Methods for population estimation and inference have evolved over the past decade to allow for the incorporation of spatial information when using capture–recapture study designs. Traditional approaches to specifying spatial capture–recapture (SCR) models often rely on an individual-based detection function that decays as a detection location is farther from an individual’s activity center. Traditional SCR models are intuitive because they incorporate mechanisms of animal space use based on their assumptions about activity centers. We modify the SCR model to accommodate a wide range of space use patterns, including for those individuals that may exhibit traditional elliptical utilization distributions. Our approach uses underlying Gaussian processes to characterize the space use of individuals. This allows us to account for multimodal and other complex space use patterns that may arise due to movement. We refer to this class of models as geostatistical capture–recapture (GCR) models. We adapt a recursive computing strategy to fit GCR models to data in stages, some of which can be parallelized. This technique facilitates implementation and leverages modern multicore and distributed computing environments. We demonstrate the application of GCR models by analyzing both simulated data and a data set involving capture histories of snowshoe hares in central Colorado, USA.

中文翻译：

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地统计捕获-再捕获模型

人口估计和推断的方法在过去十年中不断发展，以便在使用捕获-再捕获研究设计时能够纳入空间信息。指定空间捕获-再捕获（SCR）模型的传统方法通常依赖于基于个体的检测函数，该函数随着检测位置距离个体活动中心越远而衰减。传统的 SCR 模型很直观，因为它们结合了基于对活动中心的假设的动物空间使用机制。我们修改 SCR 模型以适应各种空间使用模式，包括那些可能表现出传统椭圆利用率分布的个体。我们的方法使用基础高斯过程来表征个体的空间使用。这使我们能够考虑因运动而可能出现的多模式和其他复杂的空间使用模式。我们将此类模型称为地质统计捕获-再捕获 (GCR) 模型。我们采用递归计算策略，使 GCR 模型分阶段适应数据，其中一些可以并行化。该技术有助于实现并利用现代多核和分布式计算环境。我们通过分析模拟数据和涉及美国科罗拉多州中部雪鞋野兔捕获历史的数据集来演示 GCR 模型的应用。