Coping with unobservable and mis-classified states in capture-recapture studies
Coping with unobservable and mis-classified states in capture-recapture studies
复制标题
应对捕获-再捕获研究中不可观察和错误分类的状态
DOI：
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复制
发表时间：
2004
影响因子：
0.9
通讯作者：
W. Kendall
中科院分区：
文献类型：
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作者：
W. Kendall
多状态标记-再捕获方法为考虑标记动物研究中的估计提供了一个很好的概念框架。传统的方法包括以下假设：（1）动物占据的每个状态是可观察的，以及（2）在每个时间点正确分配状态。这些假设中任何一个的失败都可能导致人口统计参数的有偏估计。我回顾了设计和分析选项，以最大限度地减少或消除这些偏差。不可观察状态可以通过将它们包括在统计模型的状态空间中来调整，具有零捕获概率，并结合稳健设计，或通过遥测、标签回收或偶然观察来观察处于不可观察状态的动物。可以通过辅助数据或结合稳健设计来调整误分类，以估计检测动物所处状态的概率。对于不可观察和错误分类的状态，稳健设计的关键特征是假设动物的状态至少在两个采样场合是静态的。
Multistate mark-recapture methods provide an excellent conceptual framework for considering estimation in studies of marked animals. Traditional methods include the assumptions that (1) each state an animal occupies is observable, and (2) state is assigned correctly at each point in time. Failure of either of these assumptions can lead to biased estimates of demographic parameters. I review design and analysis options for minimizing or eliminating these biases. Unobservable states can be adjusted for by including them in the state space of the statistical model, with zero capture probability, and incorporating the robust design, or observing animals in the unobservable state through telemetry, tag recoveries, or incidental observations. Mis-classification can be adjusted for by auxiliary data or incorporating the robust design, in order to estimate the probability of detecting the state an animal occupies. For both unobservable and mis-classified states, the key feature of the robust design is the assumption that the state of the animal is static for at least two sampling occasions.