U.S. Geological Survey, Southeast Ecological Science Center, Gainesville, Florida, United States of America.
PLoS One. 2013 Dec 27;8(12):e84017. doi: 10.1371/journal.pone.0084017. eCollection 2013.
In capture-recapture and mark-resight surveys, movements of individuals both within and between sampling periods can alter the susceptibility of individuals to detection over the region of sampling. In these circumstances spatially explicit capture-recapture (SECR) models, which incorporate the observed locations of individuals, allow population density and abundance to be estimated while accounting for differences in detectability of individuals. In this paper I propose two Bayesian SECR models, one for the analysis of recaptures observed in trapping arrays and another for the analysis of recaptures observed in area searches. In formulating these models I used distinct submodels to specify the distribution of individual home-range centers and the observable recaptures associated with these individuals. This separation of ecological and observational processes allowed me to derive a formal connection between Bayes and empirical Bayes estimators of population abundance that has not been established previously. I showed that this connection applies to every Poisson point-process model of SECR data and provides theoretical support for a previously proposed estimator of abundance based on recaptures in trapping arrays. To illustrate results of both classical and Bayesian methods of analysis, I compared Bayes and empirical Bayes esimates of abundance and density using recaptures from simulated and real populations of animals. Real populations included two iconic datasets: recaptures of tigers detected in camera-trap surveys and recaptures of lizards detected in area-search surveys. In the datasets I analyzed, classical and Bayesian methods provided similar - and often identical - inferences, which is not surprising given the sample sizes and the noninformative priors used in the analyses.
在捕获-再捕获和标记-重见调查中，个体在采样期内和采样期之间的运动可以改变个体在采样区域内的易感性，从而影响其被检测到的概率。在这种情况下，空间明确的捕获-再捕获（SECR）模型可以整合个体的观测位置，在考虑个体可检测性差异的同时，估计种群密度和丰度。在本文中，我提出了两种贝叶斯 SECR 模型，一种用于分析陷阱阵中观察到的再捕获，另一种用于分析区域搜索中观察到的再捕获。在构建这些模型时，我使用了不同的子模型来指定个体活动范围中心的分布以及与这些个体相关的可观测再捕获。这种将生态和观测过程分开的方法使我能够在之前没有建立的基础上，在贝叶斯和经验贝叶斯种群丰度估计器之间建立正式的联系。我表明，这种联系适用于 SECR 数据的每个泊松点过程模型，并为基于陷阱阵中再捕获的丰度的先前提出的估计器提供了理论支持。为了说明经典和贝叶斯分析方法的结果，我使用模拟和真实动物种群的再捕获数据比较了贝叶斯和经验贝叶斯的丰度和密度估计值。真实种群包括两个标志性数据集：在相机陷阱调查中检测到的老虎的再捕获数据和在区域搜索调查中检测到的蜥蜴的再捕获数据。在我分析的数据集，经典和贝叶斯方法提供了相似的 - 甚至经常是相同的 - 推断，这并不奇怪，因为样本量和分析中使用的非信息先验值。
