Climate change impacts agricultural pests in complicated ways, and accounting for these responses should form an integral part of pest management programmes. Process-based models are often used in agriculture to forecast population dynamics of pests within a growing season, however these are often constrained to assessing the impacts of temperature in isolation of other factors. These models are therefore unable to fully explore species’ responses to climate change, which may be driven by multiple abiotic and biological stressors. Here, we build a mechanistic model of a globally distributed agricultural aphid pest (Myzus persicae (Sulzer)), and an associated parasitic wasp (Diaeretiella rapae (M'Intosh)) that is known to biologically control the aphid. We simulate temporal dynamics of the crop with a well-established canola growing degree-day model (APSIM) and incorporate the impacts of temperature and rainfall on insect survival. The model was parameterised with laboratory-measured datasets from around the globe, and we have calibrated and validated the model to Australian broadacre cropping systems using regional observation records. We then ran the validated models with future temperature and rainfall scenarios to reveal that suppression of aphid populations by the wasp is enhanced under stressful abiotic conditions, which are predicted to occur more frequently in the future. The process-based modelling approach affords valuable and novel insights into physiological traits that influence population dynamics of both species and highlights gaps in our current understanding of the system. In the future, farmers could have greater confidence in the bio-control potential of D. rapae under different conditions, and be in a position to adjust their M. persicae management programmes accordingly. This is the first model to explore the interaction of these two cosmopolitan species in the field, which is applicable across broad geographic regions, while also providing insights as to how both species could be better managed on a local scale.

气候变化以复杂的方式影响农业害虫，考虑这些反应应该成为害虫管理计划的一个组成部分。基于过程的模型通常用于农业中来预测生长季节内害虫的种群动态，但是这些模型通常仅限于评估温度对其他因素的影响。因此，这些模型无法充分探索物种对气候变化的反应，这可能是由多种非生物和生物压力因素驱动的。在这里，我们建立了一个全球分布的农业蚜虫（Myzus persicae (Sulzer)）和相关寄生蜂（Diaeretiella rapae）的机械模型(M'Intosh)) 已知可以生物控制蚜虫。我们使用完善的双低油菜籽生长度日模型 (APSIM) 模拟作物的时间动态，并结合温度和降雨对昆虫生存的影响。该模型使用来自全球的实验室测量数据集进行参数化，我们已经使用区域观察记录对澳大利亚大面积种植系统进行了校准和验证。然后，我们在未来的温度和降雨情景下运行经过验证的模型，以揭示黄蜂对蚜虫种群的抑制在压力非生物条件下得到增强，预计这种情况在未来会更频繁地发生。基于过程的建模方法提供了对影响两个物种种群动态的生理特征的有价值和新颖的见解，并突出了我们目前对该系统理解的差距。未来，农民可以对生物防治潜力有更大的信心。D. rapae在不同条件下，并能够相应地调整他们的M. persicae管理计划。这是在该领域探索这两种世界性物种相互作用的第一个模型，适用于广泛的地理区域，同时还提供了有关如何在当地范围内更好地管理这两种物种的见解。