Use of multiple modes of flight subsidy by a soaring terrestrial bird, the golden eagle Aquila chrysaetos, when on migration

Experience reduces route selection for conspecifics by the collectively migrating white stork

Migration can be an energetically costly behavior with strong fitness consequences in terms of mortality and reproduction.1,2,3,4,5,6,7,8,9,10,11 Migrants should select migratory routes to minimize their costs, but both costs and benefits may change with experience.12,13,14 This raises the question of whether experience changes how individuals select their migratory routes. Here, we investigate the effect of age on route selection criteria in a collectively migrating soaring bird, the white stork (Ciconia ciconia). We perform step-selection analysis on a longitudinal dataset tracking 158 white storks over up to 9 years to quantify how they select their routes based on the social and atmospheric environments and to examine how this selection changes with age. We find clear ontogenetic shifts in route selection criteria. Juveniles choose routes that have good atmospheric conditions and high conspecific densities. Yet, as they gain experience, storks' selection on the availability of social information reduces-after their fifth migration, experienced birds also choose routes with low conspecific densities. Thus, our results suggest that as individuals age, they gradually replace information gleaned from other individuals with information gained from experience, allowing them to shift their migration timing and increasing the timescale at which they select their routes.

Built up areas in a wet landscape are stepping stones for soaring flight in a seabird

The energy exchange between the Earth's surface and atmosphere results in a highly dynamic habitat through which birds move. Thermal uplift is an atmospheric feature which many birds are able to exploit in order to save energy in flight, but which is governed by complex surface-atmosphere interactions. In mosaic landscapes consisting of multiple land uses, the spatial distribution of thermal uplift is expected to be heterogenous and birds may use the landscape selectively to maximise flight over areas where thermal soaring opportunities are best. Flight generalists such as the lesser black-backed gull, Larus fuscus, are expected to be less reliant on thermal uplift than obligate soaring birds. Nevertheless, gulls may select flight behaviours and routes in response to or in anticipation of thermal uplift in order to reduce their transport costs, even in landscapes where thermal uplift isn't prevalent. We explore thermal soaring over land in lesser black-backed gulls by using high-resolution GPS tracking to characterise individual instances of thermal soaring and detailed energy exchange modelling to map the thermal landscape which gulls experience. We determine that lesser black-backed gulls are regularly able to undertake thermal soaring, even in a wet temperate landscape below sea level. By examining the relationship between lesser black-backed gull flight, thermal uplift and land use, we determine that built up areas, particularly towns and cities, provide thermal uplift hotspots which lesser black-backed gulls preferentially make use of, resulting in more opportunities for energy saving flight through thermal soaring.

Assessing the accuracy of altitude estimates in avian biologging devices

Advances in animal biologging technologies have greatly improved our understanding of animal movement and distribution, particularly for highly mobile species that travel across vast spatial scales. Assessing the accuracy of these devices is critical to drawing appropriate conclusions from resulting data. While understanding the vertical dimension of movements is key to assessing habitat use and behavior in aerial species, previous studies have primarily focused on assessing the accuracy of biologging devices in the horizontal plane with far less emphasis placed on the vertical plane. Here we use an Unaccompanied Aircraft System (UAS) outfitted with a laser altimeter to broadly assess the accuracy of altitude estimates of three commonly used avian biologging devices during three field trials: stationary flights, continuous horizontal movements, and continuous vertical movements. We found that the device measuring barometric pressure consistently provided the most accurate altitude estimates (mean error of 1.57m) and effectively captured finer-scale vertical movements. Conversely, devices that relied upon GPS triangulation to estimate altitude typically overestimated altitude during horizontal movements (mean error of 6.5m or 40.96m) and underestimated amplitude during vertical movements. Additional factors thought to impact device accuracy, including Horizontal- and Position- Dilution of Precision and the time intervals over which altitude estimates were assessed, did not have notable effects on results in our analyses. Reported accuracy values for different devices may be useful in future studies of aerial species’ behavior relative to vertical obstacles such as wind turbines. Our results suggest that studies seeking to quantify altitude of aerial species should prioritize pressure-based measurements, which provide sufficient resolution for examining broad and some fine-scale behaviors. This work highlights the importance of considering and accounting for error in altitude measurements during avian studies relative to the scale of data needed to address particular scientific questions.

Demographic partitioning of dynamic energy subsidies revealed with an Ornstein-Uhlenbeck space use model

In populations across many taxa, a large fraction of sexually mature individuals do not breed but are attempting to enter the breeding population. Such individuals, often referred to as "floaters," can play critical roles in the dynamics and stability of these populations and buffer them through periods of high adult mortality. Floaters are difficult to study, however, so we lack data needed to understand their roles in the population ecology and conservation status of many species. Here, we analyzed satellite telemetry data with a newly developed mechanistic space use model based on an Ornstein-Uhlenbeck process to help overcome the paucity of data in studying the differential habitat selection and space use of floater and territorial golden eagles Aquila chrysaetos. Our sample consisted of 49 individuals tracked over complete breeding seasons across 4 years, totaling 104 eagle breeding seasons. Modeling these data mechanistically was required to disentangle key differences in movement and particularly to separate aspects of movement driven by resource selection from those driven by use of a central place. We found that floaters generally had more expansive space use patterns and larger home ranges, as well as evidence that they partition space with territorial individuals seemingly on fine scales through differential habitat and resource selection. Floater and territorial eagle home ranges overlapped markedly, suggesting that floaters use the interstices between territories. Furthermore, floater and territorial eagles differed in how they selected for uplift variables, key components of soaring birds' energy landscape, with territorial eagles apparently better able to find and use thermal uplift. We also found relatively low individual heterogeneity in resource selection, especially among territorial individuals, suggesting a narrow realized niche for breeding individuals, which varied from the level of among-individual variation present during migration. This work furthers our understanding of floaters' potential roles in the population ecology of territorial species and suggests that conserving landscapes occupied by territorial eagles also protects floaters.

A predictive flight-altitude model for avoiding future conflicts between an emblematic raptor and wind energy development in the Swiss Alps

Deployment of wind energy is proposed as a mechanism to reduce greenhouse gas emissions. Yet, wind energy and large birds, notably soaring raptors, both depend on suitable wind conditions. Conflicts in airspace use may thus arise due to the risks of collisions of birds with the blades of wind turbines. Using locations of GPS-tagged bearded vultures, a rare scavenging raptor reintroduced into the Alps, we built a spatially explicit model to predict potential areas of conflict with future wind turbine deployments in the Swiss Alps. We modelled the probability of bearded vultures flying within or below the rotor-swept zone of wind turbines as a function of wind and environmental conditions, including food supply. Seventy-four per cent of the GPS positions were collected below 200 m above ground level, i.e. where collisions could occur if wind turbines were present. Flight activity at potential risk of collision is concentrated on south-exposed mountainsides, especially in areas where ibex carcasses have a high occurrence probability, with critical areas covering vast expanses throughout the Swiss Alps. Our model provides a spatially explicit decision tool that will guide authorities and energy companies for planning the deployment of wind farms in a proactive manner to reduce risk to emblematic Alpine wildlife.

Classifying behavior from short-interval biologging data: An example with GPS tracking of birds

Recent advances in digital data collection have spurred accumulation of immense quantities of data that have potential to lead to remarkable ecological insight, but that also present analytic challenges. In the case of biologging data from birds, common analytical approaches to classifying movement behaviors are largely inappropriate for these massive data sets.We apply a framework for using K-means clustering to classify bird behavior using points from short time interval GPS tracks. K-means clustering is a well-known and computationally efficient statistical tool that has been used in animal movement studies primarily for clustering segments of consecutive points. To illustrate the utility of our approach, we apply K-means clustering to six focal variables derived from GPS data collected at 1-11 s intervals from free-flying bald eagles (Haliaeetus leucocephalus) throughout the state of Iowa, USA. We illustrate how these data can be used to identify behaviors and life-stage- and age-related variation in behavior.After filtering for data quality, the K-means algorithm identified four clusters in >2 million GPS telemetry data points. These four clusters corresponded to three movement states: ascending, flapping, and gliding flight; and one non-moving state: perching. Mapping these states illustrated how they corresponded tightly to expectations derived from natural history observations; for example, long periods of ascending flight were often followed by long gliding descents, birds alternated between flapping and gliding flight.The K-means clustering approach we applied is both an efficient and effective mechanism to classify and interpret short-interval biologging data to understand movement behaviors. Furthermore, because it can apply to an abundance of very short, irregular, and high-dimensional movement data, it provides insight into small-scale variation in behavior that would not be possible with many other analytical approaches.

Non-territorial GPS-tagged golden eagles Aquila chrysaetos at two Scottish wind farms: Avoidance influenced by preferred habitat distribution, wind speed and blade motion status

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.

Turbulence explains the accelerations of an eagle in natural flight

Turbulent winds and gusts fluctuate on a wide range of timescales from milliseconds to minutes and longer, a range that overlaps the timescales of avian flight behavior, yet the importance of turbulence to avian behavior is unclear. By combining wind speed data with the measured accelerations of a golden eagle (Aquila chrysaetos) flying in the wild, we find evidence in favor of a linear relationship between the eagle's accelerations and atmospheric turbulence for timescales between about 1/2 and 10 s. These timescales are comparable to those of typical eagle behaviors, corresponding to between about 1 and 25 wingbeats, and to those of turbulent gusts both larger than the eagle's wingspan and smaller than large-scale atmospheric phenomena such as convection cells. The eagle's accelerations exhibit power spectra and intermittent activity characteristic of turbulence and increase in proportion to the turbulence intensity. Intermittency results in accelerations that are occasionally several times stronger than gravity, which the eagle works against to stay aloft. These imprints of turbulence on the bird's movements need to be further explored to understand the energetics of birds and other volant life-forms, to improve our own methods of flying through ceaselessly turbulent environments, and to engage airborne wildlife as distributed probes of the changing conditions in the atmosphere.

Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds

Human modification of landscapes includes extensive addition of linear features, such as roads and transmission lines. These can alter animal movement and space use and affect the intensity of interactions among species, including predation and competition. Effects of linear features on animal movement have seen relatively little research in avian systems, despite ample evidence of their effects in mammalian systems and that some types of linear features, including both roads and transmission lines, are substantial sources of mortality. Here, we used satellite telemetry combined with step selection functions designed to explicitly incorporate the energy landscape (el-SSFs) to investigate the effects of linear features and habitat on movements and space use of a large soaring bird, the golden eagle Aquila chrysaetos, during migration. Our sample consisted of 32 adult eagles tracked for 45 spring and 39 fall migrations from 2014 to 2017. Fitted el-SSFs indicated eagles had a strong general preference for south-facing slopes, where thermal uplift develops predictably, and that these areas are likely important aspects of migratory pathways. el-SSFs also provided evidence that roads and railroads affected movement during both spring and fall migrations, but eagles selected areas near roads to a greater degree in spring compared to fall and at higher latitudes compared to lower latitudes. During spring, time spent near linear features often occurred during slower-paced or stopover movements, perhaps in part to access carrion produced by vehicle collisions. Regardless of the behavioural mechanism of selection, use of these features could expose eagles and other soaring species to elevated risk via collision with vehicles and/or transmission lines. Linear features have previously been documented to affect the ecology of terrestrial species (e.g. large mammals) by modifying individuals' movement patterns; our work shows that these effects on movement extend to avian taxa.

Functional Morphology of Gliding Flight I: Modeling Reveals Distinct Performance Landscapes Based on Soaring Strategies

The physics of flight influences the morphology of bird wings through natural selection on flight performance. The connection between wing morphology and performance is unclear due to the complex relationships between various parameters of flight. In order to better understand this connection, we present a holistic analysis of gliding flight that preserves complex relationships between parameters. We use a computational model of gliding flight, along with analysis by uncertainty quantification, to (1) create performance landscapes of gliding based on output metrics (maximum lift-to-drag ratio, minimum gliding angle, minimum sinking speed, and lift coefficient at minimum sinking speed) and (2) predict what parameters of flight (chordwise camber, wing aspect ratio [AR], and Reynolds number) would differ between gliding and nongliding species of birds. We also examine performance based on the soaring strategy for possible differences in morphology within gliding birds. Gliding birds likely have greater ARs than non-gliding birds, due to the high sensitivity of AR on most metrics of gliding performance. Furthermore, gliding birds can use two distinct soaring strategies based on performance landscapes. First, maximizing distance traveled (maximizing lift-to-drag ratio and minimizing gliding angle) should result in wings with high ARs and middling-to-low wing chordwise camber. Second, maximizing lift extracted from updrafts should result in wings with middling ARs and high wing chordwise camber. Following studies can test these hypotheses using morphological measurements.

Orographic lift shapes flight routes of gulls in virtually flat landscapes

Interactions between landscape and atmosphere result in a dynamic flight habitat which birds may use opportunistically to save energy during flight. However, their ability to utilise these dynamic landscapes and its influence on shaping movement paths is not well understood. We investigate the degree to which gulls utilise fine scale orographic lift created by wind deflected upwards over landscape features in a virtually flat landscape. Using accelerometer measurements and GPS tracking, soaring flight is identified and analysed with respect to orographic lift, modelled using high-resolution digital elevation models and wind measurements. The relationship between orographic lift and flight routes suggests gulls have advanced knowledge of their aerial surroundings and the benefits to be gained from them, even regarding small features such as tree lines. We show that in a landscape constantly influenced by anthropogenic change, the structure of our landscape has an aerial impact on flight route connectivity and costs.

Wind turbines cause functional habitat loss for migratory soaring birds

Wind energy production has expanded to meet climate change mitigation goals, but negative impacts of wind turbines have been reported on wildlife. Soaring birds are among the most affected groups with alarming fatality rates by collision with wind turbines and an escalating occupation of their migratory corridors. These birds have been described as changing their flight trajectories to avoid wind turbines, but this behaviour may lead to functional habitat loss, as suitable soaring areas in the proximity of wind turbines will likely be underused. We modelled the displacement effect of wind turbines on black kites (Milvus migrans) tracked by GPS. We also evaluated the impact of this effect at the scale of the landscape by estimating how much suitable soaring area was lost to wind turbines. We used state-of-the-art tracking devices to monitor the movements of 130 black kites in an area populated by wind turbines, at the migratory bottleneck of the Strait of Gibraltar. Landscape use by birds was mapped from GPS data using dynamic Brownian bridge movement models, and generalized additive mixed modelling was used to estimate the effect of wind turbine proximity on bird use while accounting for orographic and thermal uplift availability. We found that areas up to approximately 674 m away from the turbines were less used than expected given their uplift potential. Within that distance threshold, bird use decreased with the proximity to wind turbines. We estimated that the footprint of wind turbines affected 3%-14% of the areas suitable for soaring in our study area. We present evidence that the impacts of wind energy industry on soaring birds are greater than previously acknowledged. In addition to the commonly reported fatalities, the avoidance of turbines by soaring birds causes habitat losses in their movement corridors. Authorities should recognize this further impact of wind energy production and establish new regulations that protect soaring habitat. We also showed that soaring habitat for birds can be modelled at a fine scale using publicly available data. Such an approach can be used to plan low-impact placement of turbines in new wind energy developments.

Static landscape features predict uplift locations for soaring birds across Europe

Soaring flight is a remarkable adaptation to reduce movement costs by taking advantage of atmospheric uplifts. The movement pattern of soaring birds is shaped by the spatial and temporal availability and intensity of uplifts, which result from an interaction of local weather conditions with the underlying landscape structure. We used soaring flight locations and vertical speeds of an obligate soaring species, the white stork (Ciconia ciconia), as proxies for uplift availability and intensity. We then tested if static landscape features such as topography and land cover, instead of the commonly used weather information, could predict and map the occurrence and intensity of uplifts across Europe. We found that storks encountering fewer uplifts along their routes, as determined by static landscape features, suffered higher energy expenditures, approximated by their overall body dynamic acceleration. This result validates the use of static features as uplift predictors and suggests the existence of a direct link between energy expenditure and static landscape structure, thus far largely unquantified for flying animals. Our uplift availability map represents a computationally efficient proxy of the distribution of movement costs for soaring birds across the world's landscapes. It thus provides a base to explore the effects of changes in the landscape structure on the energy expenditure of soaring birds, identify low-cost movement corridors and ultimately inform the planning of anthropogenic developments.

Local meteorological conditions reroute a migration

For migrating animals, realized migration routes and timing emerge from hundreds or thousands of movement decisions made along migration routes. Local weather conditions along migration routes continually influence these decisions, and even relatively small changes in en route weather may cumulatively result in major shifts in migration patterns. Here, we analysed satellite tracking data to score a discrete navigation decision by a large migratory bird as it navigated a high-latitude, 5000 m elevation mountain range to understand how those navigational decisions changed under different weather conditions. We showed that wind conditions in particular areas along the migration pathway drove a navigational decision to reroute a migration; conditions encountered predictably resulted in migrants routing either north or south of the mountain range. With abiotic conditions continuing to change globally, simple decisions, such as the one described here, might additively emerge into new, very different migration routes.

Where eagles soar: Fine-resolution tracking reveals the spatiotemporal use of differential soaring modes in a large raptor

Unlike smaller raptors, which can readily use flapping flight, large raptors are mainly restricted to soaring flight due to energetic constraints. Soaring comprises of two main strategies: thermal and orographic soaring. These soaring strategies are driven by discrete uplift sources determined by the underlying topography and meteorological conditions in an area. High-resolution GPS tracking of raptor flight allows the identification of these flight strategies and interpretation of the spatiotemporal occurrence of thermal and orographic soaring. In this study, we develop methods to identify soaring flight behaviors from high-resolution GPS tracking data of Verreaux's eagle Aquila verreauxii and analyze these data to understand the conditions that promote the use of thermal and orographic soaring. We use these findings to predict the use of soaring flight both spatially (across the landscape) and temporally (throughout the year) in two topographically contrasting regions in South Africa. We found that topography is important in determining the occurrence of soaring flight and that thermal soaring occurs in relatively flat areas which are likely to have good thermal uplift availability. The predicted use of orographic soaring was predominately determined by terrain slope. Contrary to our expectations, the topography and meteorology of eagle territories in the Sandveld promoted the use of soaring flight to a greater extent than in territories in the more mountainous Cederberg region. Spatiotemporal mapping of predicted flight behaviors can broaden our understanding of how large raptors like the Verreaux's eagle use their habitat and how that links to energetics (as the preferential use of areas that maximize net energy gain is expected), reproductive success, and ultimately population dynamics. Understanding the fine-scale landscape use and environmental drivers of raptor flight can also help to predict and mitigate potential detrimental effects of anthropogenic developments, such as mortality via collision with wind turbines.

Relation between travel strategy and social organization of migrating birds with special consideration of formation flight in the northern bald ibis

A considerable proportion of the world's bird species undertake seasonal long-distance migrations. These journeys are energetically demanding. Two major behavioural means to reduce energy expenditure have been suggested: the use of thermal uplifts for a soaring-gliding migration style and travelling in echelon or V-shaped formation. Both strategies have immediate consequences for the social organization of the birds as they either cause large aggregations or require travelling in small and stable groups. Here, we first discuss those consequences, and second present an analysis of formation flight in a flock of northern bald ibis on their first southbound migration. We observe clear correlations between leading and trailing on the dyadic level but only a weak correlation on the individual level during independent flight and no convincing correlation during the human guided part of the migration. This pattern is suggestive of direct reciprocation as a means for establishing cooperation during formation flight. In general, we conclude that behavioural adaptations for dealing with physiological constraints on long-distance migrations either necessitate or ultimately foster formation of social groups with different characteristics. Patterns and social organization of birds travelling in groups have been elusive to study; however, new tracking technology-foremost lightweight GPS units-will provide more insights in the near future.This article is part of the themed issue 'Physiological determinants of social behaviour in animals'.

Decision-making by a soaring bird: time, energy and risk considerations at different spatio-temporal scales

Natural selection theory suggests that mobile animals trade off time, energy and risk costs with food, safety and other pay-offs obtained by movement. We examined how birds make movement decisions by integrating aspects of flight biomechanics, movement ecology and behaviour in a hierarchical framework investigating flight track variation across several spatio-temporal scales. Using extensive global positioning system and accelerometer data from Eurasian griffon vultures (Gyps fulvus) in Israel and France, we examined soaring-gliding decision-making by comparing inbound versus outbound flights (to or from a central roost, respectively), and these (and other) home-range foraging movements (up to 300 km) versus long-range movements (longer than 300 km). We found that long-range movements and inbound flights have similar features compared with their counterparts: individuals reduced journey time by performing more efficient soaring-gliding flight, reduced energy expenditure by flapping less and were more risk-prone by gliding more steeply between thermals. Age, breeding status, wind conditions and flight altitude (but not sex) affected time and energy prioritization during flights. We therefore suggest that individuals facing time, energy and risk trade-offs during movements make similar decisions across a broad range of ecological contexts and spatial scales, presumably owing to similarity in the uncertainty about movement outcomes.This article is part of the themed issue 'Moving in a moving medium: new perspectives on flight'.

Match between soaring modes of black kites and the fine-scale distribution of updrafts

Understanding how soaring birds use updrafts at small spatial scales is important to identify ecological constraints of movement, and may help to prevent conflicts between wind-energy development and the conservation of wildlife. We combined high-frequency GPS animal tracking and fine-spatial-scale uplift modelling to establish a link between flight behaviour of soaring birds and the distribution of updrafts. We caught 21 black kites (Milvus migrans) and GPS-tracked them while flying over the Tarifa region, on the Spanish side of the Strait of Gibraltar. This region has a diverse topography and land cover, favouring a heterogeneous updraft spatial distribution. Bird tracks were segmented and classified into flight modes from motion parameters. Thermal and orographic uplift velocities were modelled from publically available remote-sensing and meteorological data. We found that birds perform circular soaring in areas of higher predicted thermal uplift and linear soaring in areas of higher predicted orographic uplift velocity. We show that updraft maps produced from publically available data can be used to predict where soaring birds will concentrate their flight paths and how they will behave in flight. We recommend the use of this methodological approach to improve environmental impact assessments of new wind-energy installations.

Atmospheric conditions create freeways, detours and tailbacks for migrating birds

The extraordinary adaptations of birds to contend with atmospheric conditions during their migratory flights have captivated ecologists for decades. During the 21st century technological advances have sparked a revival of research into the influence of weather on migrating birds. Using biologging technology, flight behaviour is measured across entire flyways, weather radar networks quantify large-scale migratory fluxes, citizen scientists gather observations of migrant birds and mechanistic models are used to simulate migration in dynamic aerial environments. In this review, we first introduce the most relevant microscale, mesoscale and synoptic scale atmospheric phenomena from the point of view of a migrating bird. We then provide an overview of the individual responses of migrant birds (when, where and how to fly) in relation to these phenomena. We explore the cumulative impact of individual responses to weather during migration, and the consequences thereof for populations and migratory systems. In general, individual birds seem to have a much more flexible response to weather than previously thought, but we also note similarities in migratory behaviour across taxa. We propose various avenues for future research through which we expect to derive more fundamental insights into the influence of weather on the evolution of migratory behaviour and the life-history, population dynamics and species distributions of migrant birds.

Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds

Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.

Modeling Late-Summer Distribution of Golden Eagles (Aquila chrysaetos) in the Western United States

Increasing development across the western United States (USA) elevates concerns about effects on wildlife resources; the golden eagle (Aquila chrysaetos) is of special concern in this regard. Knowledge of golden eagle abundance and distribution across the western USA must be improved to help identify and conserve areas of major importance to the species. We used distance sampling and visual mark-recapture procedures to estimate golden eagle abundance from aerial line-transect surveys conducted across four Bird Conservation Regions in the western USA between 15 August and 15 September in 2006–2010, 2012, and 2013. To assess golden eagle-habitat relationships at this scale, we modeled counts of golden eagles seen during surveys in 2006–2010, adjusted for probability of detection, and used land cover and other environmental factors as predictor variables within 20-km² sampling units randomly selected from survey transects. We found evidence of positive relationships between intensity of use by golden eagles and elevation, solar radiation, and mean wind speed, and of negative relationships with the proportion of landscape classified as forest or as developed. The model accurately predicted habitat use observed during surveys conducted in 2012 and 2013. We used the model to construct a map predicting intensity of use by golden eagles during late summer across our ~2 million-km² study area. The map can be used to help prioritize landscapes for conservation efforts, identify areas where mitigation efforts may be most effective, and identify regions for additional research and monitoring. In addition, our map can be used to develop region-specific (e.g., state-level) density estimates based on the latest information on golden eagle abundance from a late-summer survey and aid designation of geographic management units for the species.