BUGS in Bayesian stock assessments

1999 ◽  
Vol 56 (6) ◽  
pp. 1078-1087 ◽  
Author(s):  
Renate Meyer ◽  
Russell B Millar
Keyword(s):  
State Space ◽  
Stock Assessment ◽  
State Space Model ◽  
Production Model ◽  
Posterior Density ◽  
Convergence Diagnostics ◽  
Surplus Production Model ◽  
Nonlinear State Space Models ◽  
Age Structured ◽  
Joint Posterior Density

This paper illustrates the ease with which Bayesian nonlinear state-space models can now be used for practical fisheries stock assessment. Sampling from the joint posterior density is accomplished using Gibbs sampling via BUGS, a freely available software package. By taking advantage of the model representation as a directed acyclic graph, BUGS automates the hitherto tedious calculation of the full conditional posterior distributions. Moreover, the output from BUGS can be read directly into the software CODA for convergence diagnostics and statistical summary. We illustrate the BUGS implementation of a nonlinear nonnormal state-space model using a Schaefer surplus production model as a basic example. This approach extends to other assessment methodologies, including delay difference and age-structured models.

scholarly journals The status of Japanese fisheries relative to fisheries around the world

2017 ◽  
Vol 74 (5) ◽  
pp. 1277-1287 ◽  
Author(s):  
Momoko Ichinokawa ◽  
Hiroshi Okamura ◽  
Hiroyuki Kurota
Keyword(s):  
Maximum Sustainable Yield ◽  
Production Model ◽  
Structured Population Model ◽  
Total Allowable Catch ◽  
Quick Recovery ◽  
Stock Status ◽  
Surplus Production Model ◽  
The Status ◽  
Stock Recruitment ◽  
Age Structured

We present the first quantitative review of the stock status relative to the stock biomass (B) and the exploitation rate (U) that achieved the maximum sustainable yield (MSY) (BMSY and UMSY, respectively) for 37 Japanese stocks contributing 61% of the total marine capture production in Japan. BMSY and UMSY were estimated by assuming three types of stock-recruitment (S-R) relationships and an age-structured population model or by applying a surplus production model. The estimated stock status shows that approximately half of the stocks were overfishing (U/UMSY > 1), and approximately half of the stocks were overfished (B/BMSY < 0.5) during 2011–2013. Over the past 15 years, U decreased and B slightly increased on average. The rate of decrease in the U of the stocks managed by the total allowable catch (TAC) was significantly greater than that of the other stocks, providing evidence of the effectiveness of TAC management in Japan. The above statuses and trends were insensitive to the assumption of the S-R relationship. The characteristics of Japanese stocks composed mainly of resources with relatively high natural mortality, i.e. productivity, suggest that Japanese fisheries have great potential of exhibiting a quick recovery and increasing their yield by adjusting the fishing intensity to an appropriate level.

scholarly journals Population changes in rattail species on the Chatham Rise

2021 ◽  
Author(s):  
◽  
Kathleen Large
Keyword(s):  
Maximum Likelihood ◽  
Stock Assessment ◽  
Production Model ◽  
Likelihood Method ◽  
Model Parameters ◽  
Important Species ◽  
Surplus Production ◽  
Surplus Production Model ◽  
Abundance Indices ◽  
The Impact

<p>The aim of this project was to conduct a stock assessment to determine the population dynamic characteristics of rattail species taken as bycatch in the hoki, hake and ling fishery on the Chatham Rise. No quantitative assessment of the current size of rattail populations , and how these may have changed over time, has been carried out before. There is interest in the need to quantify the impact of commercial fishing on the rattail populations, as rattails (Macrouridae family) are considered to be an ecologically important species complex in the deep ocean, and there may be the potential for the development of a commercial fishery based on their value as processed fishmeal. The minimum data required for a stock assessment are an abundance index and a catch history. Abundance indices are available for over 20 species of rattail produced from scientific surveys conducted annually on the Chatham Rise since 1992. Catch histories for individual rattail species in the same area are not available. A method was developed to reconstruct commercial catches of rattails from commercial effort data and survey catch and effort data. A surplus production model was fitted to the reconstructed catch data and survey abundance indices, using maximum likelihood and Bayesian methods to estimate model parameters and uncertainty. A surplus production model has two components: an observation model for abundance indices and a process model for population dynamics. Maximum likelihood estimation was applied to a model that specified errors for the observations only, and this produced estimates that had wide confidence intervals. A Bayesian approach was then taken to fit a statespace version of the model that incorporates errors associated with the observation and process models. While the Bayesian method produced more plausible parameter estimates (in comparison to the maximum likelihood method) and parameter uncertainty was reduced, our analysis indicated the posterior estimates were highly sensitive to the specification of different priors. There may be several reasons for these results, including: the small number of observations, lack of contrast in the data and mis-specification of the model. Meaningful estimates of the absolute size of rattail populations are not possible with these results, where estimates can vary by orders of magnitude depending on prior specification. This implies that more work needs to be done to develop more effective methods that can be used to help inform decisions regarding the management of these fish populations. Improving data collection, investigating informative priors and extending/respecifying the model are considered worthwhile avenues of future work to improve stock assessments of rattails.</p>

scholarly journals Modelling Complexity and Uncertainty in Fisheries Stock Assessment

2021 ◽  
Author(s):  
◽  
D'Arcy Webber
Keyword(s):  
New Zealand ◽  
State Space ◽  
Ross Sea ◽  
Stock Assessment ◽  
State Space Model ◽  
Computational Cost ◽  
Original Model ◽  
Agent Based Models ◽  
Agent Based ◽  
Space Model

<p>Stock assessment models are used to determine the population size of fish stocks. Although stock assessment models are complex, they still make simplifying assumptions. Generally, they treat each species separately, include little, if any, spatial structure, and may not adequately quantify uncertainty. These assumptions can introduce bias and can lead to incorrect inferences. This thesis is about more realistic models and their inference. This realism may be incorporated by explicitly modelling complex processes, or by admitting our uncertainty and modelling it correctly.  We develop an agent-based model that can describe fish populations as a collection of individuals which differ in their growth, maturation, migration, and mortality. The aim of this model is to better capture the richness in natural processes that determine fish abundance and subsequent population response to anthropogenic removals. However, this detail comes at considerable computational cost. A single model run can take many hours, making inference using standard methods impractical. We apply this model to New Zealand snapper (Pagurus auratus) in northern New Zealand.  Next, we developed an age-structured state-space model. We suggest that this sophisticated model has the potential to better represent uncertainty in stock assessment. However, it pushes the boundaries of the current practical limits of computing and we admit that its practical application remains limited until the MCMC mixing issues that we encountered can be resolved.   The processes that underpin agent-based models are complex and we may need to seek new sources of data to inform these types of models. To make a start here we derive a state-space model to estimate the path taken by individual fish from the day they are tagged to the day of their recapture. The model uses environmental information collected using pop-up satellite archival tags. We use tag recorded depth and oceanographic temperature to estimate the location at any given time. We apply this model to Antarctic toothfish (Dissostichus mawsoni) in the Ross Sea.  Finally, to reduce the computational burden of agent-based models we use Bayesian emulation. This approach replaces the simulation model with an approximating algorithm called an emulator. The emulator is calibrated using relatively few runs of the original model. A good emulator provides a close approximation to the original model and has significant speed gains. Thus, inferences become tractable.  We have made the first steps towards developing a tractable approach to fisheries modelling in complex settings through the creation of realistic models, and their emulation. With further development, Bayesian emulation could result in the increased ability to consider and evaluate innovative methods in fisheries modelling. Future avenues for application and exploration range from spatial and multi species models, to ecosystem-based models and beyond.</p>

Bayesian stock assessment using a state–space implementation of the delay difference model

1999 ◽  
Vol 56 (1) ◽  
pp. 37-52 ◽  
Author(s):  
Renate Meyer ◽  
Russell B Millar
Keyword(s):  
State Space ◽  
Bayesian Approach ◽  
Stock Assessment ◽  
State Space Model ◽  
Random Errors ◽  
Difference Model ◽  
Spawning Stock ◽  
The Common ◽  
Biomass Dynamics ◽  
Delay Difference

This paper presents a Bayesian approach to fisheries stock assessment using the delay difference model to describe nonlinear population dynamics. Given a time series of annual catch and effort data, models in the Deriso-Schnute family predict exploitable biomass in the following year from biomass in the current and previous year and from past spawning stock. A state-space model is used, as it allows incorporation of random errors in both the biomass dynamics equations and the observations. Because the biomass dynamics are nonlinear, the common Kalman filter is generally not applicable for parameter estimation. However, it is demonstrated that the Bayesian approach can handle any form of nonlinear relationship in the state and observation equations as well as realistic distributional assumptions. Difficulties with posterior calculations are overcome by the Gibbs sampler in conjunction with the adaptive rejection Metropolis sampling algorithm.

Comparative performance of data-poor CMSY and data-moderate SPiCT stock assessment methods when applied to data-rich, real-world stocks

2020 ◽  
Author(s):  
Paul Bouch ◽  
Cóilín Minto ◽  
Dave G Reid
Keyword(s):  
Stock Assessment ◽  
Assessment Methods ◽  
Production Model ◽  
Fishing Mortality ◽  
Comparative Performance ◽  
Fish Stocks ◽  
Surplus Production ◽  
Stock Biomass ◽  
Surplus Production Model ◽  
The Status

Abstract All fish stocks should be managed sustainably, yet for the majority of stocks, data are often limited and different stock assessment methods are required. Two popular and widely used methods are Catch-MSY (CMSY) and Surplus Production Model in Continuous Time (SPiCT). We apply these methods to 17 data-rich stocks and compare the status estimates to the accepted International Council for the Exploration of the Sea (ICES) age-based assessments. Comparison statistics and receiver operator analysis showed that both methods often differed considerably from the ICES assessment, with CMSY showing a tendency to overestimate relative fishing mortality and underestimate relative stock biomass, whilst SPiCT showed the opposite. CMSY assessments were poor when the default depletion prior ranges differed from the ICES assessments, particularly towards the end of the time series, where some stocks showed signs of recovery. SPiCT assessments showed better correlation with the ICES assessment but often failed to correctly estimate the scale of either F/FMSY of B/BMSY, with the indices lacking the contrast to be informative about catchability and either the intrinsic growth rate or carrying capacity. Results highlight the importance of understanding model tendencies relative to data-rich approaches and warrant caution when adopting these models.

A state-space spatial survey-based stock assessment (SSURBA) model to inform spatial variation in relative stock trends

2020 ◽  
Vol 77 (10) ◽  
pp. 1638-1658
Author(s):  
Rajeev Kumar ◽  
Noel G. Cadigan ◽  
Nan Zheng ◽  
Divya A. Varkey ◽  
M. Joanne Morgan
Keyword(s):  
State Space ◽  
Stock Assessment ◽  
State Space Model ◽  
Logistic Function ◽  
Fish Growth ◽  
Assessment Model ◽  
Fish Length ◽  
Stock Size ◽  
Age Cohort ◽  
Size At Age

An age-structured, spatial survey-based assessment model (SSURBA) is developed and applied to the Grand Banks stock (NAFO Divisions 3LNO) of American plaice (Hippoglossoides platessoides) in Newfoundland and Labrador. The state-space model is fit to annual spatial (i.e., three divisions) stock size-at-age research vessel (RV) survey indices that are assumed to be proportional to abundance. We model index catchability (q) as a logistic function of fish length, which varies with age, cohort, and the time of the survey; therefore, the model facilitates the estimation of q values that change spatially and temporally following changes in fish growth and survey gears. The SSURBA model produces division-level estimates of fishing mortality rates (F), stock productivity, and stock size relative to the logistic catchability assumption with q = 1 for fully selected ages. The spatial model allows us to include additional survey information compared with the space-aggregated assessment model (all of 3LNO) that is currently used to assess stock status. The model can provide estimates of relative catch, which we compare with reported catch trends to partially validate the model.

scholarly journals Modelling Complexity and Uncertainty in Fisheries Stock Assessment

2021 ◽  
Author(s):  
◽  
D'Arcy Webber
Keyword(s):  
New Zealand ◽  
State Space ◽  
Ross Sea ◽  
Stock Assessment ◽  
State Space Model ◽  
Computational Cost ◽  
Original Model ◽  
Agent Based Models ◽  
Agent Based ◽  
Space Model

<p>Stock assessment models are used to determine the population size of fish stocks. Although stock assessment models are complex, they still make simplifying assumptions. Generally, they treat each species separately, include little, if any, spatial structure, and may not adequately quantify uncertainty. These assumptions can introduce bias and can lead to incorrect inferences. This thesis is about more realistic models and their inference. This realism may be incorporated by explicitly modelling complex processes, or by admitting our uncertainty and modelling it correctly.  We develop an agent-based model that can describe fish populations as a collection of individuals which differ in their growth, maturation, migration, and mortality. The aim of this model is to better capture the richness in natural processes that determine fish abundance and subsequent population response to anthropogenic removals. However, this detail comes at considerable computational cost. A single model run can take many hours, making inference using standard methods impractical. We apply this model to New Zealand snapper (Pagurus auratus) in northern New Zealand.  Next, we developed an age-structured state-space model. We suggest that this sophisticated model has the potential to better represent uncertainty in stock assessment. However, it pushes the boundaries of the current practical limits of computing and we admit that its practical application remains limited until the MCMC mixing issues that we encountered can be resolved.   The processes that underpin agent-based models are complex and we may need to seek new sources of data to inform these types of models. To make a start here we derive a state-space model to estimate the path taken by individual fish from the day they are tagged to the day of their recapture. The model uses environmental information collected using pop-up satellite archival tags. We use tag recorded depth and oceanographic temperature to estimate the location at any given time. We apply this model to Antarctic toothfish (Dissostichus mawsoni) in the Ross Sea.  Finally, to reduce the computational burden of agent-based models we use Bayesian emulation. This approach replaces the simulation model with an approximating algorithm called an emulator. The emulator is calibrated using relatively few runs of the original model. A good emulator provides a close approximation to the original model and has significant speed gains. Thus, inferences become tractable.  We have made the first steps towards developing a tractable approach to fisheries modelling in complex settings through the creation of realistic models, and their emulation. With further development, Bayesian emulation could result in the increased ability to consider and evaluate innovative methods in fisheries modelling. Future avenues for application and exploration range from spatial and multi species models, to ecosystem-based models and beyond.</p>

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