PAN AFRICAN JOURNAL OF LIFE SCIENCES
e-ISSN: 2672-5924
Volume 10, No. 1, April 2026
Pages 675-684
DOI: 10.36108/pajols/6202/01.0160
Distribution and Environmental Drivers of Mosquito Larvae in Rural Communities of Asa Local Government Area, Nigeria
Yusuf H. Olayinka, Sunday O. Joseph, Oso G. Opeyemi*
Department of Zoology, Kwara State University, Malete, Kwara State, Nigeria.
Abstract
Background: Mosquito-borne diseases continue to pose a significant public health challenge in Nigeria, partic-ularly in rural areas where environmental conditions favour persistent mosquito breeding and routine vector sur-veillance is limited. This study integrated field-based larval surveys with remotely sensed environmental varia-bles to examine mosquito larval abundance, habitat productivity, and species-specific spatial risk in Asa Local Government Area (LGA), Kwara State, Nigeria.
Methods: Larval sampling was conducted across sixty-one aquatic habitats, and specimens were morphologi-cally identified. Landsat 8 OLI imagery was used to derive vegetation, moisture, soil, and built indices, which were analyzed alongside entomological data using appropriate regression models and geographic information systems. Statistical significance was evaluated at p<0.05. Predictive risk maps were generated using QGIS.
Results: A total of 1,516 mosquito larvae were collected, yielding a mean larval abundance of 25.3 larvae per habitat. Two genera were identified: Culex spp. accounted for 59.3% of the total larvae, while Anopheles spp. constituted 40.7%. Larval productivity was highly heterogeneous across habitat types, with used tyres con-tributing the largest proportion of larvae (40.0%) while the least occurred in open water bodies (12.5%). Habitat positivity was highest in used tyres and buckets (100%) and lowest in open water (66.7%). Regression analyses revealed that remotely sensed vegetation and surface-moisture indices were significant predictors of larval dy-namics. Anopheles larval abundance was significantly associated with LSWI (p=0.014), MNDWI (p=0.015), NDWI (p=0.013), MSAVI (p=0.004), and NDVI (p=0.006). Culex larval occurrence was significantly influ-enced by LSWI (p=0.002), MNDWI (p=0.002), MSAVI (p<0.001), and NDVI (p=0.002). Predictive risk maps showed spatial clustering of Anopheles larvae in moist, vegetated zones, whereas Culex risk was more wide-spread across peri-domestic and stagnant-water environments.
Conclusion: The pronounced heterogeneity in larval distribution and the strong associations between veg-etation, surface-moisture indices, and larval dynamics underscored the value of satellite-derived metrics for identifying high-risk habitats and guiding targeted vector surveillance.
Keywords: Mosquito, Remote sensing, QGIS, Environmental variables