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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.


Distribution of the mean fishing mortality in the final year of the assessment for all model variants and model averaged results.The model labelling on the y-axis refers to the combination of the stock assessment submodels and the natural mortality model (see Table 3). GCV is the model averaged result using median GCV weighting. The points show the median value, the lines extend to the 5% and 95% quantile.
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pone.0154922.g008: Distribution of the mean fishing mortality in the final year of the assessment for all model variants and model averaged results.The model labelling on the y-axis refers to the combination of the stock assessment submodels and the natural mortality model (see Table 3). GCV is the model averaged result using median GCV weighting. The points show the median value, the lines extend to the 5% and 95% quantile.

Mentions: The effect of the model averaging process on the resulting variability in the results can be seen by looking at the estimated mean fishing mortality in the final year of the assessment for each of the individual fitted model variants and the model averaged results (Fig 8). The values for the model averaged results are taken from the full suite of 26 fitted model variants and therefore cover the full range of those values. As some of those fitted model variants contain fits that would be thought of as implausible, the model averaged results also contains some implausible results. For example, the maximum value of mean fishing mortality for the model averaged restuls is 8.46. However, the median value is 1.2 which is quite reasonable and the 90% quantile range is from 0.32 to 2.36. The averaged results contain all of the assumptions that were generated during the first step of the process. The results are therefore more robust than if only a single stock assessment result was selected from the model variants.


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

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

Distribution of the mean fishing mortality in the final year of the assessment for all model variants and model averaged results.The model labelling on the y-axis refers to the combination of the stock assessment submodels and the natural mortality model (see Table 3). GCV is the model averaged result using median GCV weighting. The points show the median value, the lines extend to the 5% and 95% quantile.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0154922.g008: Distribution of the mean fishing mortality in the final year of the assessment for all model variants and model averaged results.The model labelling on the y-axis refers to the combination of the stock assessment submodels and the natural mortality model (see Table 3). GCV is the model averaged result using median GCV weighting. The points show the median value, the lines extend to the 5% and 95% quantile.
Mentions: The effect of the model averaging process on the resulting variability in the results can be seen by looking at the estimated mean fishing mortality in the final year of the assessment for each of the individual fitted model variants and the model averaged results (Fig 8). The values for the model averaged results are taken from the full suite of 26 fitted model variants and therefore cover the full range of those values. As some of those fitted model variants contain fits that would be thought of as implausible, the model averaged results also contains some implausible results. For example, the maximum value of mean fishing mortality for the model averaged restuls is 8.46. However, the median value is 1.2 which is quite reasonable and the 90% quantile range is from 0.32 to 2.36. The averaged results contain all of the assumptions that were generated during the first step of the process. The results are therefore more robust than if only a single stock assessment result was selected from the model variants.

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.