Journal article

JABBA-Select: Incorporating life history and fisheries' selectivity into surplus production models

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Publication Details

Author list: Winker H, Carvalho F, Thorson JT, Kell LT, Parker D, Kapur M, Sharma R, Booth A.J, Kerwath KE

Publisher: Elsevier

Publication year: 2020

Journal: Fisheries Research


Volume number: 222

Start page: 1

End page: 17

Total number of pages: 17

ISSN: 0165-7836


Age-structured production models (ASPMs) are often preferred over biomass aggregated surplus production models (SPMs) as the former can track the propagation of cohorts and explicitly account for the effects of selective fishing, even in the absence of reliable size- or age data. Here, we introduce ‘JABBA-Select’, an extension of the JABBA software (Just Another Bayesian Biomass Assessment; Winker et al., 2018), that is able to overcome some of the shortcomings of conventional SPMs and allows a direct comparison to ASPMs. JABBA-Select incorporates life history parameters and fishing selectivity and distinguishes between exploitable biomass (used to fit indices given fishery selectivity) and spawning biomass (used to predict surplus production). Applying JABBA-Select involves using an age-structured equilibrium model to convert the input parameters into multivariate normal priors for surplus-production productivity parameters. We illustrate the main elements of JABBA-Select using the stock parameters of South African silver kob (Argyrosomus inodorus, Scienidae) as a case study. This species is exploited by multiple fisheries and was selected as an example of a data moderate fishery that features strong contrast in selectivity over time and across fleets. For proof-of-concept, we use an age-structured simulation framework to compare the performance of JABBA-Select to: 1) a conventional Bayesian state-space model using a Pella-Tomlinson (PT) production function, 2) an ASPM with deterministic recruitment; and 3) an ASPM with stochastic recruitment. The PT model produced biased estimates of relative and absolute spawning biomass trajectories and associated reference points, which by contrast could be fairly accurately estimated by JABBA-Select. JABBA-Select performed at least as well as the ASPMs in accuracy for most of the performance metrics and best characterized the stock status uncertainty. The results indicate that JABBA-Select is able to accurately account for moderate changes in selectivity and fleet dynamics over time and to provide a robust tool for data-moderate stock assessments.


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Last updated on 2021-12-03 at 22:43