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 generalize SCR models 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 space use patterns as well as nonlinear corridors and barriers 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.

翻译：过去十年来，种群估计与推断方法不断发展，使得在采用捕获-再捕获研究设计时能够纳入空间信息。传统空间捕获-再捕获（SCR）模型的构建通常依赖基于个体的探测函数，该函数随探测位置远离个体活动中心而衰减。传统SCR模型具有直观性，因其基于活动中心假设整合了动物空间利用机制。我们对SCR模型进行推广，使其能够适应更广泛的空间利用模式，包括那些可能呈现传统椭圆形利用分布的个体。该方法利用高斯过程刻画个体的空间利用特征，从而能够处理多峰空间利用模式以及非线性运动廊道与屏障。我们称这类模型为地理统计捕获-再捕获（GCR）模型。采用递归计算策略分阶段对GCR模型进行数据拟合，其中部分阶段可并行化处理。该技术简化了模型实现过程，并充分利用现代多核与分布式计算环境。通过分析模拟数据及美国科罗拉多州中部雪靴兔的捕获历史数据集，我们展示了GCR模型的实际应用效果。