Food availability and larval density-dependent competition differentially impact Wolbachia pipientis (Rickettsiales: Rickettsiaceae) transinfected Aedes aegypti
Chun (Jessica) Cheng, Sebastián Duran-Ahumada, Abdiel Martin-Park, Yamili Contreras-Perera, Gabriela González-Olvera, Azael Che-Mendoza, Pablo Manrique-Saide, Gonzalo M Vazquez-ProkopecAbstract
Two Wolbachia pipientis (Hertig, 1936) strains, wAlbB and wMel, are currently used for the biocontrol of Aedes aegypti (Linnaeus, 1762) and urban arboviral diseases. As Wolbachia-infected mosquitoes are released into heterogeneous environmental conditions, the ability of Wolbachia to propagate is strongly dependent on external factors potentially influencing mosquito fitness. A fully-factorial laboratory experiment was conducted to comparatively assess the effects of larval food availability and conspecific density on the fitness of Ae. aegypti mosquitoes infected with wAlbB and wMel, compared to an uninfected wild-type mosquito line. Outcomes measured included adult survivorship, wing length, and Wolbachia relative density. Generalized linear and mixed-effects models evaluated overall and interactive effects of food availability and larval density on mosquito performance, fitness, and Wolbachia infection. Wolbachia-infected mosquitoes, particularly those infected with the wAlbB strain, were more sensitive to environmental stressors than wild-type mosquitoes. A significant 3-way interaction showed that the combination of high larval density and limited food imposed the greatest fitness costs, especially in wAlbB-infected individuals. While no significant differences in body size were observed between the 2 Wolbachia-infected emerging adults, wAlbB-infected mosquitoes exhibited greater variation in Wolbachia relative density across treatments. These findings suggest that ecological constraints such as limited resources and increased larval competition may differentially impact Wolbachia strains and the outcomes of field releases. These insights are essential for optimizing deployment and predicting the success of such interventions in variable real-world settings.