The physical characteristics of the environment determine the energetic cost of transport for animals, defining their ‘energy landscape’ (Wilson et al., 2012; Shepard et al., 2013). Consequently, temporal dynamics in environmental conditions can affect the inherently spatially explicit energy landscapes across time. For example, patterns of thawing and freezing of lakes and rivers shape the migratory routes of Caribou, as walking on ice is energetically preferable to swimming (Leblond et al., 2016). Variations of the energy landscape along the temporal axis are most notable for animals that move in moving media: water or air. Sharks adjust their core habitat use to make the most of temporary updrafts formed by tidal currents colliding with upward-facing slopes, which help them reduce the energetic costs of remaining buoyant (Papastamatiou et al., 2021). Temporal variations in atmospheric conditions shape hourly (Shepard et al., 2013) and seasonally (Nourani et al., 2016) optimal flight routes for birds.

The spatio-temporal variation in the energy landscape is predictable to a great extent. Animals reduce and thus optimize the cost of transport by tracking these changes as they move through the energy landscapes. As such, the distribution and movement patterns of animals can be largely explained by the energy landscape (Shepard and Lambertucci, 2013), making energy landscapes useful for understanding ecological and evolutionary processes. This knowledge is increasingly important for policymaking to reduce human–wildlife conflicts, particularly for development of infrastructures (Péron et al., 2017; Scacco et al., 2023), and more generally in conservation planning (Berti et al., 2023). Based on the timing and location of movement, the energy landscape can be specific yet predictable for animals with similar morphology and movement modes, at the guild (Shepard et al., 2013; Masello et al., 2021), species (Scacco et al., 2023), or sub-species (Nourani et al., 2020) levels.

To date, our understanding of animals’ interaction with the energy landscape has depended on factors extrinsic to the moving animal. Reconstructing the energy landscape solely based on the characteristics of the environment can be conceptualized as similar to Hutchinson’s concept of the fundamental ecological niche (Hutchinson, 1957; Colwell and Rangel, 2009). Hutchinson defined the fundamental niche as the full range of environmental conditions in which a species can potentially exist and reproduce. Here, we argue that the energy landscape too can, or possibly should, be conceptualized as the fundamental movement niche, defining the full range of physical environmental conditions within which a species can move while adhering to its movement-related energy budget or sustainable cost of transport. However, just as animals do not occupy the entirety of their fundamental Hutchinsonian niche in reality (Colwell and Rangel, 2009), for example, due to competition or predation risk, various factors can contribute to an animal not having access to the entirety of its fundamental movement niche. Our current understanding of the energy landscape lacks a differentiation between a fundamental and a realized energy landscape.

An animal’s realized energy landscape should depend on the motive to move and its internal state. For example, an animal’s decision-making when moving might be the outcome of its strategy to maximize long-term fitness rather than short-term energy savings. Halsey, 2016 introduced the concept of ‘individual energy landscapes’, stating that the energy landscape fluctuates, for example, when an animal increases its speed to out-run a predator (Halsey, 2016) or when infection or illness reduces an animal’s movement capacity (Binning et al., 2017; Risely et al., 2018). As a consequence, the realized energy landscape varies in time from the perspective of the moving animal, sometimes irrespective of, but in interaction with and as a consequence of, the immediate environment.

We would like to argue that the animals’ ability to exploit the energy landscape is transformed across one of the most fundamental progressions in all animals’ lives: the ontogenetic axis. In addition to morphological changes, as young animals progress through their developmental stages, their movement proficiency (Corbeau et al., 2020) and cognitive capabilities (Fuster, 2002) improve and memory manifests (Ramsaran et al., 2019). Cognitive capabilities allow the animal to improve the perception of its environment and make optimal decisions when choosing energy-efficient paths (Harten et al., 2020; Abrahms et al., 2021). Improved motor skills enable the animal to respond more appropriately to the perceived environment to utilize the maximum available energy and/or to avoid areas expensive to traverse (Scott et al., 2014; Sergio et al., 2014; Harel et al., 2016; Sergio et al., 2022). Finally, memory allows the animal to recall past combinations of space and environmental conditions that affected its costs of movement (Abrahms et al., 2019). Developmental changes and improvements in all these elements should be reflected in the animal’s realized energy landscape, where hotspots of energy gain within the landscape expand as young animals gain experience and master their specific movement behaviors.

We investigated the demographic shift in the realized energy landscape by gaining experience as a movement specialist, the golden eagle Aquila chrysaetos. As obligate soaring birds, golden eagles utilize ascending air currents, known as uplifts, to gain vertical altitude, which subsequently enables them to glide for horizontal displacement, without relying on energetically expensive wing flapping. Soaring flight is a learned and acquired behavior (Harel et al., 2016; Ruaux et al., 2020), requiring advanced cognitive skills to locate uplifts as well as fine-tuned locomotor skills for optimal adjustment of the body and wings to extract the most energy from them, for example, by adopting high bank angles in narrow and weak thermals (Williams et al., 2018a) and reducing gliding airspeed when the next thermal has not been detected (Williams et al., 2018b). This flight mode enables soaring birds to cover long distances with minimal energy investments, almost as low as when resting (Duriez et al., 2014). We approach the energy landscape concept with a focus on energy availability. This is commonly done for soaring birds, as uplift and wind support can directly provide energy for flying birds (Nourani et al., 2020; Scacco et al., 2019).

We analyzed 46,000 hr of flight data collected from bio-logging devices attached to 55 wild-ranging golden eagles in the Central European Alps. These data covered the transience phase of natal dispersal (hereafter post-emigration). In this population, juveniles typically achieve independence by emigrating from the parental territory within 4–10 months after fledging. However, due to the high density of eagles and consequently the scarcity of available territories, the transience phase between emigration and settling by eventually winning over a territory is exceptionally long at well over 4 years. Our hypothesis posited that the realized energy landscape during this transience phase gradually expands as the birds age. More specifically, we expected the habitat to become progressively cheaper to traverse as the birds aged, resulting in an expansion of flyable areas within the Alpine region as a consequence of gaining experience and flight proficiency.

We explored the movement decisions of 55 juvenile golden eagles in the Central European Alps during their post-emigration commuting flights (i.e., nonstop flight bouts lasting at least 1 hr) from the first week until 3 years after emigration. Following a step-selection approach (Avgar et al., 2016), at each movement step, we compared the observed step to 50 alternative steps that were available to each bird in space and time. These steps were compared with respect to the topography of the terrain, specifically Topographic Ruggedness Index (TRI) and distance to ridge lines, both useful proxies for occurrence of uplifts (Scacco et al., 2019; Murgatroyd et al., 2018). The resulting step-selection model distinguishes between the used and available conditions with a good predictive performance based on the normalized root mean squared error (RMSE = 0.14). Overall, the birds preferred to fly over areas with high potential for uplift formation, characterized by high values of TRI and lower distance to ridge lines (Figure 1). The birds also preferred to fly with long step lengths, a trend that increased with age. As the birds grew older, they were less likely to avoid areas with lower TRI and higher distance to ridge lines (Figure 2). We found individual variation in the coefficients for TRI and distance to ridge line (Figure 1—figure supplement 1).

Figure 1 with 1 supplement see all

Download asset Open asset

Figure 2

Download asset Open asset

We used the step-selection model to predict the flyability of the Alpine region for each developmental stage (week since emigration). This flyability index quantified the potential energy that a bird could obtain from its surroundings by considering topography and the bird’s movement. This index serves as a measure of the suitability of a location for energy-efficient flight, reflecting the bird’s ability to exploit favorable uplift conditions. We constructed energy landscapes for the juvenile golden eagles for each week since independence by predicting the flyability within the Alpine region. Based on these predictions, we determined the flyable area for the juvenile and immature golden eagles during the developmental phase at weekly increments. We defined flyable areas as the cells within a 100 * 100 m grid with a flyability value larger than 0.7. Our analysis shows that hotspots of energy availability in the birds’ landscape expanded over time (Figure 3, Video 1). We found that the flyable area increased 2170-fold from the first week until 3 years after emigration. From an initial 0.038% in the first week after emigration, the flyable area followed a logistic growth curve to plateau at 81% of the entire Alpine region becoming flyable in the third year (Figure 4). The tracking data indicated that the golden eagles flew over an area of 92,000 km² by the third year after emigration (Figure 4—figure supplement 1).

Figure 3

Download asset Open asset

Figure 4 with 1 supplement see all

Download asset Open asset

Video 1

Download asset

We show that the flyable area for juvenile golden eagles increased during the developmental period. This increase, we argue, is most likely linked to the birds’ improving ability to negotiate their atmospheric environment better as they aged due to an increasing capacity to perceive and exploit the energy available in the landscape. Because we estimated flyability based on proxies of uplift formation, an important component shaping the energy availability landscape for soaring birds (Nourani et al., 2020; Scacco et al., 2019), the gradual increase in flyable areas can be interpreted as a change in the realized energy landscape.

Our findings assert the concept of ‘individual energy landscapes’. Initially described by Halsey, 2016, this concept suggests that the energy expenditure of an animal when moving is more complex than the cost of transport as determined solely by its morphology and the external environment. The locomotor decisions that an animal makes to maximize its fitness at any given point in time would also affect its energy expenditure. We present evidence that ontogeny also serves as a potential mechanism giving rise to individual energy landscapes not only due to its impact on decision-making, but also due to the influence it exerts on the available options for the animal. During the early stages of their development, animals may not possess the full capacity to perceive their environment, as they will in later stages (Harten et al., 2020; Abrahms et al., 2021). Additionally, their motor skills are still being honed (Harel et al., 2016). Consequently, younger individuals may respond less efficiently to the environmental conditions they must navigate, in contrast to older, more proficient individuals.

By relying on static topographic variables to build the energy landscapes, we ensured that the environment remained constant over time. Juvenile golden eagles complete their morphological development before gaining independence from their parents, with their size and wing morphology remaining stable during the post-emigration phase (Bortolotti, 1984; Katzner et al., 2020). Consequently, variations in flyability of the landscape for these birds predominantly reflect their improved mastery of soaring flight, rather than changes in their morphology. Our findings also reveal that as the eagles aged, they adopted longer step lengths, which could indicate an increasing ability to sustain longer uninterrupted flight bouts.

Furthermore, our results provide insight into the development of different flight modes. These birds can switch between orographic and thermal soaring (Katzner et al., 2015). Orographic soaring refers to the utilization of upward air currents generated when wind encounters and flows over elevated topographic features such as hills, mountains, or cliffs. Locating orographic uplifts requires less experience, as they form predictably at mountain ridges (Bohrer et al., 2012; Duerr et al., 2012), compared to locating and using more transient thermals which form less predictably over flat surfaces (Scacco et al., 2019). Our findings suggest that with increasing age, golden eagles place decreasing importance on ruggedness and proximity to ridge lines when deciding where to fly, indicating a learning progression from mastering orographic uplifts before tackling the more challenging thermal soaring.

The energy landscapes maps showed the amount of energy potentially available to juvenile golden eagles during progressing weeks since emigration (Video 1). These maps are based on a step-selection model built on the birds’ empirical use of the landscape, the energy availability thus represents the energy that would be theoretically perceivable and exploitable by the birds in a specific developmental phase or age. The detailed perspective on an energy landscape which transforms over time with the accumulation of experience highlights the process of changes in the realized movement niche that would be available to the animals at a certain age. These changes in the ability to extract energy from the atmospheric column will induce age-specific consequences on movement capacity and the cost of transport.

We argue that the realized energy landscape does not represent the realized ecological niche nor the distribution range of animals, but rather the subset of the fundamental energy landscape that is available to the individual based on its ability to perceive and exploit the energy in the landscape. In our study, the realized movement patterns of the golden eagles within the Alpine region covered a small portion of the area predicted as flyable. This is because the birds’ movement (and thus their realized individual and age-specific movement niches) will be defined not only by the energy landscape, but also by the interplay of philopatry, developmental requirements, the social landscape, and anthropogenic features. As golden eagles age, other crucial behaviors such as transitioning from predominantly scavenging to active hunting are also happening (Nygård et al., 2016). A transition that in itself also might depend on advanced flying skills. Thus, the choice of where to fly will not solely follow an energy-minimizing strategy while moving, but naturally also increasingly accommodate resource use opportunities. Additionally, older transient golden eagles are more susceptible to potentially deadly conflicts with territorial adult birds, influencing their movement decisions as they avoid established territories (Poessel et al., 2016) affecting their landscape of fear and thus their decision landscape overall (Laundré et al., 2001; Gallagher et al., 2017; Williams and Safi, 2021). The distribution of anthropogenic features could also impact the eagles’ decision-making on where to fly (Tack et al., 2020). The strong individual variation that we observed in the birds’ response to distance to ridge lines (Figure 1—figure supplement 1) could indicate variable landscape utilization strategies among the juvenile golden eagles in response to these factors and their interaction.

All data needed to replicate the findings of this article can be accessed via an Edmond repository 'Energy landscape Ontogeny' (https://doi.org/10.17617/3.FM4EJC). The code for data preparation, analysis, and plotting can be found in the GithHub repository 'golden_eagle_energy_landscape' (https://github.com/mahle68/golden_eagle_energy_landscape/, copy archived at Nourani, 2024).

1. 1. Nourani E

   (2023) Edmond

   Energy landscape ontogeny.

   https://doi.org/10.17617/3.FM4EJC

1. 1. Abrahms B
   2. Hazen EL
   3. Aikens EO
   4. Savoca MS
   5. Goldbogen JA
   6. Bograd SJ
   7. Jacox MG
   8. Irvine LM
   9. Palacios DM
   10. Mate BR

   (2019) Memory and resource tracking drive blue whale migrations

   PNAS 116:5582–5587.

   https://doi.org/10.1073/pnas.1819031116

   • PubMed
   • Google Scholar
2. 1. Abrahms B
   2. Teitelbaum CS
   3. Mueller T
   4. Converse SJ

   (2021) Ontogenetic shifts from social to experiential learning drive avian migration timing

   Nature Communications 12:7326.

   https://doi.org/10.1038/s41467-021-27626-5

   • PubMed
   • Google Scholar
3. 1. Avgar T
   2. Potts JR
   3. Lewis MA
   4. Boyce MS

   (2016) Integrated step selection analysis: Bridging the gap between resource selection and animal movement

   Methods in Ecology and Evolution 7:619–630.

   https://doi.org/10.1111/2041-210X.12528

   • Google Scholar
4. 1. Berti E
   2. Rosenbaum B
   3. Brose U
   4. Vollrath F

   (2023) Energy landscapes direct the movement preferences of elephants

   Authorea 01:e39/v1.

   https://doi.org/10.22541/au.168373276.62196439/v1

   • Google Scholar
5. 1. Binning SA
   2. Shaw AK
   3. Roche DG

   (2017) Parasites and host performance: Incorporating infection into our understanding of animal movement

   Integrative and Comparative Biology 57:267–280.

   https://doi.org/10.1093/icb/icx024

   • PubMed
   • Google Scholar
6. 1. Bohrer G
   2. Brandes D
   3. Mandel JT
   4. Bildstein KL
   5. Miller TA
   6. Lanzone M
   7. Katzner T
   8. Maisonneuve C
   9. Tremblay JA

   (2012) Estimating updraft velocity components over large spatial scales: Contrasting migration strategies of golden eagles and turkey vultures

   Ecology Letters 15:96–103.

   https://doi.org/10.1111/j.1461-0248.2011.01713.x

   • PubMed
   • Google Scholar
7. Software

   1. Bolker B
   2. Warnes GR
   3. Lumley T

   (2022) Package gtools: R package “gtools” version, version 3.9.5

   CRAN.

   https://cran.r-project.org/web/packages/gtools/index.html
8. 1. Bortolotti GR

   (1984)

   Age and sex size variation in golden eagles

   Journal of Field Ornithology 55:54–66.

   • Google Scholar
9. 1. Bracis C
   2. Bildstein KL
   3. Mueller T

   (2018) Revisitation analysis uncovers spatio‐temporal patterns in animal movement data

   Ecography 41:1801–1811.

   https://doi.org/10.1111/ecog.03618

   • Google Scholar
10. 1. Brooks M
    2. Kristensen K
    3. Benthem K
    4. Magnusson A
    5. Berg C
    6. Nielsen A
    7. Skaug H
    8. Mächler M
    9. Bolker B

    (2017) glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling

    The R Journal 9:378.

    https://doi.org/10.32614/RJ-2017-066

    • Google Scholar
11. 1. Colwell RK
    2. Rangel TF

    (2009) Hutchinson’s duality: the once and future niche

    PNAS 106 Suppl 2:19651–19658.

    https://doi.org/10.1073/pnas.0901650106

    • PubMed
    • Google Scholar
12. 1. Conrad O
    2. Bechtel B
    3. Bock M
    4. Dietrich H
    5. Fischer E
    6. Gerlitz L
    7. Wehberg J
    8. Wichmann V
    9. Böhner J

    (2015) System for Automated Geoscientific Analyses (SAGA) v. 2.1.4

    Geoscientific Model Development 8:1991–2007.

    https://doi.org/10.5194/gmd-8-1991-2015

    • Google Scholar
13. 1. Corbeau A
    2. Prudor A
    3. Kato A
    4. Weimerskirch H

    (2020) Development of flight and foraging behaviour in a juvenile seabird with extreme soaring capacities

    The Journal of Animal Ecology 89:20–28.

    https://doi.org/10.1111/1365-2656.13121

    • PubMed
    • Google Scholar
14. 1. Das A
    2. Nagendra H
    3. Anand M
    4. Bunyan M

    (2015) Topographic and bioclimatic determinants of the occurrence of forest and grassland in tropical montane forest-grassland mosaics of the Western Ghats, India

    PLOS ONE 10:e0130566.

    https://doi.org/10.1371/journal.pone.0130566

    • PubMed
    • Google Scholar
15. 1. Duerr AE
    2. Miller TA
    3. Lanzone M
    4. Brandes D
    5. Cooper J
    6. O’Malley K
    7. Maisonneuve C
    8. Tremblay J
    9. Katzner T

    (2012) Testing an emerging paradigm in migration ecology shows surprising differences in efficiency between flight modes

    PLOS ONE 7:e35548.

    https://doi.org/10.1371/journal.pone.0035548

    • PubMed
    • Google Scholar
16. 1. Duriez O
    2. Kato A
    3. Tromp C
    4. Dell’Omo G
    5. Vyssotski AL
    6. Sarrazin F
    7. Ropert-Coudert Y

    (2014) How cheap is soaring flight in raptors? A preliminary investigation in freely-flying vultures

    PLOS ONE 9:e84887.

    https://doi.org/10.1371/journal.pone.0084887

    • PubMed
    • Google Scholar
17. 1. Fuster JM

    (2002) Frontal lobe and cognitive development

    Journal of Neurocytology 31:373–385.

    https://doi.org/10.1023/a:1024190429920

    • PubMed
    • Google Scholar
18. 1. Gallagher AJ
    2. Creel S
    3. Wilson RP
    4. Cooke SJ

    (2017) Energy landscapes and the landscape of fear

    Trends in Ecology & Evolution 32:88–96.

    https://doi.org/10.1016/j.tree.2016.10.010

    • Google Scholar
19. 1. Garriga J
    2. Palmer JRB
    3. Oltra A
    4. Bartumeus F

    (2016) Expectation-maximization binary clustering for behavioural annotation

    PLOS ONE 11:e0151984.

    https://doi.org/10.1371/journal.pone.0151984

    • PubMed
    • Google Scholar
20. 1. Halsey LG

    (2016) Terrestrial movement energetics: Current knowledge and its application to the optimising animal

    The Journal of Experimental Biology 219:1424–1431.

    https://doi.org/10.1242/jeb.133256

    • PubMed
    • Google Scholar
21. 1. Harel R
    2. Horvitz N
    3. Nathan R

    (2016) Adult vultures outperform juveniles in challenging thermal soaring conditions

    Scientific Reports 6:27865.

    https://doi.org/10.1038/srep27865

    • PubMed
    • Google Scholar
22. 1. Harten L
    2. Katz A
    3. Goldshtein A
    4. Handel M
    5. Yovel Y

    (2020) The ontogeny of a mammalian cognitive map in the real world

    Science 369:194–197.

    https://doi.org/10.1126/science.aay3354

    • PubMed
    • Google Scholar
23. 1. Hutchinson GE

    (1957) Concluding remarks in cold spring harbor symposia on quantitative biology 22

    Scientific Research 1:415–427.

    https://doi.org/10.1101/SQB.1957.022.01.039

    • Google Scholar
24. 1. Ilia I
    2. Rozos D
    3. Koumantakis I

    (2016) Landform classification using GIS techniques. The case of Kimi municipality area, Euboea Island, Greece

    Bulletin of the Geological Society of Greece 47:264.

    https://doi.org/10.12681/bgsg.10940

    • Google Scholar
25. 1. Katzner TE
    2. Turk PJ
    3. Duerr AE
    4. Miller TA
    5. Lanzone MJ
    6. Cooper JL
    7. Brandes D
    8. Tremblay JA
    9. Lemaître J

    (2015) Use of multiple modes of flight subsidy by a soaring terrestrial bird, the golden eagle Aquila chrysaetos, when on migration

    Journal of the Royal Society, Interface 12:20150530.

    https://doi.org/10.1098/rsif.2015.0530

    • PubMed
    • Google Scholar
26. Book

    1. Katzner TE
    2. Kochert MN
    3. Steenhof K
    4. McIntyre CL
    5. Craig EH
    6. Miller TA

    (2020) Birds of the World

    Ithaca, NY, USA: Cornell Lab of Ornithology.

    https://doi.org/10.2173/bow.goleag.02

    • Google Scholar
27. 1. Laundré JW
    2. Hernández L
    3. Altendorf KB

    (2001) Wolves, elk, and bison: Reestablishing the “landscape of fear” in Yellowstone National Park, U.S.A

    Canadian Journal of Zoology 79:1401–1409.

    https://doi.org/10.1139/z01-094

    • Google Scholar
28. 1. Leblond M
    2. St-Laurent MH
    3. Côté SD

    (2016) Caribou, water, and ice - fine-scale movements of a migratory arctic ungulate in the context of climate change

    Movement Ecology 4:14.

    https://doi.org/10.1186/s40462-016-0079-4

    • PubMed
    • Google Scholar
29. 1. Lüdecke D
    2. Ben-Shachar MS
    3. Patil I
    4. Waggoner P
    5. Makowski D

    (2021) Performance: An r package for assessment, comparison and testing of statistical models

    Journal of Open Source Software 6:3139.

    https://doi.org/10.21105/joss.03139

    • Google Scholar
30. 1. Masello JF
    2. Barbosa A
    3. Kato A
    4. Mattern T
    5. Medeiros R
    6. Stockdale JE
    7. Kümmel MN
    8. Bustamante P
    9. Belliure J
    10. Benzal J
    11. Colominas-Ciuró R
    12. Menéndez-Blázquez J
    13. Griep S
    14. Goesmann A
    15. Symondson WOC
    16. Quillfeldt P

    (2021) How animals distribute themselves in space: Energy landscapes of Antarctic avian predators

    Movement Ecology 9:24.

    https://doi.org/10.1186/s40462-021-00255-9

    • PubMed
    • Google Scholar
31. 1. Muff S
    2. Signer J
    3. Fieberg J

    (2020) Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation

    The Journal of Animal Ecology 89:80–92.

    https://doi.org/10.1111/1365-2656.13087

    • PubMed
    • Google Scholar
32. 1. Murgatroyd M
    2. Photopoulou T
    3. Underhill LG
    4. Bouten W
    5. Amar A

    (2018) Where eagles soar: Fine-resolution tracking reveals the spatiotemporal use of differential soaring modes in a large raptor

    Ecology and Evolution 8:6788–6799.

    https://doi.org/10.1002/ece3.4189

    • PubMed
    • Google Scholar
33. 1. Nourani E
    2. Yamaguchi NM
    3. Manda A
    4. Higuchi H

    (2016) Wind conditions facilitate the seasonal water‐crossing behaviour of Oriental Honey‐buzzards Pernis ptilorhynchus over the East China Sea

    Ibis 158:506–518.

    https://doi.org/10.1111/ibi.12383

    • Google Scholar
34. 1. Nourani E
    2. Vansteelant WMG
    3. Byholm P
    4. Safi K

    (2020) Dynamics of the energy seascape can explain intra-specific variations in sea-crossing behaviour of soaring birds

    Biology Letters 16:20190797.

    https://doi.org/10.1098/rsbl.2019.0797

    • PubMed
    • Google Scholar
35. Software

    1. Nourani E

    (2024) Golden_eagle_energy_landscape, version V1

    Zenodo.

    https://doi.org/10.5281/zenodo.12733950
36. 1. Nygård T
    2. Jacobsen KO
    3. Johnsen TV
    4. Systad GH

    (2016) Dispersal and survival of juvenile golden eagles ( Aquila chrysaetos ) from Finnmark, Northern Norway

    Journal of Raptor Research 50:144–160.

    https://doi.org/10.3356/rapt-50-02-144-160.1

    • Google Scholar
37. 1. Papastamatiou YP
    2. Iosilevskii G
    3. Di Santo V
    4. Huveneers C
    5. Hattab T
    6. Planes S
    7. Ballesta L
    8. Mourier J

    (2021) Sharks surf the slope: Current updrafts reduce energy expenditure for aggregating marine predators

    The Journal of Animal Ecology 90:2302–2314.

    https://doi.org/10.1111/1365-2656.13536

    • PubMed
    • Google Scholar
38. 1. Péron G
    2. Fleming CH
    3. Duriez O
    4. Fluhr J
    5. Itty C
    6. Lambertucci S
    7. Safi K
    8. Shepard ELC
    9. Calabrese JM

    (2017) The energy landscape predicts flight height and wind turbine collision hazard in three species of large soaring raptor

    Journal of Applied Ecology 54:1895–1906.

    https://doi.org/10.1111/1365-2664.12909

    • Google Scholar
39. 1. Poessel SA
    2. Bloom PH
    3. Braham MA
    4. Katzner TE

    (2016) Age- and season-specific variation in local and long-distance movement behavior of golden eagles

    European Journal of Wildlife Research 62:377–393.

    https://doi.org/10.1007/s10344-016-1010-4

    • Google Scholar
40. 1. Ramsaran AI
    2. Schlichting ML
    3. Frankland PW

    (2019) The ontogeny of memory persistence and specificity

    Developmental Cognitive Neuroscience 36:100591.

    https://doi.org/10.1016/j.dcn.2018.09.002

    • PubMed
    • Google Scholar
41. Software

    1. R Development Core Team

    (2022) R: A language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.R-project.org
42. 1. Riley S
    2. Degloria S
    3. Elliot S

    (1999)

    A terrain ruggedness index that quantifies topographic heterogeneity

    Intermountain Journal of Sciences 5:23–27.

    • Google Scholar
43. 1. Risely A
    2. Klaassen M
    3. Hoye BJ

    (2018) Migratory animals feel the cost of getting sick: A meta-analysis across species

    The Journal of Animal Ecology 87:301–314.

    https://doi.org/10.1111/1365-2656.12766

    • PubMed
    • Google Scholar
44. 1. Ruaux G
    2. Lumineau S
    3. Margerie E

    (2020) The development of flight behaviours in birds

    Proceedings of the Biological Sciences 287:20200668.

    https://doi.org/10.1098/rspb.2020

    • Google Scholar
45. 1. Scacco M
    2. Flack A
    3. Duriez O
    4. Wikelski M
    5. Safi K

    (2019) Static landscape features predict uplift locations for soaring birds across Europe

    Royal Society Open Science 6:181440.

    https://doi.org/10.1098/rsos.181440

    • PubMed
    • Google Scholar
46. 1. Scacco M
    2. Arrondo E
    3. Donázar JA
    4. Flack A
    5. Sánchez-Zapata JA
    6. Duriez O
    7. Wikelski M
    8. Safi K

    (2023) The species-specificity of energy landscapes for soaring birds, and its consequences for transferring suitability models across species

    Landscape Ecology 38:239–252.

    https://doi.org/10.1007/s10980-022-01551-4

    • Google Scholar
47. 1. Scott R
    2. Marsh R
    3. Hays GC

    (2014) Ontogeny of long distance migration

    Ecology 95:2840–2850.

    https://doi.org/10.1890/13-2164.1

    • Google Scholar
48. 1. Sergio F
    2. Tanferna A
    3. De Stephanis R
    4. Jiménez LL
    5. Blas J
    6. Tavecchia G
    7. Preatoni D
    8. Hiraldo F

    (2014) Individual improvements and selective mortality shape lifelong migratory performance

    Nature 515:410–413.

    https://doi.org/10.1038/nature13696

    • PubMed
    • Google Scholar
49. 1. Sergio F
    2. Barbosa JM
    3. Tanferna A
    4. Silva R
    5. Blas J
    6. Hiraldo F

    (2022) Compensation for wind drift during raptor migration improves with age through mortality selection

    Nature Ecology & Evolution 6:989–997.

    https://doi.org/10.1038/s41559-022-01776-1

    • Google Scholar
50. 1. Shepard ELC
    2. Lambertucci SA

    (2013) From daily movements to population distributions: Weather affects competitive ability in a guild of soaring birds

    Journal of the Royal Society, Interface 10:20130612.

    https://doi.org/10.1098/rsif.2013.0612

    • PubMed
    • Google Scholar
51. 1. Shepard ELC
    2. Wilson RP
    3. Rees WG
    4. Grundy E
    5. Lambertucci SA
    6. Vosper SB

    (2013) Energy landscapes shape animal movement ecology

    The American Naturalist 182:298–312.

    https://doi.org/10.1086/671257

    • PubMed
    • Google Scholar
52. 1. Tack JD
    2. Noon BR
    3. Bowen ZH
    4. Fedy BC

    (2020) Ecosystem processes, land cover, climate, and human settlement shape dynamic distributions for golden eagle across the western US

    Animal Conservation 23:72–82.

    https://doi.org/10.1111/acv.12511

    • Google Scholar
53. Conference

    1. Weiss A

    (2001)

    Topographic position and landforms analysis

    ESRI User Conference.

    • Google Scholar
54. 1. Williams HJ
    2. Duriez O
    3. Holton MD
    4. Dell’Omo G
    5. Wilson RP
    6. Shepard ELC

    (2018a) Vultures respond to challenges of near-ground thermal soaring by varying bank angle

    The Journal of Experimental Biology 221:jeb174995.

    https://doi.org/10.1242/jeb.174995

    • PubMed
    • Google Scholar
55. 1. Williams HJ
    2. King AJ
    3. Duriez O
    4. Börger L
    5. Shepard ELC

    (2018b) Social eavesdropping allows for a more risky gliding strategy by thermal-soaring birds

    Journal of the Royal Society, Interface 15:20180578.

    https://doi.org/10.1098/rsif.2018.0578

    • PubMed
    • Google Scholar
56. 1. Williams HJ
    2. Safi K

    (2021) Certainty and integration of options in animal movement

    Trends in Ecology & Evolution 36:990–999.

    https://doi.org/10.1016/j.tree.2021.06.013

    • Google Scholar
57. 1. Wilson RP
    2. Quintana F
    3. Hobson VJ

    (2012) Construction of energy landscapes can clarify the movement and distribution of foraging animals

    Proceedings Biological Sciences 279:975–980.

    https://doi.org/10.1098/rspb.2011.1544

    • PubMed
    • Google Scholar
58. Book

    1. Zimmermann SS

    (2021)

    The Early Life of Juvenile Golden Eagles (Aquila Chrysaetos): Sex and Activity Drive Fledging Time and Pre-Dispersal Exploratory Behaviour MA Thesis

    Albert-Ludwig University Freiburg.

    • Google Scholar

Author details

1. Elham Nourani

   1. Department of Migration, Max Planck Institute of Animal Behavior, Radolfzell, Germany
   2. Department of Biology, University of Konstanz, Konstanz, Germany

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   enourani@ab.mpg.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4420-3902

2. Louise Faure

   1. Department of Migration, Max Planck Institute of Animal Behavior, Radolfzell, Germany
   2. Department of Biology, University of Konstanz, Konstanz, Germany
   3. Section Géographie, École normale supérieure de Lyon, Lyon, France

   Contribution

   Formal analysis, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Hester Brønnvik and Martina Scacco

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0007-4504-3310

3. Hester Brønnvik

   1. Department of Migration, Max Planck Institute of Animal Behavior, Radolfzell, Germany
   2. Department of Biology, University of Konstanz, Konstanz, Germany

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Contributed equally with

   Louise Faure and Martina Scacco

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3061-0630

4. Martina Scacco

   1. Department of Migration, Max Planck Institute of Animal Behavior, Radolfzell, Germany
   2. Department of Biology, University of Konstanz, Konstanz, Germany

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Contributed equally with

   Louise Faure and Hester Brønnvik

   Competing interests

   No competing interests declared

5. Enrico Bassi

   ERSAF-Direzione Parco Nazionale dello Stelvio, Bormio, Italy

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

6. Wolfgang Fiedler

   1. Department of Migration, Max Planck Institute of Animal Behavior, Radolfzell, Germany
   2. Department of Biology, University of Konstanz, Konstanz, Germany

   Contribution

   Data curation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

7. Martin U Grüebler

   Swiss Ornithological Institute, Sempach, Switzerland

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4468-8292

8. Julia S Hatzl

   1. Swiss Ornithological Institute, Sempach, Switzerland
   2. Landscape Ecology Institute of Terrestrial Ecosystems , ETH Zürich, Zürich, Switzerland

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

9. David Jenny

   Swiss Ornithological Institute, Sempach, Switzerland

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

10. Andrea Roverselli

    ERSAF-Direzione Parco Nazionale dello Stelvio, Bormio, Italy

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

11. Petra Sumasgutner

    Konrad Lorenz Research Center (KLF), Core Facility for Behavior and Cognition, Department of Behavioral and Cognitive Biology, University of Vienna, Grünau/Almtal, Austria

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-7042-3461

12. Matthias Tschumi

    Swiss Ornithological Institute, Sempach, Switzerland

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7991-7780

13. Martin Wikelski

    1. Department of Migration, Max Planck Institute of Animal Behavior, Radolfzell, Germany
    2. Department of Biology, University of Konstanz, Konstanz, Germany

    Contribution

    Data curation, Funding acquisition, Writing – review and editing

    Competing interests

    No competing interests declared

14. Kamran Safi

    1. Department of Migration, Max Planck Institute of Animal Behavior, Radolfzell, Germany
    2. Department of Biology, University of Konstanz, Konstanz, Germany

    Contribution

    Conceptualization, Data curation, Validation, Writing – original draft, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8418-6759

• 619

  views

• 88

  downloads

• 1

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.