Abstract
Anopheles gambiae and An. arabiensis are major malaria vectors in sub-Saharan Africa. Knowledge of how geographical factors drive the dispersal and gene flow of malaria vectors can help in combatting insecticide resistance spread and planning new vector control interventions. Here, we used a landscape genetics approach to investigate population relatedness and genetic connectivity of An. gambiae and An. arabiensis across Kenya and determined the changes in mosquito population genetic diversity after 20 years of intensive malaria control efforts. We found a significant reduction in genetic diversity in An. gambiae, but not in An. arabiensis as compared to prior to the 20-year period in western Kenya. Significant population structure among populations was found for both species. The most important ecological driver for dispersal and gene flow of An. gambiae and An. arabiensis was tree cover and cropland, respectively. These findings highlight that human induced environmental modifications may enhance genetic connectivity of malaria vectors.
Highlights
Anopheles gambiae s.l. are the primary vectors of human malaria in sub-Saharan Africa, a disease responsible for 405,000 deaths worldwide annually, with around 90% occurring in Africa[1]
An. arabiensis specimens included in analyses originated from ten populations in western Kenya, Great Rift Valley and coastal Indian Ocean; whereas, An. gambiae specimens included in analyses originated from six populations in western Kenya only
To assess genetic diversity and structure of vector populations, six microsatellite loci were genotyped in An. arabiensis and five microsatellites were genotyped in An. gambiae specimens (Supplementary Table 2)
Summary
Anopheles gambiae s.l. are the primary vectors of human malaria in sub-Saharan Africa, a disease responsible for 405,000 deaths worldwide annually, with around 90% occurring in Africa[1]. The approach involves inferring population movement from the distribution of genetic markers, quantifying the distribution of ecological factors hypothesized to drive dispersal, and statistically testing the relationships between genetic variation and landscape h eterogeneity[15,16,17]. These inferences enable us to identify potential hotspot areas of disease movement for targeted public health interventions and containment of disease and drug and insecticide resistance[8]. By testing relationships between population genetic structure of malaria vectors and ecological factors, we can parse out confounding factors and determine the importance of key variables influencing malaria vector dispersal[8,15]
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