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, pro-
moting 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 condition-
ing 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 avail-
ability 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.Versión del edito