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.

“The Elephant in the Room”: Exploring Natural Mortality Uncertainty in Statistical Catch at Age Models

“…Recent research has been focusing on implementing a more objective model selection approach for experts to reach an agreement on which is the best supported model based on the performance of model diagnostics (Carvalho et al, 2017;Maunder and Piner, 2017;Rudd et al, 2019). Specifically, model ensembles for future stock assessment advice have been proposed as a promising approach to capture structural uncertainty surrounding important biological processes, including M (Scott et al, 2016). Elsewhere, such approaches are already implemented.…”

“…Accounting for uncertainty in M is fundamental not only to estimate the range of variability in the output but also to evaluate the outputs' robustness against model assumptions (Scott et al, 2016), as already highlighted with regards to deterministic Virtual Population Analysis (VPA) (Pope, 1972) and Extended Survival Analysis (XSA) models (Cheilari and Raetz, 2009). In agestructured models, the link between the population estimates and M occurs on two levels: in the basic population dynamics equations:…”

Model uncertainty and simulated multispecies fisheries management advice in the Baltic Sea

“…Existing methods to conduct such sensitivity analyses are highly model-specific and the comparability of the resulting parameter uncertainty ranges is questionable. In addition, complete sensitivity analysis may be impossible in case of some models such as EwE and Gadget [26,76]. This is an argument to use a number of complementary modelling approaches to provide advice, as model uncertainty tends to be larger than parameter uncertainty.…”

Stock assessment and management of cephalopods: advances and challenges for short-lived fishery resources

“…Alternative modelling approaches might include the use of Models of Intermediate Complexity for Ecosystem assessment (MICE) (e.g. Scott et al, 2016) and economic models (e.g. Colla-De-Robertis et al, 2019).…”