Occupancy-based methods are commonly used to model the distribution, habitat use, and relative abundance of species. In particular, occupancy-based methods are often recommended for monitoring species, allowing researchers to track population trends using detection–non-detection data alone. Occupancy models, however, have proven difficult to apply to rare, highly mobile species. Species’ movements outside of sampling areas may lead to violation of the geographic closure assumption of occupancy models and overestimated occupancy probability. Low detection probability may further inflate occupancy probability estimates. We developed a novel continuous-time, multi-scale occupancy model to simultaneously account for closure assumption violations and low detection probability. We used a simulation study to test our model relative to a discrete-time multi-scale model, and we conducted a power analysis to assess the ability of an instantaneous occupancy parameter to detect trends in abundance, relative to standard occupancy alone. The continuous-time model was competitive with the discrete-time model and was generally computationally faster than and outperformed the discrete-time model when detection probability was low. The instantaneous occupancy parameter outperformed occupancy in terms of power to detect trends when we used an implicit (i.e., estimating occupancy independently in each primary occasion) dynamic occupancy model, but performed no better when we used an explicit (i.e., estimating colonization and extinction) dynamic occupancy model. We applied both discrete-time and continuous-time multi-scale occupancy models to a case study of data collected on wolverines (Gulo gulo) in Washington, USA. We found improved precision in estimates with the continuous-time model and that asymptotic occupancy of wolverines was high, but short-term use of any given area was low. Our multi-scale, continuous-time occupancy model can be used to detect trends in abundance of rare, highly mobile species, regardless of how occupancy dynamics are modeled. Furthermore, our model can allow for more efficient data collection and analysis than traditional discrete-time or spatially multi-scale approaches, as our model uses all available detections and requires only one detector per sampling unit by substituting time for space.

中文翻译：

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建模多尺度占用以监测稀有和高度流动的物种

基于占用的方法通常用于对物种的分布、栖息地利用和相对丰度进行建模。特别是，经常推荐使用基于占用的方法来监测物种，使研究人员能够仅使用检测-非检测数据来跟踪种群趋势。然而，事实证明占用模型难以应用于稀有、高度流动的物种。样本区域外的物种移动可能会导致违反占用模型的地理闭合假设和高估占用概率。低检测概率可能进一步夸大占用概率估计。我们开发了一种新颖的连续时间、多尺度占用模型，以同时解决关闭假设违规和低检测概率的问题。我们使用模拟研究来测试我们相对于离散时间多尺度模型的模型，并且我们进行了功效分析以评估瞬时占用参数检测丰度趋势的能力，相对于单独的标准占用。连续时间模型与离散时间模型具有竞争力，并且在检测概率较低时通常比离散时间模型在计算上更快并优于离散时间模型。当我们使用隐式（即，在每个主要场合独立估计占用率）动态占用模型时，瞬时占用参数在检测趋势的能力方面优于占用率，但在我们使用显式（即估计殖民化和灭绝）时表现并没有更好动态占用模型。Gulo gulo ) 在美国华盛顿。我们发现连续时间模型的估计精度提高了，而且狼獾的渐近占用率很高，但任何给定区域的短期使用率都很低。我们的多尺度、连续时间占用模型可用于检测稀有、高度移动物种的丰度趋势，无论占用动态如何建模。此外，我们的模型可以允许比传统的离散时间或空间多尺度方法更有效的数据收集和分析，因为我们的模型使用所有可用的检测，并且通过用时间代替空间，每个采样单元只需要一个检测器。