README.md

Description

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.

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")

Second, please install the following: TMB (Template Model Builder): https://github.com/kaskr/adcomp INLA (integrated nested Laplace approximations): http://www.r-inla.org/download

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

# devtools command to get TMB from GitHub
install_github("kaskr/adcomp/TMB") 
# source script to get INLA from the web
source("http://www.math.ntnu.no/inla/givemeINLA.R")

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

# Install package
install_github("james-thorson/VAST") 
# Load package
library(VAST)

Known installation/usage issues

none

Example code

Please see examples folder for single-species and multi-species examples of how to run the model. This folder also contains a User Manual

This code illustrates how to loop through different default model configurations, plot diagnostics for each model, and obtain the AIC for each model.

Please also read the instructions from the single-species SpatialDeltaGLMM package, Guidelines for West Coast users wiki page, which is a living document and will evolve over time as best practices become apparent.

Description of package

Please cite if using the software

Description of individual features

Correlated spatio-temporal variation among species

Index of abundance

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

Funding and support for the tool



James-Thorson/VAST documentation built on Oct. 8, 2017, 1:15 a.m.