title: An Applied Framework for Incorporating Multiple Sources of Uncertainty in Fisheries Stock Assessments.
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.
authors.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.
authors.forename: Finlay
authors.initials: F
authors.lastname: Scott
authors.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.
authors.forename: Ernesto
authors.initials: E
authors.lastname: Jardim
authors.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.|Marine Scotland, Freshwater Laboratory, Faskally, Pitlochry, PH16 5LB, United Kingdom.
authors.forename: Colin P
authors.initials: CP
authors.lastname: Millar
authors.affiliation: Instituto Español de Oceanografía, Centro Oceanográfico de Vigo, Subida a Radio Faro 50, 36390 Vigo, Spain.
authors.forename: Santiago
authors.initials: S
authors.lastname: Cerviño
mesh_terms: D000818:Animals; D003247:Conservation of Natural Resources / Q000706:statistics & numerical data*; D005060:Europe; D005398:Fisheries / Q000706:statistics & numerical data*; D048388:Gadiformes / Q000502:physiology*; D007391:International Cooperation; D015233:Models, Statistical; D011157:Population Dynamics; D012098:Reproduction / Q000502:physiology*; D035501:Uncertainty