Albert-Ludwig University Freiburg
Faculty of Environment and Natural Resources

THE EARLY LIFE OF JUVENILE GOLDEN EAGLES (AQUILA CHRYSAETOS):SEX AND ACTIVITY DRIVE FLEDGING TIME AND PRE-DISPERSALEXPLORATORY BEHAVIOUR

Master thesis submitted in partial fulfilment of the requirements for the degree ofMaster of Science Environmental Sciencesby
Svea-Sophie Zimmermann
3732523First supervisor: Prof. Dr. Ilse Storch, University of Freiburg
Second supervisor: PD Dr. Michael Schaub, Swiss Ornithological Institute & University of Bern

Freiburg im Breisgau / Germany
Date of submission:
March 22nd 2021

TABLE OF CONTENT
ABSTRACT ................................................................................................................................................... 4
1.

INTRODUCTION .................................................................................................................................... 5

2.

MATERIALS AND METHODS ................................................................................................................... 8
2.1

STUDY AREA ................................................................................................................................. 8

2.2

STUDY SPECIES............................................................................................................................... 9

2.3

DATA COLLECTION .......................................................................................................................... 9

2.3.1 TAGGING ................................................................................................................................. 9
2.3.2 FIELD OBSERVATIONS................................................................................................................. 10
2.4

DATA PROCESSING ........................................................................................................................ 11

2.4.1 TIMING OF FLEDGING ................................................................................................................. 122.4.2 EXPLORATION PATTERNS AND START OF EMIGRATION .......................................................................... 122.4.3 AVERAGE ACTIVITY.................................................................................................................... 13
2.4.4 BEHAVIOURAL CLASSIFICATION ..................................................................................................... 13
2.4.5 INTRINSIC AND EXTRINSIC VARIABLES .............................................................................................. 14
2.5
3.

4.

STATISTICAL ANALYSIS .................................................................................................................... 15

RESULTS ........................................................................................................................................... 18
3.1

EFFECTS OF INTRINSIC TRAITS AND HABITAT ON ODBA ............................................................................ 18

3.2

EFFECTS OF INTRINSIC TRAITS AND HABITAT ON FEEDING .......................................................................... 20

3.3

EFFECTS OF ODBA AND FEEDING ON TIMING OF FLEDGING AND THE POST-FLEDGING DEPENDENCE PERIOD ............ 22

3.4

EFFECTS OF ODBA, FEEDING AND EXPLORATION PATTERNS ON EMIGRATION ................................................. 27

DISCUSSION ...................................................................................................................................... 30
4.1

NESTLING AND POST-FLEDGING ACTIVITY ............................................................................................. 30

4.2

SEX-SPECIFIC DIFFERENCES IN FLEDGING, ACTIVITY AND EXPLORATORY BEHAVIOUR .......................................... 34

4.3

NESTLING AND POST-FLEDGING FEEDING BEHAVIOUR .............................................................................. 35

4.4

GENERAL LIMITATION .................................................................................................................... 36

4.5

CONCLUSION AND OUTLOOK ............................................................................................................ 37

ACKNOWLEDGMENTS ................................................................................................................................. 39
REFERENCES .............................................................................................................................................. 40
LIST OF TABLES .......................................................................................................................................... 44
LIST OF FIGURES ........................................................................................................................................ 45
APPENDIX ................................................................................................................................................. 45

3

ABSTRACTABSTRACTThe nestling and post-fledging dependence period (PFDP) represent a pivotal time in the life-history ofyoung altricial birds. Before gaining independence at the onset of natal dispersal, it is imperative that
individuals learn how to survive in a challenging environment. Intrinsic factors and extrinsic conditions
as well as individual development and behaviour during the nestling period and PFDP have criticaleffects on decision-making, fitness, and survival in the future. Yet, the relationships between nestling
behaviour, exploratory behaviour during PFDP and the onset of natal dispersal, as well as the factors
influencing them, remain largely understudied. We used high-resolution GPS and body-acceleration
data of 32 juvenile golden eagles (Aquila chrysaetos) to investigate the links between nestling and PFDP
activity (including feeding proportion in specific models), and timing of fledging, exploratory
behaviour, as well as timing of emigration. We further tested the influence of intrinsic factors andenvironmental conditions on activity and feeding proportions of juveniles during the nestling period
and PFDP. Higher nestling activity and better body condition were associated with earlier fledging.
Higher activity was measured in more northward exposed nests. Moreover, nestlings with higher
activity also showed higher activity during PFDP inside the natal territory. A higher number of
excursions was associated with higher activity inside the natal territory, but lower activity during
excursions. Additionally, we found strong differences between the sexes in both periods: while males
fledged earlier than females, females explored more often and for a longer mean duration than males,
even though males showed higher activity levels inside the natal territory. The effects of feeding on
fledging and exploration remained inconclusive as well as the influence of nestling and post-fledging
activity, feeding and exploratory behaviour on the age at emigration. Nevertheless, the results of thisstudy suggest that higher activity levels in the nest reflect faster skill development and perhaps a moreproactive personality that have carry-over effects shaping activity and exploratory behaviour during
PFDP. While we found no link between personality types and the onset of natal dispersal, they may
well become influential during subsequent dispersal phases. Furthermore, we suggest that the sex
differences in activity and exploratory behaviour are indicative of early development of dispersal
phenotypes that become apparent during subsequent dispersal phases and roles taken on as territorial
adults.

4

INTRODUCTION

1. INTRODUCTION
Natal dispersal is a complex and highly variable process as well as a key mechanism for enabling andmaintaining gene flow, genetic diversity and adaptation within and among populations (Ronce, 2007).
It generally describes the movement of an individual from its parental home range to its first breedingsite and is characterised by three subsequent phases (Greenwood, 1980). Initiated by emigration fromthe natal home range, also termed departure, and followed by a transience or wandering phase, natal
dispersal eventually ends with immigration or settlement at a new breeding site (Andreassen et al.,
2002; Bowler & Benton, 2005). Strong evidence for the interconnectedness of these phases suggeststhat behavioural decisions taken in one phase of dispersal can influence decision-making in thefollowing phases and ultimately shape individual survival and reproductive success (Bonte et al., 2012;
Bowler & Benton, 2005; Clobert et al., 2009; Delgado et al., 2009; Delgado et al., 2010).
Before gaining independence and leaving the natal territory, young animals must acquire locomotive
skills and learn how to survive in a challenging environment (Delgado et al., 2009; Soutullo et al., 2006).
In altricial birds, the nestling period has been shown to play a crucial role in shaping post-fledging
behaviour and survival (Naef-Daenzer et al., 2001; Naef‐Daenzer & Grüebler, 2016; van de Pol et al.,2006). The period after fledging up to emigration from the natal territory is known as the post-fledging
dependence period (PFDP). As emigration from the natal territory entails independence from parental
care, the decision to leave is complex and likely modulated by a major trade-off between costs andbenefits influenced by a combination of individual intrinsic traits and extrinsic environmental factors(Bowler & Benton, 2005; Clutton-Brock, 1991; Weston et al., 2018). Consequently, the length of PFDP
and age at emigration vary greatly among individuals. Factors such as sex, body condition, conspecificdensity, food availability, risk of predation, and habitat quality have all been found to affect individual
dispersal strategies (Bowler & Benton, 2005; Fattebert et al., 2019). While individuals raised inresource-poor habitats may benefit from early emigration to find better conditions elsewhere,
individuals from habitats with high resource abundance may delay emigration to build up energy andexperience before leaving (Fattebert et al., 2019), profiting from parental protection from predators.
Greater energy and experience may be especially desirable when emigration and the following
transience phase are energy demanding and risky, as is often observed. Furthermore, such a trade-off
is likely affected by resource competition with parents and siblings within the natal territory, which isoften relaxed under good habitat conditions (Clutton-Brock, 1991). Conversely, nourishment andphysical condition have been shown to affect the age at emigration, where individuals in worse
condition emigrated later than their conspecifics in better condition (Delgado et al., 2010; Ferrer,1992). While physical condition can be linked to intrinsic and genetic factors, it may also be a result of
habitat conditions in the natal territory. It seems likely then, that delaying dispersal may be a5

INTRODUCTION
consequence of individuals evaluating their readiness to disperse by matching it with the conditions
prevailing outside their natal territories (Weston et al., 2018).
As part of the concept of informed dispersal, excursions outside the natal territory prior to emigration
allow individuals to gather information while still being able to return home. As such, exploratory
behaviour may serve as a mechanism to lower dispersal costs and associated risks, and facilitate an
optimal dispersal outcome (Clobert et al., 2009; Weston et al., 2018). Unsurprisingly, exploration is acommon behaviour among dispersing individuals, with great inter- but also intraspecific variation(Cadahía et al., 2007; Debeffe et al., 2013; Delgado et al., 2009; Weston, 2014). In conjunction withother traits, exploration has been described as a behavioural syndrome, also termed personality (Réaleet al., 2007; Sih et al., 2004). As such, the timing of emigration could be part of personality types thatinvolve the expression of exploratory behaviour before and after emigration from the natal territory.
Exploration of new habitats after emigration from the natal territory, also termed prospecting, isthought to be a key component of natal dispersal (Reed et al., 1999). Yet, the drivers of inter-individual
variation in exploratory behaviour before emigration as well as its link to natal dispersal remain poorly
investigated.
Most studies from previous decades focus on movement and behaviour of different taxa, using direct
observations, camera traps and a range of radio or satellite trackers (Bustamante, 1995; Dahlén, 2019;
Fattebert et al., 2019; Soutullo et al., 2006; Weston, 2014). GPS trackers have been crucial in advancing
movement ecology, with devices becoming smaller and lighter, cheaper, more efficient, and moreaccessible. High-resolution GPS data enables identification of specific movement patterns and timingof certain events such as fledging and emigration. To some extent, even specific behaviours such as
searching for prey can be inferred from GPS tracks (Bennison et al., 2018). Recently, GPS trackers are
increasingly combined with other sensors that measure, for example, acceleration, allowing for new
in-depth analyses of animal behaviour (Brown et al., 2013). Tri-axial acceleration (ACC) loggers capture
motion in three orthogonal directions - sway (side-to-side), heave (up-and-down) and surge (back-andforth). With this information, it is possible to infer posture, activity levels and a range of specificbehaviours of an animal (Fehlmann & King, 2016; Nathan et al., 2012). A measure of individual activity
can be derived from ACC data by calculating an individual’s overall dynamic body acceleration (ODBA)(Gleiss et al., 2011; Wilson et al., 2006). ODBA values vary substantially according to different
behaviours, where active behaviours and faster movements are associated with higher values (Ryanet al., 2013; Wilson et al., 2006). Additionally, ODBA has since been considered to reflect activityrelated energy expenditure (Gleiss et al., 2011; Halsey et al., 2009). In a recent study on juvenile white
storks (Ciconia ciconia), individual early-life activity, especially during the post-fledging dependence
period, was shown to influence the timing of emigration and subsequent first-year survival
6

INTRODUCTION(Rotics et al., 2021). The classification of different behaviours from ACC data has since gained muchattention. With increasing computational power, different machine-learning algorithms can be trained
and implemented to discriminate between behaviours using so called annotated or ground-truthed
ACC data (Nathan et al., 2012; Shepard et al., 2008; Wilson et al., 2006). Annotated ACC data is
generated by matching ACC data with the corresponding field observations of the tagged animals’
behaviours. However, while high classification accuracy can be achieved for a number or different
behaviours, some are more prone to misclassification due to motion pattern similarity or because they
are rare, resulting in an insufficient identification dataset (Nathan et al., 2012). Nevertheless, in theirstudy with griffon vultures, Nathan et al. (2012) were able to identify seven different behaviours, forexample feeding and soaring vs. flapping flight, using a combination of GPS and annotated ACC data.
As a highly mobile species, golden eagles (Aquila chrysaetos) show a great inter-individual variety in
exploratory behaviour and the timing of emigration (Soutullo et al., 2006; Weston, 2014; Weston etal., 2018). In this study, we first examined the influence of different intrinsic and extrinsic factors on
activity and feeding behaviour of golden eagles during their first year of life. Secondly, we investigated
how early life activity and feeding are linked to exploratory behaviour and the onset of natal dispersal.
More specifically, we assessed how (i) different intrinsic and extrinsic variables affect activity andfeeding behaviour of individuals during the nestling and post-fledging dependence period, how (ii)
activity and feeding behaviour affect age at fledging and exploratory behaviour, and finally, how (iii)
activity and feeding behaviour, as well as age at fledging and exploratory behaviour affect the age at
emigration. Since different landscape features such as roughness or southward exposure have beensuggested to increase the movement probability of golden eagles (Sur et al., 2020), we expected tofind an influence of extrinsic factors (such as nest exposure, territory size, landscape roughness andsouthward exposure) on individual activity in both periods while considering effects of intrinsic factors(sex and body condition). Furthermore, we expected higher activity levels and feeding proportions aswell as better body condition to reflect earlier fledging, a higher propensity to explore, as well as
younger age at emigration with differences between females and males. The results of this study willcontribute to the understanding of intrinsic and environmental drivers shaping early-life behaviour andthe onset of natal dispersal in a highly mobile raptor species.

7

MATERIALS AND METHODS

2. MATERIALS AND METHODS
2.1 STUDY AREA
Fieldwork was conducted in Grisons (46.6570° N, 9.5780° E), Switzerland’s largest and easternmost
state (in Switzerland called canton) (Figure 1). Grisons is part of the central Alpine area with 68 % of its
area laying above 1800 m above sea level (Gutersohn, 1971). Grisons is characterised by its long major
valleys, from which a multitude of smaller side valleys diverge, creating a diverse topography. Forests
cover approximately a quarter of the canton’s area (Kanton Graubünden, 2020a). These forests often
stretch along the valleys’ slopes up to elevations between 1900 m and 2300 m where they give way toopen ground and vegetation dominated by grasses and heather.
Large protected areas throughout the canton and strict hunting regulations favour high numbers ofgame species including Alpine chamois (Rupicapra rupicapra), Alpine ibex (Capra ibex), red deer
(Cervus elaphus) and European roe deer (Capreolus capreolus). Additionally, Grisons is home topopulations of other medium-sized mammals and birds such as the Alpine marmot (Marmota
marmota), capercaillie (Tetrao urogallus) and black grouse (Lyrurus tetrix) (Haller, 1996; Kanton
Graubünden, 2020b).

Figure 1. Map depicting the study area in Grisons, Switzerland. The purple line represents the canton’s administrative
boundary, and the orange circles the centres of territories where eagles were tagged throughout the study period.

8

MATERIALS AND METHODS

2.2 STUDY SPECIES
Golden eagles (Aquila chrysaetos) are long-lived, territorial raptors that form stable pairs for many
years. With a wingspan of 1.90 m to 2.20 m and a body mass between 2.85 kg and 6.70 kg, they areamong the largest raptors in Europe (Keller et al., 2020). Actively hunted for decades and reaching an
all-time low in population density in the beginning of the 20th century, golden eagle numbers haverebounded substantially since the implementation of strict protection laws in 1953 (Haller, 1996;
Jenny, 1992, 2018). Now, there are between 350 and 360 breeding pairs in Switzerland, with around
120 pairs residing in Grisons (Jenny, 2018). Strong relief, bare cliffs and open vegetation comprise thegolden eagle’s preferred natural habitat, with southern exposed slopes creating thermal updrafts
crucial for soaring raptors (Haller, 1996). However, due to their large territory size of 50 km² onaverage, the amount of available and suitable space is limited. Today’s population is therefore
regulated by negative density dependence, as young, non-territorial birds called floaters disrupt
breeding efforts of territorial pairs, thereby limiting the overall breeding success (Chambert et al.,2020; Haller, 1982, 1996; Jenny, 1992).
Territories usually accommodate multiple nests which are used over many years in either consecutive
or sometimes alternating order (Haller, 1996; Watson, 2010). Among breeding pairs, clutches of one
to two eggs are most common in Europe and years with higher proportions of multiple fledglings areusually associated with increased prey abundance (Jenny, 1992; Watson, 2010). Fledging occurs at 60
to 85 days of age (Jenny, 1992; Watson, 2010) and juvenile golden eagles usually become independent
from their parents’ care after six to 11 months of age, which is when they permanently leave theparental territory to become floaters. They spend four to five years in transience before trying to finda mate and establishing their own territory as adults (Haller, 1996). It is a period of considerable risk,
and fatalities due to starvation or aggressive encounters with conspecific are common.
The main diet of golden eagles in Switzerland is subject to age-related and seasonal changes. Whileduring summer adults prefer to hunt Alpine marmot, young chamois and ibex, they mostly rely on
carcasses of chamois, ibex, red and roe deer in winter. Parents provide their chicks with predominantly
fresh, small to medium sized kills (Haller, 1996), and until they reach independence, juvenile golden
eagles rely on their parents for food. After reaching independence, carcasses are of great importance
until juveniles learn to perfect their hunting skills (Haller, 1996; Watson, 2010).

2.3 DATA COLLECTION
2.3.1 TAGGING
A total of 33 juvenile golden eagles from 27 broods were equipped with combined tri-axial
accelerometer (ACC) and GPS transmitters and ringed between 2017 and 2020 (2017: n = 3, 2018:9

MATERIALS AND METHODSn = 8, 2019: n = 14, 2020: n = 8). Of the 27 broods, five nests included two chicks, where both individuals
were tagged in the same year. The remaining nests contained only one chick. In four territories, single
nestlings were tagged in two consecutive years within the same nest, and in one territory, single
nestlings were tagged two years apart and in two different nests. In total, 23 different territories wereused in this study. Nests were chosen based on their accessibility by climbers, and an even distribution
across the entire study area. Tagging took place between late June and mid-July when nestlings wereapproximately 50 days of age. Experts determined the age based on plumage development (Bloom &Clark, 2001), and exact tagging dates were chosen accordingly. True age ranged from 42 to 60 days,
due to the varying speed at which nestlings developed. In cooperation with the Amt für Jagd undFischerei (AJF), Grisons, nestlings were captured in the nest and carefully transferred to a nearby
location. Meanwhile, a climber stayed at the nest to prevent parents from returning to an empty nest.
Nestlings were weighed, measured, and checked for parasites and hunger stripes. Blood feathers weretaken from the neck or tail for genetic identification of the nestlings’ sex. All nestlings were fitted withBird Solar Tags UMTS 42g (hereafter called loggers) from e-obs Gmbh, Grünwald, Germany (provided
by the Max Planck Institute of Ornithology, Radolfzell, Germany), using a leg-loop harness system madeof two-layered Teflon ribbon with a silicon core to allow for relative elasticity of 20 %. With a bodymass between 2.7 kg and 4.6 kg at the time of tagging, the combined weight of transmitters andharnesses of 70 g made up less than the 3 % maximum of the birds’ body weight recommended by
Kenward (2001).
Loggers were deployed immediately after tagging and recorded high-definition GPS points, ground
speed, air temperature and acceleration data thereafter. Temperature and acceleration sensors were
calibrated prior to deployment. Geographic positions, ground speed and temperature were recorded
every 20 minutes for a duration of one second, whereas acceleration data was recorded every fiveminutes for a duration of 7.9 seconds (hereafter called burst), on three orthogonal axes (x, y, and z) ata frequency of 33.3 Hz (1188 bytes, 33.3 Hz, resulting in 7.9 seconds). In addition, the loggers emitted
a VHF signal to facilitate their detection in the field. In summer (May-October), loggers recorded datafrom 04:00 to 17:00 UTC plus one extra location at 19:00 UTC, whereas in the winter (November-April),the recording time was reduced to 09:00 to 16:00 UTC to account for shorter day length and lower
temperatures. All data was automatically transmitted and stored at the Movebank data repository(movebank.org).

2.3.2 FIELD OBSERVATIONS
Field observations of juvenile golden eagles during the nestling period and the post-fledging
dependence period were conducted to create a detailed activity profile and later match the observed
behaviour with the recorded ACC data. While behavioural observations during the nestling period were
10

MATERIALS AND METHODS
conducted for eagles tagged in 2019 and 2020 (n = 22), observations during the post-fledging
dependence period were conducted for eagles tagged between 2018 and 2020 (n = 30). Binoculars anda scope were used to spot and observe the eagles in the field. Nest sites were observed from a vantage
point at least twice before fledging, for a duration of four to six hours depending on local weather
conditions. After fledging, observation time invested in each juvenile differed due to variability andaccessibility of their locations as well as weather conditions. Once a suitable vantage point was found,observations were conducted for a minimum of two hours and for as long as possible. To find juveniles
in their natal territory, their most likely current position was predicted based on the last recorded
location from the previous day and a VHF antenna was used to narrow down the individual’s locationin the field.
Once visual contact was established, the individual’s movements and behaviours were followed closelywith the scope. Behaviours were documented every five minutes for a duration of eight seconds, atthe exact time of ACC bursts. If behaviour changed within 12 seconds after the initial eight seconds, it
was also documented to buffer for any delay of ACC data recording by the loggers. All field observations
were documented using the same project protocol in QField for mobile devices (OPENGIS.ch, 2020)
over the years.

2.4 DATA PROCESSING
Of the 33 tagged individuals, one was excluded from the dataset as it had lost its tracker one week
after deployment and before it had fledged. For 24 of the remaining 32 individuals, GPS and ACC data
were included from the time of logger deployment until May 31st of the following year (a maximum of
337 days). The end date was based on the assumption that all individuals would have emigrated fromtheir natal territories by that time, due to the beginning of the new breeding season. For the eightindividuals from the year 2020, data was included until December 29th of the same year, due to theongoing post-fledging dependence period, and time constraints of this thesis. A multi-step process wasused to clean and filter the data. Duplicated timestamps and locations were removed from the GPSdata set, which was then reduced from its original 20-minute interval sampling to one location perhour at the full hour, retaining a total of 90.895 locations. Where locations were missing at the fullhour, the nearest fix within the hour was retained. Any locations recorded outside the sampling
schedule (before 04:00 or after 17:00 UTC except 19:00 UTC) were removed. Likewise, ACC data waschecked for duplicates but kept at the original 5-minute interval sampling, resulting in a total of
1.003.759 data points. Due to the loggers’ high dependency on sun exposure for charging theirbatteries, the dataset contains data gaps of varying timespans, particularly during the winter months.
All spatial (except timing of fledging, see below) analyses were conducted using the statistical softwareR, version 4.0.3 (R Core Team, 2020).
11

MATERIALS AND METHODS
2.4.1 TIMING OF FLEDGING
Due to the lack of direct observation of fledging events, the time and day each individual left the nest
was identified based on expert-based assessment of their GPS tracks using the software Wildlife
Monitor (SWILD, 2021). The first track with consecutive locations leading away from the nest site waschosen as the fledging event and the first timestamp discernibly located away from the nest wasmarked as time of fledging. The scatter of GPS signal inaccuracies around the nest site resulting frominterferences caused by cliffsides and trees was examined and omitted during the process ofdetermining the time of fledging.

2.4.2 EXPLORATION PATTERNS AND START OF EMIGRATIONOf the 32 individuals, only 25 were considered for the extraction of exploration patterns and the startof emigration because the other seven individuals lost their loggers, died before emigrating, or had
not yet left their natal territory at the time of evaluation.
The start of emigration was determined first. This was done visually, using a set of rules adapted from
“Method 7” by Weston et al. (2013). In this publication, Weston et al. (2013) determined thatemigration had occurred when a juvenile wandered more than 9 km from its nest and stayed morethan 6 km away for a period of 10 consecutive days. For our study, an adapted spaciotemporalthreshold was chosen. The minimum number of days of absence from the natal territory was increased
to 14 days based on the expert opinion of golden eagle falconers (personal communication, December
3rd, 2020) and visual examination of trajectories. Short visits to the territory within the 14-day period
were exempt from the rule if they only lasted for a few hours and did not extend overnight. A single
distance threshold of 7 km was chosen as the border for defining both excursions and emigration(Figure 2A). The 7 km distance was based on visual examination of all birds’ GPS scatter plots andchosen so it would fully include the main areas of use in larger territories. The area within thatthreshold was considered to encompass the natal territory, favouring overestimation of smaller
territories to underestimation of large ones. To centre the 7 km threshold around the main area of use
within the territory, individual centroids of the area of use were set as reference points instead of thenest location (Figure 2B). This was based on the observation that nest sites were sometimes located at
the border of juvenile golden eagles’ area of use within the territory, rather than in the centre, as
territories are mostly limited by topographical entities. Centroids were calculated from a Kernel
Smoothed Intensity of Point Patterns (R-package ‘spatstat’, Baddeley et al., 2016) with a Gaussian
kernel using a reduced dataset containing locations from the time after fledging until emigration only.

12

MATERIALS AND METHODSA)

B)

Figure 2. (A) Movement pattern of Tasna18 depicted as distance to the centroid starting on the day of tagging. The horizontal
black line represents the 7 km threshold and the vertical red dashed line marks the time of emigration. (B) Trajectory of
Tasna18 before and after emigration. The black dot represents the centroid of Tasna18’s main area of use within the natal
territory. The black circle represents the 7 km threshold around the natal territory.

Excursions during the post-fledging dependence period were defined as movements outside the natal
territory for a maximum duration of up to 14 days. The start of each excursion was marked by the firstpoint beyond the 7 km threshold, and the first point back within the threshold as its end point.

2.4.3 AVERAGE ACTIVITY
Overall dynamic body acceleration (ODBA) reflects the aggregated acceleration of an individual andserves as a valid measure of activity from which energy expenditure can be inferred (Shepard et al.,2009; Wilson et al., 2006). ODBA was calculated for each burst by subtracting the ACC data’s static
components from the total value for each axis and summing the resulting dynamic components acrossall three axes (Shepard et al., 2008; Wilson et al., 2006). From these values, mean ODBA was calculated
for the nestling period (tagging until fledging) and the post-fledging dependence period (fledging until
emigration), respectively. The post-fledging dependence period was then divided into “time spent in
the natal territory” and “time spent on excursion”, and mean ODBA was calculated separately for thetwo sub-phases.

2.4.4 BEHAVIOURAL CLASSIFICATION
Feeding behaviour was identified from ACC data using a random forest (RF) model (R-package‘randomForest’, version 4.6-14, Liaw & Wiener, 2002) fitted by Julia Hatzl (unpublished data). The
model was trained using 5046 classified ACC bursts in order to extract 21 behavioural classes. Twoclassified datasets were used to fit the RF model: The first dataset was based on an experiment with
13

MATERIALS AND METHODS
falconry golden eagles. The second dataset was obtained by observing tagged golden eagles in thewild. Falconry golden eagles were equipped with the same logger models as free-ranging golden
eagles, but in contrast to the loggers of free-ranging individuals, the collection interval was adapted torecord ACC bursts every ten seconds. Standardised ethograms of both falconry and free-ranging golden
eagle behaviour were used to define the different behavioural classes. These defined behaviours werethen assigned to the ACC data according to their matching timestamps to create an annotated ACC
dataset. 55 summary statistics were calculated for every burst, using integrated functions andoptimised code of the R-package ‘moveACC’ version 0.1 (Scharf, 2018). For the RF, the annotateddataset was split into a training (80% of all classified bursts) and testing (20%) dataset maintaining classratios. The model was developed using the training subset and model performance was assessed byusing the prediction accuracy on the testing subset. 1000 trees were grown using seven variables foreach split. The model reached an out of bag error of 19.35% and a prediction accuracy of 11% for class“feeding”, which combined three behavioural classes called “feeding-ripping”, “feeding- gulping” and“plucking”. The resulting dataset was then split into nestling period and post-fledging dependence
period (divided into “time spent inside the natal territory” and “time spent on excursion”).

2.4.5 INTRINSIC AND EXTRINSIC VARIABLES
Body condition was calculated as deviation of recorded body mass from the group mean of body mass.
Group means were calculated separately for males and females, to factor in the reversed sexual
dimorphism occurring in golden eagles. In contrast to sex, the different ages at tagging did not have asignificant effect on body mass in a linear model and were thus not accounted for.
Nest exposure was documented for each nest as compass direction (cardinal, intercardinal andsecondary intercardinal points). Each direction was converted to degrees, ranging from South = 0° toNorth = 180°. No distinction was made between eastern and western directions.
Territory size was calculated using QGIS 3.14 (QGIS.org, 2021). Territory boundaries were derived froma map created by David Jenny (unpublished data), depicting adult home ranges for the whole cantonof Grisons. The map was created using semi-standardised observations of adult birds over the previous
decades, starting with Haller (1982) and consistently updated with current, expert-based observations(David Jenny and local gamekeepers, AJF).
We used a terrain ruggedness index (TRI) to provide information about landscape roughness. TRI was
calculated as the difference in elevation between a central cell and its surrounding cells (Riley et al.,1999). A mean TRI value was obtained for each territory, using the same territory boundaries as
described above.

14

MATERIALS AND METHODS
We used the proportion of area with open vegetation, which reflects marmot habitat, as a proxy forprey availability per territory. The proportion of area with open vegetation (open area) per territory
was calculated by combining the area covered by different land use categories within each territory(Alpwiesen, günstige Alp- und Juraweiden, versteinte Alp- und Juraweiden, Schafalpen, andunproduktive Gras- und Krautvegetation) (Bundesamt für Statistik, 2015), and dividing it by the total
area of each territory. Seven of the territories partially stretch across country borders into Austria and
Italy, and land use categories were not available for those areas. In these cases, the proportion of open
area represents area with open vegetation per territory divided by the total Swiss area within eachterritory.
Finally, the proportion of South-exposed area per territory was calculated using an exposition index
ranging from South = -1 to North = +1. Cells with values < 0 were defined as having a southward
exposition and the number of cells with values < 0 within each territory were divided by the total
number of cells within each territory to obtain proportions.

2.5 STATISTICAL ANALYSIS
Due to differing availability of data across phases and years, analyses were based on multiple datasetswith different sample sizes. All 32 individuals were included in the analyses of the nestling period.
However, for the post-fledging dependence period, juveniles that had died, lost their loggers, or had
not

yet

emigrated

by

the

time

of

evaluationwere

excluded

from

the

analyses(n = 25). For the analyses of exploration patterns during the post-fledging dependence period, twomore juveniles were excluded as they emigrated exceptionally early from their natal territories,
without any prior excursions (n = 23). Both individuals were observed to be forced to leave their natal
territory, one getting attacked by its own parents (David Jenny, personal communication, December
23rd, 2020), and the other one due to an assumed change in territory holders (Swiss game keeper,
personal communication, December 3rd, 2020). Both cases indicated that these individuals did not
emigrate under comparable circumstances to the other juveniles. Therefore, their exclusion from thedataset seemed reasonable to avoid potential bias.
Additional subsets were used for models including feeding, since behavioural data had been classified
for individuals from 2019 and 2020 only (n = 22). In this “feeding subset”, all 22 individuals wereconsidered for the nestling period. Again, juveniles that had died, lost their loggers, or had not yet
emigrated by the time of evaluation were excluded from the analyses of the post-fledging dependence
period (n = 16). Here, only one more juvenile was excluded from the analyses of exploration patterns
due to early emigration without prior excursions (n = 15).

15

MATERIALS AND METHODSAll statistical analyses were performed using the software R version 4.0.3 (R Core Team, 2020). Linear
mixed effect models (LMM) and generalised linear mixed effect models (GLMM) were fitted using Rpackage ‘lme4’ (Bates et al., 2015) and ‘rstanarm’ (Goodrich et al., 2020) to test for (i) the effects of
intrinsic and habitat variables on ODBA (as a measure of activity) and feeding during the nestling andpost-fledging dependence period, (ii) the effect of ODBA and feeding on age at fledging and exploration
patterns, and (iii) the effect of age at fledging, exploration patterns, ODBA and feeding on the age at
emigration. To disentangle the effects of intrinsic and habitat variables on ODBA during the nestling
period (ia), nest exposure, open area, territory size, body condition and sex were included as fixed
effects in an LMM. For ODBA during the post-fledging dependence period (ib), effects of open area,
mean TRI territory size and sex, as well as ODBA in the nest were included as fixed effects in an LMM.
Age at fledging (iia) was assessed by fitting an LMM, including ODBA in the nest, body condition andsex as fixed effects. Exploration patterns (number of excursions and mean excursion duration) (iib)were assessed by fitting a poisson GLMM and an LMM, respectively, including ODBA inside the natal
territory, ODBA during excursions, body condition and sex as fixed effects. Finally, to assess the age at
emigration (iii), age at fledging, number of excursions, mean excursion duration, ODBA inside the natal
territory, ODBA during excursions, body condition and sex were included as fixed effects in an LMM.
To account for the non-independence of data between siblings from the same brood or territory, as
well as across years, territory ID and year were included as crossed random effects in all models.
Restricted maximum-likelihood (REML) estimation of the associated variance components was used
when models were fitted with R-package ‘lme4’.
Separate models were fitted for analyses of the feeding subset. To assess the effects of intrinsic andhabitat variables on feeding (i) in the nest, a binomial GLMM was fitted (with the ratio of the numberof records (i.e. ACC bursts) showing feeding to not-feeding as response variable), including nest
exposure, open area, territory size, body condition and sex as well as year as fixed effects. Year was
included as fixed effect instead of random effect since it only had two levels (2019 and 2020). Insteadof territory ID as random effect, an observation-level random effect was included in this model to deal
with an issue of overdispersion. To evaluate the influence of the data’s non-independent structure due
to siblings, an extra model was fitted with data where siblings had been removed. The two models’estimates and standard errors were then compared, and the lack of not controlling for nonindependence of siblings in the first model was found negligible. For feeding during the post-fledging
dependence period inside the natal territory, open area, mean TRI, territory size, sex and year were
included as fixed effects in a binomial GLMM. To assess effects on age at fledging and exploration
patterns (ii), the corresponding models previously mentioned (without feeding) were used and feeding
proportions inside the natal territory and during excursions as well as year were added as fixed effects.
Age at emigration (iii) was assessed by including ODBA inside the natal territory, ODBA during
16

MATERIALS AND METHODS
excursions, feeding proportion inside the natal territory, feeding proportion during excursions, body
condition, sex and year as fixed effects in an LMM. In all except the first feeding model, territory ID
was included as random factor, to account for the non-independence of data between siblings andterritories. Restricted maximum-likelihood (REML) estimation of the associated variance components
was used when models were fitted with R-package ‘lme4’.
Explanatory variables were checked for collinearity using pairplots, and none showed a correlation≥ 0.7. Thus, all variables were retained, and all continuous explanatory variables were scaled andcentred. Full models were fitted based on ecological hypotheses, and inferences and prediction basedon 95% credible intervals (CrI) calculated using a simulated Bayesian posterior distribution of 5000
simulations (R-package ‘arm’, Gelman et al., 2020). No model selection was performed (cf. KornerNievergelt et al., 2015). Additionally, posterior probabilities (PP) were calculated for all model
estimates, indicating the probability of the estimates to differ from zero. Estimates were considered
to have a strong effect if PP ≥ 0.95 and a weak effect if 0.95 > PP > 0.9.

17

RESULTS

3. RESULTS
3.1 EFFECTS OF INTRINSIC TRAITS AND HABITAT ON ODBADuring the nestling period, mean ODBA across all individuals was 0.052 g ± 0.011 SD per burst (n = 32).
Nest exposure had a strong positive effect on nestling ODBA. Thereby, individuals from more
northward exposed nests showed a higher mean ODBA than individuals from more southward exposed
nests (Table 1; Figure 3). We found no difference between the sexes and neither body condition nor
any of the tested habitat variables affected nestling ODBA (Table 1).
Table 1. Intrinsic and extrinsic variables affecting mean ODBA (in g) during the nestling period.

Fixed Effects

Estimates

95% CrI

PP (< or > 0)

Intercept
Body condition
Sex (male)
Nest exposure
Open area
Territory size

0.052
0.001
0.002
0.002
0.001
0.002

0.042 to 0.063-0.002 to 0.004-0.003 to 0.007< 0.000 to 0.005-0.002 to 0.004-0.001 to 0.005

1.000
0.676
0.804
0.952
0.766
0.872

Random Effects

SD

95% CrI

7.59e-5
2.87e-6

1.86e-5 to 3.93e-4
4.42e-9 to 3.40e-5

Year
Territory ID

Parameter estimates (mean of posterior distribution) and 95% CrI from the LMM modelling nestling ODBA (in g) are shown,as well as standard deviation (SD; median of posterior distribution) and 95% CrI of random effects. The intercept represents
females with average body condition (in g) and average values for all habitat variables. Body condition, nest exposure, open
area and territory size were standardised before entering the model. Posterior probability (PP) describes the probability ofthe effect differing from zero. Strong effects with PP ≥ 0.95 are shown in bold and weak effects with 0.95 > PP > 0.9 are shownin bold and italics. n = 32 individuals from 22 territories and four years.

Figure 3. Mean ODBA (in g) in the nest in response to nest exposure (from 0 = south to 180 = north). Filled circles represent
raw data points for females and males, respectively. The solid line represents the mean value for males and the shaded areashows the 95% CrI of model predictions with all other model predictors set to their mean values. n = 32 individuals from 22
territories and four years.

18

RESULTS
During the post-fledging dependence period inside the natal territory, juveniles showed a mean ODBA
of 0.083 g ± 0.011 SD per burst (n = 25). Mean ODBA inside the natal territory was higher than in thenest, and highest during excursion flights (Figure 4). Mean ODBA inside the natal territory was higher
in males than in females, and positively correlated with mean ODBA in the nest (Table 2; Figure 5). Yet,
we found no effect of the proportion of area covered by open vegetation (open area), mean terrain
ruggedness index (TRI), or proportion of south-exposed area on mean ODBA inside the natal territory(Table 2).
Table 2. Intrinsic and extrinsic variables affecting mean ODBA (in g) during the post-fledging dependence period inside thenatal territory.

Fixed Effects
Intercept
Sex (male)
ODBA in nest
Open area
Mean TRI
South-exposed area
Random Effects
Year
Territory ID

Estimates

95% CrI

PP (< or > 0)

0.080
0.007
0.007
-0.003-0.0020.003

0.073 to 0.088< 0.000 to 0.015
0.002 to 0.011-0.007 to 0.002-0.007 to 0.003-0.002 to 0.007

1.000
0.976
0.994
0.877
0.813
0.892

SD

95% CrI-6

5.24e
1.01e-5

8.57e-9 to 1.39e-4
1.64e-8 to 9.29e-5

Parameter estimates (mean of posterior distribution) and 95% CrI from the LMM modelling ODBA (in g) in the natal territory
are shown, as well as standard deviation (SD; median of posterior distribution) and 95% CrI of random effects. The intercept
represents females with average ODBA in the nest and average extrinsic variables. ODBA in the nest, open area, mean TRI,
and south-oriented area were standardised before entering the model. Posterior probability (PP) describes the probability ofthe effect differing from zero. Effects with PP ≥ 0.95 are shown in bold and effects with 0.95 > PP > 0.9 are shown in bold anditalics. n = 25 individuals from 18 territories and four years.

Figure 4. Comparison of mean ODBA (in g) (boxplot of raw data) between the nestling and post-fledging dependence period,
with the latter subdivided into time spent inside the natal territory and on excursion. Nest: n = 32 individuals from 22
territories and four years. Natal territory: n = 25 individuals from 18 territories and four years. Excursion: n = 23 individuals
from 18 territories and four years.

19

RESULTS

Figure 5. Mean ODBA (in g) during the post-fledging dependence period inside the natal territory in response to mean ODBA
in the nest. Solid lines show the mean value per sex, and the shaded areas show 95% CrI of model predictions with all othermodel predictors set to their mean values. Filled circles and open diamonds show raw data points for females and males,
respectively. n = 25 individuals from 18 territories and four years.

3.2 EFFECTS OF INTRINSIC TRAITS AND HABITAT ON FEEDING
During the nestling period, individuals from the feeding subset (n = 22; years 2019 and 2020) werefeeding in 4.55% to 16.61% of the recorded number of bursts (mean 8.99% ± 2.53 SD). Nest exposure
had a weak positive effect on the feeding proportion of nestlings, with nestlings in more northwardexposed nests feeding proportionally more often (Table 3; Figure 6). Neither body condition nor anyof the tested habitat variables affected the feeding proportion of nestlings and we found no difference
between the sexes or years (Table 3).
Table 3. Intrinsic and extrinsic variables affecting the feeding proportion during the nestling period.

Fixed Effects

Estimates

95% CrI

PP (< or > 0)

Intercept
Sex (male)
Body condition
Nest exposure
Open area
Territory size
Year 2020-2.279-0.084-0.0430.118-0.007-0.020-0.046-2.438 to -2.123-0.289 to 0.121-0.166 to 0.083
0.021 to 0.220-0.114 to 0.101-0.134 to 0.093-0.269 to 0.177

1.000
0.788
0.747
0.989
0.555
0.639
0.647

Random Effects

SD

Observation ID

0.218

Parameter estimates (mean of posterior distribution) and 95% CrI from the binomial GLMM modelling nestling feeding
proportion are shown, as well as standard deviation (SD) of random effects. The intercept represents females with average
body condition (in g), and average extrinsic variables in 2019. Body condition, nest exposure, open area, and territory size
were standardised before entering the model. Posterior probability (PP) describes the probability of the effect differing fromzero. Effects with PP ≥ 0.95 are shown in bold and effects with 0.95 > PP > 0.9 are shown in bold and italics. n = 22 individuals
from 16 territories and two years.

20

RESULTS

Figure 6. Feeding proportion in the nest in response to nest exposure (from 0 = south to 180 = north). The solid line shows
the mean value for females and the shaded area corresponds to 95% CrI of model predictions with all other model predictors
set to their mean values. Filled circles represent raw data points for females and males, respectively. n = 22 from 16 territories
and two years.

Feeding time during the post-fledging dependence period inside the natal territory ranged from 6.03%
to 9.41% (mean 7.78% ± 1.08 SD) of the recorded number of bursts. Males had a higher feeding
proportion than females and the proportion of area covered by open vegetation (open area) had astrong, positive effect on feeding proportion (Table 4; Figure 7A). Additionally, mean TRI had a weak,negative effect on feeding proportion (Figure 7B), and feeding proportion was higher in 2020 than in2019 (Table 4). We did not observe any effect of the proportion of south-exposed area within the
territory on feeding proportion.
Table 4. Intrinsic and extrinsic variables affecting the feeding proportion of juveniles during the post-fledging dependence
period inside the natal territory.

Fixed Effects

Estimates

95% CrI

PP (< or > 0)-2.0880.030
0.649-0.458-0.1340.242-2.606 to -1.560-0.012 to 0.072
0.104 to 1.181-1.087 to 0.201-0.635 to 0.365
0.124 to 0.355

1.000
0.920
0.988
0.932
0.732
1.000

Random Effects

SD

95% CrI

Territory ID

0.570

0.226 to 1.918

Intercept
Sex (male)Open area
Mean TRI
South-exposed area
Year 2020

Parameter estimates (mean of posterior distribution) and 95% CrI from the LMM modelling feeding proportion during thepost-fledging dependence period inside the natal territory are shown, as well as standard deviation (SD; median of posterior
distribution) and 95% CrI of the random effect. The intercept represents females with average habitat variables in 2019. Openarea, mean TRI, and south-exposed area were standardised before entering the model. Posterior probability (PP) describes
the probability of the effect differing from zero. Effects with PP ≥ 0.95 are shown in bold and effects with 0.95 > PP > 0.9 areshown in bold and italics. n = 16 individuals from 11 territories and two years.

21

RESULTS

A)

B)

Figure 7. Feeding proportion inside the natal territory in response to the proportion of area with open vegetation (A), andmean territory ruggedness index (TRI) (B). The solid lines show the mean value per sex and the shaded areas correspond to
95% CrI of model predictions with all other model predictors set to their mean values. n = 16 individuals from 11 territories
and two years.

3.3 EFFECTS OF ODBA AND FEEDING ON TIMING OF FLEDGING AND THE POST-FLEDGING
DEPENDENCE PERIOD
Individuals fledged at a mean age of 77.91 days ± 6.43 SD (n = 32, range: 62 to 90 days). In the full
dataset, males fledged an estimated 3.73 days earlier than females (Table 5). We found a strong effectof ODBA in the nest on the age at fledging, with individuals with higher ODBA in the nest fledging
earlier (Table 5; Figure 8A). Additionally, body condition had a strong, negative effect on age at fledging(Table 5; Figure 8B).
Likewise, males fledged earlier than females within the feeding subset (n = 22, years 2019 and 2020).
Here, the estimated difference was larger with a mean of 5.11 days (Table 5). In this model, mean
ODBA and feeding proportion in the nest did not affect fledging age. However, body condition still had
a strong effect on fledging age, with individuals with higher body condition leaving the nest earlier(Table 5). Additionally, fledging age differed between the two years, with nestlings fledging earlier in
2020.

22

RESULTS
Table 5. Intrinsic and behavioural variables affecting nestling age at fledging (in days) tested for the full dataset andadditionally a subset to test the effect of feeding proportion on age at fledging (feeding subset).

Fixed Effects

Estimates

95% CrI

PP (< or > 0)

Full set n = 32
Intercept
Sex (male)
ODBA in nest
Body condition

79.441-3.730-2.885-1.47275.984 to 82.979-6.832 to -0.576-5.143 to -0.616-3.186 to 0.170

1.000
0.990
0.992
0.960

Random Effects

SD

Year
Territory ID

1.744
2.380

Feeding subset n = 22
Intercept
Sex (male)
ODBA in nest
Feeding prop. in nest
Body condition
Year 2020

80.810-5.113-1.040-0.407-2.162-5.75577.619 to 83.971-8.513 to -1.717-2.943 to 0.930-2.087 to 1.288-4.207 to -0.075-9.640 to -1.757

1.000
0.996
0.864
0.693
0.978
0.995

Random Effects

SD

Territory ID

3.674

Parameter estimates and 95% CrI from the LMM modelling nestlings’ age at fledging (in days) are shown, as well as standard
deviation (SD) of random effects. The intercept represents females with average ODBA (in g) and body condition (in g). ODBA
and body condition were standardised before entering the model. In the feeding subset, the intercept represents femaleswith average ODBA (in g), feeding proportion and body condition in 2019. Posterior probability (PP) describes the probabilityof the effect differing from zero. Effects with PP > 0.95 are shown in bold and effects with PP < 0.95 but > 0.9 are shown inbold and italic. Full dataset: n = 32 individuals from 22 territories and four years. Subset: n = 22 individuals from 16 territories
and two years.

A)

B)

Figure 8. Age at fledging (in days) in response to mean ODBA (in g) in the nest (A) and body condition (in g) (B). The solid lines
show the mean value per sex and the shaded areas correspond to 95% CrI of model predictions with all other model predictors
set to their mean values. Filled circles and open diamonds represent raw data points for females and males, respectively.
n = 32 individuals from 22 territories and four years.

23

RESULTS
Of the 25 individuals considered for the post-fledging dependence period, 23 undertook excursions
outside their natal territory. We counted a total of 255 such excursions. With a median of 12 excursions(range 0-17 excursions), females left their natal territory more often than males with a median of 9
excursions (range 3-21 excursions), which was confirmed by the strong sex effect in the model(Table 6). Males, however, showed a greater variation in the number of excursions. The number of
excursions (excluding zero) was positively correlated with mean ODBA inside the natal territory(Table 6; Figure 9A) but negatively correlated with mean ODBA during excursions (Table 6; Figure 9B).
We found no effect of body condition on the number of excursions (Table 6).
Within the feeding subset, we found no effect by either activity inside the natal territory, or the feeding
proportion on the number of excursions among the 15 individuals considered for the post-fledging
dependence period from 2019 and 2020 (Table 6). There was no difference between the sexes or years,
but body condition had a weak, positive effect on the number of excursions (Table 6).
Table 6. Intrinsic and behavioural variables affecting the number of excursions (> 0) undertaken outside the natal territory
for the full data set and additionally a subset to test for the effect of feeding proportion (feeding subset).

Fixed Effects
Full set n = 23
Intercept
Sex (male)
ODBA in natal territory
ODBA during excursion
Body condition
Random Effects

Estimates

95% CrI

PP (< or > 0)2.532
-0.5170.329-0.382-0.0962.314 to 2.769-0.866 to -0.176
0.097 to 0.558-0.601 to -0.171-0.258 to 0.062

1.000
0.999
0.998
1.000
0.877

1.000
0.649
0.756
0.646
0.887
0.944
0.763

SD

Year
Territory ID< 0.000
0.213

Feeding subset n = 15Intercept
Sex (male)
ODBA in natal territory
ODBA during excursion
Feeding prop. in natal t.
Body condition
Year 2020

2.361-0.1240.115-0.0540.190
0.255-0.2901.966 to 2.762-0.777 to 0.489-0.221 to 0.463-0.383 to 0.330-0.126 to 0.501-0.047 to 0.586-1.159 to 0.575

Random Effects
Territory ID

SD
0.062

95% CrI< 0.000 to 0.540

Parameter estimates and 95% CrI from the poisson GLMM modelling the number of excursions are shown, as well as standard
deviation (SD) of random effects. Within the full dataset, the intercept represents females with average ODBA (in g) and body
condition (in g). In the feeding subset, the intercept represents females with average ODBA, feeding proportions and body
condition in 2019. ODBA, feeding proportion and body condition were standardised before entering the models. Posterior
probability (PP) describes the probability of the effect differing from zero. Effects with PP ≥ 0.95 are shown in bold and effects
with 0.95 > PP > 0.9 are shown in bold and italics. Full dataset: n = 23 individuals from 18 territories and four years. Feeding
subset: n = 15 individuals from 11 territories and two years.

24

RESULTS

A)

B)

Figure 9. Number of excursions undertaken outside the natal territory by juveniles in response to mean ODBA (in g) inside
the natal territory (A) and during excursions (B). The solid lines show the mean value per sex and the shaded areas correspondto 95% CrI of model predictions with all other model predictors set to their mean values. Filled circles and open diamonds
represent raw data points. n = 23 individuals from 18 territories and four years.

Within the full data set (n = 23), excursions lasted a minimum of 0.03 days (ca. 40 minutes) up to 13.83
days. While neither ODBA inside the natal territory, ODBA during excursion, nor body condition
affected the mean excursion duration, we did find a weak effect of sex (Table 7). For females, theaverage excursion lasted 1.45 days ± 0.66 SD (range: 0.08 to 3.51 days). For males, excursions lasted
an average of 0.90 days ± 0.48 SD, with a narrower range of 0.05 to 1.91 days.
Within the feeding subset (n = 15), mean ODBA inside the natal territory had a strongly positive effecton mean excursion duration, but mean duration was shorter when ODBA during excursions was higher(Table 7). Additionally, we found a strongly negative effect of feeding proportion in the natal territory
on mean excursion duration (Figure 10). However, we found no effect of feeding proportion during
excursions, although all individuals, with large inter-individual variation, had been recorded feeding
while on excursion (Figure 11). Males had a shorter mean excursion duration than females, with nodifference between years (Table 7).

25

RESULTS
Table 7. Intrinsic and behavioural variables affecting the mean duration of excursions (in days) outside the natal territory
from the full data set and additionally a subset to test for the effect of feeding proportions (feeding subset).

Fixed Effects

Estimates

95% CrI

PP (< or > 0)1.436
-0.547-0.0830.086-0.0980781 to 2.068-1.258 to 0.194-0.591 to 0.444-0.347 to 0.518-0.488 to 0.291

1.000
0.935
0.633
0.662
0.695

Random Effects

SD

95% CrI

Year
Territory ID

0.031
0.250

< 0.000 to 0.763
0.001 to 0.986

Feeding subset n = 15Intercept
Sex (male)
ODBA in natal territory
ODBA during excursion
Feeding prop. in natal t.
Feeding prop. during e.
Body condition
Year 2020

1.364-0.7550.389-0.560-0.3750.077-0.2590.298

0.873 to 1.862-1.675 to 0.183-0.075 to 0.831-0.969 to -0.180-0.794 to 0.046-0.337 to 0.492-0.734 to 0.209-0.740 to 1.338

Random Effects

SD

95% CrI

Territory ID

0.034< 0.000 to 0.385

Full set n = 23
Intercept
Sex (male)
ODBA in natal territory
ODBA during excursion
Body condition

1.000
0.949
0.956
0.995
0.961
0.662
0.885
0.739

Parameter estimates and 95% CrI from the LMM modelling the mean excursion length are shown, as well as standard
deviation (SD; median of posterior distribution) and 95% CrI of random effects. In the full set, the intercept represents femaleswith average ODBA (in g) and body condition (in g). In the feeding subset, the intercept represents females with average
ODBA, feeding proportions, and body condition in 2019. ODBA and feeding proportions were standardised before entering
the models. Posterior probability (PP) describes the probability of the effect differing from zero. Effects with PP ≥ 0.95 areshown in bold and effects with 0.95 > PP > 0.9 are shown in bold and italics. Full dataset: n = 23 individuals from 18 territories
and four years. Feeding subset: n = 15 individuals from 11 territories and two years.

26

RESULTS

Figure 10. Mean excursion duration (in days) in response to feeding proportion inside the natal territory. Solid lines represent
the mean of model predictions for the year 2019. The shaded areas show the 95% CrI of model predictions for 2019. Filled
circles and open diamonds represent raw data points. n = 15 individuals from 11 territories and two years.

Figure 11. Bar plot showing the number of recorded feeding events (from raw data) during excursions per individual. n = 15
individuals from 11 territories and two years.

3.4 EFFECTS OF ODBA, FEEDING AND EXPLORATION PATTERNS ON EMIGRATION
Juveniles emigrated from the natal territory at a mean age of 267.92 days ± 57.52 SD (n = 25). The age
at emigration varied highly among individuals with a range from 118 to 328 days (Figure 12).
Accordingly, the post-fledging dependence period ranged from 34 to 252 days. The two juveniles thatemigrated from their natal territory without having undertaken excursion flights left earliest, at 118
and 139 days of age. When excluding these two individuals from the dataset (n = 23), none of the27

RESULTS
tested variables had an effect on the age at emigration and we found no difference between the sexes(Table 8).
In the feeding subset, juveniles emigrated at a mean age of 256.62 days ± 55.17 SD (n = 16). Excluding
one individual which emigrated without having undertaken any excursions, overall activity inside thenatal territory had a weak, positive effect on age at fledging, and overall activity during excursions had
a strong, negative effect (n = 15; Table 8). Additionally, juveniles emigrated earlier in 2020, compared
to 2019. We found no effect of feeding proportion, both inside the natal territory and during
excursions, on the age at fledging and there was no difference between the sexes (Table 8).
Table 8. Intrinsic and behavioural variables, as well as exploratory behaviour affecting the age at emigration tested for thefull dataset (n = 23) and additionally a subset (n = 15) to test for the effect of feeding proportion (feeding subset).

Fixed Effects

Estimates

95% CrI

PP (< or > 0)272.712-1.6290.034-7.1504.595
9.661-3.897-2.021

213.555 to 333.481-42.896 to 39.948-17.771 to 18.254-28.885 to 14.214-24.463 to 33.033-7.728 to 27.071-33.050 to 24.888-22.827 to 18.048

1.000
0.598
0.646
0.754
0.629
0.868
0.605
0.582

1.000
0.876
0.837
0.932
0.958
0.846
0.762
0.968

Full set n = 23
Intercept
Sex (male)
Body condition
Age at fledging
Number of excursions
Mean duration of exc.
ODBA in natal territory
ODBA during excursion
Random Effects

SD

Year
Territory ID

45.36027.620Feeding subset n = 15Intercept
Sex (male)
Body condition
ODBA in natal territory
ODBA during excursion
Feeding prop. in natal t.
Feeding prop. during e.
Year 2020

286.445-32.051-12.729
19.135-21.257-10.968-8.305-56.346254.152 to 316.063-84.941 to 28.502-40.117 to 16.223-7.619 to 45.240-46.067 to 3.550-34.578 to 13.653-32.226 to 18.858-121.084 to 4.221

SD

95% CrI

354.5370.940 to 1945.452

Random Effect
Territory ID

Parameter estimates and 95% CrI from the LMM modelling the age at emigration are shown, as well as the standard deviation(SD) of random effects. In the full dataset, the intercept represents females with all other variables set to their mean. In thefeeding subset, the intercept represents females in 2019 with all other variables set to their mean. Body condition (in g), age
at fledging, number of excursions and mean duration, ODBA (in g) and feeding proportions were standardised before entering
the models. Posterior probability (PP) describes the probability of the effect differing from zero. Effects with PP ≥ 0.95 areshown in bold and effects with 0.95 > PP > 0.9 are shown in bold and italics. Full set: n = 23 individuals from 18 territories andfour years. Feeding subset: n = 15 individuals from 11 territories and two years.

28

RESULTS

Figure 12. The number of individuals according to their age at emigration (in days) is shown for n = 25 individuals from 18
territories and four years. Red boxes mark the two individuals which emigrated without prior excursions undertaken outside
the natal territory.

29

DISCUSSION

4. DISCUSSION
In examining the potential for intrinsic factors and extrinsic conditions to affect individual early-life
activity (research question (i)), we found higher nestling activity to be associated with a more
northward nest exposure. Post-fledging juvenile activity inside the natal territory differed between thesexes with males showing higher activity than females. Assessing how individual activity would affectthe age at fledging and exploratory behaviour (research question (ii)), we found that higher nestling
activity was associated with earlier fledging and higher activity during the post-fledging dependence
period inside the natal territory. Moreover, the number of excursions was correlated with activity,
both inside the natal territory as well as during excursions. Additionally, we found that while males
fledged earlier than females, females explored more often and for a longer mean duration than males.
Whether early-life activity and exploratory behaviour had any effect on the start of dispersal in termsof age at emigration (research question (iii)) remained inconclusive, as did the effect of feeding
proportions on fledging, exploration, and emigration.

4.1 NESTLING AND POST-FLEDGING ACTIVITY
In this study, individuals with a higher activity during the approximately last three weeks of the nestling
period and with better body condition fledged at a younger age than individuals with lower activity
and worse body condition. It confirms our expectations and implies that higher activity in the nest
reflects faster individual development of locomotive skills required to leave the nest. When nestlings
approach their time of fledging, they can be observed increasingly flapping their wings and jumping
around the nest or onto nearby branches. Such movements are fast and abrupt, and in turn associatedwith higher ODBA values. As movement within the nest is otherwise limited, and nestlings spend mostof their time standing or lying, it is likely that higher overall activity is the result of increased flighttraining. However, we cannot exclude a possible methodological influence on this result, as individuals
that fledged later spent more days, and possibly more inactive days, in the nest. A more detailed lookat how individual behaviours contribute to overall ODBA would be required to shed light onto which
factors affect activity in general. Related to our findings concerning activity, the negative effect of body
condition on age at fledging suggests that individuals in better condition may have had more resources
to invest in their growth and development, thus advancing their ability to fledge (Freeman et al., 2020;
Takenaka et al., 2005).
Overall activity of the post-fledging dependence period during time spent within the territory was
higher than in the nest, and highest during excursions. Although not imperative, it makes sense giventhat mobility increases drastically when individuals leave the nest, and subsequently, are in motion forextended periods of time when on excursion. Inter-individual variation in activity was consistent over
30

DISCUSSION
both periods, indicated by the positive correlation between ODBA in the nest and during the postfledging dependence period inside the natal territory. Such individual consistency over time may
reflect activity as a consistent individual trait, i.e. a behavioural syndrome. However, environmental
conditions may equally evoke differences in activity. Still, our results corroborate findings by Rotics etal. (2021) who observed the same correlation of pre- and post-fledging activity in white storks andlinked it to first-year survival. Moreover, we found that higher activity inside the natal territory
correlated positively with an increased number of excursions undertaken outside the territory. Bothfindings align with literature on animal personality and behavioural syndromes, where individuals are
consistent in their behaviour across time or environments and different behaviours are often
correlated, e.g. activity, exploration, aggressiveness and boldness (Sih et al., 2004; Wolf et al., 2007).
Based on these results, we suggest that individuals with higher activity and a greater propensity to
explore would likely fall under the category of a “proactive” personality type (Koolhaas et al., 1999; Sihet al., 2004).
In contrast, we found an inverse relationship between activity during excursions and the number of
excursions. Judging by these results, it seems that individuals which undertook more excursions
showed a smaller increase in activity from inside the natal territory to excursions outside the natal
territory. The underlying causes for a greater increase of activity in individuals which showed lower
activity inside the natal territory remain unknown at this point. Considering this pattern from the angleof energy expenditure, it could mean that less active individuals had to spend a much higher amountof energy on exploration, which represents a high-risk endeavour. As such, the lower number of
excursions undertaken by these individuals might reflect a risk-avoidance strategy, a behaviour that isfrequently associated with less proactive and less explorative personality types (Sih et al., 2004).
Interestingly, overall activity was not associated with mean excursion duration, which is not necessarily
contradicting the link between activity and exploratory behaviour. In fact, it seems more likely that the
duration of each excursion depends on the individual’s internal state, conditions inside the natal
territory and environmental as well as perhaps social conditions encountered during each excursion.
As such, we might have overlooked fine-scale information and individual differences by summing
across all excursions per individual and using the mean duration. Additionally, we lacked information
on environmental conditions outside the natal territories. To gain deeper insight into exploratory
behaviour, we intend to address these points in more detail in future research.
In contrast to other studies, we found no correlation between early-life activity and age at emigration.
Rotics et al. (2021) were able to show that higher post-fledging activity in juvenile white storks was
associated with earlier emigration from the natal territory. Similarly, in their study with juvenile golden
eagles in Scotland, Weston et al. (2018) found that all individuals which emigrated early had a shorter
31

DISCUSSION
ontogenic phase in which they developed mobility faster than their conspecifics that emigrated later.
However, both studies looked at continuous activity and development slopes, respectively, and since
we assessed activity as a mean of the entire PFDP, such an effect might have been masked.
In accordance with previous studies on pre-dispersal behaviour of mammals and birds (e.g. Debeffe etal., 2013; Weston, 2014), our results show that exploration prior to emigration is common among
juvenile golden eagles. It was previously suggested that pre-emigration excursions outside the natal
territory may serve as a mechanism for juveniles to test their readiness to disperse (Weston, 2014).
Yet, to our knowledge, no study has hitherto investigated the link between exploratory behaviour
during the post-fledging dependence period and the timing of emigration. However, contrary to our
expectation, we did not find a connection between the number or mean duration of excursions andthe age at emigration for juveniles that did explore prior to emigration. This finding may be attributed
to our sample size (n = 23) being too small to detect such patterns. In addition, perhaps it is the qualityof information gathered on excursions rather than the quantity of excursions that informs the decision
to disperse. Accordingly, rather than being a mechanism to test individual readiness to disperse andinfluence the age at emigration, pre-emigration excursions might lower dispersal costs during thetransience and settlement phase by providing information early on, i.e. how best to navigate theenvironmental and social landscape.
Direct causes of inter-individual variation in activity levels and exploratory behaviour remained partly
ambiguous. Body condition, which often reflects habitat quality or better parental provisioning or
quality (Bańbura et al., 2011; Benson & Bednarz, 2010; Johnson, 2007; Velando & Alonso-Alvarez,
2003), was not associated with activity levels in either the nestling or post-fledging dependence period,
nor with the number and mean duration of excursions. Defining body condition as the deviation ofmass from the female or male group mean might not have been an ideal index in this case, consideringthat golden eagles can hold a significant amount of food in both their stomach and crop (Collopy, 1986;
Haller, 1996). The assessment of body condition could therefore be improved by including body size
measurements such as wing or feather length. Conversely, body condition as we defined it might
simply not be an important predictor of early-life activity, or changes in body condition occurred fromthe time we tagged and weighed individuals to when they fledged roughly three weeks later.
We did, however, find different extrinsic environmental factors associated with pre- and post-fledging
activity. Nestling activity was lower in nests with a more southward exposure. This could be explainedby means of thermoregulation and the need to increase or lower body temperature, depending on
ambient temperature conditions. It is conceivable that cooler temperatures in more northward
exposed nests required nestlings to move around more to stay warm. On the other end of the gradient,southward facing nests are exposed to more sunshine and thus higher temperatures during summer,
32

DISCUSSION
which may force individuals to lower their activity to avoid overheating. This is in line with results by
Rodríguez and Barba (2016) who found that higher temperatures experienced during the nestling
period can cause heat stress and impair nestling development and fitness in great tits (Parus major).
Moreover, high summer temperatures, southward nest exposure and little to no shade were linked to
declining nestling survival in a golden eagle population in Idaho, USA (Kochert et al., 2019).
Thermoregulation by panting or seeking out shade to sit or lie in was found to be a common behaviourin nestling bald eagles (Haliaeetus leucocephalus) and golden eagles (Ellis, 1979; Jenkins, 1988). We
did in fact observe individuals increasingly panting and seeking out shade on hot days with high sun
exposure. Hence, such shading and panting behaviour may explain lower activity levels in more
southward facing nests.
In contrast to nestling activity, we found no clear effects of extrinsic environmental conditions on
activity levels during the post-fledging dependence period. Nevertheless, there were indications that
individual activity was slightly higher in territories with greater proportions of south-facing areas(Table 2; Appendix I). Such an effect is in line with findings by Sur et al. (2020), who demonstrated thatthe likelihood of golden eagles to be in motion increases over south- and west-facing slopes, as theyfavour the generation of thermal winds which golden eagles use for soaring and gaining height.
Additionally, they found that greater topographic roughness (defined as terrain ruggedness index; TRI)
increased the likelihood of eagles to be in motion, because steeper cliffs, too, generate thermal
updrafts. Thus, we expected to find higher activity levels in territories with greater mean TRI, but ourdata did not show a correlation between activity and mean TRI. However, Sur et al. (2020) inferred
information on whether a bird was in motion from GPS trajectories by calculating the hourly speed
between GPS points. Hence ODBA as our measure of activity might not be correlated to TRI in the samemanner, especially because ODBA is the sum of many different behaviours, and gliding and soaring
flight are not necessarily related to high ODBA values.
Individual differences in activity and development of locomotive skills during the pre-emigration
period have been shown to shape the dispersal behaviour of different species (Rotics et al., 2021;
Weston et al., 2013). Our results suggest that differences in activity begin to shape events that occur
before emigration, namely the timing of fledging and exploratory behaviour during the post-fledging
dependence period. This implies that the foundations of dispersal syndromes are determined early on.
We believe that our results contribute to a refined understanding of individual variation in early activity
and movement behaviour in the wild in connection with personality types. Further research is currently
being conducted on the transience and settlement phase of juvenile golden eagles, including the same
individuals investigated in this study. In turn, we expect to gain more insight on if and how the earlylife effects observed here will carry over to influence dispersal and later life-history stages.
33

DISCUSSION

4.2 SEX-SPECIFIC DIFFERENCES IN FLEDGING, ACTIVITY AND EXPLORATORY BEHAVIOUR
In assessing whether the timing of certain life-history events during the first year of life was related to
an individual’s sex, we found that male nestlings fledged earlier than females. Attributed to their larger
size, females most likely take longer to develop physiologically due to their slower growth rates
compared to males (Collopy, 1986). Faster development and earlier fledging of the smaller sex has
been observed in many size-dimorphic species (Bildstein, 1992; Richter, 1983; Teather &Weatherhead, 1994). The fact that we found no discernible difference in nestling activity between
females and males implies that the sex-specific difference in age at fledging is influenced more byphysiological constraints rather than skill development.
During the PFDP inside the natal territory, however, males were on average more active than females.
After fledging, a range of new behaviours must be learned and perfected. In females, this process
might be constrained by their larger size compared to males. Indeed, Weston et al. (2018) observed afaster development of mobility in male golden eagles compared to females. Alternatively, the limb
stroke frequency (in our case wingbeat frequency) in larger animals is usually lower compared tosmaller ones (Shepard et al., 2008). As a result, higher activity levels of male golden eagles might simply
be the result of higher wingbeat frequencies compared to larger females. As a third explanation, theobserved sex-specific difference in activity could be indicative of different behavioural phenotypes andterritorial roles of male and female golden eagles. In golden eagles, as in most raptor species, maleterritorial adults are mostly responsible for hunting during the breeding season, while the female
incubates and tends to the nestlings (Haller, 1982; Jenny, 1992; Watson, 2010). Moreover, males, as
the smaller and more agile sex, are often more involved in territorial defence than their female mates(Haller, 1982; Jenny, 1992; Newton, 1979).
During the PFDP, females explored more often and for a longer mean duration than males. The
difference in exploratory behaviour between the sexes may reflect their different dispersal behaviour
during the transience and settlement phase. Females have been observed to disperse further fromtheir natal territory than males and cover larger areas (Soutullo et al., 2006; Watson, 2010). The higher
exploratory behaviour of females compared to males may thus reflect their overall higher movement
propensity and/or satisfy their elevated need for information and experience for subsequent longerrange dispersal movements.
Contrary to Weston (2014) who found that female juvenile golden eagles in Scotland emigrated later
than males, we did not find such an effect of sex on the age at emigration in our study. Although thepredictions from our model did not show any effect and variation was large, the negative estimate formales would suggest a similar pattern. Nevertheless, our results on differences between the sexes
support the extant literature on sex-biases in size-dimorphic species. Our results also showcase that in
34

DISCUSSION
golden eagles, such differences which become most apparent later in life, already prevail during thenestling and post-fledging dependence period.

4.3 NESTLING AND POST-FLEDGING FEEDING BEHAVIOUR
Identifying individual behaviours beyond just activity is of fundamental importance to understand themechanisms driving differential movement behaviour as well as fitness and survival. Various studies
with food supplementation experiments have highlighted the importance and influence of food intake
on physical condition, fledging and the onset of dispersal in raptors (Fattebert et al., 2019; Freeman etal., 2020; Scherler, 2020; Takenaka et al., 2005). Our study, however, is among the first to include
specific, highly fitness-relevant behaviours such as feeding events identified from ACC data undernatural conditions.
We found indications that the feeding proportion inside the natal territory was positively correlatedwith the number of excursions an individual undertook outside the natal territory (Table 6;
Appendix II). One could expect individuals with lower feeding proportions to escape poor conditions(Matthysen, 2012) and leave the natal territory more often in search for food abroad. However,considering that well-fed individuals are usually in better condition than less-well fed individuals (e.g.
Takenaka et al., 2005), we assume that well-fed individuals can sustain the costs and risks of
exploration better than less well-fed individuals. Conversely, feeding proportion inside the natal
territory was negatively correlated with excursion duration, suggesting that perhaps in this case, lesswell-fed individuals undertook longer excursions to evade adverse conditions in the natal territory andsearch for food elsewhere. This would partially align with findings by Spiegel et al. (2013), who showedthat adult griffon vultures (Gyps fulvus) increased their daily flight distance and time spent foraging
with increasing food deprivation.
It is noteworthy that all individuals from the feeding subset that undertook excursions seemed to havebeen able to locate and consume food during their excursions. We assumed that independent
localisation of food resources outside the known environment of the natal territory would be a strong
cue for individual readiness to disperse. Yet, we found no link between feeding proportions, either
inside the natal territory or on excursion, and the age at emigration. Looking at feeding frequencies
over time instead of using the mean value of feeding proportion in each period may be a possibility toelicit such effects, especially when considering that food availability changes throughout the seasons.
We found different extrinsic environmental factors associated with pre- and post-fledging feeding
proportions. Nestling feeding proportion was lower in more southward facing nests. This aligns with
our results obtained for activity inside the nest, which was lower in more southward facing nests.
Indeed, ambient temperature-induced heat stress was previously found to lower chick food intake
35

DISCUSSION
beyond affecting activity levels (Geraert et al., 1996). As we were lacking data on both parental
provisioning and prey availability, we used the proportion of area covered by open vegetation (openarea), representing marmot habitat, and thus serving as a proxy for potential hunting grounds and preyavailability. Open area did indeed correlate positively with the feeding proportion inside the natal
territory. Additionally, feeding proportion during the PFDP inside the natal territory decreased withincreasing topographic roughness (mean TRI). Perhaps high topographic roughness impeded preyaccessibility, thus leading to lower feeding proportions. Conversely, it is also possible that individuals
in steeper territories were able to benefit from more or stronger updrafts facilitating soaring andgliding flight. This might have lowered their overall energy expenditure and, likewise, their food intake.
Although the statistical power of the respective model might have been rather low, both results
suggest that our habitat variables are acceptable proxies for prey availability and highlight the effectof habitat quality on food consumption.
Regarding intrinsic factors affecting feeding, we found higher feeding proportions inside the natal
territory for males compared to females. At first glance, this seems counterintuitive considering thelarger body size of females and thus assuming that their food intake should be higher as well. However,when taking into account that males showed higher activity levels inside the natal territory, the higher
feeding proportion of males might simply be a result of higher energy expenditure through elevatedactivity. Considering the large variation in our feeding models due to the small sample size, ourinferences about causes and consequences of feeding proportions have to be treated carefully.
Nevertheless, we believe that our results provide important first insights and underline that using
specific behaviours derived from ACC data is highly promising to advance our understanding of driversof juvenile golden eagle behaviour and movement.

4.4 GENERAL LIMITATIONS
One of the main constraints of this study was the limited sample size which was further reduced forcertain analyses due to logger losses, deaths, and the fact that most individuals that hatched in 2020
had not yet emigrated at the time of assessment. This proved problematic for the assessment of theage at emigration, as it is subject to large inter-individual variation. Regarding this study’s time
constraints, we chose to work with mean values of ODBA and feeding proportions per period.
However, considering extended post-fledging dependence periods of over 200 days in someindividuals, using activity slopes for the different periods may prove more informative. We will increase
the sample size in future research and suggest exploiting the high temporal resolution of GPS and ACC
data more fully. As indicated by the strong influence of year, both as fixed and as random effect,
between year variation was large. This variation may be attributed to, for example, differences in
36

DISCUSSION
weather conditions such as prolonged rain and heavy snowfall, or delayed snowmelt. These effects
may strongly increase variability that may obscure potential differences in juvenile movement andbehaviour.
One of the main challenges with our combined GPS and ACC loggers was missing data due to low
battery states, especially during the winter months. For some individuals, this created data gaps ofseveral days or weeks. Although accounted for in the models by using a binomial distribution with thenumber of bursts as denominator, it might have biased our results by, for example, potentially missing
excursions. In addition, it may also have impacted our calculations of mean ODBA per period if crucial
events were missed, although chances of batteries running low are higher when individuals remain
immobile for extended periods of time (e.g. inside the territory) than when they are in motion (e.g. on
excursion). As such, data gaps should not have significantly impacted our ability to detect excursions.
Additionally, ODBA remains somewhat of a black box when used on its own, as it gives no information
about specific behaviours which generate different ODBA values. This makes its interpretation partiallydifficult, especially in soaring birds such as the golden eagle, because active flight will generate higher
ODBA values compared to the more passive soaring flight. Consequently, it is hard to tell whether anindividual was more active because it moved around more, or because it was a less proficient flyer thatspent more time in active flight. Nevertheless, ODBA is still among the best tools for gaining long-term
insight into animal activity and behaviour and how they relate to life-history events, fitness, andsurvival. However, ACC data posed yet another challenge concerning the classification of behaviours.
Here, our dataset had been reduced to individuals from two years because loggers deployed in 2017and 2018 recorded at a lower frequency. The comparability of behavioural classification from two
different frequencies had not been tested at the time of assessment and consequently, data collectedwith lower frequencies were excluded. Furthermore, being an indirect measure of food intake, feeding
records from ACC data only allow to infer how often but not how much an individual fed. This could
perhaps be mitigated by using feeding frequencies over time, including fasting intervals.

4.5 CONCLUSION AND OUTLOOKAn animal’s first year of life is usually the most critical time in terms of survival. Pressed for time,
individuals need to gain experience and locomotive skills in order to prevail in a challenging
environment after initiating natal dispersal. High-resolution GPS and ACC data allowed us to pry into
the lives of a highly mobile and elusive species and enabled us to quantify behaviours that areotherwise difficult to observe. This thesis therefore adds to the existing body of literature. It
demonstrates that intrinsic factors and extrinsic conditions during as early as the nestling period shapelife-history events such as the timing of fledging, and have carry-over effects onto subsequent phases.
37

DISCUSSION
Beyond that, our results demonstrate that intrinsic and extrinsic conditions also, in part, explain theinter-individual variation in behaviour, here activity. Importantly, we show that activity in the nest
affects fledging age and carries over to the post-fledging dependence period in which individual activity
correlated with the individual propensity to explore outside the natal territory. This strongly highlights
the interconnectedness of early-life periods. Additionally, the consistency in individual activity over
the two periods and its connection to exploratory behaviour point towards the manifestation of
personalities. While we could not identify exploratory behaviour as a mechanism with which
individuals test their readiness to disperse, we believe that pre-emigration excursions form asubstantial part of the dispersal process by informing decision-making after emigration. Furthermore,
our results suggest that sex differences in activity and exploratory behaviour are indicative of early
development of dispersal phenotypes that become apparent during subsequent dispersal phases.
Consequently, investigating the sex-specific link between pre-emigration excursions and postemigration movement in future research seems promising, especially regarding population dynamics
and the social landscape. As other studies have linked pre-emigration behaviour to post-emigration
fitness and survival, we argue that future research on natal dispersal should include pre-emigration
behaviour, especially exploration, to increase our mechanistic understanding of these crucial early life
processes and how they contribute to informed dispersal.

38

ACKNOWLEDGMENTS

ACKNOWLEDGMENTS
I would hereby like to express my gratitude to everyone involved in the making of this thesis and thankIlse Storch at the University of Freiburg and Michael Schaub at the Swiss Ornithological Institute for
their straightforward supervision and examination of my work.
I would like to give special thanks to the entire Golden Eagle Team at the Swiss Ornithological Institute.
To Matthias Tschumi, Martin Grüebler and Julia Hatzl for their incredible and constant support in all
matters during all phases of the project, including planning, fieldwork, statistical analyses andrevisions, as well as David Jenny for sharing his extensive knowledge on Golden Eagles, as well as hisrevisions. Thank you all so much! I would also like to thank Jérôme Guélat for providing me with habitat
data of my study area and Kamran Safi from the Max Planck Institute for Ornithology for his invaluableadvice concerning the handling and analysis of movement data.
I would like to give thanks to the Swiss Ornithological Institute for providing the financial resourcesthat supported me through the field season.
This project would not have been possible without the involvement and vital contributions of the Amt
für Jagd und Fischerei, Grisons, and the efforts of their staff of game keepers, for which I am grateful.
To my family and dearest friends, I would like to express my deepest appreciation for their unwavering
emotional support and proof-reading efforts. I could not have done this without you, so thank you!

ETHICAL NOTE
Handling and ringing of golden eagle nestlings in Switzerland was carried out under the permit by theAmt für Lebensmittelsicherheit und Tiergesundheit (ALT) of the canton Grisons (licence No. GR
2017_06, GR 2018_05E, GR 2019_03E). All procedures followed the ASAB/ABS guidelines for the
ethical treatment of animals in behavioural research and teaching and all applicable international,
national, and/or institutional guidelines for the care and use of animals were followed. The handlingof birds was performed with maximum care and disturbance to nests kept to a minimum. Ethicalapproval for involving animals in this study was received through the application procedure for ringing
permits and the scientific commission of the Swiss Ornithological Institute.
39

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LIST OF TABLES

LIST OF TABLESTABLE 1. INTRINSIC AND EXTRINSIC VARIABLES AFFECTING MEAN ODBA (IN G) DURING THE NESTLING PERIOD. ............................ 18
TABLE 2. INTRINSIC AND EXTRINSIC VARIABLES AFFECTING MEAN ODBA (IN G) DURING THE POST-FLEDGING DEPENDENCE PERIOD INSIDE
THE NATAL TERRITORY. ....................................................................................................................................... 19
TABLE 3. INTRINSIC AND EXTRINSIC VARIABLES AFFECTING THE FEEDING PROPORTION DURING THE NESTLING PERIOD. .................... 20
TABLE 4. INTRINSIC AND EXTRINSIC VARIABLES AFFECTING THE FEEDING PROPORTION OF JUVENILES DURING THE POST -FLEDGING
DEPENDENCE PERIOD INSIDE THE NATAL TERRITORY. ................................................................................................. 21
TABLE 5. INTRINSIC AND BEHAVIOURAL VARIABLES AFFECTING NESTLING AGE AT FLEDGING (IN DAYS) TESTED FOR THE FULL DATASET AND
ADDITIONALLY A SUBSET TO TEST THE EFFECT OF FEEDING PROPORTION ON AGE AT FLEDGING (FEEDING SUBSET). ................. 23
TABLE 6. INTRINSIC AND BEHAVIOURAL VARIABLES AFFECTING THE NUMBER OF EXCURSIONS (> 0) UNDERTAKEN OUTSIDE THE NATALTERRITORY FOR THE FULL DATA SET AND ADDITIONALLY A SUBSET TO TEST FOR THE EFFECT OF FEEDING PROPORTION (FEEDINGSUBSET). .......................................................................................................................................................... 24
TABLE 7. INTRINSIC AND BEHAVIOURAL VARIABLES AFFECTING THE MEAN DURATION OF EXCURSIONS (IN DAYS) OUTSIDE THE NATALTERRITORY FROM THE FULL DATA SET AND ADDITIONALLY A SUBSET TO TEST FOR THE EFFECT OF FEEDING PROPORTIONS (FEEDINGSUBSET). .......................................................................................................................................................... 26
TABLE 8. INTRINSIC AND BEHAVIOURAL VARIABLES, AS WELL AS EXPLORATORY BEHAVIOUR AFFECTING THE AGE AT EMIGRATION TESTEDFOR THE FULL DATASET (N = 23) AND ADDITIONALLY A SUBSET (N = 15) TO TEST FOR THE EFFECT OF FEEDING PROPORTION(FEEDING SUBSET). ............................................................................................................................................ 28

44

LIST OF FIGURESLIST OF FIGURESFIGURE 1. MAP DEPICTING THE STUDY AREA IN GRISONS, SWITZERLAND. THE PURPLE LINE REPRESENTS THE CANTON’S ADMINISTRATIVEBOUNDARY, AND THE ORANGE CIRCLES THE CENTRES OF TERRITORIES WHERE EAGLES WERE TAGGED THROUGHOUT THE STUDYPERIOD. ............................................................................................................................................................. 8
FIGURE 2. (A) MOVEMENT PATTERN OF TASNA18 DEPICTED AS DISTANCE TO THE CENTROID STARTING ON THE DAY OF TAGGING. THE
HORIZONTAL BLACK LINE REPRESENTS THE 7 KM THRESHOLD AND THE VERTICAL RED DASHED LINE MARKS THE TIME OF
EMIGRATION. (B) TRAJECTORY OF TASNA18 BEFORE AND AFTER EMIGRATION. THE BLACK DOT REPRESENTS THE CENTROID OFTASNA18’S MAIN AREA OF USE WITHIN THE NATAL TERRITORY. THE BLACK CIRCLE REPRESENTS THE 7 KM THRESHOLD AROUND THE
NATAL TERRITORY. ............................................................................................................................................. 13
FIGURE 3. MEAN ODBA (IN G) IN THE NEST IN RESPONSE TO NEST EXPOSURE (FROM 0 = SOUTH TO 180 = NORTH). FILLED CIRCLESREPRESENT RAW DATA POINTS FOR FEMALES AND MALES, RESPECTIVELY. THE SOLID LINE REPRESENTS THE MEAN VALUE FOR MALES
AND THE SHADED AREA SHOWS THE 95% CRI OF MODEL PREDICTIONS WITH ALL OTHER MODEL PREDICTORS SET TO THEIR MEAN
VALUES. N = 32 INDIVIDUALS FROM 22 TERRITORIES AND FOUR YEARS. ........................................................................ 18
FIGURE 4. COMPARISON OF MEAN ODBA (IN G) (BOXPLOT OF RAW DATA) BETWEEN THE NESTLING AND POST-FLEDGING DEPENDENCEPERIOD, WITH THE LATTER SUBDIVIDED INTO TIME SPENT INSIDE THE NATAL TERRITORY AND ON EXCURSION . NEST: N = 32
INDIVIDUALS FROM 22 TERRITORIES AND FOUR YEARS. NATAL TERRITORY: N = 25 INDIVIDUALS FROM 18 TERRITORIES AND FOURYEARS. EXCURSION: N = 23 INDIVIDUALS FROM 18 TERRITORIES AND FOUR YEARS. ........................................................ 19
FIGURE 5. MEAN ODBA (IN G) DURING THE POST-FLEDGING DEPENDENCE PERIOD INSIDE THE NATAL TERRITORY IN RESPONSE TO MEAN
ODBA IN THE NEST. SOLID LINES SHOW THE MEAN VALUE PER SEX, AND THE SHADED AREAS SHOW 95% CRI OF MODEL
PREDICTIONS WITH ALL OTHER MODEL PREDICTORS SET TO THEIR MEAN VALUES. FILLED CIRCLES AND OPEN DIAMONDS SHOW RAW
DATA POINTS FOR FEMALES AND MALES, RESPECTIVELY. N = 25 INDIVIDUALS FROM 18 TERRITORIES AND FOUR YEARS........... 20
FIGURE 6. FEEDING PROPORTION IN THE NEST IN RESPONSE TO NEST EXPOSURE (FROM 0 = SOUTH TO 180 = NORTH). THE SOLID LINESHOWS THE MEAN VALUE FOR FEMALES AND THE SHADED AREA CORRESPONDS TO 95% CRI OF MODEL PREDICTIONS WITH ALL
OTHER MODEL PREDICTORS SET TO THEIR MEAN VALUES. FILLED CIRCLES REPRESENT RAW DATA POINTS FOR FEMALES AND MALES,RESPECTIVELY. N = 22 FROM 16 TERRITORIES AND TWO YEARS. .................................................................................. 21
FIGURE 7. FEEDING PROPORTION INSIDE THE NATAL TERRITORY IN RESPONSE TO THE PROPORTION OF AREA WITH OPEN VEGETATION (A),AND MEAN TERRITORY RUGGEDNESS INDEX (TRI) (B). THE SOLID LINES SHOW THE MEAN VALUE PER SEX AND THE SHADED AREAS
CORRESPOND TO 95% CRI OF MODEL PREDICTIONS WITH ALL OTHER MODEL PREDICTORS SET TO THEIR MEAN VALUES. N = 16
INDIVIDUALS FROM 11 TERRITORIES AND TWO YEARS................................................................................................ 22
FIGURE 8. AGE AT FLEDGING (IN DAYS) IN RESPONSE TO MEAN ODBA (IN G) IN THE NEST (A) AND BODY CONDITION (IN G) (B). THE
SOLID LINES SHOW THE MEAN VALUE PER SEX AND THE SHADED AREAS CORRESPOND TO 95% CRI OF MODEL PREDICTIONS WITH
ALL OTHER MODEL PREDICTORS SET TO THEIR MEAN VALUES. FILLED CIRCLES AND OPEN DIAMONDS REPRESENT RAW DATA POINTSFOR FEMALES AND MALES, RESPECTIVELY. N = 32 INDIVIDUALS FROM 22 TERRITORIES AND FOUR YEARS. ........................... 23
FIGURE 9. NUMBER OF EXCURSIONS UNDERTAKEN OUTSIDE THE NATAL TERRITORY BY JUVENILES IN RESPONSE TO MEAN ODBA (IN G)
INSIDE THE NATAL TERRITORY (A) AND DURING EXCURSIONS (B). THE SOLID LINES SHOW THE MEAN VALUE PER SEX AND THE
SHADED AREAS CORRESPOND TO 95% CRI OF MODEL PREDICTIONS WITH ALL OTHER MODEL PREDICTORS SET TO THEIR MEAN
VALUES. FILLED CIRCLES AND OPEN DIAMONDS REPRESENT RAW DATA POINTS. N = 23 INDIVIDUALS FROM 18 TERRITORIES AND
FOUR YEARS...................................................................................................................................................... 25
FIGURE 10. MEAN EXCURSION DURATION (IN DAYS) IN RESPONSE TO FEEDING PROPORTION INSIDE THE NATAL TERRITORY. SOLID LINESREPRESENT THE MEAN OF MODEL PREDICTIONS FOR THE YEAR 2019. THE SHADED AREAS SHOW THE 95% CRI OF MODEL
PREDICTIONS FOR 2019. FILLED CIRCLES AND OPEN DIAMONDS REPRESENT RAW DATA POINTS. N = 15 INDIVIDUALS FROM 11
TERRITORIES AND TWO YEARS............................................................................................................................... 27
FIGURE 11. BAR PLOT SHOWING THE NUMBER OF RECORDED FEEDING EVENTS (FROM RAW DATA) DURING EXCURSIONS PER INDIVIDUAL.
N = 15 INDIVIDUALS FROM 11 TERRITORIES AND TWO YEARS. .................................................................................... 27
FIGURE 12. THE NUMBER OF INDIVIDUALS ACCORDING TO THEIR AGE AT EMIGRATION (IN DAYS) IS SHOWN FOR N = 25 INDIVIDUALSFROM 18 TERRITORIES AND FOUR YEARS. RED BOXES MARK THE TWO INDIVIDUALS WHICH EMIGRATED WITHOUT PRIOR
EXCURSIONS UNDERTAKEN OUTSIDE THE NATAL TERRITORY. ....................................................................................... 29

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APPENDIX

APPENDIX

Appendix I. ODBA (in g) inside the natal territory in response to the proportion of south-exposed area per territory. The
solid lines show the mean values for females and males, respectively. The shaded areas correspond to 95% CrI of model
predictions with all other model predictors (body condition, ODBA (g) in nest, open area, mean territory ruggedness index(TRI)) set to their mean values. Filled circles and raw diamonds represent raw data points for females and males,
respectively. n = 25 individuals from 18 territories and four years.

Appendix II. Number of excursions in response to feeding proportion inside the natal territory. The solid line shows the meanvalue for females in 2019, and the shaded area corresponds to 95% CrI of model predictions with all other model predictors(ODBA (in g) inside natal territory, ODBA during excursion, body condition (in g)) set to their mean values. Filled circles
represent raw data points for both females and males. n = 15 from 11 territories and two years.

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STATUTORY DECLARATION___________________________Name of the student

I herewith confirm
that the submitted master thesis is my own independent work and that I only used sources andresources listed therein and I have not made use of any inadmissible help from any other third party.
In particular, I have clearly identified matter from other works, cited verbatim or paraphrased, as such.
The submitted thesis or parts thereof have not been presented at any institution or higher education
for the examination procedure.

______________________
Date, Place_______________________
Signature