Estimating seal pup production in the Greenland Sea using Bayesian hierarchical modeling

TL;DR

This paper introduces a Bayesian hierarchical model using satellite data and SPDE-INLA for estimating seal pup production in the Greenland Sea, improving local estimates but highlighting sampling limitations.

Contribution

It presents a novel Bayesian hierarchical approach with satellite covariates and SPDE-INLA for seal pup estimation, enhancing local accuracy over existing methods.

Findings

Improved local estimation performance.

Increased prediction uncertainty for calibration.

Sampling density may limit estimate reliability.

Abstract

The Greenland Sea is an important breeding ground for harp and hooded seals. Estimates of the annual seal pup production are critical factors in the abundance estimation 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, due to its large extent. To estimate the total seal pup production, we propose a Bayesian hierarchical modeling approach motivated by viewing the seal pup appearances as a realization of a log-Gaussian Cox process using covariate information from satellite imagery as a proxy for ice thickness. For inference, we utilize the stochastic partial differential equation (SPDE) module of the integrated nested Laplace approximation (INLA) 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 prediction uncertainty 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 the seal pup production.