JABBAbeta

Development repository for JABBA (https://github.com/jabbamodel)

New JABBA beta version JABBAv1.2beta.R is now available!

This new beta version has been developed and tested during stock assessments of:

• ICCAT Atlantic blue marlin (BUM)
• ICCAT Atlantic bigeye tuna (BET)
• IOTC Indian Ocean striped marlin (MLS)
• IOTC Indian Ocean black marlin (BLM)
• NOAA Hawaii Kona crab benchmark assessment (KONA)

New Features include:

• Plotting code is outsouced in JABBA_plots_v1.2.R to facilitate debugging
• Settings.txt saved for reference in Input folder
• Preliminary estimate shape m option with an informative on the inflection point B_MSY/K
• Catch.CV option: Allows admitting uncertainty about the catch
• CatchOnly option: JABBA run with catch and priors, but without fitting any abundance indices
• Lower and upper values of P_bound, K_bound, q_bound can be set manually to enforce "soft" boundaries (CV=0.1)
• Option to manually set starting values for r, q and K

A detailed Tutorial describes how to set up the JABBA 'Prime' file

See examples SWO_SA_NewFeatures_v1.2.R

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 Summer of 2015 as a collaboration between the South Africa Department of Agriculture, Forestry and Fisheries 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

Reference

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

A self-contained R package of JABBA is forthcoming.