Empirical dynamic modeling for prediction and control of pest populations

Insect pests pose a threat to humans by jeopardizing food security in agricultural systems, acting as vectors for infectious diseases, and damaging forests and other ecosystems. Despite decades of research, effective pest management remains challenging. Incomplete understanding of the mechanisms behind pest population dynamics limits our ability to anticipate outbreaks. Hence, pest management is often reactive, meaning control actions are taken once outbreaks have already begun, allowing for damage to occur. Here we show that a data-driven model can effectively predict outbreaks, allowing us to optimize control strategies, targeting pests before outbreaks occur. Specifically, we explore empirical dynamic modeling paired with stochastic dynamic programming to keep insect populations within acceptable bounds. We show that this framework reduces outbreaks in several simulated and empirical scenarios. Our study provides a promising framework to reduce losses from pests.