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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

Example age-based stock data after the length-based data has been sliced using the uncertain von Bertalanffy growth parameters.Natural mortality, catch numbers and mean weights by age after slicing the length-based data. Median (solid line) and 5% and 95% quantiles (dashed line) are shown. The values are for the year 2012. Only ages up to 12 are shown for brevity. The two different natural mortality models are shown in the top panel. The ‘Gislason’ model is black and the ‘0.4’ model is blue. The variance in the ‘Gislason’ model represents the process uncertainty. The ‘0.4’ model has no process uncertainty and therefore no variance.
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pone.0154922.g004: Example age-based stock data after the length-based data has been sliced using the uncertain von Bertalanffy growth parameters.Natural mortality, catch numbers and mean weights by age after slicing the length-based data. Median (solid line) and 5% and 95% quantiles (dashed line) are shown. The values are for the year 2012. Only ages up to 12 are shown for brevity. The two different natural mortality models are shown in the top panel. The ‘Gislason’ model is black and the ‘0.4’ model is blue. The variance in the ‘Gislason’ model represents the process uncertainty. The ‘0.4’ model has no process uncertainty and therefore no variance.

Mentions: Example results of converting the length-based stock data to age-based data using the slicing method can be seen in Fig 4. The ‘Gislason’ model has higher values of natural mortality in the first age class than the ‘0.4’ model and also has uncertainty around the values (the ‘0.4’ model has no process uncertainty and therefore no variance). It can be argued that this is more biologically plausible than using the same values for all ages and the high variance reflects the high level of uncertainty in estimates of natural mortality in the early ages. The variance in the catch numbers in the younger ages is also very high reflecting high uncertainty in these ages. The variance in the mean weights at age increases as individuals get older, following the same pattern as the growth curve in Fig 3.


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

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

Example age-based stock data after the length-based data has been sliced using the uncertain von Bertalanffy growth parameters.Natural mortality, catch numbers and mean weights by age after slicing the length-based data. Median (solid line) and 5% and 95% quantiles (dashed line) are shown. The values are for the year 2012. Only ages up to 12 are shown for brevity. The two different natural mortality models are shown in the top panel. The ‘Gislason’ model is black and the ‘0.4’ model is blue. The variance in the ‘Gislason’ model represents the process uncertainty. The ‘0.4’ model has no process uncertainty and therefore no variance.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0154922.g004: Example age-based stock data after the length-based data has been sliced using the uncertain von Bertalanffy growth parameters.Natural mortality, catch numbers and mean weights by age after slicing the length-based data. Median (solid line) and 5% and 95% quantiles (dashed line) are shown. The values are for the year 2012. Only ages up to 12 are shown for brevity. The two different natural mortality models are shown in the top panel. The ‘Gislason’ model is black and the ‘0.4’ model is blue. The variance in the ‘Gislason’ model represents the process uncertainty. The ‘0.4’ model has no process uncertainty and therefore no variance.
Mentions: Example results of converting the length-based stock data to age-based data using the slicing method can be seen in Fig 4. The ‘Gislason’ model has higher values of natural mortality in the first age class than the ‘0.4’ model and also has uncertainty around the values (the ‘0.4’ model has no process uncertainty and therefore no variance). It can be argued that this is more biologically plausible than using the same values for all ages and the high variance reflects the high level of uncertainty in estimates of natural mortality in the early ages. The variance in the catch numbers in the younger ages is also very high reflecting high uncertainty in these ages. The variance in the mean weights at age increases as individuals get older, following the same pattern as the growth curve in Fig 3.

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