Predicting the Spread of Vector-Borne Diseases in a Warming World - Endo, Andrew; Amarasekare, Priyanga (Frontiers in Ecology and Evolution, 2022)
Predicting how climate warming affects vector borne diseases is a key research priority. The prevailing approach uses the basic reproductive number (R_0) to predict warming effects. However, R_0 is derived under assumptions of stationary thermal environments; using it to predict disease spread in nonstationary environments could lead to erroneous predictions. Here we develop a trait-based mathematical framework that can predict disease spread and prevalence under any type of nonstationary environment. We use the model to test the latest IPCC predictions on warmer-than-average winters and hotter-than-average summers. We report three key findings. First, the R_0 formulation commonly used to warming effects on disease spread violates the assumptions underlying its derivation as the dominant eigenvalue of a linearized host-vector model. As a result, it overestimates disease spread in cooler environments and underestimates it in warmer environments, proving its predictions to be unreliable even in a constant thermal environment. Second, hotter-than-average summers both narrow the thermal limits for disease prevalence, and reduce prevalence within those limits, to a much greater degree than warmer-than-average winters, highlighting the importance of hot extremes in driving disease burden. Third, while warming reduces infected vector populations through the compounding effects of adult mortality, and infected host populations through the interactive effects of mortality and transmission, uninfected vector populations prove surprisingly robust to warming. This suggests that ecological predictions of warming-induced reductions in disease burden should be tempered by the evolutionary possibility of vector adaptation to both cooler and warmer climates.