There are uncertainties associated with every phase of the stock
assessment process, ranging from the collection of data, assessment model
choice and assumptions, interpretation of risk, and implementation of
management advice. The dynamics of the modeled fish populations are very
complex, and our incomplete understanding of those dynamics (and limited
observations of important mechanisms) necessitate that our models are simpler
than nature. The aim is for the model to capture enough of the dynamics to accurately
estimate trends and abundance, and to provide advice to managers about
sustainable harvest.
The status quo approach to assessment modeling has been to
identify the 'best' model, based on diagnostics and model selection criteria,
and to generate advice from that single model. This procedure essentially
ignores advice from other model congurations regardless of how closely they
performed relative to the 'best' model. The ensemble modeling paradigm proposes
to more fully capture uncertainty in the assessment model selection and advice
provision—but is this the case?
This network session will summarize the status quo and ensemble
modeling processes, highlighting the characterization of uncertainty in both
assessments and forecasts, model performance metrics, and the practicalities of
reviewing the assessment and providing management advice.
Participants
in this network session are invited to share their assessment experience with
uncertainties in their model structure, performance of their model over time,
and sensitivities of management advice to model structure. Focused questions
will lead participants through the important steps of the model ensemble
process, and feedback about feasibility, and impacts on the review and advice
aspect will be summarized.