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Scottish Marine and Freshwater Science Reports

Formal report series, containing results of research and monitoring carried out by Marine Scotland Science


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Attributing seabirds at sea to appropriate breeding colonies and populations (CR/2015/18)

Scottish Marine and Freshwater Science Vol 11 No 8
The Scottish Government has set a target of 100% of Scottish demand for electricity to be met by renewable sources by 2020. Offshore renewables have the potential to make a significant contribution to achieving this target. However, the Scottish Government has a duty to ensure that offshore renewable developments (ORDs) are achieved in a sustainable manner, by protecting habitats and species from adverse impacts.

ORDs may negatively affect seabirds, in particular due to collisions with turbine blades, displacement to less favourable habitats and barrier effects to movement. In assessing the potential effects of ORDs on the environment the potential for impacts on protected populations for seabirds is assessed.

To inform environmental assessments ORD developers conduct at sea surveys of seabirds. Most seabirds at sea data are collected during surveys from ships or planes, but the breeding colony a seabird has come from cannot be identified from these data. It is important to understand which breeding colonies birds at sea come from, to ensure that the impacts upon the appropriate colony can be assessed as part of the licensing process. The process by which the origin colonies of seabirds at sea is identified is called apportioning.

In a project led by the UK Centre for Ecology & Hydrology a new tool was developed that built upon an existing tool that attributes seabirds at sea to specific breeding colonies. By comparing several new approaches to analysing seabirds at sea data new improved methods were developed. The resulting apportioning tool allows users to produce a more accurate estimate of the relative proportions of seabirds present in a specific location that can be attributed to different breeding colonies.

This research is part of the Scottish Marine Energy Research Programme (ScotMER)

Butler, A., Carroll, M., Searle, K., Bolton, M., Waggitt, J., Evans, P., Rehfisch, M., Goddard, B., Brewer, M., Burthe, S. and Daunt, F. 2020. Attributing seabirds at sea to appropriate breeding colonies and populations (CR/2015/18). Scottish Marine and Freshwater Science Vol 11 No 8, 140pp. DOI: 10.7489/2006-1
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Spatial / Geographical Coverage Location
Temporal Coverage
2015-01-01 to 2017-12-31
UK Open Government Licence (OGL)
Data Dictionary

We implemented four different statistical methods for apportioning birds to breeding colonies. The four methods include the existing approach that is currently used in practice (the “SNH tool”) and three novel approaches based upon statistical modelling of GPS data.

The first of these novel approaches (“WAKE”) derived the apportioning percentages associated with a statistical model (Wakefield et al., 2017) which describes the utilisation distribution of birds from a particular colony in terms of variables relating to accessibility, competition and environmental effects, and which can be used to predict the utilisation distribution of birds originating from each breeding colony in the British Isles.

Wakefield et al. (2017) used colony size data derived from the Seabird 2000 census. The second novel approach (“UCC”) is similar to the first, but revised the calculations to use more recent colony size data, where available (and to impute more recent colony sizes in situations where data were not available).

Wakefield et al. (2017) only considered breeding birds. The third novel approach (“BNB”) extends this, by using spatial survey data (both at-sea and aerial) to estimate the distribution of non-breeding as well as breeding birds, and thereby to calculate the apportioning percentages associated with all birds (whether breeding or non-breeding).

The BNB model for kittiwake and razorbill estimated the distribution of breeding and non-breeding birds to be identical. This may either suggest that the distributions are genuinely similar, or that the data are insufficiently informative to be able to detect differences between the distributions. Therefore, the WAKE and BNB approaches only provide different results for guillemot.

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Marine Scotland
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