Ecologists increasingly rely on Bayesian methods to fit capture-recapture models. Capture-recapture models are used to estimate abundance while accounting for imperfect detectability in individual-level data. A variety of implementations exist for such models, including integrated likelihood, parameter-expanded data augmentation, and combinations of those. Capture-recapture models with latent random effects can be computationally intensive to fit using conventional Bayesian algorithms. We identify alternative specifications of capture-recapture models by considering a conditional representation of the model structure. The resulting alternative model can be specified in a way that leads to more stable computation and allows us to fit the desired model in stages while leveraging parallel computing resources. Our model specification includes a component for the capture history of detected individuals and another component for the sample size which is random before observed. We demonstrate this approach using three examples including simulation and two data sets resulting from capture-recapture studies of different species.

翻译：生态学家日益依赖贝叶斯方法拟合捕获-再捕获模型。该模型用于估计物种丰度，同时校正个体水平数据中因不完全可检测性导致的偏差。此类模型存在多种实现方式，包括整合似然法、参数扩展数据增广法及其组合形式。采用传统贝叶斯算法拟合具有潜在随机效应的捕获-再捕获模型时，计算强度可能很高。我们通过考虑模型结构的条件表示，识别出捕获-再捕获模型的替代规格。这种替代模型能以更稳定的计算方式构建，使得我们能够分阶段拟合目标模型，同时利用并行计算资源。该模型规格包含两个组成部分：一是检测个体捕获历史的模块，二是观测前样本量视为随机变量的模块。我们通过三个实例（包括模拟研究及两个来自不同物种捕获-再捕获研究的数据集）展示了该方法的有效性。