Where boundaries become bridges: Mosquito community composition, key vectors, and environmental associations at forest edges in the central Brazilian Amazon

Abstract

Risk of spillover and spillback of mosquito-borne viruses in the neotropics, including yellow fever, dengue, Zika (Flaviviridae: Flavivirus), chikungunya, and Mayaro (Togaviridae: Alphavirus) viruses, is highest at ecotones where humans, monkeys, and mosquitoes coexist. With a view to identifying potential bridge vectors, we investigated changes in mosquito community composition and environmental variables at ground level at distances of 0, 500, 1000, and 2000 m from the edge of a rainforest reserve bordering the city of Manaus in the central Brazilian Amazon. During two rainy seasons in 2019 and 2020, we sampled 9,467 mosquitoes at 244 unique sites using BG-Sentinel traps, hand-nets, and Prokopack aspirators. Species richness and diversity were generally higher at 0 m and 500 m than at 1000 m and 2000 m, while mosquito community composition changed considerably between the forest edge and 500 m before stabilizing by 1000 m. Shifts in environmental variables mainly occurred between the edge and 500 m, and the occurrence of key taxa (Aedes albopictus, Ae. scapularis, Limatus durhamii, Psorophora amazonica, Haemagogus, and Sabethes) was associated with one or more of these variables. Sites where Ae. aegypti and Ae. albopictus were detected had significantly higher surrounding mean NDBI (Normalized Difference Built-up Index) values than sites where they were not detected, while the opposite was true for Sabethes mosquitoes. Our findings suggest that major changes in mosquito communities and environmental variables occur within 500 m of the forest edge, where there is high risk for contact with both urban and sylvatic vectors. By 1000 m, conditions stabilize, species diversity decreases, and forest mosquitoes predominate. Environmental variables associated with the occurrence of key taxa may be leveraged to characterize suitable habitat and refine risk models for pathogen spillover and spillback.