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

Author:

KAI MIKIHIKO1,TSUKAHARA YOHEI1,HASHIMOTO MIDORI1

Affiliation:

1. Fisheries Resources Institutes, Japan Fisheries Research and Education Agency

Publisher

Japanese Society of Fisheries Science

Subject

Aquatic Science

Reference36 articles.

1. 1) Zuur AF, Hilbe JM, Ieno EN. Beginner's guide to GLM and GLMM with R: a frequentist and Bayesian perspective for ecologists. Highland Statistics. 2013.

2. 2) Wood SN. Generalized additive models: An Introduction with R (2nd Edition). Chapman & Hall/CRC, Boca Raton. 2017.

3. 3) Hinton MG, Nakano H. Standardizing catch and effort statistics using physiological, ecological, or behavioral constraints and environmental data, with an application to blue marlin (Makaira nigricans) catch and effort data from the Japanese longline fisheries in the Pacific. Bull. Int. Am. Trop. Tuna Comm. 1996; 21: 171-200.

4. 4) Thorson JT, Shelton AO, Ward EJ, Skaug H. Geostatistical delta-generalized linear mixed models improve precision for estimated abundance indices for West Coast groundfishes. ICES J. Mar. Sci. 2015; 72: 1297-1310.

5. 5) Thorson JT, Barnett LAK. Comparing estimates of abundance trends and distribution shifts using single- and multispecies models of fishes and biogenic habitat. ICES J. Mar. Sci. 2017; 74: 1311-1321.

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