We have derived within-windfarm avoidance rates for a variety of species for specific sites. In some cases, these differ from those presented elsewhere using, apparently, the same data (see Natural England/ JNCC note). For this reason, we include an appendix ( Appendix 7) detailing how each of our values has been derived. Note that the values in Appendix 7 are supplied for illustrative purposes only and that we would recommend the use of the total avoidance rates presented in Table 7.2. Given the variability in the values that have been presented for some datasets, we believe that this level of transparency is crucial to enable readers to come to an informed opinion as to what represents a robust avoidance rate. The derivation of the flux rate through the windfarm is particularly important, as it can have quite a strong influence on the predicted number of collisions, and therefore, the final avoidance rate.
Very little data were available describing separate meso-responses or micro-avoidance. There were limitations in the data from each of the studies we identified. However, observations of flight behaviour around individual turbines indicate that birds very rarely pass close to the rotor blades, suggesting that a significant proportion of avoidance behaviour is likely to occur at a meso-scale. We identified evidence from several sites to suggest that avoidance behaviour may be influenced by both the layout of the windfarm ( e.g. the inter-turbine spacing) and the operational status of turbines. There is some limited evidence to suggest that overall avoidance rates may be lower during the breeding season than the non-breeding season, although significantly more data are required to confirm this hypothesis (see section 184.108.40.206).
The availability of suitable data has been a key problem throughout this review. In part, this relates to the difficulty in collecting collision data at sea, leading to gaps in data for key species such as northern gannet and black-legged kittiwake. It is to be hoped that the ongoing ORJIP work will help to address this issue. However, it also relates to the way in which data have been collected as part of post-construction monitoring at offshore windfarms. We identified extremely limited evidence for macro-response rates for our priority species. In many instances, this may be because when impacts which may contribute to macro-avoidance, such as displacement or barrier effects, are considered, the focal species are usually auks, divers and sea-ducks. As a consequence, the impacts on other species, such as northern gannet are less well understood.
Our review highlights that there are still significant data gaps in relation to avoidance rates for marine birds and offshore windfarms, particularly in relation to micro- and meso-responses, as opposed to the correction factors often used as avoidance rates at present. Despite this, we feel that our review represents a significant step forward. We are able to recommend for the first time within-windfarm avoidance rates for gulls using both the basic Band (2012) model (options 1 and 2) and extended Band (2012) model (option 3) based on significantly more data than has been used to make recommendations for geese and raptors in the past ( e.g. Pendlebury 2006, Whitfield 2009). Significant data gaps still remain for within-windfarm avoidance behaviour in the northern gannet.
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