Efford Murray G, Hunter Christine M
Department of Mathematics and Statistics, University of Otago, P.O. Box 56, Dunedin, New Zealand.
Department of Conservation, Private Bag 5, Nelson 7042, New Zealand.
Biometrics. 2018 Jun;74(2):411-420. doi: 10.1111/biom.12766. Epub 2017 Aug 23.
Sightings of previously marked animals can extend a capture-recapture dataset without the added cost of capturing new animals for marking. Combined marking and resighting methods are therefore an attractive option in animal population studies, and there exist various likelihood-based non-spatial models, and some spatial versions fitted by Markov chain Monte Carlo sampling. As implemented to date, the focus has been on modeling sightings only, which requires that the spatial distribution of pre-marked animals is known. We develop a suite of likelihood-based spatial mark-resight models that either include the marking phase ("capture-mark-resight" models) or require a known distribution of marked animals (narrow-sense "mark-resight"). The new models sacrifice some information in the covariance structure of the counts of unmarked animals; estimation is by maximizing a pseudolikelihood with a simulation-based adjustment for overdispersion in the sightings of unmarked animals. Simulations suggest that the resulting estimates of population density have low bias and adequate confidence interval coverage under typical sampling conditions. Further work is needed to specify the conditions under which ignoring covariance results in unacceptable loss of precision, or to modify the pseudolikelihood to include that information. The methods are applied to a study of ship rats Rattus rattus using live traps and video cameras in a New Zealand forest, and to previously published data.
对先前标记动物的观察可以扩展捕获-再捕获数据集，而无需额外花费捕捉新动物进行标记的成本。因此，标记与再观察相结合的方法在动物种群研究中是一个有吸引力的选择，并且存在各种基于似然的非空间模型，以及一些通过马尔可夫链蒙特卡罗抽样拟合的空间版本。到目前为止，在实际应用中，重点一直是仅对观察进行建模，这就要求预先标记动物的空间分布是已知的。我们开发了一套基于似然的空间标记-再观察模型，这些模型要么包括标记阶段（“捕获-标记-再观察”模型），要么需要已知标记动物的分布（狭义的“标记-再观察”）。新模型在未标记动物计数的协方差结构中牺牲了一些信息；估计是通过最大化一个伪似然，并对未标记动物观察中的过度离散进行基于模拟的调整来实现的。模拟表明，在典型抽样条件下，由此得到的种群密度估计具有低偏差和足够的置信区间覆盖率。需要进一步开展工作来确定在哪些条件下忽略协方差会导致不可接受的精度损失，或者修改伪似然以纳入该信息。这些方法被应用于一项在新西兰森林中使用活捕陷阱和摄像机对船鼠（褐家鼠）进行的研究，以及先前发表的数据。
