A framework for predicting the effects of climate warming on arthropod disease vectors

Predicting the effects of climate warming on vector-borne disease transmission is a crucial research priority. Predictions that can reliably inform policy need to be based on vector biology, but models that incorporate biological realism are often difficult to test with the limited amount of information available for most disease vectors. Here we present a framework for predicting warming effects on vector population dynamics based solely on the vector's life history trait responses to temperature and the characteristics of the vector's thermal environment. We show that life history trait responses alone can make reasonably accurate predictions of a vector population's propensity for extinction under warming, while trait responses combined with the life stage at which density-dependence operates can predict whether vector populations exhibit intrinsic cycles in the absence of temperature variation. By incorporating the vector's life history traits into a population model that explicitly incorporates the vector's developmental delay, we show that the interplay between intrinsic cycles and temperature variation can lead to distinctive signatures in vector abundance patterns that can be detected in time series data without having to fit the model to such data. Importantly, we can use the model to predict the level of warming at which population regulation fails altogether, causing vector extinction. We find that the threshold warming level for extinction is lower when warming is driven by hot extremes compared to other scenarios.