Zhang Yuzi, Lyles Robert H
Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA.
Department of Biostatistics and Bioinformatics, The Rollins School of Public Health of Emory University, Atlanta, GA, USA.
J Agric Biol Environ Stat. 2025 Jun 25. doi: 10.1007/s13253-025-00701-w.
Capture-recapture (CRC) experiments conducted over discrete time points motivate the development of models characterizing animal behavioral responses to facilitate estimating sizes of closed animal populations. We propose a multinomial distribution-based CRC modeling framework allowing for flexibly incorporating behavioral response patterns. In the proposed modeling framework, behavioral patterns of animals are reflected by specifying desirable constraints among conditional probabilities used to parameterize overall probabilities of different capture histories. We explicitly introduce various sets of crucial constraints which encode interpretable assumptions of behavioral patterns and lead to a unique estimate of the animal population size. Bias corrections and Bayesian credible intervals previously designed for disease surveillance are adapted to accommodate sparse CRC data which are commonly encountered in ecological studies. The proposed method incorporating minimal constraints is demonstrated to provide comparatively robust estimates in real data applications and simulation studies. To improve estimation when data are sparse, we also illustrate the use of Akaike's information criterion (AIC) to potentially justify additional noncrucial modeling constraints.
在离散时间点进行的捕获-再捕获（CRC）实验推动了用于表征动物行为反应的模型的发展，以促进对封闭动物种群规模的估计。我们提出了一个基于多项分布的CRC建模框架，允许灵活纳入行为反应模式。在所提出的建模框架中，通过在用于参数化不同捕获历史的总体概率的条件概率之间指定理想的约束来反映动物的行为模式。我们明确引入了各种关键约束集，这些约束集编码了行为模式的可解释假设，并导致对动物种群规模的唯一估计。先前为疾病监测设计的偏差校正和贝叶斯可信区间被调整以适应生态研究中常见的稀疏CRC数据。在实际数据应用和模拟研究中，所提出的包含最小约束的方法被证明能提供相对稳健的估计。为了在数据稀疏时改进估计，我们还说明了使用赤池信息准则（AIC）来潜在地证明额外的非关键建模约束的合理性。
