While ecologists know that models require assumptions, the consequences of their violation become vague as model complexity increases. Integrated population models (IPMs) combine several datasets to inform a population model and to estimate survival and reproduction parameters jointly with higher precision than is possible using independent models. However, accuracy actually depends on an adequate fit of the model to datasets. We first investigated bias of parameters obtained from integrated population models when specific assumptions are violated. For instance, a model may assume that all females reproduce although there are non-breeding females in the population. Our second goal was to identify which diagnostic tests are sensitive to detect violations of the assumptions of IPMs. We simulated data mimicking a short- and a long-lived species under five scenarios in which a specific assumption is violated. For each simulated scenario, we fitted an IPM that violates the assumption (simple IPM) and an IPM that does not violate each specific assumption. We estimated bias and uncertainty of parameters and performed seven diagnostic tests to assess the fit of the models to the data. Our results show that the simple IPM was quite robust to violation of many assumptions and only resulted in small bias of the parameter estimates. Yet, the applied diagnostic tests were not sensitive to detect such small bias. The violation of some assumptions such as the absence of immigrants resulted in larger bias to which diagnostic tests were more sensitive. The parameters informed by the least amount of data were the most biased in all scenarios. We provide guidelines to identify misspecified models and to diagnose the assumption being violated. Simple models should often be sufficient to describe simple population dynamics, and when data are abundant, complex models accounting for specific processes will be able to shed light on specific biological questions.

虽然生态学家知道模型需要假设，但随着模型复杂性的增加，其违反的后果变得模糊不清。综合种群模型 (IPM) 将多个数据集结合起来，以告知种群模型并以比使用独立模型更高的精度联合估计生存和繁殖参数。然而，准确性实际上取决于模型与数据集的充分拟合。我们首先调查了违反特定假设时从综合总体模型获得的参数的偏差。例如，模型可能假设所有雌性都会繁殖，尽管种群中存在非繁殖雌性。我们的第二个目标是确定哪些诊断测试对检测违反 IPM 假设的行为很敏感。我们模拟了在违反特定假设的五种情况下模拟短寿命和长寿命物种的数据。对于每个模拟场景，我们拟合了一个违反假设的 IPM（简单 IPM）和一个不违反每个特定假设的 IPM。我们估计了参数的偏差和不确定性，并进行了七项诊断测试来评估模型对数据的拟合。我们的结果表明，简单的 IPM 对违反许多假设非常稳健，并且只会导致参数估计的小偏差。然而，应用的诊断测试对检测如此小的偏差并不敏感。违反某些假设（例如没有移民）导致了更大的偏差，诊断测试对哪些更敏感。数据量最少的参数在所有场景中偏差最大。我们提供指南来识别错误指定的模型并诊断被违反的假设。简单的模型通常足以描述简单的种群动态，当数据丰富时，解释特定过程的复杂模型将能够阐明特定的生物学问题。