First‐order dynamic occupancy models (FODOMs) are a class of state‐space model in which the true state (occurrence) is observed imperfectly. An important assumption of FODOMs is that site dynamics only depend on the current state and that variations in dynamic processes are adequately captured with covariates or random effects. However, it is often difficult to understand and/or measure the covariates that generate ecological data, which are typically spatiotemporally correlated. Consequently, the non‐independent error structure of correlated data causes underestimation of parameter uncertainty and poor ecological inference. Here, we extend the FODOM framework with a second‐order Markov process to accommodate site memory when covariates are not available. Our modeling framework can be used to make reliable inference about site occupancy, colonization, extinction, turnover, and detection probabilities. We present a series of simulations to illustrate the data requirements and model performance. We then applied our modeling framework to 13 yr of data from an amphibian community in southern Arizona, USA. In this analysis, we found residual temporal autocorrelation of population processes for most species, even after accounting for long‐term drought dynamics. Our approach represents a valuable advance in obtaining inference on population dynamics, especially as they relate to metapopulations.

中文翻译：

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适应位点记忆在动态物种分布模型中的作用

一阶动态占用模型（FODOM）是一类状态空间模型，其中不完美地观察到真实状态（发生）。FODOM的一个重要假设是，站点动态仅取决于当前状态，并且动态过程中的变化可以通过协变量或随机效应得到充分捕获。然而，通常难以理解和/或测量产生生态数据的协变量，这些协变量通常是时空相关的。因此，相关数据的非独立错误结构导致对参数不确定性的低估和不良的生态推断。在这里，我们使用二阶马尔可夫过程扩展了FODOM框架，以在协变量不可用时容纳站点内存。我们的建模框架可用于对网站的使用情况做出可靠的推断，定植，灭绝，周转和检测概率。我们提供了一系列仿真来说明数据需求和模型性能。然后，我们将建模框架应用于来自美国亚利桑那州南部两栖动物群落的13年数据。在此分析中，即使考虑了长期干旱动态，我们也发现了大多数物种的种群过程的剩余时间自相关。我们的方法代表了在推断种群动态方面的宝贵进展，尤其是当它们与种群相关时。我们发现，即使考虑了长期干旱动态，大多数物种的种群过程仍存在剩余的时间自相关。我们的方法代表了在推断种群动态方面的宝贵进展，尤其是当它们与种群相关时。我们发现，即使考虑了长期干旱动态，大多数物种的种群过程仍存在剩余的时间自相关。我们的方法代表了在推断种群动态方面的宝贵进展，尤其是当它们与种群相关时。