| one publication added to basket [393355] | Answering the key stakeholder questions about the impact of offshore wind farms on marine life using hypothesis testing to inform targeted monitoring
Cresci, A.; Degraer, S.; Zhang, G.; Dannheim, J.; Browman, H.I. (2024). Answering the key stakeholder questions about the impact of offshore wind farms on marine life using hypothesis testing to inform targeted monitoring. ICES J. Mar. Sci./J. Cons. int. Explor. Mer Accepted. https://dx.doi.org/10.1093/icesjms/fsae066
In: ICES Journal of Marine Science. Academic Press: London. ISSN 1054-3139; e-ISSN 1095-9289, meer
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| Trefwoorden |
Renewable energy
Marien/Kust |
| Author keywords |
offshore renewables, marine energy, offshore wind turbines, impact assessment, marine spatial planning, marine ecosystems, energy transition, offshore wind development, hypothesis testing |
| Auteurs | | Top |
- Cresci, A.
- Degraer, S., meer
- Zhang, G.
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- Dannheim, J.
- Browman, H.I.
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| Abstract |
Stakeholders need scientific advice on the environmental impacts of offshore wind (OW) before the facilities are installed. The utility of conventional environmental monitoring methods as a basis for forecasting OW impacts is limited because they do not explain the causes of the observed effects. We propose a multistep approach, based on process-oriented hypothesis testing, targeted monitoring and numerical modeling, to answer key stakeholder questions about planning an OW facility: Q1—Where do we place future OW farms so that impacts on the ecosystem are minimized? Q2—Which species and ecosystem processes will be impacted and to what degree? Q3—Can we mitigate impacts and, if so, how? and Q4—What are the risks of placing an OW facility in one location vs. another? Hypothesis testing can be used to assess impacts of OW facilities on target species-ecological process. This knowledge is transferable and is broadly applicable, a priori, to assess suitable locations for OW (Q1). Hypothesis testing can be combined with monitoring methods to guide targeted monitoring. The knowledge generated can identify the species/habitats at risk (Q2), help selecting/developing mitigation measures (Q3), and be used as input parameters for models to forecast OW impacts at a large spatial scale (Q1; Q4). |
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