Abstract
IntroductionIncreasing the knowledgebase of anopheline larval ecology could enable targeted deployment of malaria control efforts and consequently reduce costs of implementation. In Malawi, there exists a knowledge gap in anopheline larval ecology and, therefore, basis for targeted deployment of larval source management (LSM) for malaria control, specifically larvicides. We set out to characterize anopheline larval habitats in the Majete area of Malawi on the basis of habitat ecology and anopheline larval productivity to create a basis for larval control initiatives in the country. MethodsLongitudinal surveys were conducted in randomly selected larval habitats over a period of fifteen months in Chikwawa district, southern Malawi. Biotic and abiotic parameters of the habitats were modelled to determine their effect on the occurrence and densities of anopheline larvae. ResultsSeventy aquatic habitats were individually visited between 1-7 times over the study period. A total of 5,123 immature mosquitoes (3,359 anophelines, 1,497 culicines and 267 pupae) were collected. Anopheline and culicine larvae were observed in sympatry in aquatic habitats. Of the nine habitat types followed, dams, swamps, ponds, borehole runoffs and drainage channels were the five most productive habitat types for anopheline mosquitoes. Anopheline densities were higher in aquatic habitats with bare soil making up part of the surrounding land cover (p<0.01) and in aquatic habitats with culicine larvae (p<0.01) than in those surrounded by vegetation and not occupied by culicine larvae. Anopheline densities were significantly lower in highly turbid habitats than in clearer habitats (p<0.01). Presence of predators in the aquatic habitats significantly reduced the probability of anopheline larvae being present (p=0.04). ConclusionsAnopheline larval habitats are widespread in the study area. Presence of bare soil, culicine larvae, predators and the level of turbidity of water are the main determinants of anopheline larval densities in aquatic habitats in Majete, Malawi. While the most productive aquatic habitats should be prioritised, for the most effective control of vectors in the area all available aquatic habitats should be targeted, even those that are not characterized by the identified predictors. Further research is needed to determine whether targeted LSM would be cost-effective when habitat characterisation is included in cost analyses and to establish what methods would make the characterisation of habitats easier.
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