Optimizing Habitat Models as a Means for Resolving Environmental Barriers for Wind Farm Developments in the Marine Environment

Abstract

The recent, rapid growth of offshore wind energy has highlighted significant gaps in our ability to properly assess impacts on wildlife species and habitats. Despite the reported and conceived small and local impacts at small and medium-sized offshore wind farms, the experience with future large-scale wind farms may show otherwise. At the same time the industry now faces daunting logistic and scientific challenges as the construction sites move offshore both in relation to the assessment of the status of habitats and species, and in relation to the estimation of environmental effects. The key problems are lack of reliable models both of the distributional dynamics and of the habitat displacement and related impacts on populations of the species in question. This situation has hampered decision-making in relation to the management of the offshore wind energy sector by introducing unnecessary conflicts with conservation interests. As shown in this paper habitat models may offer solutions to many environmental barriers by providing data in high spatio-temporal resolution about the distribution of sensitive species. Detailed data about the distribution of sensitive species is required in order to:  Predict likely changes in distribution arising from natural dynamic change in the marine environment;  Evaluate more accurately the potential loss of habitat arising from exclusion (displacement) of priority and sensitive fauna from offshore wind farm areas as induced by disturbance and underwater noise emissions;  Assess the impact of cumulative habitat loss on priority and sensitive species arising from wind farm construction;  Avoid conflicts in future offshore wind energy schemes associated with environmentally sensitive areas. The programmes of biological sampling that are typically carried out for the offshore industry have documented problems associated with biological sampling in a dynamic environment. Even benthic habitats are not stable, and as the weather windows during which sampling of species and habitats is typically undertaken are relatively small interpretation and generalisation of results from baseline surveys is often constrained. Examples of such constraints are the lack of information on the distribution of food supply to higher trophic levels like birds, and the lack of information on the variation of habitats at