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：过去十年间，种群估计与推断方法在利用捕获-重捕研究设计时逐步发展出整合空间信息的能力。传统空间捕获-重捕（SCR）模型的构建通常依赖于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。此类传统SCR模型因基于活动中心假设引入动物空间利用机制而具有直观性。我们通过模型泛化处理，涵盖包括可能呈现传统椭圆利用分布的个体在内的广泛空间利用模式。该方法采用高斯过程刻画个体空间利用特征，从而能够解释多峰空间利用模式及非线性运动廊道与障碍。我们将这类模型命名为地理统计捕获-重捕（GCR）模型。通过采用递归计算策略，我们可对GCR模型进行分阶段数据拟合（部分阶段可并行处理），该技术有助于模型实施并充分利用现代多核与分布式计算环境。基于模拟数据与科罗拉多州中部美洲兔的捕获历史数据集，我们展示了GCR模型的应用实践。