Limits...
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

Regression trees showing which stock assessment and natural mortality model components had the biggest impact on the estimated stock assessment summary results.The summary stock assessment results are spawning stock biomass (SSB), mean fishing mortality (Fbar) and recruitment. The notation for F, Q and R refers to the submodel number in Table 3. For example, Q = qmd1 means the second qmodel (the logistic model). M refers to the natural mortality model, either ‘0.4’ or ‘Gislason’. The numbers are the mean residuals from each model component on the logarithm of each summary measure.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4862649&req=5

pone.0154922.g006: Regression trees showing which stock assessment and natural mortality model components had the biggest impact on the estimated stock assessment summary results.The summary stock assessment results are spawning stock biomass (SSB), mean fishing mortality (Fbar) and recruitment. The notation for F, Q and R refers to the submodel number in Table 3. For example, Q = qmd1 means the second qmodel (the logistic model). M refers to the natural mortality model, either ‘0.4’ or ‘Gislason’. The numbers are the mean residuals from each model component on the logarithm of each summary measure.

Mentions: The impact of the different natural mortality, fishing mortality, survey catchability and stock-recruitment model components on important fisheries variables (spawning stock biomass (SSB), recruitment and mean fishing mortality) was investigated using classification regression trees [38] to recursively partition the variables across the distinct model components. The analysis identifies the model components which have the biggest effect on the variable estimates (Fig 6). The analysis was carried out with the R package rpart [39].


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

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

Regression trees showing which stock assessment and natural mortality model components had the biggest impact on the estimated stock assessment summary results.The summary stock assessment results are spawning stock biomass (SSB), mean fishing mortality (Fbar) and recruitment. The notation for F, Q and R refers to the submodel number in Table 3. For example, Q = qmd1 means the second qmodel (the logistic model). M refers to the natural mortality model, either ‘0.4’ or ‘Gislason’. The numbers are the mean residuals from each model component on the logarithm of each summary measure.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0154922.g006: Regression trees showing which stock assessment and natural mortality model components had the biggest impact on the estimated stock assessment summary results.The summary stock assessment results are spawning stock biomass (SSB), mean fishing mortality (Fbar) and recruitment. The notation for F, Q and R refers to the submodel number in Table 3. For example, Q = qmd1 means the second qmodel (the logistic model). M refers to the natural mortality model, either ‘0.4’ or ‘Gislason’. The numbers are the mean residuals from each model component on the logarithm of each summary measure.
Mentions: The impact of the different natural mortality, fishing mortality, survey catchability and stock-recruitment model components on important fisheries variables (spawning stock biomass (SSB), recruitment and mean fishing mortality) was investigated using classification regression trees [38] to recursively partition the variables across the distinct model components. The analysis identifies the model components which have the biggest effect on the variable estimates (Fig 6). The analysis was carried out with the R package rpart [39].

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