Spatially explicit inference for open populations: estimating demographic parameters from camera-trap studies
Spatially explicit inference for open populations: estimating demographic parameters from camera-trap studies
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DOI：
10.1890/09-0804.1
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发表时间：
2010-11-01
期刊：
影响因子：
4.8
通讯作者：
Royle, J. Andrew
中科院分区：
文献类型：
--
作者：
Gardner, Beth;Reppucci, Juan;Royle, J. Andrew
我们开发了一个分层捕获-再捕获模型的人口开放的辅助空间信息时，捕获的位置。这种空间捕获-再捕获数据来自于基于相机捕获、DNA采样和其他情况的研究，在这些情况下，空间阵列的设备记录了独特个体的遭遇。我们整合了一个基于个人的配方的Jolly-Seber型模型与最近开发的空间显式捕获-再捕获模型估计密度和人口参数的生存和招聘。在此模型下，我们采用贝叶斯框架进行推理，使用数据增强的方法，在软件程序WinBUGS中实现。该模型的动机是相机诱捕研究的潘帕斯草原猫Leopardus colocolo从阿根廷，我们目前作为一个例子，在本文中的模型。我们提供估计的密度和生命率的第一个定量评估的潘帕斯草原猫在高安第斯山脉。这些估计的精度很差，可能是由于稀疏的数据集。与传统的推理方法，通常依赖于渐近参数，贝叶斯推理是有效的，在任意的样本量，因此该方法是理想的研究稀有或濒危物种的小数据集是典型的。
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.