# A non-invasive genetic approach for estimating the apparent survival rate of a long-lived territorial raptor: a case study on the Golden Eagle Aquila chrysaetos

Sébastien Waterlot, Christian Itty, Arzhela Hemery, Yoann Bunz, Cécile Kaerle, Guillaume Queney, Aurélien Besnard

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## ► To cite this version:

Sébastien Waterlot, Christian Itty, Arzhela Hemery, Yoann Bunz, Cécile Kaerle, et al.. A non-invasive genetic approach for estimating the apparent survival rate of a long-lived territorial raptor: a case study on the Golden Eagle Aquila chrysaetos. Ibis, 2025, 167 (2), pp.452-467. 10.1111/ibi.13361. hal-04927716

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Ibis (2024)

doi: 10.1111/ibi.13361

# A non-invasive genetic approach for estimating the apparent survival rate of a long-lived territorial raptor: a case study on the Golden Eagle *Aquila chrysaetos*

SÉBASTIEN WATERLOT,\*<fnref n="1" />,<fnref n="2" /> ID CHRISTIAN ITTY,<fnref n="3" /> ARZHELA HEMERY,<fnref n="2" /> YOANN BUNZ,<fnref n="4" />

CÉCILE KAERLE,<fnref n="5" /> ID GUILLAUME QUENEY<fnref n="5" /> & AURÉLIEN BESNARD<fnref n="2" /> ID

For long-lived species, adult survival is the parameter that theoretically has the strongest impact on population dynamics; this makes its estimate crucial for conservation management. The use of non-invasive genetic approaches coupled with capture-recapture methods is being used increasingly to estimate adult survival. Despite being regularly employed in mammals, it has been little-used in birds and notably in long-lived territorial species. This study aimed to determine if feathers obtained from eagle nests could be used to estimate the adult apparent survival rate and the costs associated with such an approach. Feather samples (n = 180) were collected in nests (n = 78) and directly from individual Golden Eagles (n = 36) *Aquila chrysaetos* in France and were genotyped using 17 microsatellite markers. The genotyping error rates obtained were low, with 3% amplification failures, 3.1% dropouts and 1.7% false alleles. The genetic variability of the markers was high, with a probability of identity between siblings of 6.8 × 10⁻⁶, allowing reliable individual identification. Of the feathers collected in nests, 90% were from breeding females; this allowed apparent survival to be estimated for adult females at a reasonable cost. The genotyping of three feathers collected from a nest ensured individual identification of the breeding female with near certainty. Our simulations showed that monitoring of at least 20 pairs over 5 years, or 10 pairs over 10 years is necessary to detect a 10% absolute decrease in adult apparent survival. Estimating the adult survival rate of long-lived birds using feathers collected in nests is possible. A substantial budget, albeit one that is likely to be lower than that for a common capture-recapture survey, would be needed to obtain precise survival estimates.

**Keywords:** capture-recapture, conservation genetics, genetic tag, monitoring, shed feathers.

Population monitoring is typically based on estimating abundance and its variation over time (Williams *et al.* 2002). However, abundance is not always the most appropriate metric for defining

conservation measures. Demographic parameters such as fecundity or survival are far more informative. It is these demographic mechanisms, not the abundance which results, that are directly affected by environmental factors (Caswell 2006). The study of demographic parameters allows for the identification of those with the greatest impact on population dynamics and contributes to designing more relevant conservation measures (Beissinger & McCullough 2002). For example, most large

<footnote n="1">¹University of Montpellier, Montpellier, France</footnote>

<footnote n="2">²CEFE, Univ Montpellier, CNRS, EPHE-PSL University, IRD, Montpellier, France</footnote>

<footnote n="3">³BECOT Association, Saint-Gervais-sur-Mare, France</footnote>

<footnote n="4">⁴Ecrins National Park, Gap, France</footnote>

<footnote n="5">⁵Antagene – Animal Genomics Laboratory, La Tour de Salvagny, France</footnote>

<footnote>*Corresponding author.</footnote>

<footnote>Email: sebastien.waterlot@gmail.com</footnote>

<footnote>TWITTER: SbastienWater11</footnote>

<footnote>[Correction added on 29 October 2024, after first online publication: The CEFE affiliation has been corrected in this version.]</footnote>
raptor species have long lifespans and late sexual maturity (Newton et al. 2016). Their population dynamics are highly sensitive to variations in adult survival and less sensitive to small variations in fecundity (Saether & Bakke 2000). Implementing conservation measures targeting adult survival is therefore likely to have a greater impact.

However, estimating demographic parameters, notably survival rate, can be challenging as it requires tracking individuals over time. For many species, this is only possible by capturing and tagging them (Shirose et al. 1993, Salewski et al. 2007). Statistical ‘capture–recapture’ methods have been developed since the 1960s to analyse data collected on marked individuals. These methods allow for estimation of the survival probability of individuals given that not all marked individuals are detected (e.g. Cormack–Jolly–Seber model; Cormack 1964, Jolly 1965, Seber 1965, Lebreton et al. 1992).

Setting up capture–recapture monitoring for elusive species is a complex task. When there are no external features that make individual identification possible (Hernández-Matías et al. 2011, Rich et al. 2014), the use of marking or banding is usually necessary. However, this requires capturing individuals and reading the bands/marks, which is difficult and time-consuming for many elusive species (Marucco et al. 2010, Rodgers & Janečka 2012). In addition, capture–recapture methods often disturb target species as they involve catching and handling individuals (e.g. Hiebert Burch et al. 2000). This can be ethically problematic, especially for endangered species. These difficulties have often resulted in small sample sizes for such species, in turn leading to an increased risk of obtaining inaccurate estimates of demographic parameters (e.g. Marucco et al. 2010).

Given these limitations, genetic methods provide interesting alternatives. Advances in molecular genetics allow for the identification of individuals from samples collected non-invasively–without the need to capture individuals (Kohn et al. 1999). Molecular markers can be used to identify individuals and then capture–mark–recapture (CMR) models can be applied to these data (Lukacs & Burnham 2005, Rodgers & Janečka 2012). For example, these methods are widely used to monitor populations of large carnivores using their hair or faeces collected from the environment (Marucco et al. 2010, Caniglia et al. 2011).

Although these molecular methods are promising, they also have limitations. The genetic

material used for such studies is generally derived from samples collected in the environment. Material for genetic analyses collected in the environment has potentially been exposed to unfavourable DNA preservation conditions such as wide fluctuations in temperature (Broquet et al. 2007), resulting in the DNA being of low quality and quantity. This can result in poor PCR-amplification rates, contamination with foreign DNA and high genotyping error rates (Taberlet et al. 1999, Waits & Leberg 2000).

Despite this limitation, non-invasive genetic approaches are now widely used to study the demography of mammals. However, such methods are much less used for bird species despite studies using moulted feathers for genetic analysis having recovered sufficient DNA quality for individual identification (Horváth et al. 2005, Rudnick et al. 2005). In some studies on birds, non-invasive sampling methods have proven to be useful: for example, faeces and feathers of the Western Capercaillie *Tetrao urogallus* have been used to estimate population size, sex ratio, survival rate and dispersal (Rutkowski et al. 2018, Cayuela et al. 2021). Shed feathers have been used for individual identification of Northern Goshawks *Accipiter gentilis* based on natural phenotypic characteristics (Hoy et al. 2015), and genetic characterization of shed feathers from large raptors has occasionally been used to estimate population size (Rudnick et al. 2008, Kapetanakis 2014). Yet, the method’s potential to estimate adult survival has rarely been tested on long-lived raptor species, even though it could be a valuable alternative to conventional methods (Newton et al. 2016). To the best of our knowledge, three studies have utilized this approach. Rudnick et al. (2005) collected and genetically analysed shed feathers in nests of the Eastern Imperial Eagle *Aquila heliaca* to detect individual replacement and then to estimate adult turnover rate as a proxy of survival rate. Vili et al. (2013) determined the turnover rate of breeding individuals of an Eastern Imperial Eagle population using the same method. Gil-Sánchez et al. (2021) collected naturally shed feathers in nests of Bonelli’s Eagle *Aquila fasciata* at the end of the breeding season to estimate the adult turnover rate, and to compare it with the value obtained using direct observations with binoculars, telescopes and camera-trapping. However, none of these studies focused on the feasibility and cost-efficiency of such an approach or how to make it operable for conservation stakeholders. The
objective of our study was to attempt to fill this gap using a case study on the Golden Eagle *Aquila chrysaetos*. The Golden Eagle is a territorial and monogamous species; individuals breed in the same territory throughout their lives (Watson 2010). Thus, genotyping of feathers annually collected from nests can identify changes in individuals within a territory that are generally indicative of the mortality of the replaced individual (Rudnick *et al.* 2005, Vili *et al.* 2013), allowing the approach to be used to estimate adult turnover rate as a proxy of adult survival. As turnover rates are a combination of surviving and philopatry, we used the term ‘apparent adult survival’ in this paper (and see Discussion for a more in-depth analysis of this hypothesis). Specifically, our aim was to determine whether genetic monitoring was possible in this species, and to assess the level of field effort and associated cost needed to obtain a relevant level of statistical power in determining apparent survival probability variation in order to provide concrete recommendations for setting up such an approach in the field.

To investigate this approach on the Golden Eagle, we genotyped feathers collected in nests and on captured individuals with the following aims:

1. To estimate genotyping error rates in order to determine if reliable genotyping can be obtained from feathers collected in nests. If the DNA in this context is highly degraded, identification may not be systematically possible. However, other studies using moulted feathers for genetic analysis have found the DNA quality to be sufficiently good for individual identification (Horváth *et al.* 2005, Rudnick *et al.* 2005).

2. To estimate how many feathers need to be collected and genotyped to ensure identification of the breeding pair.

3. To characterize the genetic variability of the loci to determine which should be preferentially used in future protocols.

4. To simulate varying sampling protocols for determining the sampling effort and associated costs necessary to detect variation in adult apparent survival.

# METHODS

## Study species

The Golden Eagle is a large raptor that is widely distributed in the Northern Hemisphere. In the

19th and early 20th centuries, the species became extinct in most non-alpine regions of Europe as a consequence of human persecution (Kenntner *et al.* 2007). From the 1960s to the 1990s, protection of the species in several European countries reversed this decline. Nonetheless, despite a significant recovery, the Golden Eagle remains subject to anthropogenic threats such as collisions with aerial infrastructure (e.g. cables, wind turbines), poisoning (e.g. lead poisoning from hunting ammunition), poaching and disturbance during the breeding season (Pedrini & Sergio 2001, Kenntner *et al.* 2007). These pressures can result in increased mortality rates that may, in turn, affect population viability.

## Sample collection

The study was carried out from 2012 to 2020 in the south of France on different Golden Eagle populations. Feathers were collected in nests and directly from chicks during banding/tagging with GPS equipment. Some adult individuals were also sampled during captures to tag them with GPS transmitter equipment. Two batches of feathers were genotyped. The first batch included 90 feathers collected during four consecutive years in four Golden Eagle territories of the Massif Central. Of these feathers, 19 were directly sampled from individuals (adults and chicks; see Table S1) and 71 were collected from nests. Nest feathers were collected in years when breeding was successful, but also in years when hatching or fledging failed (adult feathers can still be found in nests). The choice of these four territories was based on having: (i) several successive years of feather collection on the same territories most likely not to have had adult turnover (well-known and established pairs monitored by GPS and rings), (ii) samples from at least one of the adults and (iii) samples from several chicks. These criteria were set to maximize the chances of verifying the correct genetic identification of individuals. This selection of feathers was not aimed at estimating apparent survival as we selected too few territories (four). They were only selected to assess the reliability of the proposed genetic approach in identifying the breeders (see below).

In addition to this first analysis, a second batch of 90 feather samples collected from nests or directly from individual birds in five different French mountainous regions was performed:
Drôme (n = 13 in nests), Vanoise (n = 1 from bird; n = 3 in nests), Hautes-Alpes (n = 9 birds; n = 13 in nests), Massif Central (n = 7 from birds; n = 33 in nests) and Pyrenees (n = 11 in nests). This analysis aimed at estimating the allelic diversity of the different markers to calculate the probability of being able to assign identity to individuals with confidence. Low genetic differentiation has been highlighted between Golden Eagles from the Alps and the Pyrenees indicating a single population, enabling us to make use of a single value of probability of identity for the five regions (Nebel et al. 2015, 2019).

## Genetic data

Each sample was prepared following a sterile process in a specific extraction room clean of DNA. For each feather sample, the tip of the feather was cut into pieces and transferred to a sterile labelled Eppendorf tube for DNA extraction. Sample tubes were surrounded by both negative extraction controls (blanks) and positive extraction controls consisting of samples previously analysed and validated, in terms of DNA quality and genotyping success on microsatellite markers. According to the manufacturer's instructions (Nucleospin 96 Tissue Kit; Macherey-Nagel, Düren, Germany), sample tubes, as well as positive and negative extraction controls, were lysed overnight at 56°C, after which DNA was purified, isolated and eluted using purification columns and vacuum filtration.

For each DNA sample, 17 microsatellite markers and one marker for sex identification (CHD1 modified from Lee *et al.* 2008) were amplified by two multiplex polymerase chain reactions (PCRs) (multiplex A (nine markers) and B (10 markers), and analysed with an automated sequencer on two lanes (one for each multiplex)). The sex identification marker (CHD1) was PCR-amplified as part of each multiplex reaction. We used the following 17 microsatellite loci: Aa02, Aa04, Aa11, Aa26, Aa27, Aa35, Aa39, Aa43, Aa49 (Martínez-Cruz *et al.* 2002), IEAAAG13, IEAAAG14, IEAAAG15, Hal10, Hal13 (Bourke & Dawson 2006), NVHfr142 (Bielikova *et al.* 2010), BV13 (Gautschi *et al.* 2000) and Hal09 (Hailer *et al.* 2005; Table S2). Each DNA sample was genotyped twice (multiple-tube approach; Taberlet *et al.* 1996).

The PCRs were prepared following a unidirectional workflow starting in a clean room with positive air pressure to prepare sensitive reagents

(enzymes and DNA primers) and continuing in a pre-PCR room to assemble DNA and reagents using filtered tips. Three negative controls (blanks) and three positive controls were included per PCR plate. The PCR amplifications then were performed in a dedicated post-PCR area in 96-well microplates in a 10-µL volume containing 5 µL Mastermix Taq Polymerase (Multiplex PCR Kit; Qiagen, Hilden, Germany), 0.66 µL of a first pool of eight pairs of primers or 0.68 µL of a second pool of nine pairs of primers at a concentration of 0.10–0.80 µM depending on the multiplex, and a mean of 30 ng genomic DNA. Each pair of primers was coupled to a fluorescent dye (Table S2). The PCR thermal protocol consisted of 95°C for 15 min, followed by eight touchdown cycles of 94°C for 30 s, 62°C to 55°C for 90 s (decreasing 1°C per cycle) and 72°C for 60 s, and then followed by 35 cycles of 94°C for 30 s, 55°C for 90 s and 72°C for 60 s, ending with an extension of 60°C for 30 min.

The PCR products were resolved on a calibrated ABI PRISM 3130 XL capillary sequencer (Thermo Fisher Scientific, Waltham, MA, USA) under denaturing conditions (HiHi Formamide; Thermo Fisher Scientific) with an internal size marker (GeneScan™ 600 LIZ™; Thermo Fisher Scientific) prepared once and dispatched equally in all sample wells of each marker run. This size marker is optimized for fragments up to 600 bp and guarantees the same calibration for all samples. As all the samples were distributed on a unique plate containing all of the positive reference controls (individuals genotyped once previously), all positive controls were run twice on each marker to provide a means to estimate PCR amplification and capillary resolution repeatability. The electropherograms were analysed using GENEMAPPER 4.1 (Thermo Fisher Scientific) independently by two analysts to determine the allele sizes for each marker of each individual. When the genotypes determined by each analyst did not agree, the electropherograms were read again, reading errors were resolved and, in case of persistent disagreement, ambiguous results were considered as missing data. The genotype of each positive control was compared to its known reference to determine the repeatability of the analysis.

## Estimation of the genotyping error rate

Each feather was genotyped twice to obtain two replicates. From these two genotyping replicates, a
consensus genotype was determined (genotype with the highest probability of being the true genotype; see Table S2) and a synthetic quality index (QI) was calculated following the method of Miquel *et al.* (2006). This indicator is used to easily assess the reliability of individual genotypes and is considered reliable when calculated with only two replicates if the genotyping error rate is low.

We thus also calculated the exact genotyping error rates. A multitube approach based on three to seven successive PCRs on the same samples is usually used to estimate the genotyping error rate (Taberlet *et al.* 1996). We relied on only two genotyping replicates per sample, yet several feathers were analysed from specific territories in the first batch of analysis which led to several genotyping of the same individuals (something that cannot usually be performed when genotyping mammals using hair or faeces sampling). These samples provided up to 15 times the genotype of some individuals from 15 different collected feathers (see Results). We then used these replicates, which we termed 'recapture replicates', to determine a consensus genotype (genotype with the highest probability of being the true genotype). We used the GIMLET v1.3.4 software (Valière 2002) to group genotypes belonging to the same individual. This software calculates the percentage of similarity between each genotype in the dataset. From the 17 microsatellite markers and the genotypes obtained on feathers collected from captured adults, 76 genotypes (84%) were easily assignable to an individual (QI > 0.7). The same software was used to determine the consensus genotype and to compare it to each replicate (both recapture and genotyping replicates), allowing for the estimation of genotyping error rates using the method proposed by Broquet and Petit (2004). We validated an allele for a given locus within the consensus genotype when it appeared at least four times in the replicates. This threshold was chosen based on visual analysis of the data; the same genotyping error never appeared four or more times for a given locus.

A chick shares 50% of its genotype with that of each of its parents. If PCR amplification success is low or if many genotyping errors are present, a genotype may have the same percentage similarity to a parent's genotype as to its chick's. This can make genotyping error rate calculations complex, because we may not be sure to which individual the genotype belongs. In this case, we assigned the

genotype to the adult resembling it, at the risk of slightly overestimating the error rate. We made this choice not only because it is rare to find chick feathers in nests (their feathers are just developing and are generally not lost, so mainly large feathers from adults were collected), but also because this approach is conservative in the sense that it tends to slightly overestimate the error rate (see Discussion).

## Statistical analyses

### Analysis of genetic variability

We assessed genetic variability using the second batch of genetic analyses (85 different genotypes) as well as the genotypes of the six adults (four females, two males) identified in the first batch of data (i.e. a total of 91 individuals). We used GENALEX (Peakall & Smouse 2012) to calculate the number of alleles per locus ($N_a$) as well as the frequency of each allele. The average fixation index ($FIS$) was calculated to determine if there was any deviation from Hardy-Weinberg equilibrium (HWE). The number of loci deviating from HWE was checked using chi-square ($\chi^2$) tests. Large deviations from HWE would not allow the use of the probability of identity formulas suggested by Woods *et al.* (1999). To calculate this index, we removed samples with a QI below 0.70, which corresponded to poor genotyping quality, because they are likely to have had a high dropout error rate and thus artificially increased the proportion of homozygous genotypes. We estimated observed ($H_o$) and expected ($H_e$) heterozygosity, and we calculated the probability of identity of each locus to determine which loci were most relevant for identifying individuals. We calculated the probability of identity (PI) for each locus as well as for all loci (PIglobal). As we collected feathers from nests, we could have collected feathers of chicks or adults. We thus also calculated the probability of identity between siblings (PIsibs) in a situation where there may be both parents and offspring in the samples. This PIsibs was the most conservative PI value to determine whether the variability of the available loci was sufficient for reliable identification of individuals (Woods *et al.* 1999). A threshold value of PIsibs < 0.01 was sufficient, given that in the case of Golden Eagles, the number of offspring does not exceed three chicks per year (<3% risk of error; Watson 2010).
## Number of feathers necessary for successful identification

One of our aims was to determine the number of feathers that needed to be genotyped per territory annually to have a probability of identifying the adults of the breeding pair of almost one (perfect detection). However, we mostly detected adult females in the samples collected from the nest in the first batch of samples (see Results). We therefore estimated the probability $P_1$ that a feather randomly drawn from the nest belongs to the breeding female using the results from the first batch of analysis. We then estimated the probability $P_2$ that the genotyping of one feather obtained a QI value superior to 0.80 corresponding to an almost certain identification of the female; a QI value of 0.80 is considered to correspond to reliable genotyping (Miquel et al. 2006, Gajdárová et al. 2023). The product of these two probabilities was used to determine the global probability ($P_g$) that a feather randomly drawn from the nest would allow with near certainty the identification of the adult female of the territory.

As the PCRs were performed using two multiplexes, we also conducted this analysis for the multiplex with the highest genetic variability. Using a single multiplex reduces the cost of genetic analysis, but also greatly reduces the number of loci genotyped. Genotyping errors are therefore more likely to lead to misidentification. As a result, we only used genotypes with a QI superior to 0.95 (almost perfect genotyping) in this second analysis to calculate the proportion of genotypes used for $P_g$.

Our objective was to have a 95% chance of obtaining the correct genotype of the breeding female at each nest sampling, to allow a high probability of detecting turnover of individuals from year to year. The number of feathers to be sampled and analysed to reach this threshold was determined as the smallest value of $x$ calculated by the following equation:

$$x < \ln(0.05)/\ln(1-P_{\text{final}})$$

## Power analysis for apparent survival estimation

We conducted power tests to determine which sampling designs (number of territories to sample and time interval between successive samplings) were required to detect yearly apparent survival probability variation over time. To this end, we

simulated a virtual population to which we applied different sampling designs in R 4.1.2 (R Core Team 2021). Specifically, we simulated life histories of individuals (alive or dead in each year of the simulation) using random draws from a Bernoulli distribution. The life histories of 40 females (for 40 territories) were simulated over 30 years for four values of apparent survival probability: 0.85, 0.875, 0.90 and 0.925. These four values represented apparent survival decreases of 10%, 7.5%, 5% and 2.5% relative to a control value of 0.95 corresponding to adult Golden Eagle survival in a stable population (Crandall et al. 2019). We conducted these simulations on females only because very few male feathers were found in nests – too few to estimate their apparent survival within a reasonable budget (see Results). When an individual died, it was replaced in the year of its death by another individual in the same territory. The history of a new individual was then simulated from this year. This situation represented an area totally occupied by territorial pairs with a systematic turnover as a consequence of the large availability of floating individuals (individuals without fixed territories; Hunt 1998).

On these simulated life histories, we applied different sampling designs from five to 30 territories for 5–20 years. The simulated sampling designs were ‘fixed’ – the same territories were sampled each year. We also simulated ‘rotating’ designs in which sample territories were drawn randomly and without replacement each year (the results of these simulations are available in Fig. S1). The simulated data were then analysed using CMR models (Cormack–Jolly–Seber model; Cormack 1964, Jolly 1965, Seber 1965) in RMARK to estimate the apparent survival probability of individuals (White & Burnham 1999, Laake 2013) with a probability of detection of 0.8 to mimic situations in which monitored eagle pairs occasionally skip reproduction and thus may be undetectable in some years. To do that, we used a Bernoulli trial with a probability of 0.8 for each territory and for each year. As our approach is based on the assumption that the adult is either detected or dead in cases where reproduction is successful, the exact same simulations were also conducted using a probability of detection of one for perfect detection (the results of these simulations are available in Fig. S2).

We conducted each simulation scenario 1000 times in order to assess the expected precision of
the apparent survival estimate and its power to
detect a lower apparent survival probability com-
pared to the reference value. For each scenario, we
used the proportion of apparent survival estimates
that did not include the reference value of 0.95
within its 95% confidence interval as the power of
the tested scenario. With the usual time con-
straints in a conservation context, we opted for an
α-level of 0.2 to favour statistical power, even
though it might lead to a higher chance of type I
error (Yoccoz 1991). Thus, if the proportion was
greater or equal to 80%, then the statistical power
was considered sufficiently good. We then coupled
these simulations with an analysis of the costs asso-
ciated with each sampling design (cost of field
sampling and cost of genetic analysis) to evaluate
their efficiency. The most efficient sampling plan
is defined here as the one that detects the given
survival variation with the smaller value of the
product of the number of years of monitoring with
the number of monitored territories. For example,
a sampling design of 15 territories for 15 years has
an efficiency value of 15 × 15 = 225 territory-years
and a sampling design of 30 territories for 10 years
has an efficiency value of 300 territory-years and is
therefore less efficient as its costs (fieldwork,
genetic analysis) are higher.

All R codes are provided in Data S1.

Cost estimates of the sampling designs

We estimated the financial and human cost (in field days, FD) of each sampling design for a national park in France as an example. Parameters that could lead to a variation in cost included: (i) the number of feathers to be genotyped per nest and per year, (ii) the number of multiplexes to be used and (iii) the number of territories to be monitored per year (depending on the sampling design chosen). Once these parameters were determined, the total cost was calculated using estimates of the genetic costs and estimates of the number of FD for monitoring of pairs and access to nests. For the extraction and genotyping (including personnel costs, consumables and equipment depreciation) of one feather, the cost is €60 to analyse both multiplexes or €40 to analyse a single multiplex. The number of FD required to detect an area occupied by a pair of eagles varies depending on the study area. For this analysis, data from Ecrins National Park (in southeastern France) as reported by Chambert et al. (2020) were utilized. They estimated that it takes 425 h, approximately

equivalent to 57 FD, to detect the breeding nests
of 20 pairs of Golden Eagles. Accessing the nest
(including approach walk and descent) to collect
feathers typically requires around one field day for
two rope access technicians (depending on the
environment; two individuals are necessary for
security reasons), totalling 2 FD.

RESULTS

Genotyping and error rate estimates

Of the 90 feathers genotyped from the first batch,
73 (81%) resulted in successful PCR amplifications
of at least 15 of the 17 loci (88%). Four feathers
(including three from captured individuals) pro-
vided unusable genotype with fewer than five suc-
cessfully amplified loci. The mean QI value for the
180 samples in both batches was 0.90 (95% CI:
0.84; 0.96); 80% of the samples had a QI superior
to 0.80.

In the 71 genotyped naturally shed feathers
from this first batch, 62 resulted in successful
amplifications of at least 15 of the 17 loci (87%).
All of these genotyped naturally shed feathers pro-
vided some information with more than five suc-
cessfully amplified loci. The average error rates
were low, with 7% amplification failures per locus,
5.2% dropouts and 0.7% false alleles (details of
error rates for each locus can be found in
Table S3). Of these 71 feathers collected in nests,
64 were attributed to the female of the breeding
pair (i.e. 90% of the samples collected). Of the
seven remaining genotypes, five were probably
chick genotypes (7%), one belonged to the breed-
ing male and one genotype was not usable (insuffi-
cient loci were amplified). Breeding males were
therefore rarely detected by this method and it
would be difficult to estimate their apparent sur-
vival with this non-invasive approach. For the rest
of the analysis, we focused on females only.

Number of feathers to collect
from nests

As mentioned above, 90% of the samples collected
and genotyped for the first batch of analysis
belonged to the territorial female (P<sub>1</sub>); 80% of the
genotypes had a QI superior to 0.80 (P<sub>2</sub>). Using
all 17 loci, we obtained 72% of genotypes that
were assignable to the breeding female with near
certainty (P<sub>g</sub>). If we considered only the loci of
multiplex A, 56% of the genotypes belonged to the breeding female and were genotyped without error (final P – multiplex A).

Using the probabilities estimated from our samples for the 17 loci (both multiplexes), it was necessary to genotype at least three feathers from each sampling of a nest in order to obtain a probability higher than 95% to identify the breeding female. For the eight loci of multiplex A only, it was necessary to genotype at least four feathers at each sampling of a nest. For all loci, the probabilities of identifying the breeding female with two, three and four feathers were 0.92, 0.98 and 0.99, respectively. For multiplex A only, they were 0.81, 0.91 and 0.96.

## Population genetics of Golden Eagles in France

The allelic diversity of these 17 loci varied from three to 17 alleles with an average allelic diversity per locus of 7.6 alleles. The average effective allele number ($N_e$) was 3.1 alleles (range 1.3–7.4 alleles). The mean probability of identity (PI) by locus is 0.2 and the mean probability of identity between siblings by locus (PIsibs) is 0.5. According to these indicators, the genetic diversity of these loci seems rather high. The average expected and observed heterozygosity values were 0.59 (range

0.23–0.86) and 0.53 (range 0.21–0.77), respectively. The average $F_{IS}$ fixation index was 0.055 (95% CI: 0.005–0.105) showing a slight average excess of homozygosity in our samples; four of the 17 microsatellite markers deviated slightly from HWE ($P < 0.05$, $\chi^2$ test). Given the low value of the $F_{IS}$ fixation index and the slight deviation from HWE, we kept these four loci for probability of identity calculations. Genetic variability appeared to be high, leading to a low total probability of identity (total PI) of $1.9 \times 10^{-13}$ and a PIsibs of $6.8 \times 10^{-6}$. By genotyping these 17 loci there was almost zero probability that two genotypes without genotyping errors were identical by chance (Table 1).

For multiplex A (eight loci), PIsibs was equal to $2.1 \times 10^{-3}$, and for multiplex B (nine loci) PIsibs was equal to $4.6 \times 10^{-3}$. Multiplex A had the lowest probability of identity but, for both multiplexes, the variability was theoretically sufficient for reliable identification of individuals (PIsibs <0.01).

## Expected accuracy of apparent survival estimates by CMR simulations

With the fixed sampling design simulation, it was not possible to detect a 2.5% decline in apparent survival regardless of the sampling effort. To

<table><caption>**Table 1.** Values of genetic variability per locus: probability of identity (PI) corresponding to the probability that two genotypes were identical by chance; probability of identity between siblings (PIsibs) corresponding to the probability that two genotypes of the same offspring were identical by chance; cumulative probability of identity (PI and PIsibs cum); number of alleles ($N_a$); number of effective alleles ($N_e$); observed heterozygosity ($H_o$) and expected heterozygosity ($H_e$).</caption><thead><tr><th>Locus</th><th>PI per locus</th><th>PI cum</th><th>Pisibs/locus</th><th>Pisibs cum</th><th>N<sub>a</sub></th><th>N<sub>e</sub></th><th>H<sub>o</sub></th><th>H<sub>e</sub></th></tr></thead><tbody><tr><td>10-BV13</td><td>0.03</td><td>3.2E-02</td><td>0.33</td><td>3.3E-01</td><td>17</td><td>7.4</td><td>0.75</td><td>0.86</td></tr><tr><td>07-Aa39</td><td>0.04</td><td>1.4E-03</td><td>0.34</td><td>1.1E-01</td><td>12</td><td>6.1</td><td>0.77</td><td>0.84</td></tr><tr><td>09-Aa49</td><td>0.08</td><td>1.1E-04</td><td>0.38</td><td>4.2E-02</td><td>7</td><td>4.5</td><td>0.65</td><td>0.78</td></tr><tr><td>04-Aa26</td><td>0.10</td><td>1.1E-05</td><td>0.40</td><td>1.7E-02</td><td>10</td><td>4.0</td><td>0.73</td><td>0.75</td></tr><tr><td>02-Aa04</td><td>0.12</td><td>1.4E-06</td><td>0.43</td><td>7.3E-03</td><td>14</td><td>3.4</td><td>0.67</td><td>0.71</td></tr><tr><td>17-NVHfr142</td><td>0.15</td><td>2.0E-07</td><td>0.45</td><td>3.2E-03</td><td>8</td><td>3.1</td><td>0.49</td><td>0.68</td></tr><tr><td>12-Hal10</td><td>0.13</td><td>2.7E-08</td><td>0.44</td><td>1.4E-03</td><td>6</td><td>3.3</td><td>0.62</td><td>0.69</td></tr><tr><td>03-Aa11</td><td>0.16</td><td>4.3E-09</td><td>0.46</td><td>6.5E-04</td><td>7</td><td>3.0</td><td>0.59</td><td>0.66</td></tr><tr><td>06-Aa35</td><td>0.20</td><td>8.6E-10</td><td>0.48</td><td>3.1E-04</td><td>6</td><td>2.7</td><td>0.54</td><td>0.63</td></tr><tr><td>13-Hal13</td><td>0.18</td><td>1.5E-10</td><td>0.46</td><td>1.5E-04</td><td>9</td><td>2.9</td><td>0.56</td><td>0.66</td></tr><tr><td>05-Aa27</td><td>0.19</td><td>3.0E-11</td><td>0.49</td><td>7.1E-05</td><td>4</td><td>2.7</td><td>0.72</td><td>0.63</td></tr><tr><td>08-Aa43</td><td>0.32</td><td>9.4E-12</td><td>0.60</td><td>4.3E-05</td><td>6</td><td>1.8</td><td>0.36</td><td>0.46</td></tr><tr><td>01-Aa02</td><td>0.33</td><td>3.1E-12</td><td>0.61</td><td>2.6E-05</td><td>5</td><td>1.8</td><td>0.43</td><td>0.45</td></tr><tr><td>11-Hal09</td><td>0.39</td><td>1.2E-12</td><td>0.65</td><td>1.7E-05</td><td>7</td><td>1.7</td><td>0.29</td><td>0.40</td></tr><tr><td>16-IEAAAG15</td><td>0.45</td><td>5.3E-13</td><td>0.67</td><td>1.1E-05</td><td>4</td><td>1.6</td><td>0.34</td><td>0.38</td></tr><tr><td>15-IEAAAG14</td><td>0.58</td><td>3.1E-13</td><td>0.77</td><td>8.7E-06</td><td>4</td><td>1.3</td><td>0.25</td><td>0.25</td></tr><tr><td>14-IEAAAG13</td><td>0.62</td><td><strong>1.9E-13</strong></td><td>0.79</td><td><strong>6.8E-06</strong></td><td>3</td><td>1.3</td><td>0.21</td><td>0.23</td></tr><tr><td>Mean</td><td><strong>0.2</strong></td><td></td><td><strong>0.5</strong></td><td></td><td><strong>7.6</strong></td><td><strong>3.1</strong></td><td><strong>0.53</strong></td><td><strong>0.59</strong></td></tr></tbody></table>
detect a 5% decline in apparent survival, the most efficient sampling design would be to collect feathers from 20 territories annually for 15 years, or from 30 territories for 10 years. To detect a 7.5% decline in apparent survival, the most efficient sampling design would be to collect feathers from 15 territories for 10 years, or from 10 territories for 15 years. Finally, to detect a 10% decline in apparent survival, the most efficient sampling design would be to sample 10 territories for 10 years, or from five territories for 20 years (Fig. 1). Results of rotating sampling designs and fixed sampling design with a higher probability of detection (P = 1) are presented in Figures S1 and S2 and require slightly less monitoring effort.

Cost estimates

Cost estimates were made for three genotyped
feathers per year and per territory with the two
multiplexes (€60 per sample). Only costs for fixed
sampling designs were estimated because they
were more efficient (see above).

To detect a 10% decrease in apparent survival,
it would be possible to monitor 20 territories for
5 years, which would represent an annual cost of
97 FD and €3600 for the genetic analysis of the
feathers. It would also be possible to monitor 10
territories for 10 years, which would represent an
annual cost of 50 FD and €1800 for the genetic
analysis of feathers (Table 2).

<figure><img src="image_6.png" /><figcaption>**Figure 1.** Results of capture–mark–recapture (CMR) simulations for fixed sampling designs (the same territories monitored each year). Simulations were performed for four values of an absolute decline in apparent survival probability: 10%, 7.5%, 5% and 2.5%. The statistical power is represented as a function of the number of years of effective sampling and the number of territories sampled: pink curve = five territories, yellow curve = 10 territories, brown curve = 15 territories, orange curve = 20 territories and red curve = 30 territories. The dotted black line (0.80) corresponds to the threshold value of statistical power at which we considered the protocol relevant.</figcaption></figure>
<table><caption>**Table 2.** Financial and field costs in number of field days (FD) of different constant sampling designs (same territories monitored each year) and their ability to detect a greater or lesser decline in apparent survival.</caption><thead><tr><th>Precision</th><th>Decline of 5%</th><th>Decline of 7.5%</th><th>Decline of 10%</th><th>Decline of 10%</th></tr></thead><tbody><tr><td>Sampling design</td><td>15 territories – 15 years</td><td>15 territories – 10 years</td><td>20 territories – 5 years</td><td>10 territories – 10 years</td></tr><tr><td>Annual cost</td><td>74 FD – €2700</td><td>74 FD – €2700</td><td>97 FD – €3600</td><td>50 FD – €1800</td></tr><tr><td>Total cost</td><td>1110 FD – €44 500</td><td>740 FD – €27 000</td><td>485 FD – €18 000</td><td>500 FD – €18 000</td></tr></tbody></table>

## DISCUSSION

We showed that the genotyping error rates obtained were very low while the genetic variability of the markers was high, thus allowing for reliable individual identification from genetic samples obtained from shed feathers in the Golden Eagle. However, most of the feathers collected in nests were from breeding females and therefore only female apparent survival could be estimated at a reasonable cost with the approach proposed here. Our simulations showed that monitoring of at least 10 pairs over 10 years is necessary to detect a 10% decrease in adult survival. Estimating the apparent adult survival rate of long-lived birds using feathers collected in nests is thus possible, but a substantial budget would be needed to obtain precise estimates.

### Genetic variability

We had access to a larger number of loci compared to other studies using non-invasive genetic methods for individual identification; studies in which five to 10 loci were usually sufficient (Woods et al. 1999, Marucco et al. 2009). The total probability of identity (PI) of our 17 loci was very low, allowing for reliable identification. PIsibs of multiplex A was theoretically sufficient for reliable identification (<0.01) but remained quite close to the threshold value of $10^{-2}$. Therefore, genotyping errors could lead to identification difficulties between related individuals. Even a few individual misidentifications can significantly influence estimation of survival rates and they should be avoided whenever possible (Mills et al. 2000, Waits & Leberg 2000). The additional cost of a multiplex is small compared to the cost of field-work for feather collection and DNA extraction. To monitor 10 territories, the annual cost represents €1800 for both multiplexes or €1200 for one multiplex, and 50 FD in either case. It therefore seems more prudent to keep both multiplexes for

genetic analysis to make individual identification as accurate as possible.

### Genotyping, error rates and quality index (QI)

Most studies using non-invasive genetic analysis methods on large fauna have been conducted using faeces or hair. The use of feathers is less common, providing few points of comparison (but see Horváth et al. 2005, Rudnick et al. 2005). Broquet et al. (2007) calculated the mean values of error rates obtained for about 30 different studies using non-invasive genetic methods on various mammals. The average dropout and PCR amplification failure rates were respectively 11.3% and 14.3% for faeces, and 18.7% and 19.8% for hair. In comparison, the dropout and amplification failure rates obtained on the initial genotyping replicates of our study were (respectively) 5.2% and 7%, so these are rather low error values. The false allele rate is difficult to compare from one study to another because of the heterogeneity of the methods used to calculate it (Broquet & Petit 2004). The false allele rate of 0.7% obtained in our study is nevertheless a rather low error value compared to other studies. Therefore, it seems possible to conclude that the feathers collected from eagle nests allow for genotyping of sufficient quality to obtain individual identification.

The method used in our study – using different samples from the same nest to obtain recapture replicates of the same genotype – has not often been used (see Rudnick et al. 2005 for another example). This type of approach is not possible in the case of opportunistic collection of faeces in mammals. Another limitation of our method for estimating error rates was that some genotypes from degraded samples were difficult to assign to an individual (male, female or chick) and in these cases they were assigned by default to the genotype of the female of the pair resembling it, slightly increasing the error rates obtained. However, there were few situations
of poor quality in our samples (fewer than five of 90); moreover, this approach is conservative as it increases the number of feathers to be genotyped to reduce errors, which minimizes the risk of indicating that a turnover on a territory has occurred when it has not.

Genotyping error rates were consistent with the QI values obtained with two genotyping replicates per sample, confirming the reliability of this synthetic indicator. QI could thus be used by conservation stakeholders using the method proposed herein to easily evaluate the genotyping reliability of each sample.

The probability of collecting feathers from breeding females in nests was very high (90%). This result seems logical because the female of the pair spends considerably more time in the nest than the male during incubation and then in caring for the chicks (Collopy 1984), thus increasing the possibility of losing feathers in the nest. Vili *et al.* (2013), in their study of the Eastern Imperial Eagle, observed a similar bias of detection of females using the same approach and thus estimated female turnover only.

Because of the large difference in detection between the sexes, only the adult apparent survival of females could be estimated. Dozens of feathers would have to be genotyped to identify males, which would be too costly. This sex bias may not be problematic, as in the literature, no significant survival differential between adult males and females of large raptors has been reported (Newton *et al.* 2016). It thus seems plausible to equate adult female survival with overall adult survival. Similarly, detecting survival variation in females should *a priori* be generalizable to males. Another possibility might be to obtain male genotypes by collecting feathers under known Golden Eagle roosts as Rudnick *et al.* (2008) did with the Eastern Imperial Eagle.

## Number of feathers to sample

The probability of identifying the breeding female of a territory from a feather collected in the nest was calculated from a limited number of samples and should be interpreted with caution. However, if in year *t* turnover is wrongly detected owing to genotyping errors, the assignment error should be corrected in year *t* + 1 (or even later) by redetecting the female that was present in year *t* − 1. Our results show that analysing three feathers per nest

is thus sufficient to unambiguously identify the breeding female. This corresponds to a cost of €180 per territory and per year (with the genetic analysis of two multiplexes), which seems reasonable. Conservation stakeholders would also have the option of regenotyping samples with a low QI (<0.80) in order to 'rescue' samples and more reliably identify the individual.

To increase the chances of genotyping adult feathers, large feathers (rectrices, remiges, coverts) were prioritized, as down feathers are more likely to belong to chicks. There is also a high risk of confusion with other raptor species (potential prey of the Golden Eagle) for down feathers, as opposed to large feathers. Fresh feathers from the surface of the nest should be prioritized to avoid collecting feathers from previous years. For more details on feather collection methodology, see the Data S1.

## Expected accuracy of apparent survival estimates and associated costs

Because long-lived species are highly sensitive to variation in adult survival (Caswell 2006), it is important to be able to detect small changes in this demographic parameter. Indeed, even small variation in adult survival may have significant demographic consequences in these long-lived species (e.g. Gaillard & Yoccoz 2003, Hernández-Matías *et al.* 2013). Our results suggest that detecting these fine-scale variations is complicated owing to the relatively low statistical power obtained with this method. On the whole, it seems that no method can easily detect such small variations of survival in this species at a reasonable cost. For example, it was not possible to detect a 2.5% variation in apparent survival in the scenarios we tested. This means that it is only possible to detect fairly strong variation in the apparent survival rate – around 10% – unless significant resources are deployed. Therefore, sampling plans that detect less than 10% variation in apparent survival do not seem realistic to implement for conservation purposes. Nonetheless, detecting a 10% variation in adult survival still seems relevant, because variations of this magnitude following management measures have been observed in large raptors over less than 10 years (Chevallier *et al.* 2015, Lieury *et al.* 2015) and because anthropogenic threats such as collision with a power line or poisoning may generate high levels of adult mortalities.

The results discussed above rely on an 80% detection rate of the breeding female at the nest
because some eagle pairs do not breed in certain years and therefore do not incubate eggs in the nest (Steenhof *et al.* 1996, McIntyre & Schmidt 2012). It may still be possible to collect feathers for these pairs to increase detection rate, as eagles often reload a nest with material even when no laying occurs, yet the number of feathers would probably be much lower. In addition, the time window during which the nest in question can be detected in the field is shorter, because the adults do not remain for as long as when they rear chicks. In addition to feathers from the nests, it may be possible to search for adult feathers at known roosting sites regularly frequented by the pair to overcome this problem and compensate for the reduced detection rate. In any case, our simulations show that a better annual detection rate (P = 100%) does not provide a better sampling design to detect a 10% variation in adult apparent survival (i.e. monitoring 10 territories for 10 years; see Fig. S2).

Given all these factors, the sampling plan that seems most feasible consists of monitoring 10 territories for 10 years in order to detect a 10% variation in adult apparent survival. To implement this, it is necessary to plan about 30 FD a year to detect the areas and about 20 FD to collect the feathers, for an annual total of 50 FD, and €1800 for the genetic analyses. These costs seem reasonable compared to those determined by Lieury *et al.* (2017) to estimate survival in a population with an approach that requires direct capture and ringing of birds (approximately 2000 FD for 10 years of monitoring). Alternative methods such as GPS tracking (Serratosa *et al.* 2024) are also far more expensive as it may require a large field effort to capture adults (at least in our studied population) and the GPS may only function for few years. If the objective is to be able to detect a 5% decrease in apparent survival, a decrease that can already have strong impact on population viability for long-lived species, it would be necessary to monitor at least 20 pairs for 15 years or 30 pairs for 10 years. However, having to sample for 15 years before obtaining any information about a decline in survival does not seem relevant for conservation purposes, and the alternative of monitoring 30 pairs each year for 10 years may also be too demanding for conservation stakeholders. Regarding the choice of monitored territories, it is preferable to randomly select them to avoid over-selecting territories of good or poor quality, which could bias apparent survival estimates.

## Relation between turnover and survival rate

Golden Eagles start breeding at 4–5 years old and are faithful to their mate for life. The species also shows a strong territory fidelity, and during non-breeding years the pair maintain their bond sometimes repairing nests or even building new ones (Brown & Amadon 1968). Replacement of a breeding adult in a territory may be the consequence of mortality, divorce or breeding dispersal (Booms *et al.* 2011, Vili *et al.* 2013, Kylmänen *et al.* 2023). However, divorce is very rare in large raptors (Watson 2010). Some cases of compulsory divorce have been observed in Barn Owls *Tyto alba* when females lay a second clutch in a year or when breeding was unsuccessful the previous year (Marti 1999). However, this has never been seen for Peregrine Falcons *Falco peregrinus* (Ponnikas *et al.* 2017) while some very occasional cases of divorce have been observed in Golden Eagles after an unsuccessful breeding attempt, but overall, cases of breeding dispersal in the scientific literature appear to be very rare regarding this species (Watson 2010). Therefore, we assume that turnover is overwhelmingly the result of mortality events in Golden Eagles. In addition, even if the turnover rate is a negatively biased estimate of absolute mortality, we think that a change in turnover rate can still be a good proxy of change in survival and thus may be reliable to detect declines in survival.

We encourage researchers to apply this method on other long-lived species that are also highly territorial and for which genetic material can be collected non-invasively. This approach may also be used to complement standard capture-resighting data to identify and monitor adults that are not ringed. It is important to note that for this method to be relevant, it is essential that breeding individuals do not frequently disperse or switch to other territories during their lifetime. For some owl species, for example, breeding dispersal can be common, making the method less relevant (Fasciolo *et al.* 2016).

## CONCLUSIONS

Estimating adult survival is necessary to assess as accurately as possible the population dynamics of long-lived species and the pressures they are subjected to. Although non-invasive genetic approaches require significant resources to obtain
accurate estimates, for raptors, the method of collecting feathers from nests seems less complex, costly and disturbing for the birds than CMR based on ringing and rereading bands, or GPS tagging. The latter methods require very high field effort and are much more invasive as individuals must be captured directly. Our results show that non-invasive sampling can be used on a regular basis in long-lived territorial birds to provide basic information on demographic parameters, crucially needed for population management

We would like to thank the numerous people involved in this programme who helped us in the field to collect feathers and capture the individuals: officers of Cévennes', Ecrins', Pyrénées' and Vanoise' National Parks, of French Office of Biodiversity, RTE, local authorities, volunteers of many naturalists NGO, but also unaffiliated volunteers, climbers and mountain guides. We also would like to thank the Cévennes', Mercantour' and Vanoise' National Parks for helping to co-finance the genetic analysis of the feathers.

## AUTHOR CONTRIBUTIONS

**Sébastien Waterlot:** Formal analysis; writing – review and editing; writing – original draft; visualization; conceptualization; methodology. **Christian Itty:** Methodology; investigation; resources; writing – review and editing. **Arzhela Hemery:** Methodology; investigation; resources; writing – review and editing. **Yoann Bunz:** Resources; funding acquisition; writing – review and editing. **Cécile Kaerle:** Resources; writing – review and editing; investigation. **Guillaume Queney:** Resources; investigation; writing – review and editing. **Aurélien Besnard:** Funding acquisition; project administration; supervision; validation; conceptualization; methodology; writing – review and editing.

## CONFLICT OF INTEREST

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## ETHICAL NOTE

Feather sampling in nests on living individuals was authorized under project no. 579 by Centre de Recherches sur la Biologie des Populations

d'Oiseaux, Muséum National d'Histoire Naturelle (CRBPO-MNHN), the bird-ringing centre designated for France by the Ministry of Environment. These procedures are considered non-invasive and do not require an ethical approval in France.

## FUNDING

The authors declare that the genetic analyses of the samples in this study were co-financed by the Vanoise National Parc, Cévennes National Parc and Mercantour National Parc. No additional funds, grants or support were received during the preparation of the manuscript.

## Data Availability Statement

The datasets generated or analysed during the current study are available on zenodo: DOI of the R code of power analysis: https://doi.org/10.5281/zenodo.11198811. DOI of the genetic data: https://doi.org/10.5281/zenodo.11198849. The R code for the power analysis is also available on Github under the public repository named 'Golden-Eagle'.

## REFERENCES

* Beissinger, S.R. & McCullough, D.R. 2002. Population viability analysis. In *Australian Mammalogy Reviews*, Vol. 24: 251–252. Chicago, IL: University of Chicago Press.

* Bielikova, M., Ficek, A., Valkova, D. & Turna, J. 2010. Multiplex PCR amplification of 13 microsatellite loci for *Aquila chrysaetos* in forensic applications. *Biologia* 65: 1081–1088.

* Booms, T.L., Talbot, S.L., Sage, G.K., McCaffery, B.J., McCracken, K.G. & Schempf, P.F. 2011. Nest-site fidelity and dispersal of gyrfalcons estimated by noninvasive genetic sampling. *Condor* 113: 768–778.

* Bourke, B. & Dawson, D. 2006. Fifteen microsatellite loci characterized in the golden eagle *Aquila chrysaetos* (Accipitridae, Aves). *Mol. Ecol. Notes* 6: 1047.

* Broquet, T. & Petit, E. 2004. Quantifying genotyping errors in noninvasive population genetics. *Mol. Ecol.* 13: 3601–3608.

* Broquet, T., Ménard, N. & Petit, E. 2007. Noninvasive population genetics: A review of sample source, diet, fragment length and microsatellite motif effects on amplification success and genotyping error rates. *Conserv. Genet.* 8: 249–260.

* Brown, L. & Amadon, D. 1968. *Eagles, Hawks, and Falcons of the World*, Vol. 2. London: Country Life Books.

* Caniglia, R., Fabbri, E., Cubaynes, S., Gimenez, O., Lebreton, J.-D. & Randi, E. 2011. An improved procedure to estimate wolf abundance using non-invasive genetic sampling and capture-recapture mixture models. *Conserv. Genet.* 13: 53–64.
Caswell, H. 2006. *Matrix Population Models: Construction, Analysis, and Interpretation*, 2nd edn. Oxford: Oxford University Press.

Cayuela, H., Prunier, J.G., Laporte, M., Gippet, J.M.W., Boualit, L., Guérold, F., Laurent, A., Foletti, F. & Jacob, G. 2021. Demography, genetics, and decline of a spatially structured population of lekking bird. *Oecologia* 195: 117-129.

Chambert, T., Imberdis, L., Bonet, R., Perfus, M., Siefert, N., Cavailhes, J., Fonderflick, J., Malafosse, J.P., Jailloux, A. & Besnard, A. 2020. *Suivi des populations d'Aigles royaux dans les Parcs Nationaux français, analyses et recommandations*: 44. OFB-CEFE cooperation. French Office of Biodiversity website. https://professionnels.ofb.fr/fr/doc/suivi-populations-daigles-royaux-dans-parcs-nationaux-francais-analyses-recommandations

Chevallier, C., Hernandez-Matías, A., Real, J., Vincent-Martin, N., Ravayrol, A. & Besnard, A. 2015. Retrofitting of power lines effectively reduces mortality by electrocution in large birds: An example with the endangered Bonelli's eagle. *J. Appl. Ecol.* 52: 1465-1473.

Collopy, M.W. 1984. Parental care and feeding ecology of golden eagle nestlings. *Auk* 101: 753-760.

Cormack, R.M. 1964. Estimates of survival from the sighting of marked animals. *Biometrika* 51: 429-438.

Crandall, R.H., Craighead, D.J., Bedrosian, B. & Slabe, V.A. 2019. Survival estimates and cause of mortality of golden eagles in south-central Montana. *J. Raptor Res.* 53: 38-45.

Fasciolo, A., Delgado, M.D.M., Cortés, G., Soutullo, Á. & Penteriani, V. 2016. Limited prospecting behaviour of juvenile eagle owls *Bubo bubo* during natal dispersal: Implications for conservation. *Bird Study* 63: 128-135.

Gaillard, J.-M. & Yoccoz, N.G. 2003. Temporal variation in survival of mammals: A case of environmental canalization? *Ecology* 84: 3294-3306.

Gajdárová, B., Belotti, E., Bufka, L., Volfová, J., Wölfl, S., Mináriková, T., Hollerbach, L., Duľa, M., Kleven, O., Kutal, M., Nowak, C., Ozolinš, J., Tám, B., Bryja, J., Koubek, P. & Krojerová-Prokešová, J. 2023. Long-term genetic monitoring of a reintroduced Eurasian lynx population does not indicate an ongoing loss of genetic diversity. *Glob. Ecol. Conserv.* 42: e02399.

Gautschi, B., Tenzer, I., MÜller, J.P. & Schmid, B. 2000. Isolation and characterization of microsatellite loci in the bearded vulture (*Gypaetus barbatus*) and cross-amplification in three old world vulture species. *Mol. Ecol.* 9: 2193-2195.

Gil-Sánchez, J.M., Bautista, J., Godinho, R. & Moleón, M. 2021. Detection of individual replacements in a long-lived bird species, the Bonelli's eagle (*Aquila fasciata*), using three noninvasive methods. *J. Raptor Res.* 55: 552-564.

Hailer, F., Gautschi, B. & Helander, B. 2005. Development and multiplex PCR amplification of novel microsatellite markers in the white-tailed Sea eagle, *Haliaeetus albicilla* (Aves: Falconiformes, Accipitridae). *Mol. Ecol. Notes* 5: 938-940.

Hernández-Matías, A., Real, J. & Pradel, R. 2011. Quick methods for evaluating survival of age-characterizable long-lived territorial birds. *J. Wildl. Manag.* 75: 856-866.

Hernández-Matías, A., Real, J., Moleón, M., Palma, L., Sánchez-Zapata, J.A., Pradel, R., Carrete, M., Gil-Sánchez, J.M., Beja, P., Balbontín, J., Vincent-Martin, N., Ravayrol, A., Benítez, J.R., Arroyo, B., Fernández, C., Ferreiro, E. &

García, J. 2013. From local monitoring to a broad-scale viability assessment: A case study for the Bonelli's Eagle in western Europe. *Ecol. Monogr.* 83: 239-261.

Hiebert Burch, S., Ramenofsky, M., Salvante, K., Wingfield, J. & Gass, C. 2000. Noninvasive methods for measuring and manipulating corticosterone in hummingbirds. *Gen. Comp. Endocrinol.* 120: 235-247.

Horváth, M., Martínez-Cruz, B., Negro, J., Kalmár, L. & Godoy, J. 2005. An overlooked DNA for non-invasive genetic analysis in birds. *J. Avian Biol.* 36: 84-88.

Hoy, S., Ball, R., Lambin, X., Whitfield, D. & Michael, M. 2015. Genetic markers validate using the natural phenotypic characteristics of shed feathers to identify individual northern goshawks *Accipiter gentilis*. *J. Avian Biol.* 47: 443-447.

Hunt, G. 1998. Raptor floaters at Moffat's equilibrium. *Olkos* 82: 191-197.

Jolly, G.M. 1965. Explicit estimates from capture-recapture data with both death and immigration-stochastic model. *Biometrika* 52: 225-247.

Kapetanakos, Y. 2014. Assessing the demography and conservation genetics of Asian vultures using non-invasive molecular techniques. Thesis, Cornell University.

Kenntner, N., Crettenand, Y., Fünfstück, H.-J., Janovsky, M. & Tataruch, F. 2007. Lead poisoning and heavy metal exposure of golden eagles (*Aquila chrysaetos*) from the European Alps. *J. Ornithol.* 148: 173-177.

Kohn, M., York, E., Kamradt, D., Haught, G., Sauvajot, R. & Wayne, R. 1999. Estimating population size by genotyping faeces. *Proc. Biol. Sci.* 266: 657-663.

Kylmänen, A., Karabanina, E., Ollila, T., Ponnikas, S. & Kvist, L. 2023. Turnover and natal dispersal in the Finnish Golden Eagle (*Aquila chrysaetos*) population. *Diversity* 15: 567.

Laake, J. 2013. *RMark: An R Interface for analysis of capture-recapture data with MARK*. AFSC Processed Rep. 2013-01. Seattle, WA: Alaska Fisheries Science Center, NOAA, National Marine Fisheries Service. https://apps-afsc.fisheries.noaa.gov/Publications/ProcRpt/PR2013-01.pdf

Lebreton, J.-D., Burnham, K., Clobert, J. & Anderson, D. 1992. Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies. *Ecol. Monogr.* 62: 67-118.

Lee, J., Tsai, L.-C., Huang, M.-T., Jhuang, J.-A., Yao, C., Chin, S.-C., Wang, L.-C., Linacre, A. & Hsieh, H.-M. 2008. A novel strategy for avian species identification by cytochrome b gene. *Electrophoresis* 29: 2413-2418.

Lieury, N., Devillard, S., Besnard, A., Gimenez, O., Hameau, O., Ponchon, C. & Millon, A. 2017. Designing cost-effective capture-recapture surveys for improving the monitoring of survival in bird populations. *Biol. Conserv.* 214: 233-241.

Lieury, N., Gallardo, M., Ponchon, C., Besnard, A. & Millon, A. 2015. Relative contribution of local demography and immigration in the recovery of a geographically-isolated population of the endangered Egyptian vulture. *Biol. Conserv.* 191: 349-356.

Lukacs, P. & Burnham, K. 2005. Review of capture-recapture methods applicable to noninvasive genetic sampling. *Mol. Ecol.* 14: 3909-3919.

Marti, C.D. 1999. Natal and breeding dispersal in barn owls. *J. Raptor Res.* 33: 1.

Martínez-Cruz, B., David, V., Godoy, J., Negro, J., O'Brien, S. & Johnson, W. 2002. Eighteen polymorphic microsatellite
markers for the highly endangered Spanish imperial eagle (Aquila adalberti) and related species. *Mol. Ecol. Notes* 2: 323–326.

Marucco, F., Pletscher, D., Boitani, L., Schwartz, M., Pilgrim, K. & Lebreton, J.-D. 2009. Wolf survival and population trend using non-invasive CMR techniques in the Western Alps. *J. Appl. Ecol.* 46: 1003–1010.

Marucco, F., Boitani, L., Pletscher, D. & Schwartz, M. 2010. Bridging the gaps between non-invasive genetic sampling and population parameter estimation. *Eur. J. Wildl. Res.* 57: 1–13.

McIntyre, C. & Schmidt, J. 2012. Ecological and environmental correlates of territory occupancy and breeding performance of migratory Golden Eagles *Aquila chrysaetos* in interior Alaska. *Ibis* 154: 124–135.

Mills, L.S., Citta, J.J., Lair, K.P., Schwartz, M.K. & Tallmon, D.A. 2000. Estimating animal abundance using noninvasive DNA sampling: Promise and pitfalls. *Ecol. Appl.* 10: 283–294.

Miquel, C., Bellemain, E., Poillot, C., Bessière, J., Durand, A. & Taberlet, P. 2006. Quality indexes to assess the reliability of genotypes in studies using noninvasive sampling and multiple-tube approach. *Mol. Ecol. Notes* 6: 985–988.

Nebel, C., Gamauf, A., Haring, E., Segelbacher, G., Villers, A. & Zachos, F. 2015. Mitochondrial DNA analysis reveals Holarctic homogeneity and a distinct Mediterranean lineage in the Golden eagle (*Aquila chrysaetos*). *Biol. J. Linn. Soc.* 116: 328–340.

Nebel, C., Gamauf, A., Haring, E., Segelbacher, G., Väli, Ü., Villers, A. & Zachos, F. 2019. New insights into population structure of the European golden eagle (*Aquila chrysaetos*) revealed by microsatellite analysis. *Biol. J. Linn. Soc.* 128: 611–631.

Newton, I., McGrady, M. & Oli, M. 2016. A review of survival estimates for raptors and owls. *Ibis* 158: 227–248.

Peakall, R. & Smouse, P.E. 2012. GenAIEx 6.5: Genetic analysis in excel. Population genetic software for teaching and research—An update. *Bioinformatics* 28: 2537–2539.

Pedrini, P. & Sergio, F. 2001. Density, productivity, diet, and human persecution of Golden Eagles (*Aquila chrysaetos*) in the central-eastern Italian Alps. *J. Raptor Res.* 35: 40–48.

Ponnikas, S., Olliila, T. & Kvist, L. 2017. Turnover and post-bottleneck genetic structure in a recovering population of Peregrine Falcons *Falco peregrinus*. *Ibis* 159: 311–323.

R Core Team 2021. *R: A Language and Environment for Statistical Computing*. Vienna: R Foundation for Statistical Computing.

Rich, L.N., Kelly, M.J., Sollmann, R., Noss, A.J., Maffei, L., Arispe, R.L., Paviolo, A., De Angelo, C.D., Di Blanco, Y.E. & Di Bitetti, M.S. 2014. Comparing capture-recapture, mark-resight, and spatial mark-resight models for estimating puma densities via camera traps. *J. Mammal.* 95: 382–391.

Rodgers, T. & Janečka, J. 2012. Applications and techniques for non-invasive faecal genetics research in felid conservation. *Eur. J. Wildl. Res.* 59: 1–16.

Rudnick, J.A., Katzner, T.E., Bragin, E.A., Rhodes, O.E. Jr. & Dewoody, J.A. 2005. Using naturally shed feathers for individual identification, genetic parentage analyses, and population monitoring in an endangered eastern imperial eagle (*Aquila heliaca*) population from Kazakhstan. *Mol. Ecol.* 14: 2959–2967.

Rudnick, J.A., Katzner, T.E., Bragin, E.A. & DeWoody, J.A. 2008. A non-invasive genetic evaluation of population size,

natal philopatry, and roosting behavior of non-breeding eastern imperial eagles (*Aquila heliaca*) in central Asia. *Conserv. Genet.* 9: 667–676.

Rutkowski, R., Dulisz, B., Szczepański, S., Nowakowski, J., Zwijacz-Kozica, T. & Krzan, P. 2018. Conservation genetics of the capercaillie in Poland: Estimating the size of the Tatra National Park population by the genotyping of non-invasive samples. *Fragm. Faunist.* 60: 119–128.

Saether, B.-E. & Bakke, Ø. 2000. Avian life history variation and contribution of demographic traits to the population growth rate. *Ecology* 81: 642–653.

Salewski, V., Thoma, M. & Schaub, M. 2007. Stopover of migrating birds: Simultaneous analysis of different marking methods enhances the power of capture-recapture analyses. *J. Ornithol.* 148: 29–37.

Seber, G.A. 1965. A note on the multiple-recapture. *Biometrika* 52: 249–259.

Serratosa, J., Oppel, S., Rotics, S., Santangeli, A., Butchart, S.H.M., Cano-Alonso, L.S., Tellería, J.L., Kemp, R., Nicholas, A., Kalvāns, A., Galarza, A., Franco, A.M.A., Andreotti, A., Kirschel, A.N.G., Ngari, A., Soutullo, A., Bermejo-Bermejo, A., Botha, A.J., Ferri, A., Evangelidis, A. & Jones, V.R. 2024. Tracking data highlight the importance of human-induced mortality for large migratory birds at a flyway scale. *Biol. Conserv.* 293: 110525.

Shirose, L.J., Brooks, R.J., Barta, J.R. & Desser, S.S. 1993. Intersexual differences in growth, mortality, and size at maturity in bullfrogs in central Ontario. *Can. J. Zool.* 71: 2363–2369.

Steenhof, K., Kochert, M. & Mcdonald, T. 1996. Interactive effects of prey and weather on golden eagle reproduction. *J. Anim. Ecol.* 66: 350–362.

Taberlet, P., Griffin, S., Goossens, B., Questiau, S., Manceau, V., Escaravage, N., Waits, L.P. & Bouvet, J. 1996. Reliable genotyping of samples with very low DNA quantities using PCR. *Nucleic Acids Res.* 24: 3189–3194.

Taberlet, P., Waits, L.P. & Luikart, G. 1999. Noninvasive genetic sampling: Look before you leap. *Trends Ecol. Evol.* 14: 323–327.

Valière, N. 2002. Gimlet: A computer program for analysing genetic individual identification data. *Mol. Ecol. Notes* 2: 377–379.

Vili, N., Szabó, K., Kovacs, S., Kabai, P., Kalmár, L. & Horváth, M. 2013. High turnover rate revealed by non-invasive genetic analyses in an expanding eastern Imperial eagle population. *Acta Zool. Acad. Sci. Hung.* 59: 279–295.

Waits, J. & Leberg, P. 2000. Biases associated with population estimation using molecular tagging. *Anim. Conserv.* 3: 191–199.

Watson, J. 2010. *The Golden Eagle*. London: A&C Black.

White, G. & Burnham, K. 1999. Program MARK: Survival estimation from populations of marked animals. *Bird Study* 46(Supplement): 120–138.

Williams, B.K., Nichols, J.D. & Conroy, M.J. 2002. *Analysis and Management of Animal Populations: Modeling, Estimation and Decision Making*. London: Academic Press.

Woods, J.G., Paetkau, D., Lewis, D., McLellan, B.N., Proctor, M. & Strobeck, C. 1999. Genetic tagging of free-ranging black and brown bears. *Wildl. Soc. Bull.* 27: 616–627.

Yoccoz, N.G. 1991. Use, overuse, and misuse of significance tests in evolutionary biology and ecology. *Bull. Ecol. Soc.* Am. 72: 106–111.
Received 8 February 2024;
Revision 31 July 2024;
revision accepted 9 September 2024.

Associate Editor: Alexandre Millon.

**SUPPORTING INFORMATION**

Additional supporting information may be found online in the Supporting Information section at the end of the article.

**Table S1.** Details of the 19 individuals that were captured and from which feathers were directly collected in this study.

**Table S2.** Details of microsatellite markers used in this study.

**Table S3.** Error rates per locus and average error rates calculated on consensus genotypes from nest-collected feathers.

**Figure S1.** Results of simulations for annual CMR sampling designs based on random draws of territories each year from 40 possible territories.

**Figure S2.** Results of capture-mark-recapture (CMR) simulations for fixed sampling designs (the same territories monitored each year) with a probability of detection of 1.