The deployment of offshore structures for renewable energy generation (wind/wave/tidal) will lead to the alteration of access to the area of installation for several users of the sea including: shipping, fishing, tourism and recreational users. Arguably, the largest impact will be upon the fishing industry where access loss may lead to displacement and reduced catch per unit effort in turn leading to conflict. To prevent conflict, it is important to understand mitigating factors. Marine renewable energy devices (MREDs) and associated infrastructure will be placed on the seabed, affecting benthic infauna and epifauna, important sources of food for many species including those of commercial importance, potentially providing benefits to the fishing industry and mitigating the causes of conflict. Two key plausible benefits of MREDs are the ‘artificial reef effect’ and the ‘exclusion zone effect’. This study investigated the utility of the Ecopath with Ecosim and Ecospace modelling software to address the implications of these ‘effects’. Two case study models were developed, one at the whole west coast of Scotland shelf scale and one at a smaller single installation scale. Our results suggested that the Ecospace model could potentially predict the effects of MRED installations, but revealed that there are a number of considerations which should be taken into account before attempting to do this. Key considerations include data availability (an issue in all modelling), spatial scale and resolution. Other limitations to this particular study such as the ability to make changes over time are currently being addressed by ongoing developments of the software. Despite the considerations and limitations, these case studies reveal the usefulness of spatial ecosystem modelling, particularly Ecospace, to investigate this issue.
Alexander, K., Meyjes, S., & Heymans, J. (2016). Spatial ecosystem modelling of marine renewable energy installations: Gauging the utility of Ecospace. Ecological Modelling, 331, 115-128. https://doi.org/10.1016/j.ecolmodel.2016.01.016