Accommodating the role of site memory in dynamic species distribution models

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 spatio-temporally 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 perf...

一阶动态占用模型（First-order Dynamic Occupancy Models, FODOMs）是一类状态空间模型，其真实状态（即物种出现情况）无法被完美观测。一阶动态占用模型的一项核心假设为：样地动态仅依赖于当前状态，且动态过程的变异可通过协变量或随机效应得到充分捕捉。然而，驱动生态数据生成的协变量往往难以被认知与量化，且这类协变量通常具有时空相关性。因此，相关数据所具备的非独立误差结构会导致参数不确定性被低估，进而削弱生态推断的可靠性。为此，我们在协变量不可获取的场景下，引入二阶马尔可夫过程拓展一阶动态占用模型框架，以适配样地的记忆效应。本建模框架可用于对样地占用率、定殖率、灭绝率、周转速率以及检测概率开展可靠的生态推断。我们通过一系列模拟实验阐明了该模型的数据需求与模型性能……