Responses of dispersing GPS-tagged Golden Eagles (Aquila chrysaetos) to multiple wind farms across Scotland - Fielding, Alan H.; Anderson, David; Benn, Stuart; Dennis, Roy; Geary, Matthew; Weston, Ewan; Whitfield, Phil (Ibis, 2021)
This is the peer reviewed version of the following article: Fielding, A. H., Anderson, D., Benn, S., Dennis, R., Geary, M., Weston, E., & Whitfield, D. P. (2022). Responses of dispersing GPS‐tagged Golden Eagles (Aquila chrysaetos) to multiple wind farms across Scotland. Ibis, 164(1), 102-117. https://doi.org/10.1111/ibi.12996, which has been published in final form at https://onlinelibrary.wiley.com/doi/10.1111/ibi.12996. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving Wind farms may have two broad potential adverse effects on birds via antagonistic processes: displacement from the vicinity of turbines (avoidance), or death through collision with rotating turbine blades. Large raptors are often shown or presumed to be vulnerable to collision and are demographically sensitive to additional mortality, as exemplified by several studies of the Golden Eagle Aquila chrysaetos. Previous findings from Scottish Eagles, however, have suggested avoidance as the primary response. Our study used data from 59 GPS-tagged Golden Eagles with 28 284 records during natal dispersal before and after turbine operation < 1 km of 569 turbines at 80 wind farms across Scotland. We tested three hypotheses using measurements of tag records’ distance from the hub of turbine locations: (1) avoidance should be evident; (2) older birds should show less avoidance (i.e. habituate to turbines); and (3) rotor diameter should have no influence (smaller diameters are correlated with a turbine’s age, in examining possible habituation). Four generalized linear mixed models (GLMMs) were constructed with intrinsic habitat preference of a turbine location using Golden Eagle Topography (GET) model, turbine operation status (before/after), bird age and rotor diameter as fixed factors. The best GLMM was subsequently verified by k-fold cross-validation and involved only GET habitat preference and presence of an operational turbine. Eagles were eight times less likely to be within ...