PREDICT is an ambitious three-year £800k project funded by Ørsted, the global leader in offshore wind, and a collaboration between the Environmental Research Institute (ERI) at the University of the Highlands and Islands and the University of Aberdeen that aims to address knowledge gaps in offshore wind environmental characterisation by improving understanding of fish migration patterns and providing a vision for next-generation monitoring techniques.
The project brings together a diverse team of expertise from academia and industry spanning a range of multi-disciplinary research disciplines including ecology, engineering, and data analysis, to investigate fish migration patterns as prey availability to better predict the locations and seasons where top-level predators (seabirds and mammals) may have increased interaction with windfarms. PREDICT will also look at how climate change may impact predictions of oceanographic changes to productive regions in time and space that may drive as well as use methodologies to assess the knock-on effects on seabird and marine mammals.
The project will support building the evidence base of strategic prediction, survey and analysis methods, and help to increase confidence in developing and consenting offshore wind arrays. Outputs of the project will enable the industry to avoid using locations that have a higher likelihood of overlap with important feeding grounds for seabird and marine mammals for offshore wind developments now and into the future.
Seasonal distributions of seabirds and marine mammals are driven by the daily, weekly, and annual migration patterns of their prey (fish). Those migrations relate to highly predictable seasonal changes in foraging and spawning grounds, which in turn are driven by environmental variables that can be influenced by climate change. Within the planning of future large-scale offshore wind developments in the North Sea, there is a growing need to understand where top-predator distributions will have an increased probability of interaction with offshore windfarm developments. However, existing environmental monitoring techniques are currently based on tagging studies or aerial/ vessel-based snapshot surveys, which generate high variance between individual species, seasons, years, and development sites.
Recreating annual fish migration routes
To reduce variance and uncertainty in assessments of top-level predator distributions, the PREDICT project brings together multiple datasets, from historical ICES International Bottom Trawl Survey (IBTS) to MMO commercial landings data, to identify the locations and timings of where multiple fish species (e.g., sandeels, herring, mackerel, sprat, and juvenile gadoid species) are available as common prey. This will enable us to recreate annual fish migration routes and generate seasonal maps of overlap for multiple species to elucidate spatio-temporal trends in growth rates and track annual cohorts with a greater degree of precision. The creation of individual seasonal maps will help us to identify where planned locations of future windfarms may overlap with high use areas in the annual cycle of fish movement during their migration.
Novel autonomous platforms
PREDICT will further identify years, regions, oceanographic and finer-scale features (e.g. frontal and highly stratified areas) to predict mechanisms driving variability in annual fish migrations that are the most likely cause of high variation in seabird and marine mammal distributions. The use of innovative technologies and novel sensor (autonomous) platforms will help facilitate this task by identifying the types and combinations of instrumentation needed in the specific locations of critical marine habitat types to simultaneously collect data on environmental drivers and prey availability. This study will increase ecological understanding and help to de-risk renewable developments in shallow and coastal seas worldwide, as offshore wind fulfils increasing demand for clean energy.
Team
For more details please contact:
Benjamin Williamson (ERI) https://eri.ac.uk/members/benjamin-williamson/