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

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