README.md
In jabbamodel/JABBA: Just Another Bayesian Biomass Assessment

JABBA: Just Another Bayesian Biomass Assessment

The materials in this repository present the stock assessment tool ‘Just Another Bayesian Biomass Assessment’ JABBA. The motivation for developing JABBA was to provide a user-friendly R to JAGS (Plummer) interface for fitting generalized Bayesian State-Space SPMs with the aim to generate reproducible stock status estimates and diagnostics. Building on recent advances in optimizing the fitting procedures through the development of Bayesian state-space modelling approaches, JABBA originates from a continuous development process of a Bayesian State-Space SPM tool that has been applied and tested in many assessments across oceans. JABBA was conceived in the Hawaiian Summer of 2015 as a collaboration between young researchers from South Africa and the Pacific Islands Fisheries Science Center (NOAA) in Honolulu, HI USA. The goal was to provide a bridge between age-structured and biomass dynamic models, which are still widely used. JABBA runs quickly and by default generates many useful plots and diagnosic tools for stock assessments.

Inbuilt JABBA features include:

Integrated state-space tool for averaging multiple CPUE series (+SE) for optional use in assessments
Automatic fitting of multiple CPUE time series and associated standard errors
Fox, Schaefer or Pella Tomlinson production function (optional as input Bmsy/K)
Kobe-type biplot plotting functions
Forecasting for alternative TACs
Residual and MCMC diagnostics
Estimating or fixing the process variance
Optional estimation additional observation variance for individual or grouped CPUE time series
Easy implementation of time-block changes in selectivity
New: Estimating Catch with Error
New: Estimating shape with prior
New: Catch-Only option with additional relative biomass priors
New: Inbuilt retrospective and hindcasting run and plotting options

library(devtools) install_github("jabbamodel/JABBA")

library(JABBA) data(iccat)

# Compile JABBA JAGS model and input object for bigeye tuna (bet) jbinput = build_jabba(catch=iccat$bet$catch,cpue=iccat$bet$cpue,se=iccat$bet$se,assessment="BET",scenario = "TestRun",model.type = "Fox",sigma.est = FALSE,fixed.obsE = 0.01)

# Fit JABBA (here mostly default value - careful) bet1 = fit_jabba(jbinput,quickmcmc=TRUE)

# Make individual plots jbplot_catcherror(bet1) jbplot_ppdist(bet1) jbplot_cpuefits(bet1) jbplot_logfits(bet1)

# Plot Status Summary par(mfrow=c(3,2),mar = c(3.5, 3.5, 0.5, 0.1)) jbplot_trj(bet1,type="B",add=T) jbplot_trj(bet1,type="F",add=T) jbplot_trj(bet1,type="BBmsy",add=T) jbplot_trj(bet1,type="FFmsy",add=T) jbplot_spphase(bet1,add=T) jbplot_kobe(bet1,add=T) # Test run end

More detailed examples of JABBA applications to ICCAT stocks can be found in Examples_iccat.R.

An updated vignette is currently under development...

Reference Winker, H., Carvalho, F., Kapur, M. (2018) JABBA: Just Another Bayesian Biomass Assessment. Fisheries Research 204: 275-288.