The Greenland Sea is an important breeding ground for harp and hooded seals. Estimates of annual seal pup production are critical factors in the estimation of abundance that is needed for management of the species. These estimates are usually based on counts from aerial photographic surveys. However, only a minor part of the whelping region can be photographed, because of its large extent. To estimate total seal pup production, we propose a Bayesian hierarchical modelling approach motivated by viewing the seal pup appearances as a realization of a log‐Gaussian Cox process by using covariate information from satellite imagery as a proxy for ice thickness. For inference, we utilize the stochastic partial differential equation module of the integrated nested Laplace approximation framework. In a case‐study using survey data from 2012, we compare our results with existing methodology in a comprehensive cross‐validation study. The results of the study indicate that our method improves local estimation performance, and that the increased uncertainty of prediction of our method is required to obtain calibrated count predictions. This suggests that the sampling density of the survey design may not be sufficient to obtain reliable estimates of seal pup production.

中文翻译：

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使用贝叶斯层次模型估算格陵兰海中的海豹幼崽产量

格陵兰海是竖琴和带帽海豹的重要繁殖地。每年对海豹幼崽产量的估计是管理该物种所需的丰度估计的关键因素。这些估计通常基于航空摄影调查的计数。然而，由于其范围很大，只能拍摄雏龙区域的一小部分。为了估计海豹幼崽的总产量，我们提出了一种贝叶斯分层建模方法，其方法是通过利用卫星图像的协变量信息作为冰厚的代理，将海豹幼崽的外观视为对数-高斯考克斯过程的一种实现。为了进行推断，我们利用集成的嵌套拉普拉斯近似框架的随机偏微分方程模块。在使用2012年调查数据进行的案例研究中，我们在全面的交叉验证研究中将我们的结果与现有方法进行了比较。研究结果表明，我们的方法提高了局部估计性能，并且为了获得校准的计数预测，需要增加我们方法的预测不确定性。这表明调查设计的抽样密度可能不足以获得对海豹幼崽产量的可靠估计。