.ac.th organization: Department of Public Health and Health Promotion, College of Allied Health Sciences, Suan Sunandha Rajabhat University, Samut Songkhram, Thailand |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/41151664$$D View this record in MEDLINE/PubMed |
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| Keywords | Mosquito-borne diseases Mosquito vectors Molecular methods Species identification Wing morphometrics |
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| SubjectTerms | Animals Culex - anatomy & histology Culex - classification Culex - genetics DNA Barcoding, Taxonomic - methods Female Machine Learning Molecular methods Mosquito vectors Mosquito Vectors - anatomy & histology Mosquito Vectors - classification Mosquito Vectors - genetics Mosquito-borne diseases Species identification Vector Borne Diseases - epidemiology Vector Borne Diseases - transmission Wing morphometrics Wings, Animal - anatomy & histology |
| Title | An integrative approach to DNA barcoding, geometric morphometrics, and machine learning for field identification of Culex mosquitoes (Diptera: Culicidae), with implications for vector-borne disease surveillance |
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