article · Pan African Journal of Life Sciences
Background: Malaria poses a significant public health challenge in Nigeria. The identification of Anopheles mosquitoes involves both morphological and molecular approaches. Morphological identification relies on physical characteristics such as body shape and wing patterns, while molecular characterization uses genetic markers to differentiate species and understand their genetic diversity. These methods play crucial roles in studying Anopheles mosquitoes, which are vectors for diseases like malaria, aiding in their surveillance, control, and research efforts. This study aimed to identify Anopheles mosquito species in six selected local governments of Ekiti State, South West, Nigeria,, using both morphological and molecular methods. Methods: Mosquito larvae were collected between April 2022 and July 2023 from eighteen locations in Ado, Oye, Ise-Orun, Ido-Osi, Efon and Ikere local governments respectively. We employed standard dipper sampling techniques to collect mosquito larvae from all types of larval habitats, including both manmade and natural sources, at each study location throughout the mosquito breeding season. The collected larvae were reared to the adult stage. The adult mosquitoes were then identified morphologically using morphological keys, and their species were confirmed through Multiplex Polymerase Chain Reaction for molecular characterisation. The data obtained were analysed using SPSS version 27.0, with a p-value set at 95%. Results: The study identified Anopheles gambiae (80.5%) and Anopheles funestus (19.5%) as the major malaria vectors. Two sibling species of Anopheles gambiae s.l were identified by PCR: Anopheles gambiae s.s (75.4%) and Anopheles arabiensis (18.8%), Anopheles coluzzii (M form) (55.3%) and Savannah (S-form) (44.7%). Conclusion: Integrating molecular techniques with morphological analysis can provide valuable insights into the species composition, population dynamics, and biological characteristics of malaria vectors.
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DOI: 10.36108/pajols/4202/80.0250
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