Incorporating diapause to predict the interannual dynamics of an important agricultural pest

We develop a new population-scale model incorporating diapause induction and termination that allows multi-year predictions of pest dynamics. In addition to predicting phenology and voltinism, the model also allows us to study the degree of overlapping among the life-stages across time; a quantity not generally predicted by previous models yet a key determinant of how frequently management must be done to maintain control. The model is a physiological, stage-structured population model that includes temperature-dependent vital rates, diapause processes, and plasticity in development. The model is statistically fitted with a 33-year long weekly term time series of Cydia pomonella adults captured in pheromone-baited traps from a research orchard in southern Pennsylvania. The multiannual model allows investigation of both within season control strategies, as well as the likely consequences of climate change for this important agricultural pest. The model predicts that warming temperatures will cause earlier spring emergence, additional generations, and increased overall abundance. Most importantly, by calculating the circular variance, we find that warmer temperatures are associated with an increase in overlap among life-stages especially at the beginning of the growing season. Our findings highlight the importance of modeling diapause to fully understand C. pomonella lifecycle and to better inform management for effectively controlling this pest in a warmer future.