- Is an R package for implementing a spatial delta-generalized linear mixed model (delta-GLMM) for use when standardizing fishery-independent index data for U.S. West Coast surveys.
- Has built in diagnostic functions and model-comparison tools
- Is intended to improve analysis speed, replicability, peer-review, and interpretation of index standardization methods
- Will eventually be improved to incorporate informative help files accessible via standard R commands.
- This tool is designed to estimate spatial variation in density using fishery-independent data, with the goal of estimating total abundance for a target species in one or more years.
- The model builds upon delta-generalized linear mixed modelling techniques (Thorson and Ward 2013,2014), 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 always 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).
- Each submodel can also estimate spatial variation (variation that is constant among years), and spatiotemporal variation (variation over space which differs among years).
- Spatial and spatiotemporal variation are approximated as Gaussian Markov random fields (Thorson Skaug et al. 2015 ICESJMS), which imply that correlations in spatial variation decay as a function of distance.
- The tool incorporates geometric anisotropy, i.e., differences and rotation of the direction of correlation, where correlations may decline faster inshore-offshore than alongshore (Thorson Shelton et al. 2015 ICESJMS).
SpatialDeltaGLMMnow has unit-testing to ensure that results are consistent across software updates
VAST(link here) has been developed as a multispecies extension to
SpatialDeltaGLMM, and unit testing confirms that it gives identical results when using data for a single species. I recommend that new users use
VASTto ease the transition to multispecies or age/size-structured index models.
- Other spatio-temporal tools are linked at www.FishStats.org
Resources for using the tool
There are three main resources for learning about and using the tool:
Please see the tutorial for example code.
Please use the R help files, e.g., model settings are documented in
?SpatialDeltaGLMM::Data_Fnafter you have installed the package
Other resources include:
You should browse abstracts and read relevant papers
You can join the FishStats listserv
You can post questions on the issue tracker but please first confirm that your question isn't answered elsewhere.
Regions that have been previously tested (and have associated meta-data):
and see FishViz.org for visualization of results for regions with a public API for their data, using package
FishData (link here).
This function depends on R version >=3.1.1 and a variety of other tools.
First, install the package
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, packages
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 package
SpatialDeltaGLMM from this GitHub repository using a function in the
# Install package install_github("nwfsc-assess/geostatistical_delta-GLMM", ref="3.3.0") # Load package library(SpatialDeltaGLMM)
Or you can always use the development version
# Install package install_github("nwfsc-assess/geostatistical_delta-GLMM")
Description of package
Please cite if using the software
- 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
Description of individual features
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
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
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. 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