Spatial analysis of fish distribution in relation to offshore wind farm developments

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2012-11

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Cranfield University

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Thesis or dissertation

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Abstract

In an effort to support the Kyoto Protocol, the government of the United Kingdom has targeted a goal of obtaining 15% of its electricity supply from renewable sources by 2015. To reach such standards, primary concentration has been placed on renewable sources from the marine environment. However, with increases in the numbers of offshore renewable energy developments (OREDs), proper monitoring and analysis techniques must be established to evaluate the potential impacts these structures and their overall environmental footprint will pose on the marine ecosystem, particularly species distribution. Monitoring techniques have been established by offshore energy developers; however, such methods currently only evaluate animal distribution trends in the short term both pre and post construction. In this study, spatio-temporal analysis of catch per unit effort (CPUE) data was undertaken, utilising geostatistics to enable long term trends to be evaluated for four elasmobranch species common to the North Sea over the 1990-2011 survey period. Overall, the mean CPUE was found to remain stable for all species. However, distribution trends were found to vary throughout the periods examined. Such trends were often correlated to migrating seasons, as well as the habitat preferences for each species. The presence of offshore wind farms and electromagnetic fields associated with subsea cable networks may affect elasmobranch migratory patterns and small-scale orientation. As these species are already vulnerable to overfishing, habitat disruption, and anthropogenic disturbance due to their long life history and low fecundity, consistent monitoring periods and survey locations are essential to their conservation and protection. It is, therefore, unlikely short monitoring periods will provide accurate information on the potential impacts offshore energy developments may have on elasmobranch populations. The approach used is generic enough to provide a basis on which to analyse spatial distribution of organisms in relation to other sources of anthropogenic influence, and environmental parameters.

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Github

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Elasmobranch, Offshore Energy, Geostatistics, Spatio-Temporal, Kriging

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© Cranfield University 2012. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright owner.

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