JABBA-Select: Incorporating life history and fisheries’ selectivity into surplus production models (original) (raw)
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North American Journal of Fisheries Management, 2018
Maximum sustainable yield (MSY)-based reference points are often prescribed by national and international laws as the basis for catch limits (e.g., the Magnuson-Stevens Reauthorization Act in the United States). However, MSY is highly dependent on the assumed selectivity pattern and catch allocation of the fisheries. The addition of bycatch fleets or mortality from discarding further complicates MSY calculations, and no prescribed approach has been agreed upon for including complex fleet dynamics in dynamic pool models. Using the Gulf of Mexico Red Snapper Lutjanus campechanus fishery as an example, we demonstrate the various ways that MSY can be computed when multiple fleets and bycatch fisheries exist, and we illustrate the tradeoffs that occur between yield and spawning stock biomass (SSB). Presenting the full array of alternative MSY proxies, however, can lead to subjective decision making that may diminish the value of scientific advice by encouraging the maximization of yield at the expense of maintaining stocks within safe biological limits. We propose that the spawning potential ratio (SPR) associated with the global (theoretical maximum) MSY can be utilized as a reasonable proxy in most fishery applications. The yield streams required to achieve SPR MSY can then be calculated conditional on extant selectivity patterns and bycatch levels. Our approach utilizes the inherently sustainable SSB associated with the global MSY as a rebuilding target while limiting disruption to the fishery by accounting for current fleet dynamics and avoiding unsustainable proxies that may result when bycatch or discard rates are high.
Background/Question/Methods Understanding the density dependence of fish reproduction is critically important for management and conservation of commercial fisheries. Stock-recruitment relationships describe how reproductive output changes relative to the number of fish in a population. Common models used to fit fisheries data include the Beverton-Holt model, which describes a system where total recruitment levels off at higher spawner densities (called compensation), and the Ricker model, where total recruitment declines at high densities (overcompensation). While large amounts of data allow an appropriate model to be chosen for a species or population, no clear method has been developed to objectively determine the appropriate stock-recruitment relationship for commercial species where limited or no stock-recruitment data exist. To address this gap, we developed a hierarchical model that uses Bayesian inference to link stock-recruitment parameters among species, taxonomic orders (...
Fisheries stock assessment and decision analysis: the Bayesian approach
Reviews in Fish Biology and Fisheries, 2000
page 35 Introduction 36 Evaluating the consequences of management actions 37 Choice of alternative hypotheses and their probabilities Specifying the management options Calculating performance indices Presenting the results Methods for assigning weights to alternative hypotheses 41 Specifying prior distributions Noninformative or informative priors Eliciting expert opinion and using data from other stocks The prior for B 0 Expressing the data in the form of a likelihood Abundance indices Age-structure data Current applications 48 Discussion 52 Advantages and disadvantages of the Bayesian approach Future needs and issues Acknowledgements 55 Appendix A: Methods for numerical integration 55 Grid search The Metropolis algorithm (Hastings, 1970) The SIR algorithm (Rubin, 1987; Van Dijk et al., 1987) Appendix B: The age-structured model 58 References 59
2019
Previous stock assessment advice for Atlantic bigeye tuna (Thunnus obesus) originated from a combination of 'A Stock Production Model Incorporating Covariates' (ASPIC) and Stock Synthesis (ss3). This contribution aims to extend the assessment toolbox for this stock by the Bayesian State-Space Surplus Production Model software 'JABBA'. While Bayesian priors for the key parameters are kept uninformative, we specifically focus on developing an informative prior for approximating the expected process error from a stochastic age-structured simulation model. The model diagnostics provided ample support for use of the newly developed split, Joint-Research CPUE index in the reference case. Based on multi-model inference from JABBA runs over a small uncertainty grid, we predict with 85.5% probability that the stock remains overfished. Whereas JABBA appears sufficiently robust for inference about the stock status, we caution against the use of JABBA projections for specific quota recommendations in the case of bigeye tuna, because the relative impact of the different fleets can currently not be explicitly accounted for with (aggregated) biomass dynamic models. RÉSUMÉ Un précédent avis découlant de l'évaluation du stock de thon obèse de l'Atlantique (Thunnus obesus) était fondé sur la combinaison d'un modèle de production de stocks incorporant des covariables (ASPIC) et de Stock Synthesis (SS3). Cette contribution vise à étendre la boîte à outils d'évaluation de ce stock au moyen du logiciel « JABBA », le modèle de production excédentaire état-espace de type bayésien. Bien que les priors bayésiens pour les paramètres clés restent non informatifs, nous nous sommes concentrés spécifiquement sur la mise au point d'un prior informatif permettant d'estimer l'erreur de processus escomptée à partir d'un modèle de simulation stochastique structuré par âge. Les diagnostics du modèle viennent appuyer l'
Fisheries Research, 2015
Stock assessment typically involves developing a set of alternative models, fitting each to the available data, and then selecting the one that gives the most accurate estimates of management quantities of interest. In this context, it is important to consider model selection uncertainty because ignoring it can lead to unreliable estimates and overconfident inferences. For this study, four Bayesian surplus production models with symmetric or asymmetric production functions and either a constant or hierarchical timevarying intrinsic growth rate (r) were developed using data for Pacific blue marlin (Makaira nigricans) and Western and Central North Pacific striped marlin (Kajikia audax). The uncertainty resulting from model selection was evaluated using Monte Carlo simulation techniques to examine the consistency of model estimates within (self-tests) and among (cross-tests) the alternative models. Specifically, these tests evaluated the performance of the deviance information criterion (DIC) and Bayesian model averaging (BMA). The results of the simulation tests suggested that mis-specification of time-varying r can lead to large estimation errors for biomass and management quantities and that DIC may not reliably identify the true data-generating model. Although BMA did not provide more accurate point estimates than just selecting the data-generating model, it did provide a more accurate characterization of uncertainty in model results. Our study shows the value of using simulations to evaluate model performance and to account for model selection uncertainty.
The model PROCEAN (PROduction Catch-Effort Analysis) is a bayesian statistical catch/effort analysis framework based on a generalized production model. The use of such a production model could be usefull in IOTC where reliable size data are missing for stock assessment. The aim of this paper is to present the PROCEAN model. PROCEAN is a multi-fleet non equilibrium generalized production model which includes process error for both catchability time series and carrying capacity of the stock. PROCEAN assumes that fluctuations of the stock surface may only have consequences on fleets catchability and on the stock carrying capacity. Our objective is not to propose a very realistic representation of the fishery. We propose a tool to extract the maximum amount of information from the data set by structuring it given a simple and well established theoretical model. Then, modeling is used here as a mean to explore data sets according to various hypothesis.
… for the 21st Century. Alaska Sea …, 1998
Catch-age analysis is a powerful tool for assessing the status of fisheries resources and catch-age analyses are routinely conducted for many commercially exploited fish stocks around the world. In this paper, we illustrate a general approach for making short-term stochastic projections from the ADAPT age-structured assessment model. Our approach uses the standard statistical techniques of bootstrapping and Monte Carlo simulation to project performance measures such as landings, discards, spawning biomass, and recruitment under alternative management policies. The key idea is to propagate variability in estimates of initial stock size forward in stochastic projections of future possibilities. We use bootstrap replicates of current population size from an age-structured ADAPT model combined with a stochastic stock-recruitment relationship to simulate population trajectories through the projection horizon. The approach is illustrated for a commercially important New England groundfish, Georges Bank yellowtail flounder, Pleuronectes ferrugineus. Although hypothetical, this illustration provides some general insight for the rebuilding of the Georges Bank yellowtail flounder stock.
Fishes
Chub mackerel (Scomber japonicus) is a major targeted species in the Northwest Pacific Ocean, fished by China, Japan, and Russia, and predominantly captured with purse seine fishing gear. A formal stock assessment of Chub mackerel in the region has yet to be implemented by the managing authority, that is, the North Pacific Fisheries Commission (NPFC). This study aims to provide a wider choice of potential models for the stock assessment of Chub mackerel in the Northwest Pacific using available data provided by members of the NPFC. The five models tested in the present study are CMSY, BSM, SPiCT, JABBA, and JABBA-Select. Furthermore, the influence of different data types and input parameters on the performance of the different models used was evaluated. These effects for each model are catch time series for CMSY, catch time series and prior of the relative biomass for BSM, prior information for SPiCT, and selectivity coefficients for JABBA-Select. Catch and CPUE (catch per unit effor...