James-Thorson-NOAA / VAST

Spatio-temporal analysis of univariate or multivariate data, e.g., standardizing data for multiple species or stages
http://www.FishStats.org
GNU General Public License v3.0
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spatial-models species-distributions thorson

Description

VAST

Background

User resources for learning about VAST

There are eleven 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.

Database

Regions available in the example script: alt text and see FishViz.org 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
install.packages("devtools")
library("devtools")

Next, please install the VAST package from this GitHub repository using a function in the "devtools" package. This may require using the INSTALL_opts option depending upon your version of R:

# Install package
install_github("james-thorson/VAST@main", INSTALL_opts="--no-staged-install")
# Load package
library(VAST)

If you are having problems with installation, please consider installing dependencies individually, e.g. using:

# Install TMB from CRAN
install.packages("TMB")
# Install INLA using currently recommended method
install.packages("INLA", repos=c(getOption("repos"), INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE)
# Install FishStatsUtils from CRAN
install_github("james-thorson/FishStatsUtils@main", INSTALL_opts="--no-staged-install")

Finally, please confirm that VAST is installed by running a model, e.g., following the simple example here.

Known installation/usage issues

  1. If using a NOAA laptop, sometimes the PATH for Rtools is not correctly specified during installation. In those cases, please follow instructions here

  2. Some versions of R are having problems downloading dependencies from GitHub, see details here

  3. People using R version 3.6.0 or MRAN 3.5.3 are having a problem with changing standards for package namespaces, see details here, which appears to be particularly a problem with loading INLA due to install issues with that package.

  4. MacOS users have specific install issues and a discussion of potential fixes is here

  5. MacOS users should be aware that significant speed-ups in model fitting can be accomplished by switching the library used for Basic Linear Algebra Subprograms (BLAS) from the default. There are a few BLAS alternatives available, though, the simplest seems to be using the vecLib library, part of Apple's Accelerate Framework and included in most recent R binaries. To switch the BLAS library, run the following lines in the terminal and then confirm the switch with a call to sessionInfo() in R.

    # Terminal commands to switch R BLAS library to increase speed
    cd /Library/Frameworks/R.framework/Resources/lib
    ln -sf /System/Library/Frameworks/Accelerate.framework/Frameworks/vecLib.framework/Versions/Current/libBLAS.dylib libRblas.dylib
  1. Windows has a speed-limit on the rate that users can access the GitHub API. You can get around this by installing each package locally from a ZIP file. You'll need to first download a ZIP file for GitHub repositories TMBhelper (here), then ThorsonUtilities (here), then FishStatsUtils (here), then VAST (here) to your harddrive in a local directory while recording the directory name (which I will reference as download_dir), and then install these packages from each ZIP file in the same order. To install each package, please click "clone or download" -> "Download ZIP" -> devtools::install_local(path=download_dir, dependencies=FALSE)

References

Core functionality

Correlated spatio-temporal variation among species (a.k.a. "joint species distribution models")

Correlated spatio-temporal variation among years (a.k.a. "Empirical Orthogonal functions")

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

Combining multiple types of data (e.g., biomass, count, encounter)

Spatially varying coefficient models and their use for fisheries oceanography

Funding and support for the tool

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Disclaimer

“The United States Department of Commerce (DOC) GitHub project code is provided on an ‘as is’ basis and the user assumes responsibility for its use. DOC has relinquished control of the information and no longer has responsibility to protect the integrity, confidentiality, or availability of the information. Any claims against the Department of Commerce stemming from the use of its GitHub project will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.”