Abstract: In the absence of entomological information, tools for predicting mosquito vector species presence can help evaluate the entomological risk of vector-borne disease transmission. Here, we illustrate how species distribution models (SDM) could quantify potential dominant vector species presence in several settings. We fitted a 250 m resolution ensemble SDM for Anopheles albimanus Wiedemann, a dominant malaria vector, and a 30 m resolution SDM for Aedes aegypti L, a dominant dengue vector. Each ensemble SDM included predictions based on several algorithms. SDM covariates included environmental variables that were selected based on their importance from a larger set of layers that included remotely and spatially interpolated locally measured variables for the land surface of Costa Rica. Goodness of fit for each ensemble SDMs was very high, with a minimum AUC of 0.79. We used the resulting ensemble SDMs 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 and Ae. aegypti presence, than in surrounding landscapes. The An. albimanus 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 two villages also affected by the epidemic. Our results illustrate how ensemble SDMs in malaria elimination settings, and more generally in vector-borne disease transmission settings, can provide information that could help to improve vector surveillance and control .
