Dynamic Spatio-temporal Zero-inflated Poisson Models for Predicting Capelin Distribution in the Barents Sea

TL;DR

This paper introduces a dynamic spatio-temporal zero-inflated Poisson model for predicting Capelin distribution in the Barents Sea, effectively capturing zero-inflation and spatial-temporal dependencies in count data.

Contribution

It develops a novel mixture model with dynamic linear and Gaussian process components, along with an efficient computational algorithm for spatio-temporal ecological data.

Findings

Model accurately predicts Capelin distribution in simulations.

Application demonstrates effective prediction of Capelin counts from 2014-2019.

Model handles zero-inflation and complex spatio-temporal dependencies.

Abstract

We consider modeling and prediction of Capelin distribution in the Barents sea based on zero-inflated count observation data that vary continuously over a specified survey region. The model is a mixture of two components; a one-point distribution at the origin and a Poisson distribution with spatio-temporal intensity, where both intensity and mixing proportions are modeled by some auxiliary variables and unobserved spatio-temporal effects. The spatio-temporal effects are modeled by a dynamic linear model combined with the predictive Gaussian process. We develop an efficient posterior computational algorithm for the model using a data augmentation strategy. The performance of the proposed model is demonstrated through simulation studies, and an application to the number of Capelin caught in the Barents sea from 2014 to 2019.