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

Abstract

Simple SummaryCosta Rica is near malaria elimination. However, sporadic outbreaks still occur, and while control strategies have been focused on delivering efficient treatments for infected patients, an open question is whether control measures targeting the dominant vector, Anopheles albimanus, are appropriately designed given their ecology and distribution. Here, we illustrate the use of an ensemble species distribution model (SDM) as a tool to assess the potential exposure to An. albimanus in palm and pineapple plantations, and to also assess the potential involvement of this mosquito vector in transmission foci where entomological surveillance is not feasible. We found that both oil palm and pineapple plantations are very likely to harbor An. albimanus. By contrast, environments at the Crucitas open-pit gold mine, the epicenter of malaria transmission in 2018 and 2019, have low suitability for this mosquito species. Our results suggest that medium to high resolution SDMs can be used to plan vector control activities. Finally, we discuss the high suitability of oil palm and pineapple plantations for An. albimanus in reference to recently developed social science theory about the Plantationocene.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.