Insect physiology is highly dependent on the environmental temperature, and the relationship can be mathematically defined. Thus, many models that aim to predict insect-pest population dynamics, use meteorological data as input to descriptive functions that predict the development rate, survival and reproduction of pest populations. In most cases, however, these functions/models are laboratory-driven and are based on data from constant-temperature experiments. Therefore, they lack an important optimization and validation steps that test their accuracy under field conditions. Here, we developed a realistic and robust regional framework for modeling the field population dynamics of the global insect pest Bemisia tabaci. First, two non-linear functions, development rate (DR) and female reproduction (EN) were fitted to data collected in constant temperature experiments. Next, nine one-generation field experiments were conducted in order to establish a field-derived database of insect performance, representing a variety of growing conditions (different seasons, regions and host plants). Then, sensitivity analyses were performed for identifying the optimal time-scale for which the running-averaged temperatures should be fed to the model. Setting the time to 6 h (i.e., each of the 24-time steps per day represents the last 6 h average) produced the best fit (RMSD score of 1.59 days, 5.7% of the mean) between the field observations and the model simulations. We hypothesize that the 6 h ‘relevant biological time-scale’ captures the insect's physiological memory of daily cycling temperature events. Lastly, we evaluated the potential of the developed modeling framework to serve as a decision support tool in pest-management programs by correlating the model predictions with field-observations of three pest control inspectors during 2019. The model successfully predicted the first notable appearance of the insect in the field (completion of the third generation in May). Also, the model correctly identified the sharp rise in abundance (outbreak point) in mid-July (completion of the fifth generation), and the persistent rise in abundance through August and September. Comparing the simulations of the 2018 and 2019 seasons indicated that the model can also serve as a tool for retrospective systematic assessment of major decisions. Taken together, these data demonstrate the model robustness and its potential to provide an excellent decision-making support platform in regional control of pest species.

昆虫的生理高度依赖于环境温度，这种关系可以用数学方法定义。因此，许多旨在预测害虫种群动态的模型都将气象数据用作描述性功能的输入，这些功能可预测害虫种群的生长速度，存活和繁殖。但是，在大多数情况下，这些功能/模型是实验室驱动的，并且基于恒温实验的数据。因此，它们缺乏重要的优化和验证步骤，无法在野外条件下测试其准确性。在这里，我们开发了一个现实而强大的区域框架，用于对全球害虫烟粉虱的田间种群动态进行建模。。首先，将两个非线性函数发育速度（DR）和雌性生殖（EN）拟合到在恒温实验中收集的数据。接下来，进行了九次单代野外实验，以建立一个代表各种生长条件（不同季节，地区和寄主植物）的昆虫表现的野外数据库。然后，进行了敏感性分析，以确定将平均运行温度输入模型的最佳时标。将时间设置为6小时（即每天24个步骤中的每个步骤代表最近的6小时平均值），可以在野外观测和模型模拟之间实现最佳拟合（RMSD分数为1.59天，平均值的5.7％） 。我们假设6小时的“相关生物时间尺度”捕获昆虫日常循环温度事件的生理记忆。最后，我们通过将模型预测与三位害虫控制检查员在2019年的实地观察相关联，评估了开发的建模框架作为害虫管理计划的决策支持工具的潜力。该模型成功地预测了害虫防治检查员的首次明显出现昆虫（5月第三代完成）。此外，该模型正确地确定了7月中旬（第五代完成）的丰度急剧上升（爆发点），以及到8月和9月的丰度持续上升。比较2018年和2019年的模拟结果表明，该模型还可以用作对重大决策进行回顾性系统评估的工具。在一起。