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 thatVAST
andSpatialDeltaGLMM
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:
- Model structure: Please see the User Manual for a document listing model equations and relating them to the input/output used in R.
- Guidance for user decisions: Please see Thorson-2019 for guidance regarding the 15 major decisions needed in every VAST model
- Examples: Please see examples folder for annoted Rmarkdown scripts that run single-species or multi-species examples for a variety of regions.
- R-help documentation: Please see the standard R-help documentation, e.g., by typing
?VAST::Data_Fn
in the R-terminal after installing the package. - Publications: Please see the publications list to identify peer-reviewed publications regarding individual features. These publications include statistical theory and model testing.
- List-serv: Consider joining the FishStats listserve for 4-6 updates per year, including training classes.
- Issue-tracker: Before posting new issues, users should explore the previous issues in the github issue tracker for VAST, SpatialDeltaGLMM, and FishStatsUtils, including a search for old and closed issues.
- Wiki: Users should read and are encouraged to actively contribute to the wiki, which is housed at the github for SpatialDeltaGLMM
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: and see FishViz.org for visualization of results for regions with a public API for their data.
Installation Instructions
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 VAST 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
Description of package
Please cite if using the software
- Thorson, J.T., Barnett, L.A.K., 2017. Comparing estimates of abundance trends and distribution shifts using single- and multispecies models of fishes and biogenic habitat. ICES J. Mar. Sci. 74, 1311–1321. https://doi.org/10.1093/icesjms/fsw193
- Thorson, J.T., 2019. Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments. Fish. Res. 210, 143–161. https://doi.org/10.1016/j.fishres.2018.10.013
Description of individual features
Correlated spatio-temporal variation among species
- Thorson, J.T., Ianelli, J.N., Larsen, E., Ries, L., Scheuerell, M.D., Szuwalski, C., and Zipkin, E. 2016. Joint dynamic species distribution models: a tool for community ordination and spatiotemporal monitoring. Glob. Ecol. Biogeogr. 25(9): 1144–1158. doi:10.1111/geb.12464. url: http://onlinelibrary.wiley.com/doi/10.1111/geb.12464/abstract.
- Thorson, J.T., Scheuerell, M.D., Shelton, A.O., See, K.E., Skaug, H.J., and Kristensen, K. 2015. Spatial factor analysis: a new tool for estimating joint species distributions and correlations in species range. Methods Ecol. Evol. 6(6): 627–637. doi:10.1111/2041-210X.12359. url: http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12359/abstract
Index of abundance
- Thorson, J.T., Shelton, A.O., Ward, E.J., Skaug, H.J., 2015. Geostatistical delta-generalized linear mixed models improve precision for estimated abundance indices for West Coast groundfishes. ICES J. Mar. Sci. J. Cons. 72(5), 1297–1310. doi:10.1093/icesjms/fsu243. URL: http://icesjms.oxfordjournals.org/content/72/5/1297
Standardizing samples of size/age-composition data
- Thorson, J. T., and Haltuch, M. A. 2018. Spatio-temporal analysis of compositional data: increased precision and improved workflow using model-based inputs to stock assessment. Canadian Journal of Fisheries and Aquatic Sciences. doi:10.1139/cjfas-2018-0015. URL: http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2018-0015#.W0oloTpKiUk
Range shift metrics
- Thorson, J.T., Pinsky, M.L., Ward, E.J., 2016. Model-based inference for estimating shifts in species distribution, area occupied, and center of gravity. Methods Ecol. Evol. 7(8), 990-1008. doi:10.1111/2041-210X.12567. URL: http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12567/full
Effective area occupied metric
- Thorson, J.T., Rindorf, A., Gao, J., Hanselman, D.H., and Winker, H. 2016. Density-dependent changes in effective area occupied for sea-bottom-associated marine fishes. Proc R Soc B 283(1840): 20161853. doi:10.1098/rspb.2016.1853. URL: http://rspb.royalsocietypublishing.org/content/283/1840/20161853.
Spatio-temporal statistical methods
- Thorson, J.T., Skaug, H.J., Kristensen, K., Shelton, A.O., Ward, E.J., Harms, J.H., Benante, J.A., 2014. The importance of spatial models for estimating the strength of density dependence. Ecology 96, 1202–1212. doi:10.1890/14-0739.1. URL: http://www.esajournals.org/doi/abs/10.1890/14-0739.1
- Shelton, A.O., Thorson, J.T., Ward, E.J., Feist, B.E., 2014. Spatial semiparametric models improve estimates of species abundance and distribution. Can. J. Fish. Aquat. Sci. 71, 1655–1666. doi:10.1139/cjfas-2013-0508. URL: http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2013-0508#.VMafDf7F_h4
Accounting for fish shoals using robust observation models
- Thorson, J. T., I. J. Stewart, and A. E. Punt. 2012. Development and application of an agent-based model to evaluate methods for estimating relative abundance indices for shoaling fish such as Pacific rockfish (Sebastes spp.). ICES Journal of Marine Science 69:635–647. URL: http://icesjms.oxfordjournals.org/content/69/4/635
- Thorson, J. T., I. Stewart, and A. Punt. 2011. Accounting for fish shoals in single- and multi-species survey data using mixture distribution models. Canadian Journal of Fisheries and Aquatic Sciences 68:1681–1693. URL: http://www.nrcresearchpress.com/doi/abs/10.1139/f2011-086#.VMafcf7F_h4
Accounting for variation among vessels
- Helser, T.E., Punt, A.E., Methot, R.D., 2004. A generalized linear mixed model analysis of a multi-vessel fishery resource survey. Fish. Res. 70, 251–264. doi:10.1016/j.fishres.2004.08.007. url: http://www.sciencedirect.com/science/article/pii/S0165783604001705
- Thorson, J.T., Ward, E.J., 2014. Accounting for vessel effects when standardizing catch rates from cooperative surveys. Fish. Res. 155, 168–176. doi:10.1016/j.fishres.2014.02.036. url: http://www.sciencedirect.com/science/article/pii/S0165783614000836
Accounting for fisher targetting in fishery-dependent data
- Thorson, J.T., Fonner, R., Haltuch, M., Ono, K., and Winker, H. In press. Accounting for spatiotemporal variation and fisher targeting when estimating abundance from multispecies fishery data. Can. J. Fish. Aquat. Sci. doi:10.1139/cjfas-2015-0598. URL: http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2015-0598
- Dolder, P.J., Thorson, J.T., Minto, C., 2018. Spatial separation of catches in highly mixed fisheries. Sci. Rep. 8, 13886. https://doi.org/10.1038/s41598-018-31881-w
Bias-correction of estimated indices of abundance
- Thorson, J.T., and Kristensen, K. 2016. Implementing a generic method for bias correction in statistical models using random effects, with spatial and population dynamics examples. Fish. Res. 175: 66–74. doi:10.1016/j.fishres.2015.11.016. url: http://www.sciencedirect.com/science/article/pii/S0165783615301399
Estimating and attributing variation in size-structured distribution
- Kai, M., Thorson, J. T., Piner, K. R., and Maunder, M. N. 2017. Spatio-temporal variation in size-structured populations using fishery data: an application to shortfin mako (Isurus oxyrinchus) in the Pacific Ocean. Canadian Journal of Fisheries and Aquatic Sciences. doi:10.1139/cjfas-2016-0327. URL: http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2016-0327#.W0olqjpKiUk.
- Thorson, J. T., Ianelli, J. N., and Kotwicki, S. 2018. The relative influence of temperature and size-structure on fish distribution shifts: A case-study on Walleye pollock in the Bering Sea. Fish and Fisheries. doi:10.1111/faf.12225. URL: https://onlinelibrary.wiley.com/doi/abs/10.1111/faf.12225.
Estimating fishing impacts using spatial surplus production modelling
- Thorson, J. T., Jannot, J., and Somers, K. 2017. Using spatio-temporal models of population growth and movement to monitor overlap between human impacts and fish populations. Journal of Applied Ecology, 54: 577–587.doi:10.1111/1365-2664.12664. URL: https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/1365-2664.12664
Estimating species interactions using multispecies Gompertz model
- Thorson, J. T., Munch, S. B., and Swain, D. P. 2017. Estimating partial regulation in spatiotemporal models of community dynamics. Ecology, 98: 1277–1289. doi:10.1002/ecy.1760. URL: https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/ecy.1760
Estimating synchrony among species and locations as measure of risk-exposure
- Thorson, J.T., Scheuerell, M.D., Olden, J.D., Schindler, D.E., 2018. Spatial heterogeneity contributes more to portfolio effects than species variability in bottom-associated marine fishes. Proc R Soc B 285, 20180915. https://doi.org/10.1098/rspb.2018.0915
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
- Ongoing: Support from Fisheries Resource Analysis and Monitoring Division (FRAM), Northwest Fisheries Science Center, National Marine Fisheries Service, NOAA
- Ongoing: Support from Danish Technical University (in particular Kasper Kristensen) for development of Template Model Builder software, URL: https://www.jstatsoft.org/article/view/v070i05
- Generous support from people knowledgeable about each region and survey! Specific contributions are listed here.
- Thorson, J., Ianelli, J., and O’Brien, L. 2015. Distribution and application of a new geostatistical index standardization and habitat modeling tool for stock assessments and essential fish habitat designation in Alaska and Northwest Atlantic regions. Habitat Assessment Improvement Plan 2014 RFP. URL: https://www.st.nmfs.noaa.gov/ecosystems/habitat/funding/projects/project15-027