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Predicting the impacts of wind farms on seabirds: An individual-based model

Warwick-Evans, V, Atkinson, PW, Walkington, I and Green, JA (2017) Predicting the impacts of wind farms on seabirds: An individual-based model. Journal of Applied Ecology, 55 (2). pp. 503-515. ISSN 0021-8901

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Open Access URL: https://doi.org/10.1111/1365-2664.12996 (Published version)

Abstract

Individual‐based models (IBMs) are a powerful tool in predicting the consequences of environmental change on animal populations and supporting evidence‐based decision making for conservation planning.
There are increasing proposals for wind farms in UK waters and seabirds are a vulnerable group, which may be at risk from these developments.
We developed a spatially explicit IBM to investigate the potential impacts of the installation of wind farms in the English Channel and North Sea on body mass, productivity and mortality of a breeding population of Northern gannets for which we have tracking data.
A baseline model with no wind farms accurately represented the status of a sample of tracked gannets at the end of the 90‐day chick‐rearing period, and the behaviour‐time budget was similar to that of tracked gannets.
Model simulations in the presence of wind farms indicated that installations should have little impact on the gannet population, when either avoidance behaviour or collision risk scenarios were simulated. Furthermore, wind farms would need to be ten times larger or in more highly used areas in order to have population‐level impacts on Alderney's gannets.
Synthesis and applications. Our spatially explicit individual‐based models (IBM) highlight that it is vital to know the colony‐specific foraging grounds of seabirds that may be impacted, when identifying potential wind farm sites, in order to account for cumulative impacts from multiple sites. Avoiding areas highly used for foraging and commuting, and avoiding large‐scale developments should be effective in limiting gannet mortality as a result of collision, competition and energy expenditure. Our IBM provides a robust approach which can be adapted for other seabird populations or to predict the impacts from other types of spatial change in the marine environment.

Item Type: Article
Uncontrolled Keywords: 0501 Ecological Applications, 0502 Environmental Science and Management, 0602 Ecology
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Q Science > QL Zoology
Divisions: Biological & Environmental Sciences (new Sep 19)
Publisher: Wiley
Date Deposited: 08 Oct 2019 10:07
Last Modified: 08 Oct 2019 10:07
DOI or Identification number: 10.1111/1365-2664.12996
Editors: Votier, S
URI: http://researchonline.ljmu.ac.uk/id/eprint/11488

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