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.
Highlights
Stock assessment can be defined as the application of quantitative and statistical models to estimate the current and historical status and trends of a fish stock, including abundance, mortality and productivity [1]
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
A more recent definition used by the World Conference on Stock Assessment Methods (WCSAM), is “Stock assessment is the synthesis of information on life history, fishery monitoring, and resource surveys for estimating stock size and harvest rate relative to sustainable reference points. . . . Stock assessment is usually carried out by applying mathematical models that fit available information to provide simplified representations of population and fishery dynamics.” [2]
Summary
Stock assessment can be defined as the application of quantitative and statistical models to estimate the current and historical status and trends of a fish stock, including abundance, mortality and productivity [1]. The remaining three types of uncertainty: observation, arising from data collection, measurement and sampling; implementation, how well a management policy is fulfilled and institutional, arising from interactions between different groups of people (e.g. scientists and fishermen) are not explored in this study. This does not mean that they are unimportant in the context of fisheries management but that they are usually used in the testing of management options, notably in management strategy evaluation (MSE) algorithms which is outside the scope of this paper
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