A Bayesian State-Space Approach for Invasive Species Management: The Case of Spotted Wing Drosophila

Spotted wing drosophila (SWD) is an invasive pest with devastating effects on soft-skinned fruit crops. Due to its high economic impacts, current SWD management strategies usually focus on preventive calendar-based insecticide sprays. The industry is calling for adoption of monitoring-based integrated pest management (IPM) strategies to reduce unnecessary insecticide applications. However, because traps are costly and do not provide perfect observations of the population size, most growers do not monitor. We develop a Bayesian state-space bioeconomic framework to inform the optimal SWD management strategy when observational uncertainty exists. We use Bayesian inference via the Markov Chain Monte Carlo method to generate the posterior distribution of population model parameters, which we then use to simulate the economic performance of alternative SWD management strategies. We find that one of the monitoring-based IPM strategies has a slightly lower total cost than the calendar-based spray strategy. We also find evidence of misalignments between public and private incentives in the adoption of IPM strategies. Profit-maximizing growers who ignore the negative externalities of insecticide spray have little incentive to adopt the IPM strategies. However, for environmentally conscious growers who take into account the external costs of insecticide sprays, IPM strategies are superior to calendar-based spray strategy. Our results indicate that IPM strategies become more appealing to both types of growers as the trapping efficiency improves. Extension efforts, support for research to improve trapping efficiency, and monetary incentives can be used to encourage grower adoption of monitoring-based IPM strategies to control SWD infestation.