No abstract

This manuscript discusses the benefits of having a stock assessment model that is intuitively close to a linear model. It creates a case for the need of such models taking into account the increase in data availability and the expansion of stock assessment requests. We explore ideas around the assessment of large numbers of stocks and the need to make stock assessment easier to run and more intuitive, so that more scientists from diverse backgrounds can be involved. We show, as an example, the model developed under the European Commission Joint Research Center’s ‘Assessment for All’ Initiative (a4a) and how it fits the a4a strategy of making stock assessment simpler and accessible to a wider group of scientists.

The current fish stock assessment process in Europe can be very resource- and time-intensive. The scientists involved require a very particular set of skills, acquired over their career, drawing from biology, ecology, statistics, mathematical modelling, oceanography, fishery policy, and computing. There is a particular focus on producing a single “best” stock assessment model, but as fishery science advances, there are clear needs to address a range of hypotheses and uncertainties, from large-scale issues such as climate change to specific ones, such as high observation error on young hake. Key to our discussion is the use of the assessment for all frameworks to translate hypotheses into models. We propose a change to the current stock assessment procedure, driven by the use of model averaging to address a range of plausible hypotheses, where increased collaboration between the varied disciplines within fishery science will result in more robust advice.