Abstract
Within the environmental impact assessment process for offshore wind farms (OWFs) and marine birds, legislation requires an understanding of the potential connectivity between designated protected populations (Special Protection Areas, SPAs) and OWFs, and the magnitude of potential impacts from specific effects, such as collision risk. At-sea survey data (e.g. boat or aerial surveys) forms the basis for assessing baseline spatial abundance and distribution of seabirds within a wind farm footprint and the surrounding area. Tagging birds from breeding colonies provides a complimentary method for estimating spatial abundance of birds of known provenance. To assess the impacts of offshore renewables upon SPAs for all types of data, it is necessary to estimate the percentage of birds that may originate from each SPA, termed apportioning. That way, the potential numbers of birds impacted by specific effects can be ascribed to SPAs through potential connective pathways. However, there are many methods of deriving such apportioning, and they vary by the type of data used at the outset and vary in complexity and assumptions used. These methods may also vary in application potential for specific bird species, and have also been the subject of specific workshops for targeted groups of species, such as gulls (e.g. Quinn 2019).
In general, apportioning relies on being able to estimate (a) the size of each breeding colony and (b) the spatial distribution (e.g. utilisation distribution; UD) of the birds from each colony, because the proportion of birds originating from each colony will be dependent on the product of the colony size and the estimated spatial distribution of birds from that colony. Apportioning methods differ largely based on the sources of data and statistical methods used to estimate colony-specific spatial distributions.
There are broadly five different approaches that are currently available:
a. Scottish Natural Heritage (SNH, now NatureScot) Apportioning Tool
b. Marine Scotland Science (MSS) Apportioning Tool
c. New methods using Global Positioning System (GPS) tracking data in a radial time-distance function approach
d. Biological Defined Meaningful Population Scales (BDMPS)
e. New methods for the non-breeding season based on light-level Geolocation (GLS) data