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Abstract：
We anticipate that unmanned aerial vehicles will become popular wildlife survey platforms. Because detecting animals from the air is imperfect, we develop a mark-recapture line transect method using two digital cameras, possibly mounted on one aircraft, which cover the same area with a short time delay between them. Animal movement between the passage of the cameras introduces uncertainty in individual identity, so individual capture histories are unobservable and are treated as latent variables. We obtain the likelihood for mark-recapture line transects without capture histories by automatically enumerating all possibilities within segments of the transect that contain ambiguous identities, instead of attempting to decide identities in a prior step. We call this method \"Latent Capture-history Enumeration\" (LCE). We include an availability model for species that are periodically unavailable for detection, such as cetaceans that are undetectable while diving. External data are needed to estimate the availability cycle length, but not the mean availability rate, if the full availability model is employed. We compare the LCE method with the recently developed cluster capture-recapture method (CCR), which uses a Palm likelihood approximation, providing the first comparison of CCR with maximum likelihood. The LCE estimator has slightly lower variance, more so as sample size increases, and close to nominal coverage probabilities. Both methods are approximately unbiased. We illustrate with semisynthetic data from a harbor porpoise survey.
摘要：
我们预计无人机将成为受欢迎的野生动物调查平台。因为从空气中探测动物是不完美的，我们使用两台数码相机开发了一种标记-重新捕获线横断面方法，可能安装在一架飞机上，覆盖相同的区域，它们之间有很短的时间延迟。摄像机之间的动物运动引入了个人身份的不确定性，因此，个体捕获历史是不可观察的，并被视为潜在变量。我们通过自动枚举包含模棱两可身份的样段中的所有可能性，来获得没有捕获历史记录的标记重新捕获线样段的可能性，而不是试图在之前的步骤中决定身份。我们将此方法称为“潜在捕获历史记录枚举”(LCE)。我们为定期无法检测的物种提供了一个可用性模型，比如潜水时无法察觉的鲸目动物。需要外部数据来估计可用性周期长度，但不是平均可用率，如果采用完全可用性模型。我们将LCE方法与最近开发的集群捕获-重新捕获方法(CCR)进行了比较，使用Palm似然近似，提供CCR与最大似然的第一次比较。LCE估计器的方差略低，随着样本量的增加，并接近名义覆盖概率。这两种方法都是近似无偏的。我们用港口海豚调查的半合成数据进行说明。
