Predicting how insects will respond to stressors through time is difficult because of the diversity of insects, environments, and approaches used to monitor and model. Forecasting models take correlative/statistical, mechanistic models, and integrated forms; in some cases, temporal processes can be inferred from spatial models. Because of heterogeneity associated with broad community measurements, models are often unable to identify mechanistic explanations. Many present efforts to forecast insect dynamics are restricted to single-species models, which can offer precise predictions but limited generalizability. Trait-based approaches may offer a good compromise that limits the masking of the ranges of responses while still offering insight. Regardless of the modeling approach, the data used to parameterize a forecasting model should be carefully evaluated for temporal autocorrelation, minimum data needs, and sampling biases in the data. Forecasting models can be tested using near-term predictions and revised to improve future forecasts.

由于昆虫、环境以及用于监测和建模的方法的多样性，预测昆虫随着时间的推移如何应对压力源是很困难的。预测模型采用相关/统计、机械模型和综合形式；在某些情况下，可以从空间模型推断时间过程。由于与广泛的群落测量相关的异质性，模型通常无法识别机制解释。目前许多预测昆虫动态的努力仅限于单物种模型，这些模型可以提供精确的预测，但普遍性有限。基于特征的方法可能提供一个很好的折衷方案，限制响应范围的掩盖，同时仍然提供洞察力。无论采用哪种建模方法，都应仔细评估用于参数化预测模型的数据的时间自相关性、最小数据需求和数据中的采样偏差。可以使用近期预测来测试预测模型，并进行修改以改进未来的预测。