U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, Maryland 20708, USA.
Ecology. 2010 Nov;91(11):3376-83. doi: 10.1890/09-0804.1.
We develop a hierarchical capture-recapture model for demographically open populations when auxiliary spatial information about location of capture is obtained. Such spatial capture-recapture data arise from studies based on camera trapping, DNA sampling, and other situations in which a spatial array of devices records encounters of unique individuals. We integrate an individual-based formulation of a Jolly-Seber type model with recently developed spatially explicit capture-recapture models to estimate density and demographic parameters for survival and recruitment. We adopt a Bayesian framework for inference under this model using the method of data augmentation which is implemented in the software program WinBUGS. The model was motivated by a camera trapping study of Pampas cats Leopardus colocolo from Argentina, which we present as an illustration of the model in this paper. We provide estimates of density and the first quantitative assessment of vital rates for the Pampas cat in the High Andes. The precision of these estimates is poor due likely to the sparse data set. Unlike conventional inference methods which usually rely on asymptotic arguments, Bayesian inferences are valid in arbitrary sample sizes, and thus the method is ideal for the study of rare or endangered species for which small data sets are typical.
我们开发了一种针对人口统计学开放群体的分层捕获-再捕获模型，当获得关于捕获位置的辅助空间信息时。这种空间捕获-再捕获数据源于基于相机陷阱、DNA 采样和其他情况下的研究，在这些情况下，设备的空间阵列记录了独特个体的遭遇。我们将基于个体的 Jolly-Seber 型模型的公式与最近开发的空间显式捕获-再捕获模型相结合，以估计生存和招募的密度和人口统计学参数。我们在这个模型下采用贝叶斯框架进行推理，使用数据增强方法，该方法在软件程序 WinBUGS 中实现。该模型的灵感来自于对阿根廷潘帕斯猫 Leopardus colocolo 的相机陷阱研究，我们将其作为本文中模型的说明。我们提供了在安第斯高地的潘帕斯猫的密度估计值和生命率的首次定量评估。由于数据集稀疏，这些估计的精度很差。与通常依赖于渐近论证的传统推理方法不同，贝叶斯推理在任意样本大小下都是有效的，因此该方法非常适合研究稀有或濒危物种，这些物种通常只有小数据集。
