Stock assessment is a scientific process aimed at providing fisheries managers with knowledge and indicators (e.g. current catch levels, abundance, and fishing mortality Maximum Sustainable Yields) to be used for the regulation of fishery stocks (harvested or managed unit of fish populations). A stock assessment framework is used by resource managers, scientists, and students to understand the status and trends of marine fishery resources. A stock assessment process starts with the collation and harmonization of data among the participating scientists, and the data formatting to provide input to dedicated assessment software. Algorithms are run multiple times to assess the sensitivity to various hypotheses and parameters. When consensus is reached, model outputs are published together with a narrative, and constitute a stock status report. This is a slow and costly process.

The challenge in stock assessments is to find or produce quality data and robust models to estimate biological parameters in order to determine fishing limitations related to the stock biomass and reproduction rate. The EU recommends that the limit for catch should be set to a value that does not compromise the species reproduction capabilities (Maximum Sustainable Yield or MSY). For many stocks, MSY today has reached an upper limit, and many commercial species are overfished or severely depleted. Currently there are many data-poor stocks where such limits are difficult to establish, and scientists have a real need for a data and computation platform.

There are several approaches in place, such as surplus production models, statistical catch at age models, and virtual population analysis models, mass balances, time dynamic simulations, spatial and temporal dynamic impact models, catch only based MSY (e.g. CMSY), or ecological models (e.g. Ecoscope, Ecoscope with EcoPath, EwE). With the ever-increasing amounts and granularity of data, there is a need to produce more timely and accurate projections of stocks, and the possibility to apply new paradigms to catch and environmental data. The increasingly sophisticated models are becoming impossible to manage for individual scientists. The major problems affecting existing approaches include:

  1. computing capacity limitations,
  2. data access limitations, and
  3. incompatibility in model output.

For computing catch limitations, a number of stock assessment models have been developed, e.g. by FAO and IRD. However, these models are becoming too demanding to be executed on researchers’ machines.

Limitations are also felt in the areas of data granularity, spatial data availability, and accessibility. Many data owners lack facilities to share disaggregated data because of technical or data formatting limitations and, during assessment working groups, much time is spent on data formatting instead of actual assessments. The current emergence of larger frameworks for managing entire food-web-based models requires swapping smaller model output to serve as input for larger models. An infrastructure that can host and orchestrate these food-web-based models is missing, and model output is difficult to re-use in another context.

BlueBRIDGE is working to implement VREs able to effectively support stock assessment (The VREs will be released in early 2018).