Non-Invasive Sex Determination of Nestlings and Adult Bonelli’s Eagles Using Morphometrics

Abstract

Simple Summary
This study explores the use of biometric analysis to identify the sex of Bonelli’s eagles, a bird species with reverse sexual dimorphism where females are usually larger than males. By using linear discriminant analysis of biometric variables, we were able to obtain equations that accurately distinguished between male and female eagles, using just two measurements. The study included 137 Bonelli’s eagles, 82 nestlings and 55 adults, sampled in eastern Spain from 2015 to 2022. The validation procedure reduced the number of variables used, increasing the accuracy of sexing and saving time. Results showed that the lateral tarsus length and dorso-ventral tarsus length measurements were the most effective in distinguishing between male and female eagles of all age classes, while other variables showed some overlap between sexes. This non-invasive method has multiple applications, including estimation of sex ratio for further studies on population dynamics and extinction risk assessments, which could contribute to the conservation of this endangered species.

Abstract
Biometric analysis allows the sexing of most vertebrates, particularly birds. Birds of prey, and, especially, the Bonelli’s eagle (Aquila fasciata), show reverse sexual dimorphism (i.e., females are usually larger than males). In contrast to blood sampling, the use of morphometrics allows sex determination using a non-invasive method, and, therefore, it facilitates fieldwork. By means of a linear discriminant analysis of biometric variables, we obtained different equations that allow the sexing of nestlings and adult Bonelli’s eagles. We sampled 137 Bonelli’s eagles, 82 nestlings and 55 adults in eastern Spain during the period 2015–2022. The sexes obtained after linear discriminant analysis were compared with their molecular sexing. The validation procedure of the linear discriminant equations facilitated the reduction of the number of variables used and, consequently, optimised working time and sexing accuracy. After validation, some equations showed a 100% sexing efficiency for Bonelli’s eagles, particularly for adults. Our results showed that the variables with smaller overlap between the sexes were the lateral tarsus length and dorso-ventral tarsus length, particularly in nestlings. The rest of the variables showed some overlap between the sexes in both age classes. The results we obtained enable the sexing of juvenile and adult Bonelli’s eagles in the field using just these two measurements. Hence, this is an easy, accurate, quick and non-invasive method with multiple applications, including in studies on population dynamics, survival analysis or extinction risk assessments, which, ultimately, could contribute to the improvement of the conservation status of this endangered species.

Full text

Citation: Estellés-Domingo, I.;
López-López, P. Non-Invasive Sex
Determination of Nestlings and
Adult Bonelli’s Eagles Using
Morphometrics. Animals 2023,13,
1201. https://doi.org/10.3390/
ani13071201
Academic Editor: Katarzyna
Wojczulanis-Jakubas
Received: 17 February 2023
Revised: 19 March 2023
Accepted: 29 March 2023
Published: 30 March 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
animals
Article
Non-Invasive Sex Determination of Nestlings and Adult
Bonelli’s Eagles Using Morphometrics
Irene Estellés-Domingo and Pascual López-López *
Movement Ecology Lab, Cavanilles Institute of Biodiversity and Evolutionary Biology, University of Valencia,
C/Catedrático JoséBeltrán 2, E-46980 Paterna, Valencia, Spain
*Correspondence: pascual.lopez@uv.es
Simple Summary:
This study explores the use of biometric analysis to identify the sex of Bonelli’s
eagles, a bird species with reverse sexual dimorphism where females are usually larger than males.
By using linear discriminant analysis of biometric variables, we were able to obtain equations that
accurately distinguished between male and female eagles, using just two measurements. The study
included 137 Bonelli’s eagles, 82 nestlings and 55 adults, sampled in eastern Spain from 2015 to 2022.
The validation procedure reduced the number of variables used, increasing the accuracy of sexing
and saving time. Results showed that the lateral tarsus length and dorso-ventral tarsus length mea-
surements were the most effective in distinguishing between male and female eagles of all age classes,
while other variables showed some overlap between sexes. This non-invasive method has multi-
ple applications, including estimation of sex ratio for further studies on population dynamics and
extinction risk assessments, which could contribute to the conservation of this endangered species.
Abstract:
Biometric analysis allows the sexing of most vertebrates, particularly birds. Birds of
prey, and, especially, the Bonelli’s eagle (Aquila fasciata), show reverse sexual dimorphism (i.e.,
females are usually larger than males). In contrast to blood sampling, the use of morphometrics
allows sex determination using a non-invasive method, and, therefore, it facilitates fieldwork. By
means of a linear discriminant analysis of biometric variables, we obtained different equations
that allow the sexing of nestlings and adult Bonelli’s eagles. We sampled 137 Bonelli’s eagles,
82 nestlings and 55 adults in eastern Spain during the period 2015–2022. The sexes obtained after
linear discriminant analysis were compared with their molecular sexing. The validation procedure
of the linear discriminant equations facilitated the reduction of the number of variables used and,
consequently, optimised working time and sexing accuracy. After validation, some equations showed
a 100% sexing efficiency for Bonelli’s eagles, particularly for adults. Our results showed that the
variables with smaller overlap between the sexes were the lateral tarsus length and dorso-ventral
tarsus length, particularly in nestlings. The rest of the variables showed some overlap between the
sexes in both age classes. The results we obtained enable the sexing of juvenile and adult Bonelli’s
eagles in the field using just these two measurements. Hence, this is an easy, accurate, quick and non-
invasive method with multiple applications, including in studies on population dynamics, survival
analysis or extinction risk assessments, which, ultimately, could contribute to the improvement of the
conservation status of this endangered species.
Keywords:
Accipitridae; birds; biometry; LDA; molecular sexing; PCR; raptors; reverse sexual
dimorphism; sexing
1. Introduction
Sex determination is a procedure with multiple applications in, for example, ecology,
including gene dispersal studies [
1
,
2
], survival studies [
3
], ethological studies [
4
] and
conservation [
5
]. In addition, sex determination is key for conservation programs of
endangered species and for species reintroduction projects [
6
,
7
]. Although most raptors
Animals 2023,13, 1201. https://doi.org/10.3390/ani13071201 https://www.mdpi.com/journal/animals

Animals 2023,13, 1201 2 of 13
have reversed sexual dimorphism, and females are usually larger than males [
8
–
10
], in
some species, sex determination is particularly difficult as the raptors can apparently be
monomorphic [11], showing an overlap in their size range [12,13].
Different techniques have been used for sexing raptors, including methods based
on the study of phenotypic characteristics such as feathers and colours [
14
], morphom-
etry (e.g., [
13
,
15
,
16
]), molecular techniques (e.g., [
17
–
19
]) or the combination of them
(e.g., [
20
,
21
]). Morphometric techniques are commonly used in the field in combination
with molecular methods. However, although DNA analysis can provide accurate sex
identification, morphometry is less invasive and easier to apply in the field.
The Bonelli’s eagle (Aquila fasciata) is a cliff-nesting raptor that occurs throughout
the mountains of the Palearctic, Afrotropical and Indomalayan regions [
22
,
23
]. Due to a
large population decline of this species at the end of the 20th century, it has been listed
as Vulnerable in Spain (Spanish Royal Decree 139/2011, which established the National
Catalogue of Endangered Species) and listed as a Regionally Endangered species in several
regions in Spain.
Previous studies compared morphometric versus DNA techniques for sexing Bonelli’s
eagle nestlings [
24
–
26
]. Other studies used morphometrics and plumage colour patterns
to sex adults [
14
]. However, to the best of our knowledge, there has been no single study
in which the sex of Bonelli’s eagle adults and nestlings of the same population has been
determined using the same methods.
In view of existing precedents, and, given the lack of agreement on which criterion is
the most appropriate for sexing both adult and juvenile Bonelli’s eagles, this paper aims to
develop simple equations to unify the sexing criteria for both age groups of the Bonelli’s
eagle. In addition, in the case of the nestlings, the aim was to enable their sexing considering
their age throughout their ontogenetic development in the nest, which has been overlooked
in most studies. The application of a quick and non-invasive method could facilitate work
on the conservation of the Bonelli’s eagle, in which the correct estimation of the sex ratio is
an important factor in terms of the analysis of survival, population dynamics or the risk of
extinction, and in other potential applications.
2. Material and Methods
2.1. Study Area
The study area is located in eastern Spain, including Albacete, Alicante, Castellón,
Cuenca, and Valencia provinces. The area covers approximately 7600 km
2
, with altitudes
ranging between the coastline and 1814 m above sea level. The climate is Mediterranean,
with an average annual temperature that varies between 17
◦
C in the coastal areas and 8
◦
C
in the mountains. The population size in the study area has decreased in the last decades,
and, currently, less than one hundred pairs remain [27].
2.2. Sample Collection
We trapped adults in the framework of an ongoing GPS tracking project aimed at
studying mortality risks and eagles’ ranging behaviour in eastern Spain (more details
in [
28
–
31
]). Adults were trapped from June to December during 2015–2022. We installed
a trap in each territory consisting of a folding ground net activated by remote control in
the territories occupied by the pairs of interest throughout the study area. The trap was
always under surveillance by the researchers who were hidden nearby. The trap was only
activated once the target individual/s were inside. In most cases, both pair members of
each territory, male and female, were trapped at the same time. Once trapped, adults were
measured, weighted and fitted with a GPS/GSM datalogger in a backpack configuration.
This procedure took a maximum time of 30 to 40 min (more details in [28–30]).
In order to obtain information on the biometry of juveniles (i.e., nestlings), field visits
every two weeks were made during December, January and early February to determine
the locations of the nests and to select which nests would be accessed [
32
]. During this
period, we used 10
×
42 binoculars (Olympus, Tokyo, Japan), a Swarovski 20–60
×
tele-

Animals 2023,13, 1201 3 of 13
scope (Swarovski Optik, Absam, Austria) and a Nikon camera with a 55–300 mm lens
(Nikon, Tokyo, Japan). We estimated the age of nestlings whose parents did not have GPS
transmitters using the feather development patterns, following the methodology described
in [
33
] and used in similar studies of the same species [
32
,
34
]. For those juveniles whose
parents had GPS transmitters, we knew the exact laying date, and, consequently, their age
was precisely assessed [
28
]. As for the adults, morphometric measurements were taken
from juveniles after reaching the nest on the cliffs with the participation of expert climbers.
We also took blood samples from all individuals in this study with a 25 G needle and a
5 mL syringe which were preserved in 3 mL EDTA and heparin tubes for further analyses.
We also took oral swabs to check for the occurrence of Trichomonas gallinae, a protozoan
parasite that causes juvenile mortality in the nest [35].
2.3. Molecular Sexing
All individuals were sexed by using molecular methods based on polymerase chain
reaction (PCR) with PM and PN/p2 25 nmol primers (ThermoFisher Scientific, Waltham,
MA, USA, product code 10336022) and separating the PCR product with a polyacrylamide
gel. We followed the procedure described in [36].
2.4. Morphometric Measurements
All measurements of adults and nestlings were taken by the same person (Pascual
López-López) to minimise measurement errors. All the measurements were taken with a
metal ruler, calipers and a tape measure to the nearest 0.01 mm (Table 1). Body mass was
measured with a digital balance to the nearest 1 g.
Table 1.
Measurements taken from adult and juvenile Bonelli’s eagles in eastern Spain in 2015–2022.
Variable Description Instrument Units
Lateral tarsus Lateral width of tarsus-metatarsus in the narrowing part situated
above the beginning of the metatarsal angle. Calipers mm
Dorso-ventral tarsus Dorso-ventral width of tarsus-metatarsus in the narrowing part
situated above the beginning of the metatarsal angle. Calipers mm
Tarsus length Distance from the tarsometatarsal joint to the articulation of the
middle toe. Calipers mm
Fore claw length Length of the claw of the central toe. Calipers mm
Hallux length Length of the claw of the hind toe. Calipers mm
Bill length Length from the distal end of the bill to the distal dorsal edge of
the cere. Calipers mm
Bill height Length from the distal edge of the cere to the base of the bill, placing
the caliper perpendicular to the bill. Calipers mm
Bill width Width of the bill taken by the calipers placed against the corners of the
bill at the level of the nostrils. Calipers mm
Head width Width of the skull measured behind the eyes in the auricular region. Calipers mm
Head length Maximum length from the back of the skull to the distal end of the bill. Calipers mm
Seventh primary length Length of the seventh primary feather (counting from the outer tip of
the wing inwards) to its insertion. Metal ruler mm
Forearm length Length of the forearm ventral to the shoulder, positioning the ruler at
the elbow to the distal end of the ulna. Metal ruler mm
Rectrix Length of the central feather of the tail to its insertion. Metal ruler mm
Wingspan Distance between the tips of the extended wings. Tape measure mm
Body length Length from the end of the bill to the end of the central feather of the
tail with the bird stretched out on a table. Tape measure mm
Body mass Body mass weighted on a digital balance. Digital balance g

Animals 2023,13, 1201 4 of 13
2.5. Statistical Analyses
Descriptive statistical analyses were performed for each variable, including the mean,
median, maximum, minimum and standard deviation. Data were divided into two age
groups: juveniles and adult birds. The normality of the variables was checked by Shapiro–
Wilk normality tests [
37
]. A nonparametric Mann–Whitney test [
37
] was then performed to
test for differences between sexes for each of the variables. The effect size was calculated
as the z-statistic divided by the square root of the sample size (n) using the “rstatix” R
package [
38
]. The effect size value varied from 0 to close to 1 and was interpreted as
follows: 0.10–<0.3 (small effect), 0.30–<0.5 (moderate effect) and
≥
0.5 (large effect). We also
computed the percentage of sexual dimorphism as 100
×
[(male mean/female mean)
−
1]
for each variable [
13
]. Negative values indicated that the variable is larger in females than
in males.
Secondly, all variables were standardised and then included in a linear discriminant
analysis (LDA) to assess which combination of variables discriminates between males and
females, generating the discriminant function [
14
,
21
,
39
]. The model was validated using a
leave-one-out (jack-knife) procedure [
40
]. To check the effectiveness of the discriminant
function, the results were compared with the results of the molecular sexing.
Then, the morphometric measurements were eliminated from each equation one by
one to determine which combination of variables best explains the differences between
the sexes using Wilk’s lambda statistic [
41
]. In this way, it was possible to calculate the
predictive capacity of the functions obtained using 2
×
2 contingency tables with their
respective 95% confidence interval (CI). All the analyses and figures were prepared using R
version 4.1.2 and the RStudio program environment [
42
] with the following packages: tydi-
verse [
43
], caret [
44
], dplyr [
43
], psycho [
45
], tibble [
46
] and corrplot [
47
]. The significance
threshold was set at p< 0.05.
3. Results
Overall, we sampled 137 Bonelli’s eagles: 82 nestlings and 55 adults. Of these, we
recorded complete information on all variables of 68 nestlings and 33 adults. According to
the molecular analysis, the eagles’ sexes were as follows: 34 males and 34 females among
nestlings and 17 males and 16 females among adults.
3.1. Bonelli’s Eagle Nestlings
Morphometrics differed between the sexes (Table 2). In general, females of similar age
were larger than males for all the variables described. However, there was some degree of
overlap in all the variables (Figure 1).
The variables that showed the greatest dimorphism were the body mass (% dimor-
phism =
−
18.27%), the lateral tarsus (% dimorphism =
−
11.34%) and the dorso-ventral
tarsus (% dimorphism =
−
12.87%), whereas those with the smallest were the rectrix feather
(% dimorphism =
−
1.73%) and the seventh primary feather (% dimorphism =
−
3.77%). All
variables showed significant differences between sex, excepting the rectrix and the seventh
primary feather (Table 2).
Considering all variables, we obtained a linear classification equation for which the
leave-one-out cross-validation showed an average of 84.54% accuracy in males (95%CI =
81.82–87.88%) and 87.62% in females (95%CI = 79.41–90.91%) (Equation (1)). In the LDA
equations scores, >0 indicated females and <0 males. The discriminant function for Bonelli’s
eagle nestlings considering all measured variables was as follows:
Sex score = 0.328 ×(lateral tarsus) + 0.107 ×(dorso-ventral tarsus) −0.026 ×
(tarsus length) −0.323 ×(fore claw) + 0.644 ×(hallux) −0.097 ×(bill length) +
0.706 ×(bill height) + 0.383 ×(bill width) −0.003 ×(head length) −0.057 ×
(head width) −0.011 ×(forearm length) + 0.008 ×(7th primary) −0.036 ×
(rectrix) + 0.004 ×(body length)
(1)

Animals 2023,13, 1201 5 of 13
Due to the large number of variables and the low statistical significance of the differ-
ences in the seventh primary feather, rectrix, tarsus length and forearm length, we decided
to make an LDA with the easiest variables to measure (Equation (2)).
Sex score = 0.439 ×(lateral tarsus) + 0.260 ×(dorso-ventral tarsus) −0.304 ×
(fore claw) + 0.443 ×(hallux) −0.093 ×(bill length) + 0.662 ×(bill height) +
0.378 ×(bill width) + 0.016 ×(head length) −0.082 ×(head width) −0.011 ×
(body length)
(2)
The cross-validated performance showed an average of 85.11% accuracy for sexing
in males (95%CI = 81.82–90.91%) and 86.91% in females (95%CI = 81.82–90.91%) using
Equation (2).
Table 2.
Summary statistics and results of the Mann–Whitney test of morphometric measurements of
male (n= 34) and female (n= 34) Bonelli’s eagles nestlings from eastern Spain sexed by molecular
techniques. Values are given in mm except body mass which is given in grams. The importance of
each variable in the identification of the sexes with respect to the other variables (effect size) and the
percentage of sexual dimorphism are shown.
Variable Sex Mean sd Median Min Max p-Value Effect Size % Dimorphism
Lateral tarsus male 10.60 0.83 10.65 8.81 13.16 <0.001 0.577 −11.34
female 11.96 1.18 11.77 9.72 14.98
Dorso-ventral tarsus male 11.40 0.96 11.43 9.64 14.08 <0.001 0.676 −12.87
female 13.09 1.15 12.98 11.03 17.62
Tarsus length male 104.43 7.92 104.71 84.53 116.55 0.006 0.309 −4.55
female 109.41 7.44 109.76 92.46 121.55
Fore claw male 23.48 2.08 23.64 18.78 31.74 0.002 0.344 −5.19
female 24.77 2.54 24.96 20.47 34.88
Hallux male 29.27 1.72 29.52 24.70 33.73 <0.001 0.476 −6.77
female 31.40 2.48 31.67 25.12 36.96
Bill length male 24.32 1.79 24.50 20.58 28.99 0.001 0.397 −6.21
female 25.93 2.08 25.96 21.42 31.12
Bill height male 19.23 1.22 19.30 16.53 22.40 <0.001 0.543 −6.53
female 20.58 0.93 20.68 18.06 22.53
Bill width male 13.68 1.32 13.86 10.79 16.84 0.002 0.356 −6.73
female 14.66 1.26 14.55 11.75 17.76
Head length male 87.02 5.04 87.15 76.14 99.47 0.003 0.335 −4.24
female 90.87 6.38 90.76 75.93 105.71
Head width male 43.48 2.56 43.46 38.42 48.54 0.008 0.295 −4.72
female 45.64 4.16 45.23 36.14 58.54
Forearm length male 161.41 13.92 165.50 121.00 184.00 0.032 0.225 −4.80
female 169.56 17.57 170.00 128.00 210.00
Seventh primary male 145.47 41.06 149.50 68.00 250.00 0.352 0.047 −3.77
female 151.18 36.22 146.50 79.00 247.00
Rectrix male 105.41 33.99 105.00 21.00 205.00 0.527 0.001 −1.73
female 107.27 30.13 104.50 46.00 188.00
Body length male 436.87 52.08 440.00 325.00 580.00 0.024 0.240 −5.03
female 460.03 47.68 465.00 349.00 585.00
Body mass male 1359.0 171.0 1387.50 920 1625 <0.001 0.597 −18.27
female 1662.9 254.1 1725.00 1015 2180

Animals 2023,13, 1201 6 of 13
Animals 2023, 13, x FOR PEER REVIEW 6 of 13
Figure 1. Morphometrics of male (yellow dots, n = 34) and female (blue triangles, n = 34) Bonelli’s
eagle nestlings from eastern Spain sexed by molecular techniques. Measurements are given in mm
and nestlings’ age in days. Density plots are shown to the right of each figure.
Figure 1.
Morphometrics of male (yellow dots, n= 34) and female (blue triangles, n= 34) Bonelli’s
eagle nestlings from eastern Spain sexed by molecular techniques. Measurements are given in mm
and nestlings’ age in days. Density plots are shown to the right of each figure.

Animals 2023,13, 1201 7 of 13
3.2. Bonelli’s Eagle Adults
As in the nestlings, morphometrics differed between sexes, and females were larger
than males in all the variables described (Table 3). There was overlap in all the variables
but to a smaller extent than in the nestlings (Figure 2).
The greatest degree of sexual dimorphism was observed in body mass (% dimorphism
=
−
24.65), lateral tarsus (% dimorphism =
−
12.70) and dorso-ventral tarsus (% dimorphism
=
−
12.17), and the lowest degree of sexual dimorphism was obtained for head length (%
dimorphism = −5.54%) and head width (% dimorphism = −4.95) (Table 3).
Considering all variables, we obtained a linear classification equation for adults for
which the leave-one-out cross-validation showed an average of 94.30% accuracy in males
(95%CI = 93.75–100%) and 93.75% in females (95%CI = 93.33–100%) (Equation (3)). As for
nestlings, in this equation, scores >0 indicated females and <0 males.
Table 3.
Summary statistics and results of the Mann–Whitney test of morphometric measurements
of adult male (n= 17) and female (n= 16) Bonelli’s eagles from eastern Spain sexed by molecular
techniques. Values are given in mm except body mass which is given in grams. The importance of
each variable in the identification of the sexes with respect to the other variables (effect size) and the
percentage of sexual dimorphism are shown.
Variable Sex Mean sd Median Min Max p-Value Effect Size % Dimorphism
Lateral tarsus male 10.70 0.75 10.40 9.70 11.80 <0.001 0.769 −12.70
female 12.26 0.57 12.35 11.40 13.12
Dorso-ventral tarsus male 12.68 0.58 12.80 11.80 13.60 <0.001 0.844 −12.17
female 14.44 0.57 14.50 13.44 15.20
Hallux male 38.59 1.39 38.70 36.30 41.90 <0.001 0.771 −9.90
female 42.82 1.79 42.85 38.90 46.00
Bill length male 34.41 5.21 32.12 29.60 44.70 0.002 0.514 −10.97
female 38.65 6.29 35.05 32.45 48.30
Bill height male 22.69 0.63 22.80 21.60 23.80 <0.001 0.738 −6.01
female 24.15 0.68 24.05 22.80 25.20
Bill width male 16.97 0.96 17.00 15.50 19.00 0.002 0.505 −7.68
female 18.38 1.36 18.70 15.90 20.10
Head length male 98.93 4.89 99.50 84.80 107.60 <0.001 0.677 −5.54
female 104.72 2.51 104.15 100.80 109.50
Head width male 52.07 2.90 52.00 47.00 56.80 0.004 0.464 −4.95
female 54.79 2.13 54.55 51.90 58.90
Forearm length male 179.47 11.34 181.00 164.00 200.00 0.017 0.373 −5.94
female 190.81 12.37 190.50 175.00 212.00
Seventh primary male 33.15 3.34 34.00 24.90 39.20 0.003 0.484 −7.64
female 35.89 2.23 36.75 31.10 39.30
Rectrix male 250.53 14.37 250.00 219.00 275.00 <0.001 0.587 −7.43
female 270.63 13.73 271.50 242.00 295.00
Body length male 634.94 17.06 638.00 600.00 665.00 <0.001 0.773 −7.38
female 685.56 22.75 693.00 641.00 710.00
Wingspan male 151.18 7.88 152.00 138.00 162.00 <0.001 0.694 −9.03
female 166.19 7.34 169.50 152.00 174.00
Body mass male 1882.2 115.2 1894.00 1700 2060 <0.001 0.851 −24.65
female 2498.1 182.9 2490.00 2160 2900

Animals 2023,13, 1201 8 of 13
Animals 2023, 13, x FOR PEER REVIEW 8 of 13
Figure 2. Morphometrics of adult male (n = 17) and female (n = 16) Bonelli’s eagles from eastern
Spain sexed by molecular techniques. Measurements are given in mm. Density plots are shown to
the right of each figure.
Figure 2.
Morphometrics of adult male (n= 17) and female (n= 16) Bonelli’s eagles from eastern
Spain sexed by molecular techniques. Measurements are given in mm. Density plots are shown to
the right of each figure.

Animals 2023,13, 1201 9 of 13
The discriminant function for Bonelli’s eagle adults considering all measured variables
is as follows:
Sex score = 1.491 ×(lateral tarsus) + 0.645 ×(dorso-ventral tarsus)) + 0.453 ×
(hallux) + 0.186 ×(bill length) + 0.380 ×(bill height) −0.450 ×(bill width) −
0.119 ×(head length) −0.102 ×(head width) + 0.044 ×(forearm length) −0.064
×(7th primary) + 0.026 ×(rectrix) + 0.030 ×(body length) −0.008 ×(wingspan)
(3)
After the removal of the feather variables, the cross-validated performance of the LDA
showed an average of 94.86% accuracy of sexing in males (95%CI = 93.75–100%) and 93.75%
in females (95%CI = 93.33–100%) (Equation (4)).
Sex score = 1.452 ×(lateral tarsus) + 0.647 ×(dorso-ventral tarsus)) + 0.493 ×
(hallux) + 0.067 ×(bill length) + 0.537 ×(bill height) −0.450 ×(bill width) −
0.096 ×(head length) −0.072 ×(head width) + 0.037 ×(body length) −0.017 ×
(wingspan)
(4)
Due to the high significance of the dorso-ventral tarsus and the lateral tarsus, we built
a model using just those measures. This model showed 100% accuracy for sexing in females
(95%CI = 100–100%) and 93.54% in males (95%CI = 87.50–100%) (Equation (5)).
Sex score = 0.745 ×(lateral tarsus) + 1.317 ×(dorso-ventral tarsus) (5)
4. Discussion
Our results reaffirm that both nestling and adult Bonelli’s eagles show reverse sexual
dimorphism, which is in agreement with previous works [
14
,
25
,
26
]. However, as reported
in other raptors [
48
], adults and nestlings require different equations for sex discrimination
as they show morphological differences. This happens because late-state nestlings of raptor
species are known to have not yet fully developed, particularly in terms of their bills [
49
,
50
],
hallux claws [51] and feathers [21,52].
We found that the lateral tarsus and the dorso-ventral tarsus showed the lowest
variation throughout the development of the nestlings. Despite the allometric growth that
characterises nestlings of birds of prey, both variables stabilise at early ages and, thus, allow
the correct distinction between sexes.
The least statistically significant variables in the LDA for the nestlings were the lengths
of the rectrix and of the seventh primary. This may be due to the fact that feathers in raptors
generally vary only slightly in size between sexes [
53
,
54
], which was previously observed in
Bonelli’s eagle nestlings [
25
], and because both variables showed high individual variation
regardless of sex.
In addition to the general function in which all variables were included, we developed
a discriminant function in which only those variables that were easy to measure were taken
into account (i.e., those excluding the feathers). The classification efficiency of more than
84% may be a consequence of the removal of the length of the feathers as sex predictors and
the fact that the variables that were easy to measure were also variables that significantly
contributed to the power of the discriminant functions.
In contrast to previous studies in which body mass and feather length (wing and
tail length) were selected as the best discriminant variables [
26
], our study shows that
sex discrimination of nestlings can be easily obtained using variables with little variation
throughout chicks’ ontogeny. This is particularly important in the case of variables such
as body mass and feather length. In raptors, nestlings’ body mass and feather length
show large variation between individuals [
55
,
56
]. The body mass varies in relation to food
availability and even within individuals during the day [
57
,
58
]. Therefore, the use of these
variables that do not stabilise their growth during chicks’ development is not recommended
for sex determination.

Animals 2023,13, 1201 10 of 13
In the case of adults, we confirmed previous studies in which Bonelli’s eagles showed a
marked reverse sexual dimorphism [
14
], being one of the Palaearctic species with the great-
est size differences between sexes [
23
]. In contrast to García et al. [
14
], which did not find
any overlap in body length and hallux length between sexes and, thus, recommended these
variables for sex determination, we found a slight degree of overlap among individuals
(Figure 2). This limits their application as single variables for sex determination.
The adults’ equation including all the variables showed a 94% average success rate
in sexing. This function may not have been fully effective due to the inclusion of many
variables with high overlap. Furthermore, the equation without feather variables showed
an average 95% success rate in males and 94% in females. Interestingly, the equation with
just the lateral tarsus and the dorso-ventral tarsus measurements included as predictors
showed 100% accuracy in females and 94% in males.
Despite the high percentage of sexual dimorphism, the body mass showed high
individual variation and some degree of overlap among individuals (Figure 2). In fact, we
weighted the same individuals at different times because we trapped some of them several
times because the GPS transmitter had fallen off. We found differences in the body mass
of these individuals throughout the annual cycle. In agreement with García et al. [
14
], the
weight of individuals can vary up to 10% depending on physical condition, the ecological
characteristics of the foraging area and the phase of the year (i.e., the highest body mass
values were recorded in months prior to reproduction). Therefore, body mass should be
used with caution in sex determination in the field.
Finally, previous studies suggested that the differences in size observed across Bonelli’s
eagle subpopulations could reflect biological differences among them [
14
,
26
]. However,
we consider that these small differences might be better explained by the fact that mea-
surements were taken by different researchers, at least in [
26
], which could have affected
the result. In our opinion, the changes in individual size can be explained solely by the
different ways of measuring used by different researchers or by limited sample size. The
metapopulation structure of the Bonelli’s eagle [
59
,
60
], in which some exchange of individ-
uals between the Iberian and southern French regions occurs [
1
,
2
,
61
,
62
], does not favour
any actual sub-regional differences.
5. Conclusions
Our results show that simple equations provide a great advantage for rapid sex deter-
mination in the field with small error. This is particularly important for nestlings, for which
nests must be accessed by experienced climbers during a short period of development,
usually when chicks are 35–45 days old. Taking body measurements when chicks are
younger than 25–30 days old might result in nest abandonment or the nestlings’ death
as a result of their lower thermoregulation ability. On the other hand, manipulation of
chicks older than 50–55 days might result in nestlings’ premature abandonment of the
nest as the climbers approach, causing serious injuries or even death in extreme cases.
Thus, the availability of simple methods for quick sex determination is essential when the
manipulation time is limited.
In conclusion, we consider the equations we proposed to allow the quick sexing of
juvenile and adult Bonelli’s eagles in the field is an easy, accurate and non-invasive approach
with multiple applications. This is particularly useful for studies on population dynamics
and survival analysis or extinction risk assessments that, ultimately, could contribute to the
improvement of the conservation status of this endangered species.
Author Contributions:
P.L.-L. conceived the ideas and designed the methodology. I.E.-D. and P.L-L.
collected and analysed the data, wrote the manuscript and contributed critically to the drafts. All
authors have read and agreed to the published version of the manuscript.

Animals 2023,13, 1201 11 of 13
Funding:
Adult trapping and GPS marking was supported by Red Eléctrica de España, ACCIONA
Eólica de Levante, LafargeHolcim and the Wildlife Service of the Valencian Community regional
government (Conselleria d’Agricultura, Desenvolupament Rural, Emergència Climàtica i Transició
Ecològica, Generalitat Valenciana, Spain). Field monitoring was self-financed by the second author.
Institutional Review Board Statement:
Ethical review and approval were waived for this study
because it does not include animals used for experimentation. Trapping and marking activities
were authorised and conducted under permissions issued by the regional authorities (Generalitat
Valenciana, Spain), and all efforts were made to minimise handling time to avoid any suffering to
the eagles.
Informed Consent Statement: Not applicable.
Data Availability Statement: All data used in this study are available upon request to authors.
Acknowledgments:
We would like to thank the regional environmental administration (Wildlife
Service of the Valencian Community Regional Government; Conselleria d’Agricultura, Desenvolupa-
ment Rural, Emergència Climàtica i TransicióEcològica, Generalitat Valenciana, Spain) for the help
during fieldwork. Special thanks are due to the personnel from the Wildlife Monitoring Team (Equipo
de Seguimiento de Fauna—VAERSA) and the members of the work at height group of the regional
forest rangers (Grupo de Intervención en Altura—Agentes Medioambientales). We would also like
to thank J. Sansano for his help in sex determination by molecular analysis. C. García, F. García,
V. García, J. de la Puente, V. Urios, S. Morollón, A. López and O. Egea provided help during the
fieldwork. Three anonymous reviewers provided valuable suggestions that improved early versions
of the original manuscript. This paper is a part of Irene Estellés’ doctoral thesis at the University
of Valencia.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Cadahía, L.; López-López, P.; Urios, V.; Soutullo, Á.; Negro, J.J. Natal dispersal and recruitment of two Bonelli’s Eagles Aquila
fasciata: A four-year satellite tracking study. Acta Ornithol. 2009,44, 193–198. [CrossRef]
2.
Cadahía, L.; López-López, P.; Urios, V.; Negro, J.J. Satellite telemetry reveals individual variation in juvenile Bonelli’s eagle
dispersal areas. Eur. J. Wildl. Res. 2010,56, 923–930. [CrossRef]
3.
Newton, I.; Marquiss, M.; Rothery, P. Age structure and survival in a sparrowhawk population. J. Anim. Ecol.
1983
,52, 591–602.
[CrossRef]
4. Clutton-Brock, T. Sex ratio variation in birds. Ibis 1986,128, 317–329. [CrossRef]
5. Griffiths, R.; Tiwari, B. Sex of the last wild Spix’s macaw. Nature 1995,375, 454. [CrossRef] [PubMed]
6.
Muriel, R.; Casado, E.; Schmidt, D.; Calabuig, C.P.; Ferrer, M. Morphometric sex determination of young Ospreys Pandion
haliaetus using discriminant analysis. Bird Study 2010,57, 336–343. [CrossRef]
7. Ferrer, M.; Delecourt, C. Sex identification in the Spanish imperial eagle. J. Field Ornithol. 1992,63, 359–364.
8. Wheeler, P.; Greenwood, P.J. The evolution of reversed sexual dimorphism in birds of prey. Oikos 1983,40, 145–149. [CrossRef]
9.
Montgomerie, R.; Lundberg, A. Reversed sexual dimorphism in raptors: Which sex changed size? Oikos
1989
,56, 283–286.
[CrossRef]
10.
Schoenjahn, J.; Pavey, C.R.; Walter, G.H. Why female birds of prey are larger than males. Biol. J. Linn. Soc.
2020
,129, 532–542.
[CrossRef]
11.
Krüger, O. The evolution of reversed sexual size dimorphism in hawks, falcons and owls: A comparative study. Evol. Ecol.
2005
,
19, 467–486. [CrossRef]
12. Morrison, J.; Maltbie, M. Methods for gender determination of Crested Caracaras. J. Raptor Res. 1999,33, 128–133.
13.
López-López, P.; Gil, J.A.; Alcántara, M. Morphometrics and sex determination in the endangered Bearded Vulture (Gypaetus
barbatus). J. Raptor Res. 2011,45, 361–366. [CrossRef]
14.
García, V.; Moreno-Opo, R.; Tintó, A. Sex differentiation of Bonelli’s eagle Aquila fasciata in western europe using morphometrics
and plumage colour patterns. Ardeola 2013,60, 261–277. [CrossRef]
15.
Harmata, A.; Montopoli, G. Morphometric sex determination of North American Golden Eagles. J. Raptor Res.
2013
,47, 108–116.
[CrossRef]
16.
Boucheker, A.; Nedjah, R.; Prodon, R.; Gillingham, M.; Dechaume-Moncharmont, F.-X.; Béchet, A.; Samraoui, B. Cohort effect on
discriminant rate: The case of greater flamingo (Phoenicopterus roseus) chicks sexed with morphological characters. Web Ecol.
2020
,
20, 153–159. [CrossRef]
17.
Griffiths, R.; Daan, S.; Dijkstra, C. Sex identification in birds using two CHD genes. Proc. R. Soc. London. Ser. B Biol. Sci.
1996
,263,
1251–1256.

Animals 2023,13, 1201 12 of 13
18.
Fridolfsson, A.-K.; Ellegren, H. A simple and universal method for molecular sexing of non-ratite birds. J. Avian Biol.
1999
,30,
116–121. [CrossRef]
19. Dubiec, A.; Zagalska-Neubauer, M. Molecular techniques for sex identification in birds. Biol. Lett. 2006,43, 3–12.
20.
Balbontín, J.; Ferrer, M.; Casado, E. Sex determination in booted eagles (Hieraaetus pennatus) using molecular procedures and
discrimiant function analysis. J. Raptor Res. 2001,35, 20–23.
21.
Dykstra, C.R.; Mays, H.L.; Hays, J.L.; Simon, M.M.; Wegman, A.R. Sexing Adult and Nestling Red-Shouldered Hawks Using
Morphometrics and Molecular Techniques1. J. Raptor Res. 2012,46, 357–364. [CrossRef]
22.
Cramp, S.; Ferguson-Lees, I.J.; Gillmor, R.; Hollom, P.; Hudson, R.; Nicholson, E.M.; Ogilvie, M.; Olney, P.; Voous, K.; Wattel, J. The
Birds of the Western Palearctic; Oxford University Press: Oxford, UK, 1983; p. 41.
23. Ferguson-Lees, J.; Christie, D.A. Raptors of the World; Houghton Mifflin Harcourt: Boston, MA, USA, 2001.
24. Mañosa, S.; Codina, J. Age estimation and growth patterns in nestling Bonelli’s Eagles. J. Raptor Res. 1995,29, 273–275.
25.
Palma, L.; Mira, S.; Cardia, P.; Beja, P.; Guillemaud, T.; Ferrand, N.; Cancela, M.; da Fonseca, L. Sexing Bonelli’s Eagle nestlings:
Morphometrics versus molecular techniques. J. Raptor Res. 2001,35, 187–193.
26.
Redondo-Gómez, D.; Bautista, J.; Gil-Sánchez, J.; Pares, F.; Hernandez-Matias, A.; Resano-Mayor, J.; Real, J.; Pacteau, C.; Madero,
A.; Moleón, M. Towards accurate and simple morphometric sex differentiation in Bonelli’s Eagle Aquila fasciata nestlings:
Interpopulation variations and influence of growth conditions. Avian Biol. Res. 2022,15, 3–12. [CrossRef]
27. Del Moral, J.C.; Molina, B. El águila Perdicera en España, Población Reproductora en 2018 y Método de Censo; SEO/BirdLife: Madrid,
Spain, 2018.
28.
López-López, P.; Perona, A.; Egea-Casas, O.; Morant, J.; Urios, V. Tri-axial accelerometry shows differences in energy expenditure
and parental effort throughout the breeding season in long-lived raptors. Curr. Zool. 2022,68, 57–67. [CrossRef]
29.
Morollón, S.; Urios, V.; López-López, P. Home-Range Size and Space Use of Territorial Bonelli’s Eagles (Aquila fasciata) Tracked by
High-Resolution GPS/GSM Telemetry. Diversity 2022,14, 1082. [CrossRef]
30.
Morollón, S.; Urios, V.; López-López, P. Fifteen days are enough to estimate home-range size in some long-lived resident eagles.
J. Ornithol. 2022,163, 849–854. [CrossRef]
31.
Perona, A.M.; Urios, V.; López-López, P. Holidays? Not for all. Eagles have larger home ranges on holidays as a consequence of
human disturbance. Biol. Conserv. 2019,231, 59–66. [CrossRef]
32.
López-López, P.; García-Ripollés, C.; Urios, V. Population size, breeding performance and territory quality of Bonelli’s eagle
Hieraaetus fasciatus in eastern Spain. Bird Study 2007,54, 335–342. [CrossRef]
33.
Gil-Sánchez, J.M. Efecto de la altitud y de la disponibilidad de presas en la fecha de puesta del águila-azor perdicera (Hieraaetus
fasciatus) en la provincia de Granada (SE de España). Ardeola 2000,47, 1–8.
34.
López-López, P. Potential negative effects of the installation of video surveillance cameras in raptors’ nests. Sci. Rep.
2022
,12,
21969. [CrossRef] [PubMed]
35.
Sansano-Maestre, J.; Garijo-Toledo, M.; Gomez-Munoz, M. Prevalence and genotyping of Trichomonas gallinae in pigeons and
birds of prey. Avian Pathol. 2009,38, 201–207. [CrossRef]
36.
Ito, H.; Sudo-Yamaji, A.; Abe, M.; Murase, T.; Tsubota, T. Sex identification by alternative polymerase chain reaction methods in
Falconiformes. Zool. Sci. 2003,20, 339–344. [CrossRef] [PubMed]
37. Zar, J. Biostatistical Analysis, 5th ed.; Pearson: New York, NY, USA, 2014.
38.
Kassambara, A. Rstatix: Pipe-Friendly Framework for Basic Statistical Tests. 2023. Version 0.7.2. Available online: https:
//CRAN.R-project.org/package=rstatix (accessed on 28 March 2023).
39.
Dillon, W.R.; Goldstein, M. Multivariate Analysis: Methods and Applications; Wiley: New York, NY, USA, 1984; ISBN 0-471-08317-8.
40.
Dechaume-Moncharmont, F.-X.; Monceau, K.; Cezilly, F. Sexing birds using discriminant function analysis: A critical appraisal.
Auk 2011,128, 78–86. [CrossRef]
41.
McLachlan, G.J. Discriminant Analysis and Statistical Pattern Recognition; John Wiley & Sons: Hoboken, NJ, USA, 2005;
ISBN 978-0-471-72528-2.
42.
R Core Team. R: A Language and Environment for Statistical Computing 2022. Available online: http://www.r-project.org/
index.html (accessed on 28 March 2023).
43.
Wickham, H.; François, R.; Henry, L.; Müller, K.; Vaughan, D. Posit PBC Dplyr: A Grammar of Data Manipulation 2023. Version
1.1.1. Available online: https://CRAN.R-project.org/package=dplyr (accessed on 28 March 2023).
44.
Kuhnaut, M.; Wing, J.; Weston, S.; Williams, A.; Keefer, C.; Engelhardt, A.; Cooper, T.; Mayer, Z.; Kenkel, B.; R Core Team; et al.
Caret: Classification and Regression Training 2022; R Core Team: Vienna, Austria, 2022.
45.
Makowskiaut, D.; Najberg, H.; Simko, V.; Epskamp, S. psycho: Efficient and Publishing-Oriented Workflow for Psychological
Science 2021. Version 0.6.1. Available online: https://CRAN.R-project.org/package=psycho (accessed on 28 March 2023).
46.
Müller, K.; Wickham, H.; Francois, R.; Bryan, J. Tibble: Simple Data Frames 2022. Version 3.2.1. Available online: https:
//CRAN.R-project.org/package=tibble (accessed on 28 March 2023).
47.
Wei, T.; Simko, V.; Levy, M.; Xie, Y.; Jin, Y.; Zemla, J.; Freidank, M.; Cai, J.; Protivinsky, T. Corrplot: Visualization of a Correlation
Matrix 2021. Version 0.92. Available online: https://CRAN.R-project.org/package=corrplot (accessed on 28 March 2023).
48.
Pay, J.M.; Katzner, T.E.; Wiersma, J.M.; Brown, W.E.; Hawkins, C.E.; Proft, K.M.; Cameron, E.Z. Morphometric Sex Identification
of Nestling and Free-Flying Tasmanian Wedge-Tailed Eagles (Aquila audax fleayi). J. Raptor Res. 2021,55, 539–551. [CrossRef]

Animals 2023,13, 1201 13 of 13
49.
Bortolotti, G.R. Physical development of nestling bald eagles with emphasis on the timing of growth events. Wilson Bull.
1984
,96,
524–542.
50.
Donohue, K.C.; Dufty, A.M., Jr. Sex determination of Red-tailed Hawks (Buteo jamaicensis calurus) using DNA analysis and
morphometrics. J. Field Ornithol. 2006,77, 74–79. [CrossRef]
51.
Bortolotti, G.R. Influence of sibling competition on nestling sex ratios of sexually dimorphic birds. Am. Nat.
1986
,127, 495–507.
[CrossRef]
52.
McPherson, S.C.; Brown, M.; Downs, C.T. Gender-related morphometric differences in mature and nestling Crowned Eagles,
with comments on ringing of eagle nestlings in KwaZulu-Natal, South Africa. Ostrich 2017,88, 195–200. [CrossRef]
53. Poole, K. Determining age and sex of nestling Gyrfalcons. J. Raptor Res. 1989,23, 45–47.
54. Sodhi, N. Growth of nestling Merlins, Falco columbarius. Can. Field-Nat. Ott. ON 1992,106, 387–389.
55.
Choi, C.-Y.; Nam, H.-Y.; Park, J.-G.; Bing, G.-C.; Park, C.; Cho, S.-Y. Morphometrics and sexual dimorphism of Chinese Goshawks
(Accipiter soloensis). J. Raptor Res. 2013,47, 385–391. [CrossRef]
56.
Smith, N.R.; Afton, A.D.; Hess, T.J., Jr. Morphometric sex determination of after-hatch-year Bald Eagles in Louisiana. J. Raptor Res.
2016,50, 301–304. [CrossRef]
57. Barton, N.W.; Houston, D. A comparison of digestive efficiency in birds of prey. Ibis 1993,135, 363–371. [CrossRef]
58.
Bordner, Z.E.; McCabe, R.A.; Brinker, D.; Rosenfield, R.N.; Jacobs, E.A.; England, C.; Wilson, M.; Goodrich, L.J. Broad-Winged
Hawk Size Varies by Sex and Latitude in North America. J. Raptor Res. 2022,56, 412–422. [CrossRef]
59.
Soutullo, A.; López-López, P.; Urios, V. Incorporating spatial structure and stochasticity in endangered Bonelli’s eagle’s population
models: Implications for conservation and management. Biol. Conserv. 2008,141, 1013–1020. [CrossRef]
60.
Hernandez-Matias, A.; Real, J.; Moleon, M.; Palma, L.; Sanchez-Zapata, J.; Pradel, R.; Carrete, M.; Gil-Sanchez, J.; Beja, P.;
Balbontin, J.; et al. From local monitoring to a broad-scale viability assessment: A case study for the Bonelli’s Eagle in western
Europe. Ecol. Monogr. 2013,83, 239–261. [CrossRef]
61. Real, J.; Manosa, S. Dispersal of juvenile and immature Bonelli’s Eagles in northeastern Spain. J. Raptor Res. 2001,35, 9–14.
62.
Hernandez-Matias, A.; Real, J.; Pradel, R.; Ravayrol, A.; Vincent-Martin, N.; Bosca, F.; Cheylan, G. Determinants of territorial
recruitment in Bonelli’s eagle (Aquila fasciata) populations. Auk 2010,127, 173–184. [CrossRef]
Disclaimer/Publisher’s Note:
The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.