Anopheles albimanus (Diptera: Culicidae) Ensemble Distribution Modeling: Applications for Malaria Elimination

In the absence of entomological information, tools for predicting Anopheles spp. presence can help evaluate the entomological risk of malaria transmission. Here, we illustrate how species distribution models (SDM) could quantify potential dominant vector species presence in malaria elimination settings. We fitted a 250 m resolution ensemble SDM for Anopheles albimanus Wiedemann. The ensemble SDM included predictions based on seven different algorithms, 110 occurrence records and 70 model projections. SDM covariates included nine environmental variables that were selected based on their importance from an original set of 28 layers that included remotely and spatially interpolated locally measured variables for the land surface of Costa Rica. Goodness of fit for the ensemble SDM was very high, with a minimum AUC of 0.79. We used the resulting ensemble SDM to evaluate differences in habitat suitability (HS) between commercial plantations and surrounding landscapes, finding a higher HS in pineapple and oil palm plantations, suggestive of An. albimanus presence, than in surrounding landscapes. The ensemble SDM suggested a low HS for An. albimanus at the presumed epicenter of malaria transmission during 2018-2019 in Costa Rica, yet this vector was likely present at the two main towns also affected by the epidemic. Our results illustrate how ensemble SDMs in malaria elimination settings can provide information that could help to improve vector surveillance and control.

The genomic signal of local environmental adaptation in Aedes aegypti mosquitoes

Local adaptation is important when predicting arthropod-borne disease risk because of its impacts on vector population fitness and persistence. However, the extent that vector populations are adapted to the environment generally remains unknown. Despite low population structure and high gene flow in Aedes aegypti mosquitoes across Panama, excepting the province of Bocas del Toro, we identified 128 candidate SNPs, clustered within 17 genes, which show a strong genomic signal of local environmental adaptation. This putatively adaptive variation occurred across fine geographical scales with the composition and frequency of candidate adaptive loci differing between populations in wet tropical environments along the Caribbean coast and dry tropical conditions typical of the Pacific coast. Temperature and vegetation were important predictors of adaptive genomic variation in Ae. aegypti with several potential areas of local adaptation identified. Our study lays the foundations of future work to understand whether environmental adaptation in Ae. aegypti impacts the arboviral disease landscape and whether this could either aid or hinder efforts of population control.