The possibility of anopheline larval control and the need to understand the contribution of larval habitat distribution to the intensity of the malaria transmission cycle have generated inquiry into the relationships of anopheline larval habitats with environmental variables, including those variables that can be remotely-sensed across the landscape. These habitats are spatially predictable but their occurrence is unstable throughout time such that a map of their locations has a short lifespan of high accuracy. In this study, I create a dynamic environmental model of aquatic habitats of anopheline larvae for Asembo, a community in western Kenya, using topography, land-use/land-cover, and rainfall variables that have shown previous success in landscape models of Anopheles spp. habitats. I compare the success of the model’s prediction maps when confronted with new data in another year at the same site to the accuracy of nearly-contemporaneous maps of the habitats as well as kriging-interpolated maps that exploit the habitat spatial clustering to increase the predictive power of the map. The dynamic environmental model shows the best predictive power of the three map types tested. The dominant input variable, the topographic position index, is further investigated, showing that the relationship is strongest at the 1710m scale and the predictions are moderately robust to elevation measurement errors. Though the prior knowledge of habitat locations does not accurately predict their future locations for long, I identify significant spatiotemporal autocorrelation in the distribution of the aquatic habitats that could be used in future prediction mapping to fine-tune generalized environmental models to site-specific patterns when some habitats have already been identified.
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