With life-history traits involving high survival, low reproductive output, years of natal dispersal and deferred maturity, the population ecology and behaviour of large raptors which occur at low densities can be difficult to study. The age at which large raptors first settle on a prospective breeding territory receives relatively little attention, but is a key metric in population modelling including, for example, projections of reintroduction projects. It can also be a barometer of the “health” of populations and the availability of breeding opportunities. The advancement of GPS-telemetry has proved invaluable in gaining insights into several aspects of large raptor ecology and behaviour. Age of first territory settlement (AFTS) is one such aspect. AFTS is important in modelling population trajectories and considering individuals’ lifetime reproductive success. We used an algorithm based on GPS-records from dispersing Golden Eagles tagged as nestlings in Scotland to estimate AFTS. While the lifespan of GPS-tags can bias against settlement dates of older birds, they can also potentially reveal settlement ages difficult or impossible to discern from other methods. We found a range of ages for AFTS, including those in their second calendar year; much younger than previously documented by other methods. Ground-truthing – when possible and if inevitably slightly delayed – confirmed territory occupation on field-based survey criteria. We found that eagles settled younger in vacant territories and when older in occupied existing territories. Birds’ sex had no effect on AFTS. Birds which dispersed earlier from their natal territory (indicative of “quality” from some previous research) had no association with AFTS. Our results indicate that within technological temporal limits GPS-data can provide for accurate and precise estimations of AFTS including early settlement not consistently or precisely recorded by other methods. Within our study’s variable competitive landscape we found that AFTS was associated with the availability of territorial opportunities but not with the timing of dispersal. These findings have consequences for studying and understanding large raptor population dynamics.

Many large raptors exploit or rely on anabatic and orographic winds which provide vertical lift, to supplement or provide the energy fuelling flight. Airspace is therefore a critical habitat for such large raptors and its use is subject to the underlying terrestrial topography, because particular topographical features are more likely to provide wind‐energetic lift. Accordingly, ridges and/or ‘rugged topography’ are common preferred features in habitat use by large raptors. Our study aimed to provide a simple model of space use for a large raptor, the Golden Eagle Aquila chrysaetos, based on thousands of GPS telemetry records during juvenile dispersal of 92 birds tagged as nestlings between 2007 and 2016 across upland Scotland. Model development was based on the hypothesis that four topographical variables would be influential: slope, aspect, altitude and distance from ridge. The telemetry dataset was divided into training and two testing components. The first testing set was derived by a temporal split resulting in approximately equal sample size on records and some temporal overlap in individuals’ records with training data. The second testing set involved no individuals from the training set. Aspect was removed early in training model development because it was not influential. The model found that young Golden Eagles preferred, or used according to availability, space above slopes greater than 10°, at an altitude of ≥ 300 m, and within 300 m of a ridge. The test data were highly correlated with those from the training data in the model variables, and performance as regard to expected preferences from the model was improved in both test datasets, indicating the model was robust. Given the apparent universal nature of large raptor dependence on topography, that topography is relatively immutable according to time and use, and that topographical data are readily available, we commend our approach to other habitat preference studies of Golden Eagles and other large raptors elsewhere.

Wind farms can 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. These effects may not be mutually exclusive. Using detailed data from 99 turbines at two wind farms in central Scotland and thousands of GPS-telemetry data from dispersing golden eagles, we tested three hypotheses. Before-and-after-operation analyses supported the hypothesis of avoidance: displacement was reduced at turbine locations in more preferred habitat and with more preferred habitat nearby. After-operation analyses (i.e. from the period when turbines were operational) showed that at higher wind speeds and in highly preferred habitat eagles were less wary of turbines with motionless blades: rejecting our second hypothesis. Our third hypothesis was supported, since at higher wind speeds eagles flew closer to operational turbines; especially–once more–turbines in more preferred habitat. After operation, eagles effectively abandoned inner turbine locations, and flight line records close to rotor blades were rare. While our study indicated that whole-wind farm functional habitat loss through avoidance was the substantial adverse impact, we make recommendations on future wind farm design to minimise collision risk further. These largely entail developers avoiding outer turbine locations which are in and surrounded by swathes of preferred habitat. Our study illustrates the insights which detailed case studies of large raptors at wind farms can bring and emphasises that the balance between avoidance and collision can have several influences.

Capsule: A complete survey of Golden Eagles Aquila chrysaetos in Britain in 2015 found that the population had increased by 15% since 2003 to 508 territorial pairs. Aims: The survey aimed to investigate the population size, distribution and breeding success of Golden Eagles in Britain, and to compare results with similar surveys since the early 1980s. Methods: Every home range was visited on a minimum of three occasions between January and August 2015. First, to look for eagles or signs of their presence (January-March), then to look for evidence of breeding or further checks for occupation (April-June) and finally to record productivity of nesting pairs (July-August). Results: The figure of 508 territorial pairs represents a 15% increase in the population from 442 pairs in 2003. The proportion of home ranges occupied was 70%. The largest increases in the proportion of occupied home ranges were in south-central Highlands (71%), northern moors and flows (38%) and northwest Highlands (29%), with modest increases of up to 10% in the other regions. Productivity was lower in 2015 than in 2003, and there was significant variation in breeding success between regions. Conclusion:The British Golden Eagle population has increased since 2003, although the species is absent from England and Wales. The population now meets the abundance target identified to define favourable conservation status in Scotland, and while home range occupancy has increased there is regional variation, with some regions falling below the target levels. A combination of increased annual monitoring and tagging of eagles, as well as the introduction of new legislation, may serve as effective deterrents against persecution of eagles thus facilitating this population increase. However, concerns remain over low levels of home range occupancy particularly in the east Highlands, but also the proportion of sub-adult pairs holding territory in that region and in the south-central Highlands. Persecution associated with grouse moor management has been highlighted as a particular population constraint in both of these areas.

Research on potentially adverse effects of wind farms is an expanding field of study and often focuses on large raptors, such as golden eagles, largely because of their life history traits and extensive habitat requirements. These features render them sensitive to either fatality (collision with turbine blades) or functional habitat loss (avoidance through wariness of turbines). Simplistically, avoidance is antagonistic to collision; although, the two processes are not necessarily mutually exclusive in risk. A bird that does not enter a wind farm or avoids flying close to turbines cannot collide with a blade and be killed. In the USA, collision fatality is implicated as the typical adverse effect. In Scotland, avoidance of functional habitat loss appears more likely, but this depends in part on the habitat suitability of turbine locations. Previous Scottish studies have largely concentrated on the responses of GPS-tagged non-territorial golden eagles during dispersal. Several arguments predict that territorial eagles may have lower avoidance (be less wary) of turbines than non-territorial birds. Hence, we contrasted the responses of GPS-tagged non-territorial (intruding) and territorial eagles to the same turbines at 11 operational Scottish wind farms. We show that territorial eagles rarely approached turbines, but, as in previous Scottish studies of non-territorial birds, the spatial extent of avoidance depended on the habitat suitability of both turbine locations and their wider surroundings. Unexpectedly, we found that territorial eagles were apparently as wary as intruding non-territorial conspecifics of the same turbines. Our results show that regardless of age or territorial status, Scottish golden eagles largely avoided wind turbine locations, but this avoidance was conditional, in part, on where those turbines were located. Responses to turbines were also strongly dependent on birds’ identities and different wind farms. We speculate on how widespread our findings of avoidance of turbines by golden eagles are elsewhere in Europe, where there appear to be no published studies showing the level of collision fatalities documented in the USA.