State–space models (SSMs) are an important modeling framework for analyzing ecological time series. These hierarchical models are commonly used to model population dynamics, animal movement, and capture–recapture data, and are now increasingly being used to model other ecological processes. SSMs are popular because they are flexible and they model the natural variation in ecological processes separately from observation error. Their flexibility allows ecologists to model continuous, count, binary, and categorical data with linear or nonlinear processes that evolve in discrete or continuous time. Modeling the two sources of stochasticity separately allows researchers to differentiate between biological variation and imprecision in the sampling methodology, and generally provides better estimates of the ecological quantities of interest than if only one source of stochasticity is directly modeled. Since the introduction of SSMs, a broad range of fitting procedures have been proposed. However, the variety and complexity of these procedures can limit the ability of ecologists to formulate and fit their own SSMs. We provide the knowledge for ecologists to create SSMs that are robust to common, and often hidden, estimation problems, and the model selection and validation tools that can help them assess how well their models fit their data. We present a review of SSMs that will provide a strong foundation to ecologists interested in learning about SSMs, introduce new tools to veteran SSM users, and highlight promising research directions for statisticians interested in ecological applications. The review is accompanied by an in-depth tutorial that demonstrates how SSMs can be fitted and validated in R. Together, the review and tutorial present an introduction to SSMs that will help ecologists to formulate, fit, and validate their models.

中文翻译：

---

生态时间序列状态空间建模指南

状态空间模型 (SSM) 是分析生态时间序列的重要建模框架。这些分层模型通常用于模拟种群动态、动物运动和捕获-重新捕获数据，现在越来越多地用于模拟其他生态过程。SSM 很受欢迎，因为它们很灵活，并且可以将生态过程中的自然变化与观测误差分开建模。它们的灵活性允许生态学家使用在离散或连续时间演化的线性或非线性过程对连续、计数、二元和分类数据进行建模。分别对随机性的两个来源进行建模使研究人员能够区分采样方法中的生物变异和不精确性，并且通常比直接模拟一个随机性来源提供更好的生态量估计。自从引入 SSM 以来，已经提出了广泛的拟合程序。然而，这些程序的多样性和复杂性会限制生态学家制定和适应他们自己的 SSM 的能力。我们为生态学家提供知识，以创建对常见且通常隐藏的估计问题具有鲁棒性的 SSM，以及可以帮助他们评估模型与数据拟合程度的模型选择和验证工具。我们对 SSM 进行了回顾，这将为有兴趣了解 SSM 的生态学家提供坚实的基础，向资深 SSM 用户介绍新工具，并为对生态应用感兴趣的统计学家强调有前途的研究方向。