Drake, J. M., & Beier, J. C. (2014). Ecological niche and potential distribution of Anopheles arabiensis in Africa in 2050. Malaria Journal, 13(1), 213–23. http://doi.org/10.1186/1475-2875-13-213
Anopheles arabiensis is an important vector of malaria in sub-Saharan Africa because it is exophilic, and therefore less likely to be controlled by current elimination efforts focused on indoor residual spraying and insecticide treated nets. Drake & Beier used a presence-only method of ecological niche modeling to predict the distribution of this vector in 2050, based on climate projection models. This is one of the few studies to use LOBAG-OC since the original paper was published in 2014. LOBAG-OC was chosen because it is a better discriminator of niche boundaries, the area at which other species distribution models tend to fail. The model used 307 occurrence points, of which 246 were in the training set, and 86 environmental features constructed from WorldClim data, all of which were clipped to the African continent. The authors conducted a principal components analysis on the environmental features to examine the gross structure and found the majority of variation was explained by the first two principal components. The fit model describes An. arabiensis as a climate generalist, because of its wide baseline distribution across the African continent. When the fit model was applied to three climate change scenarios in 2050 based on IPCC projections, all three scenarios predicted significant reductions in area suitable for An. arabiensis. Variation amongst the three sceanarios was calculated as a measure of uncertainty, finding strong congruence among models. The key drivers of the predicted decrease in area are temperature and precipitation during the dry season. It is suggested that a cordon sanitaire may help control this fragmented, reduced population of malaria vectors in the future. Given the importance of urban areas in current and future vector-borne disease risk, I would be interested in seeing a similar method applied to incorporate predictions of population growth. It may be that these reductions (many in rural areas) are counter-balanced by increases in urban areas, and the overall per capita burden is unchanged.