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An Applied Framework for Incorporating Multiple Sources of Uncertainty in Fisheries Stock Assessments.

Scott F, Jardim E, Millar CP, Cerviño S - PLoS ONE (2016)

Bottom Line: Additionally, although multiple candidate models may be considered, only one is selected as the 'best' result, effectively rejecting the plausible assumptions behind the other models.The final step integrates across all of the results to reconcile the multi-model outcome.Simple model averaging is used to integrate across the results and produce a single assessment that considers the multiple sources of uncertainty.

View Article: PubMed Central - PubMed

Affiliation: European Commission, Joint Research Centre (JRC), Institute for the Protection and Security of the Citizen (IPSC), Maritime Affairs Unit, via Enrico Fermi 2749, 21027 Ispra (VA), Italy.

ABSTRACT
Estimating fish stock status is very challenging given the many sources and high levels of uncertainty surrounding the biological processes (e.g. natural variability in the demographic rates), model selection (e.g. choosing growth or stock assessment models) and parameter estimation. Incorporating multiple sources of uncertainty in a stock assessment allows advice to better account for the risks associated with proposed management options, promoting decisions that are more robust to such uncertainty. However, a typical assessment only reports the model fit and variance of estimated parameters, thereby underreporting the overall uncertainty. Additionally, although multiple candidate models may be considered, only one is selected as the 'best' result, effectively rejecting the plausible assumptions behind the other models. We present an applied framework to integrate multiple sources of uncertainty in the stock assessment process. The first step is the generation and conditioning of a suite of stock assessment models that contain different assumptions about the stock and the fishery. The second step is the estimation of parameters, including fitting of the stock assessment models. The final step integrates across all of the results to reconcile the multi-model outcome. The framework is flexible enough to be tailored to particular stocks and fisheries and can draw on information from multiple sources to implement a broad variety of assumptions, making it applicable to stocks with varying levels of data availability The Iberian hake stock in International Council for the Exploration of the Sea (ICES) Divisions VIIIc and IXa is used to demonstrate the framework, starting from length-based stock and indices data. Process and model uncertainty are considered through the growth, natural mortality, fishing mortality, survey catchability and stock-recruitment relationship. Estimation uncertainty is included as part of the fitting process. Simple model averaging is used to integrate across the results and produce a single assessment that considers the multiple sources of uncertainty.

No MeSH data available.


Related in: MedlinePlus

The impact of model uncertainty on the summary stock assessment results.Summary stock assessment results (recruitment, spawning stock biomass (SSB), mean fishing mortality (Fbar) and catch) from fitting a single iteration of the biological parameters with the 26 combinations of stock assessment and natural mortality models. This is equivalent to performing stock assessments without process or estimation uncertainty and only including model uncertainty. There are clear differences between the patterns and trends of the fits from each model, particularly in the most recent years. Note that the recruitment and SSB are shown on a log scale to allow the differences between the model results to be more visible. The recruitment, SSB and Fbar results can be broadly separated into two groups, driven by the natural mortality model. The ‘Gislason’ natural mortality model (blue lines) estimates higher recruitment and Fbar and lower SSB than the ‘0.4’ model (black lines). The results from the most recent ICES stock assessment are shown as the thick, dashed, red line.
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pone.0154922.g005: The impact of model uncertainty on the summary stock assessment results.Summary stock assessment results (recruitment, spawning stock biomass (SSB), mean fishing mortality (Fbar) and catch) from fitting a single iteration of the biological parameters with the 26 combinations of stock assessment and natural mortality models. This is equivalent to performing stock assessments without process or estimation uncertainty and only including model uncertainty. There are clear differences between the patterns and trends of the fits from each model, particularly in the most recent years. Note that the recruitment and SSB are shown on a log scale to allow the differences between the model results to be more visible. The recruitment, SSB and Fbar results can be broadly separated into two groups, driven by the natural mortality model. The ‘Gislason’ natural mortality model (blue lines) estimates higher recruitment and Fbar and lower SSB than the ‘0.4’ model (black lines). The results from the most recent ICES stock assessment are shown as the thick, dashed, red line.

Mentions: Model uncertainty is not often considered in stock assessments other than attempting to find the single ‘best’ model within a suite of candidate models and discarding the other plausible models. Different stock assessment and natural mortality model combinations will obviously result in different stock assessment results. The impact of the model uncertainty can be illustrated by fitting each model combination with only a single iteration of the biological parameters (Fig 5). This ignores process and estimation uncertainty. There are clear differences in the patterns and trends in the results, particularly in the most recent years. For example, the mean fishing mortality in the final year ranges from 0.04 to 1.95. Models with the ‘Gislason’ natural mortality model all tend to have higher mean fishing mortality and recruitment but lower SSB than the models with ‘0.4’ natural mortality model, driven by the high natural mortality in the younger ages and low natural mortality in the older age.


An Applied Framework for Incorporating Multiple Sources of Uncertainty in Fisheries Stock Assessments.

Scott F, Jardim E, Millar CP, Cerviño S - PLoS ONE (2016)

The impact of model uncertainty on the summary stock assessment results.Summary stock assessment results (recruitment, spawning stock biomass (SSB), mean fishing mortality (Fbar) and catch) from fitting a single iteration of the biological parameters with the 26 combinations of stock assessment and natural mortality models. This is equivalent to performing stock assessments without process or estimation uncertainty and only including model uncertainty. There are clear differences between the patterns and trends of the fits from each model, particularly in the most recent years. Note that the recruitment and SSB are shown on a log scale to allow the differences between the model results to be more visible. The recruitment, SSB and Fbar results can be broadly separated into two groups, driven by the natural mortality model. The ‘Gislason’ natural mortality model (blue lines) estimates higher recruitment and Fbar and lower SSB than the ‘0.4’ model (black lines). The results from the most recent ICES stock assessment are shown as the thick, dashed, red line.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4862649&req=5

pone.0154922.g005: The impact of model uncertainty on the summary stock assessment results.Summary stock assessment results (recruitment, spawning stock biomass (SSB), mean fishing mortality (Fbar) and catch) from fitting a single iteration of the biological parameters with the 26 combinations of stock assessment and natural mortality models. This is equivalent to performing stock assessments without process or estimation uncertainty and only including model uncertainty. There are clear differences between the patterns and trends of the fits from each model, particularly in the most recent years. Note that the recruitment and SSB are shown on a log scale to allow the differences between the model results to be more visible. The recruitment, SSB and Fbar results can be broadly separated into two groups, driven by the natural mortality model. The ‘Gislason’ natural mortality model (blue lines) estimates higher recruitment and Fbar and lower SSB than the ‘0.4’ model (black lines). The results from the most recent ICES stock assessment are shown as the thick, dashed, red line.
Mentions: Model uncertainty is not often considered in stock assessments other than attempting to find the single ‘best’ model within a suite of candidate models and discarding the other plausible models. Different stock assessment and natural mortality model combinations will obviously result in different stock assessment results. The impact of the model uncertainty can be illustrated by fitting each model combination with only a single iteration of the biological parameters (Fig 5). This ignores process and estimation uncertainty. There are clear differences in the patterns and trends in the results, particularly in the most recent years. For example, the mean fishing mortality in the final year ranges from 0.04 to 1.95. Models with the ‘Gislason’ natural mortality model all tend to have higher mean fishing mortality and recruitment but lower SSB than the models with ‘0.4’ natural mortality model, driven by the high natural mortality in the younger ages and low natural mortality in the older age.

Bottom Line: Additionally, although multiple candidate models may be considered, only one is selected as the 'best' result, effectively rejecting the plausible assumptions behind the other models.The final step integrates across all of the results to reconcile the multi-model outcome.Simple model averaging is used to integrate across the results and produce a single assessment that considers the multiple sources of uncertainty.

View Article: PubMed Central - PubMed

Affiliation: European Commission, Joint Research Centre (JRC), Institute for the Protection and Security of the Citizen (IPSC), Maritime Affairs Unit, via Enrico Fermi 2749, 21027 Ispra (VA), Italy.

ABSTRACT
Estimating fish stock status is very challenging given the many sources and high levels of uncertainty surrounding the biological processes (e.g. natural variability in the demographic rates), model selection (e.g. choosing growth or stock assessment models) and parameter estimation. Incorporating multiple sources of uncertainty in a stock assessment allows advice to better account for the risks associated with proposed management options, promoting decisions that are more robust to such uncertainty. However, a typical assessment only reports the model fit and variance of estimated parameters, thereby underreporting the overall uncertainty. Additionally, although multiple candidate models may be considered, only one is selected as the 'best' result, effectively rejecting the plausible assumptions behind the other models. We present an applied framework to integrate multiple sources of uncertainty in the stock assessment process. The first step is the generation and conditioning of a suite of stock assessment models that contain different assumptions about the stock and the fishery. The second step is the estimation of parameters, including fitting of the stock assessment models. The final step integrates across all of the results to reconcile the multi-model outcome. The framework is flexible enough to be tailored to particular stocks and fisheries and can draw on information from multiple sources to implement a broad variety of assumptions, making it applicable to stocks with varying levels of data availability The Iberian hake stock in International Council for the Exploration of the Sea (ICES) Divisions VIIIc and IXa is used to demonstrate the framework, starting from length-based stock and indices data. Process and model uncertainty are considered through the growth, natural mortality, fishing mortality, survey catchability and stock-recruitment relationship. Estimation uncertainty is included as part of the fitting process. Simple model averaging is used to integrate across the results and produce a single assessment that considers the multiple sources of uncertainty.

No MeSH data available.


Related in: MedlinePlus