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Developing an avian collision risk model to incorporate variability and uncertainty


Scottish Marine and Freshwater Science Vol 6 No 14

The report describes the data required, and the methods used, to estimate collision risk. It is accompanied by a worked example and R code (available at, which enables the collision risk calculations to be performed in a standardised and reproducible way. In the UK, the most frequently used avian collision risk model is commonly known as ‘the Band model’ (Band, Madders & Whitfield 2007) and was originally conceived in 1995. Since then it has undergone several iterations with the most recent associated with the Strategic Ornithological Support Services (SOSS) (Band 2012a; b). The Band model (Band 2012b) provides four different options for calculating collision risk. • Option 1 - Basic model, i.e. assuming that a uniform distribution of flight heights between the lowest and the highest levels of the rotors and using the proportion of birds at risk height as derived from site survey. • Option 2 - Basic model, but using the proportion of birds at risk height as derived from a generic flight height distribution provided. • Option 3 - Extended model and using a generic flight height distribution. • Option 4 - Extended model and using a flight height distribution generated from site survey.

Masden, E. (2015) Developing an avian collision risk model to incorporate variability and uncertainty. Scottish Marine and Freshwater Science Vol 6 No 14. Edinburgh: Scottish Government, 43pp. DOI: 10.7489/1659-1
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Thursday, January 1, 2015 - 00:00 to Thursday, December 31, 2015 - 00:00
UK Open Government Licence (OGL)
Masden, E.
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The general purpose of this collision risk model update is to further develop the application of the Band model using a simulation approach to incorporate variability and uncertainty. In this report we refer to variability as the inherent heterogeneity of the environment and uncertainty as a lack of data or incomplete knowledge. The simulation model randomly samples from distributions for each of the model parameters and the simulations can then be used to derive average collision estimates, with associated confidence intervals. The model update will therefore allow for a better understanding of the uncertainty associated with the predicted collision impact of a wind farm development and provide confidence limits, something which has previously been absent. In addition, the incorporation of uncertainty would reduce the possibility that a collision estimate was driven by the choice of a single input parameter value. Ultimately, the update should aid streamlining of the planning/consenting stages of a development by providing information not only on the magnitude of collisions i.e. the number of collision events, but also the likelihood of that number of collisions occurring. In this model update, variability and uncertainty are considered together in combination, rather than separately. Some model input parameters will have associated variability, for example bird body length, others may be expected to be point estimates with associated uncertainty, such as turbine rotor radius, and some parameters may have both variability and uncertainty. Ideally it would be possible to differentiate between variability and uncertainty but at present this is not possible due to a lack of data. However, including variability and uncertainty in combination in the model still provides a significant step forward.

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Marine Scotland Science
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