Satellite telemetry data are commonly used to quantify habitat selection, examine animal movements, and delineate home ranges. These data also contain valuable information concerning dens, nests, roosts, and other central places that are often associated with important life history events and may exhibit unique characteristics; however, using satellite telemetry data to study central places is complicated by common nuances like locational error and animal movement. We coupled a novel modeling framework that accounts for these nuances with an Argos satellite telemetry dataset to examine the spatiotemporal behavior associated with harbor seal haul‐out sites on Kodiak Island, Alaska, USA. The methodology incorporates an observation model that accommodates multiple sources of uncertainty in telemetry data and a flexible Bayesian nonparametric model to uncover latent clustering in the telemetry locations. We also contribute extensions to examine the effect of covariates on site selection and to obtain population‐level inference concerning central place use. Harbor seal haul‐out sites generally occurred in inlets and bays, areas that are isolated from the open water of the Gulf of Alaska. Most individuals selected haul‐out sites that were protected from wave exposure. The effects of bathymetry and shoreline complexity on haul‐out site selection were variable among individual seals, as were the effects of time of day, time since low tide, and day of year on temporal patterns of haul‐out use. As repositories of satellite telemetry data on a wide variety of species accumulate, so do opportunities for using this information to learn about the locations of central places, as well as the temporal patterns in their use. The model‐based approach we describe offers a practical and rigorous means for gaining insight concerning these sensitive locations, knowledge of which is important for the effective management and conservation of many species.

中文翻译：

---

基于模型的聚类揭示了海洋顶级捕食者在中央场所使用的模式

卫星遥测数据通常用于量化栖息地选择，检查动物运动并描绘出家园范围。这些数据还包含与巢穴，巢穴，栖息地和其他中心位置有关的有价值的信息，这些信息通常与重要的生活史事件有关，并可能表现出独特的特征；但是，使用卫星遥测数据来研究中心位置会因为诸如位置错误和动物运动之类的细微差别而变得复杂。我们将一个解释这些细微差别的新颖建模框架与一个Argos卫星遥测数据集结合起来，以检查与美国阿拉斯加科迪亚克岛上的海豹捕捞站点相关的时空行为。该方法包括一个观测模型和一个灵活的贝叶斯非参数模型，该观测模型可容纳遥测数据中的多个不确定性源，以发现遥测位置中的潜在聚类。我们还提供扩展功能，以检查协变量对选址的影响，并获得有关中心场所使用的总体水平推断。港口海豹捕捞场通常发生在与阿拉斯加湾的开阔水域隔离开来的入口和海湾。大多数人选择的避难场所应避免受到波的照射。在各个海豹中，测深和海岸线复杂度对拖网地点选择的影响是可变的，一天中的时间，自退潮以来的时间和一年中的一天对拖网使用时间模式的影响也不同。随着各种物种的卫星遥测数据存储库的积累，利用这些信息来了解中心地点的位置及其使用时态的机会也越来越多。我们描述的基于模型的方法提供了一种实用而严格的方法，以获取有关这些敏感位置的见识，而这些知识对于有效管理和保护许多物种非常重要。