McClintock Brett T, White Gary C, Pryde Moira A
Marine Mammal Laboratory, Alaska Fisheries Science Center, NOAA National Marine Fisheries Service, Seattle, Washington.
Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado.
Biometrics. 2019 Sep;75(3):799-809. doi: 10.1111/biom.13058. Epub 2019 Apr 25.
Over the past decade, there has been much methodological development for the estimation of abundance and related demographic parameters using mark-resight data. Often viewed as a less-invasive and less-expensive alternative to conventional mark recapture, mark-resight methods jointly model marked individual encounters and counts of unmarked individuals, and recent extensions accommodate common challenges associated with imperfect detection. When these challenges include both individual detection heterogeneity and an unknown marked sample size, we demonstrate several deficiencies associated with the most widely used mark-resight models currently implemented in the popular capture-recapture freeware Program MARK. We propose a composite likelihood solution based on a zero-inflated Poisson log-normal model and find the performance of this new estimator to be superior in terms of bias and confidence interval coverage. Under Pollock's robust design, we also extend the models to accommodate individual-level random effects across sampling occasions as a potentially more realistic alternative to models that assume independence. As a motivating example, we revisit a previous analysis of mark-resight data for the New Zealand Robin (Petroica australis) and compare inferences from the proposed estimators. For the all-too-common situation where encounter rates are low, individual detection heterogeneity is non-negligible, and the number of marked individuals is unknown, we recommend practitioners use the zero-inflated Poisson log-normal mark-resight estimator as now implemented in Program MARK.
在过去十年中，利用标记重捕数据估计种群数量及相关种群统计学参数的方法有了很大发展。标记重捕方法常被视为传统标记重捕法的一种侵入性较小且成本较低的替代方法，它联合对标记个体的遇见情况和未标记个体的数量进行建模，并且最近的扩展方法考虑到了与不完全检测相关的常见挑战。当这些挑战包括个体检测异质性和未知的标记样本大小时，我们证明了当前在流行的捕获 - 重捕免费软件Program MARK中实现的最广泛使用的标记重捕模型存在若干缺陷。我们提出了一种基于零膨胀泊松对数正态模型的复合似然解，并发现这种新估计器在偏差和置信区间覆盖方面表现更优。在波洛克的稳健设计下，我们还扩展了模型，以适应不同采样场合下个体水平的随机效应，作为假设独立性的模型的一种可能更现实的替代方案。作为一个激励性的例子，我们重新审视了之前对新西兰知更鸟（Petroica australis）标记重捕数据的分析，并比较了所提出估计器的推断结果。对于遇见率低、个体检测异质性不可忽略且标记个体数量未知这种非常常见的情况，我们建议从业者使用现在Program MARK中实现的零膨胀泊松对数正态标记重捕估计器。
