  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.
