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A paradigm for understanding whole ecosystem effects of offshore wind farms in shelf seas

  • Natalie Isaksson* (Corresponding Author)
  • , Beth Scott* (Corresponding Author)
  • , Georgina Hunt
  • , Ella-Sophia Benninghaus
  • , Morgane Marie-Eva Declerck
  • , Kate Gormley
  • , Caitlin Harris
  • , Sandra Sjostrand
  • , Neda Trifonova
  • , James J. Waggitt
  • , Juliane Wihsgott
  • , Charlotte A.J Williams,
  • , Arianna Zampollo
  • , Benjamin Williamson
  • *Corresponding author for this work
  • University of the Highlands and Islands
  • Bangor University
  • National Oceanography Centre
  • University of Aberdeen

Research output: Contribution to journalArticlepeer-review

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Abstract

With the rapid expansion of offshore windfarms (OWFs) globally, there is an urgent need to assess and predict effects on marine species, habitats, and ecosystem functioning. Doing so at shelf-wide scale while simultaneously accounting for the concurrent influence of climate change will require dynamic, multitrophic, multiscalar, ecosystem-centric approaches. However, as such studies and the study system itself (shelf seas) are complex, we propose to structure future environmental research according to the investigative cycle framework. This will allow the formulation and testing of specific hypotheses built on ecological theory, thereby streamlining the process, and allowing adaptability in the face of technological advancements (e.g. floating offshore wind) and shifting socio-economic and political climates. We outline a strategy by which to accelerate our understanding of environmental effects of OWF development on shelf seas, which is illustrated throughout by a North Sea case study. Priorities for future studies include ascertaining the extent to which OWFs may change levels of primary production; whether wind energy extraction will have knock-on effects on biophysical ecosystem drivers; whether pelagic fishes mediate changes in top predator distributions over space and time; and how any effects observed at localized levels will scale and interact with climate change and fisheries displacement effects.
Original languageEnglish
Article number fsad194
Number of pages12
JournalICES Journal of Marine Science
Volume82
Issue number3
Early online date12 Dec 2023
DOIs
Publication statusPublished - Mar 2025

Bibliographical note

We would like to thank Emma Ahart, David Bould, Constance Schéré, Marie Toulon, and Inne Withouck for comments on drafts. Also thanks to three anonymous reviewers and Howard Browman for constructive review.

Data Availability Statement

Data availability:
The data underlying this article are available in the article and in its online supplementary material.

Funding

This work was supported by the PREDICT project funded by Ørsted (https://eri.ac.uk/predict/; https://www.abdn.ac.uk/sbs/research/predict-938.php) and by PELAgIO NE/X008835/1 and NE/X008770/1. PELAgIO is part of the ‘The Ecological Consequences of Offshore Wind’ (ECOWind) programme, funded by the Natural Environment Research Council (NERC), The Crown Estate through its Offshore Wind Evidence and Change Programme and is also supported by the Department for Environment, Food, and Rural Affairs (Defra). C.H. is supported by an NERC Scottish Universities Partnership for Environmental Research (SUPER) Doctoral Training Partnership (DTP) grant (grant reference number NE/S007342/1 and website https://superdtp.st-andrews.ac.uk).

FundersFunder number
Ørsted A/S
Natural Environment Research CouncilNE/X008835/1 , NE/X008770/1, NE/S007342/1
The Crown Estate
UK Department for Environment, Food and Rural Affairs

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 7 - Affordable and Clean Energy
      SDG 7 Affordable and Clean Energy
    2. SDG 13 - Climate Action
      SDG 13 Climate Action
    3. SDG 14 - Life Below Water
      SDG 14 Life Below Water

    Keywords

    • marine renewable energy
    • bio-physical indicators
    • predator-prey interactions
    • scaling
    • multitrophic
    • autonomous platforms
    • dynamic Bayesian network modelling
    • cumulative impact assessment

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