Structured demographic models are among the most common and useful tools in population biology. However, the introduction of integral projection models (IPMs) has caused a profound shift in the way many demographic models are conceptualized. Some researchers have argued that IPMs, by explicitly representing demographic processes as continuous functions of state variables such as size, are more statistically efficient, biologically realistic, and accurate than classic matrix projection models, calling into question the usefulness of the many studies based on matrix models. Here, we evaluate how IPMs and matrix models differ, as well as the extent to which these differences matter for estimation of key model outputs, including population growth rates, sensitivity patterns, and life spans. First, we detail the steps in constructing and using each type of model. Second, we present a review of published demographic models, concentrating on size‐based studies, which shows significant overlap in the way IPMs and matrix models are constructed and analyzed. Third, to assess the impact of various modeling decisions on demographic predictions, we ran a series of simulations based on size‐based demographic data sets for five biologically diverse species. We found little evidence that discrete vital rate estimation is less accurate than continuous functions across a wide range of sample sizes or size classes (equivalently bin numbers or mesh points). Most model outputs quickly converged with modest class numbers (≥10), regardless of most other modeling decisions. Another surprising result was that the most commonly used method to discretize growth rates for IPM analyses can introduce substantial error into model outputs. Finally, we show that empirical sample sizes generally matter more than modeling approach for the accuracy of demographic outputs. Based on these results, we provide specific recommendations to those constructing and evaluating structured population models. Both our literature review and simulations question the treatment of IPMs as a clearly distinct modeling approach or one that is inherently more accurate than classic matrix models. Importantly, this suggests that matrix models, representing the vast majority of past demographic analyses available for comparative and conservation work, continue to be useful and important sources of demographic information.

中文翻译：

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人口统计分析中积分投影模型和矩阵投影模型的重要比较

结构化的人口模型是人口生物学中最常见和最有用的工具之一。但是，积分投影模型（IPM）的引入已使许多人口模型的概念化方式发生了深刻的变化。一些研究人员认为，IPM通过将人口统计过程明确表示为状态变量（例如大小）的连续函数，比经典的矩阵投影模型具有更高的统计效率，生物学上的真实性和准确性，这使许多基于矩阵的研究的有用性产生了疑问。楷模。在这里，我们评估IPM和矩阵模型的差异，以及这些差异对关键模型输出（包括人口增长率，敏感性模式和寿命）的估计程度。第一的，我们详细介绍了构建和使用每种类型的模型的步骤。其次，我们对已发布的人口模型进行回顾，重点是基于规模的研究，这表明IPM和矩阵模型的构建和分析方法存在显着重叠。第三，为了评估各种建模决策对人口预测的影响，我们基于五个生物多样性物种基于大小的人口数据集进行了一系列模拟。我们发现很少有证据表明，离散生命率估算的准确性不及在广泛的样本大小或大小类别（相当于箱数或网格点）上的连续函数。不管其他大多数建模决策如何，大多数模型输出都会迅速地与适度的类编号（≥10）融合。另一个令人惊讶的结果是，最常用的离散化IPM分析增长率的方法会在模型输出中引入实质性误差。最后，我们证明，对于人口统计输出的准确性，经验样本大小通常比建模方法更重要。基于这些结果，我们为那些构建和评估结构化人口模型的人提供具体建议。我们的文献回顾和模拟都质疑IPM是一种明显不同的建模方法，还是固有地比经典矩阵模型更准确的建模方法。重要的是，这表明代表了过去可用于比较和保护工作的绝大多数人口分析的矩阵模型仍然是有用且重要的人口信息来源。