Overview of the spatio-temporal model, R package VAST, and its application to highly migratory species

Abstract

　The Vector Autoregressive Spatio-Temporal (VAST) software package for R, which enables researchers to analyze fishery data using a spatio-temporal generalized linear mixed model, has recently attracted attention as a new approach and is now commonly used globally to predict spatial changes in species distribution and temporal variations in a population range and density, based on spatial and temporal autocorrelation among catch rates and correlations with various biotic and abiotic environmental factors. However, a Japanese overview of the package including the spatio-temporal model has not yet been published. The main purpose of this paper is to provide an overview of VAST, describing the model structure, methods of estimating parameters and predicting abundance indices, and technical terms associated with the spatio-temporal model. We also demonstrate how VAST can be applied to studies on highly migratory species such as the Pacific bluefin tuna Thunnus orientalis, Pacific saury Cololabis saira, and North Pacific shortfin mako shark Isurus oxyrinchus. We hope that this overview of VAST and its application to actual species will assist Japanese end-users in understanding VAST and thus contribute to the wider application of this package in Japan and improved assessments of domestic fishery stocks.