VAST Is an R package for implementing a spatial delta-generalized linear mixed model (delta-GLMM) for multiple categories (species, size, or age classes) when standardizing survey or fishery-dependent data. Builds upon a previous R package SpatialDeltaGLMM (public available here), and has unit-testing to automatically confirm that VAST and SpatialDeltaGLMM give identical results (to the 3rd decimal place for parameter estimates) for several varied real-world case-study examples Has built in diagnostic functions and model-comparison tools Is intended to improve analysis speed, replicability, peer-review, and interpretation of index standardization methods

Background This tool is designed to estimate spatial variation in density using spatially referenced data, with the goal of habitat associations (correlations among species and with habitat) and estimating total abundance for a target species in one or more years. The model builds upon spatio-temporal delta-generalized linear mixed modelling techniques (Thorson Shelton Ward Skaug 2015 ICESJMS), which separately models the proportion of tows that catch at least one individual ("encounter probability") and catch rates for tows with at least one individual ("positive catch rates"). Submodels for encounter probability and positive catch rates by default incorporate variation in density among years (as a fixed effect), and can incorporate variation among sampling vessels (as a random effect, Thorson and Ward 2014) which may be correlated among categories (Thorson Fonner Haltuch Ono Winker In press). Spatial and spatiotemporal variation are approximated as Gaussian Markov random fields (Thorson Skaug Kristensen Shelton Ward Harms Banante 2014 Ecology), which imply that correlations in spatial variation decay as a function of distance.

User resources for learning about VAST

There are eight main resources for learning about VAST:

If there are questions that arise after this, please look for a VAST Point-of-Contact at your institution and consider contacting them prior to posting an issue.


Regions available in the example script: alt text and see for visualization of results for regions with a public API for their data.

Installation Instructions

Build Status DOI

This function depends on R version >=3.1.1 and a variety of other tools.

First, install the "devtools" package from CRAN

# Install and load devtools package

Second, please install the following: TMB (Template Model Builder): INLA (integrated nested Laplace approximations):

Note: at the moment, TMB and INLA can be installed using the commands

# devtools command to get TMB from GitHub
# source script to get INLA from the web

Next, please install the VAST package from this GitHub repository using a function in the "devtools" package:

# Install package
# Load package

Known installation/usage issues


Description of package

Please cite if using the software

Description of individual features

Correlated spatio-temporal variation among species

Index of abundance

Standardizing samples of size/age-composition data

Range shift metrics

Effective area occupied metric

Spatio-temporal statistical methods

Accounting for fish shoals using robust observation models

Accounting for variation among vessels

Accounting for fisher targetting in fishery-dependent data

Bias-correction of estimated indices of abundance

Estimating and attributing variation in size-structured distribution

Estimating fishing impacts using spatial surplus production modelling

Estimating species interactions using multispecies Gompertz model

Estimating synchrony among species and locations as measure of risk-exposure

Forecasting future changes in distribution or abundance

Thorson, In press. Forecast skill for predicting distribution shifts: A retrospective experiment for marine fishes in the Eastern Bering Sea. Fish Fish.

Funding and support for the tool

James-Thorson/VAST documentation built on Nov. 24, 2018, 11:06 p.m.