Ndjomatchoua Frank Thomas, Stutt Richard Olaf James Hamilton, Guimapi Ritter A, Rossini Luca, Gilligan Christopher A
Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.
Biotechnology and Plant Health Division, Norwegian Institute of Bioeconomy Research, Ås, Norway.
J R Soc Interface. 2025 May;22(226):20250059. doi: 10.1098/rsif.2025.0059. Epub 2025 May 7.
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 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.
经验性田间数据和模拟模型通常被分别用于监测和分析害虫种群随时间的动态变化。当直接使用田间数据为种群动态模型设定参数时，可能会获得更深入的见解。在本文中，我们使用差分进化算法来整合基于生理机制的种群模型和监测数据，以估计田间监测开始时第一代种群的密度和生理年龄。我们引入了一个临时的温度驱动生命周期模型，并结合田间监测数据。估计了当地粉虱入侵的可能日期，随后提高了模型的预测准确性。该方法可以计算害虫首次田间入侵的可能日期，并表明先前研究中有些被忽视的初始生理年龄可以提高模型模拟的准确性。鉴于描述陆生节肢动物的监测数据和模型越来越多，整合监测数据和模拟模型以改善模型预测和先驱入侵日期估计将有助于在害虫管理中做出更好的决策。
