Navigating management trade-offs between fisheries and marine top-predators

My research focuses on the social and ecological dynamics influencing forage fish in marine ecosystems. More specifically, I investigate how commercial fisheries and marine top predators (seabirds) affect and are affected by changes in forage fish populations. Forage fish play a key role in marine ecosystems by transferring energy from primary production to top predators. They also play a crucial role in global human nutrition by constituting 30% of the world’s fisheries landings. I base my research on a social-ecological systems (SES) approach, where I combine ecological science with social science and where the use of cutting-edge research technologies is imperative.

My research provides the much-needed knowledge base and decision support for ecosystem-based management (EBM) of forage fish, which in contrast to traditional, sectorial management can handle complex relationships and trade-offs and thereby be better equipped for achieving sustainability in a dynamic and changing world. My planned research specifically targets the following 4 areas:

• Unmanned Surface Vessel (USV) based technology for data collection. USVs – marine drones – can revolutionize how we gather knowledge on fish stocks and marine ecosystems. I will continue my pioneering efforts in developing drone technology. As a scientific leader for one of the annual expeditions with SLU’s research vessel R/V Svea, I will develop benchmarking procedures for integrating drone and research vessel data. Drones are cheap to operate and can dramatically increase data collection efforts.
• Automated and non-invasive data acquisition systems for top-predators. Top-predators are valuable indicators of ecosystem health. I have studied common murres in the Baltic Sea since 2001, increasingly by using innovative research methods such as artificial breeding ledges with camera surveillance. I will continue developing such automated and non-invasive data acquisition systems for seabird ecology.
• Artificial Intelligence for processing of big data. The USV data collection as well as the automated systems for seabirds generates massive amounts of data that is unrealistic to process with traditional statistical methods. Therefore, I work with AI Sweden at the to develop machine learning algorithms. Automated data acquisition systems in combination with AI accelerated data analysis in the lab as well as onboard research vessels and USVs can provide a knowledge base for a dynamic, real-time based EBM.
• Knowledge and tools for solving management trade-offs. Developing EBM is a key to handle trade-offs between top-predator conservation and forage fisheries. To this end, I have developed a bio-energetic framework for estimating the forage fish requirement for top predators in relation to fisheries management. My current focus is to generalize this work to any marine top-predator, which will be executed through collaborating with international colleagues on empirical meta-analyses, further bio-energetic modelling and USV deployments in additional ecosystems.

In summary, my forthcoming research will provide the basis for a knowledge intensive EBM of forage fish. My research will be fundamental for understanding the dynamics of marine SES, for acquiring timely and accurate data on ecosystem health and for solving management trade-offs. My research will thereby contribute to a sustainable future for the world’s oceans, where management of forage fish ensures both their role in the ecosystem and their important contribution to human populations worldwide.