Closer to Sustainable Fisheries with a New Advanced and User-Friendly Stock Assessment Model

“Stock assessment is an extremely difficult discipline. It requires large volumes of different types of data, many years of experience, and specialist expertise – and the models must be both robust and precise,” says Nis Sand Jacobsen, Senior Researcher and lead author of the new study published in Ecological Solutions and Evidence.

A State-of-the-Art Stock Assessment Model

At the heart of smsR is its ability to address a challenge that has long faced stock assessments: fish populations change significantly throughout the year.

Traditional stock assessment models treat the year as a single period and may therefore overlook important seasonal changes and biological processes. Fish grow, spawn, migrate and are affected by changing natural mortality, such as disease and predation, throughout the seasons, while fishing activities and scientific surveys take place at selected times of the year.

By explicitly incorporating these seasonal variations, smsR can describe stock dynamics much more realistically.

This makes the model particularly suitable for species with rapid growth and pronounced seasonal patterns, such as many small fish species.

“Small pelagic fish such as lesser sandeel and North Sea sprat grow and develop rapidly over short periods. It is therefore crucial that models can utilise data from different times of the year if we are to describe and assess the status of these stocks as accurately as possible,” says Nis Sand Jacobsen.

The researchers point out that future stock assessments will increasingly need to account for climate change, environmental conditions and increasingly complex ecosystem processes. It is precisely this flexibility that smsR has been designed to provide.

“These stock assessments already require very long time series and large amounts of data. We typically need time series spanning ten years to have solid, valid and robust models. We are not working with just a few years of observations,” says Nis Sand Jacobsen.

Between the Ecosystem and the Fishery

Species such as lesser sandeel and North Sea sprat are of great interest to commercial fisheries and also play a special role in marine ecosystems. These small fish are known as key forage species because they provide a vital food source for larger fish, seabirds and marine mammals.

With such key species, the challenge of balancing ecosystem needs and fishing interests becomes clear.

If a stock is overestimated, catch quotas may be set too high, leading to overfishing. Conversely, underestimation may result in unnecessarily low quotas with economic consequences for the fishing sector.

“It is essential to strive for this level of precision by continually improving stock assessment models. Even small improvements can make a major difference. Otherwise, we risk setting quotas too high and overfishing commercially important species. Until now, our models have not always been good enough.”

Nis Sand Jacobsen points to the historical example of Newfoundland cod:

“Cod there was overfished during the 1970s because the stock was assessed to be larger than it actually was. The result is that cod has still not recovered in Newfoundland waters.”

Continuous Development Towards Greater Precision in Scientific Advice
The better the models describe stock development, the stronger the scientific basis for the advice that ICES provides to the EU and national authorities.

In Denmark, DTU Aqua communicates this advice to the fishing industry and advises authorities and ministries on fish stocks and fisheries management.

Behind this visible process, that often attracts great attention in the media, stands a group of statisticians, mathematicians and modellers at DTU Aqua who have been among the world leaders for decades in developing stock assessment models underpinning international fisheries management.

Nis Sand Jacobsen repeatedly emphasises the purpose of this passion for data and statistic models:

“The goal of developing ever more advanced stock assessment models is simple: to make scientific advice as accurate as possible so that fisheries can be managed more sustainably. This is a necessity in a marine environment that is visibly and demonstrably under pressure,” says Nis Sand Jacobsen.

The statisticians, mathematicians and modellers at DTU Aqua are already facing the next stage of development. In a new project funded by Innovation Fund Denmark, the researchers will develop and work with spatial aspects of stock assessment models.

These models will be better able to account for where fish are located and how they move between different areas. This is a field of research with considerable potential for the future of fisheries management, but one in which significant development is still needed.

A user-friendly new model

In the article, the researchers point out that future stock assessments will increasingly need to account for climate change, environmental conditions and ever more complex ecosystem processes.
This is precisely the kind of flexibility that smsR was designed to provide.

"These stock assessments already require very long time series and large volumes of data. Typically, we need time series covering around ten years to produce sound, valid and robust models. We are not working with just a few years of observations in stock assessments," says Nis Sand Jacobsen.

Although stock assessment models are required to handle increasingly complex datasets, smsR has now been made much more user-friendly.

Where previous versions were primarily internal tools that could be used by only a small number of specialists, smsR has now been released as open-source software with comprehensive documentation.

This means that researchers and working groups involved in stock assessments and fisheries advice around the world can download the model, run analyses in the R programming environment, and gain access to the same advanced methods that have until now been available only to a limited group of model developers and researchers. See the fact box for further details