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Background  A better understanding of the ecology and spatial-temporal distribution of malaria vectors is essential to design more effective and sustainable strategies for malaria control and elimination. In a previous study, we analyzed presence-absence data of  An. funestus, An. coluzzii,  and  An. gambiae s.s.  in an area of southern Benin with high coverage of vector control measures. Here, we further extend the work by analysing the positive values of the dataset to assess the determinants of the abundance of these three vectors and to produce predictive maps of vector abundance.    Methods  Positive counts of the three vectors were assessed using negative-binomial zero-truncated (NBZT) mixed-effect models according to vector control measures and environmental covariates derived from field and remote sensing data. After 8-fold cross-validation of the models, predictive maps of abundance of the sympatric  An. funestus, An. coluzzii,  and  An. gambiae s.s.  were produced.    Results  Cross-validation of the NBZT models showed a satisfactory predictive accuracy. Almost all changes in abundance between two surveys in the same village were well predicted by the models but abundances for  An. gambiae s.s.  were slightly underestimated. During the dry season, predictive maps showed that abundance greater than 1 bite per person per night were observed only for  An. funestus  and  An. coluzzii . During the rainy season, we observed both increase and decrease in abundance of  An. funestus , which are dependent on the ecological setting. Abundances of both  An. coluzzii  and  An. gambiae s.s.  increased during the rainy season but not in the same areas.    Conclusions  Our models helped characterize the ecological preferences of three major African malaria vectors. This works highlighted the importance to study independently the binomial and the zero-truncated count processes when evaluating vector control strategies. The study of the bio-ecology of malaria vector species in time and space is critical for the implementation of timely and efficient vector control strategies.

Spatio-temporal analysis of abundances of three malaria vector species in southern Benin using zero-truncated models