Integration of temperature-driven population model and pest monitoring data to estimate initial conditions and timing of first field invasion: application to the cassava whitefly, Bemisia tabaci.

Empirical field data and simulation models are often used separately to monitor and analyse the dynamics of insect pest populations over time. Greater insight may be achieved when field data are used directly to parametrize population dynamic models. In this paper, we use a differential evolution algorithm to integrate mechanistic physiological-based population models and monitoring data to estimate the population density and the physiological age of the first cohort at the start of the field monitoring. We introduce an ad hoc temperature-driven life-cycle model of Bemisia tabaci in conjunction with field monitoring data. The likely date of local whitefly invasion is estimated, with a subsequent improvement of the model's predictive accuracy. The method allows computation of the likely date of the first field incursion by the pest and demonstrates that the initial physiological age somewhat neglected in prior studies can improve the accuracy of model simulations. Given the increasing availability of monitoring data and models describing terrestrial arthropods, the integration of monitoring data and simulation models to improve model prediction and pioneer invasion date estimate will lead to better decision-making in pest management.