Is black coat color in wolves of Iran an
evidence of admixed ancestry with dogs?
Rasoul Khosravi, Marzieh Asadi
Aghbolaghi, Hamid Reza Rezaei, Elham
Nourani & Mohammad Kaboli
Journal of Applied Genetics
Microorganisms and Organelles
ISSN 1234-1983
J Appl Genetics
DOI 10.1007/s13353-014-0237-6
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J Appl Genetics
DOI 10.1007/s13353-014-0237-6
ANIMAL GENETICS • ORIGINAL PAPER
Is black coat color in wolves of Iran an evidence of admixed
ancestry with dogs?
Rasoul Khosravi & Marzieh Asadi Aghbolaghi &
Hamid Reza Rezaei & Elham Nourani &
Mohammad Kaboli
Received: 12 August 2013 / Revised: 6 May 2014 / Accepted: 23 July 2014
# Institute of Plant Genetics, Polish Academy of Sciences, Poznan 2014
Abstract Melanism is not considered a typical characteristic
in wolves of Iran and dark wolves are believed to have
originated from crossbreeding with dogs. Such hybrid individuals can be identified with the combined use of genetic and
morphological markers. We analyzed two black wolves using
a 544 base pairs (bp) fragment of the mtDNA control region
and 15 microsatellite loci in comparison with 28 dogs, 28
wolves, and four known hybrids. The artificial neural networks (ANNs) method was applied to microsatellite data to
separate genetically differentiated samples of wolves, dogs,
and hybrids, and to determine the correct class for the black
specimens. Individual assignments based on ANNs showed
that black samples were genetically closer to wolves. Also, in
the neighbor-joining network of mtDNA haplotypes, wolves
and dogs were separated, with the dark specimens located in
the wolf branch as two separate haplotypes. Furthermore, we
compared 20 craniometrical characters of the two black individuals with 14 other wolves. The results showed that
craniometrical measures of the two black wolves fall within
the range of wolf skulls. We found no trace of recent hybridization with free-ranging dogs in the two black wolves. Dark
coat color might be the result of a natural combination of
alleles in the coat-color-determining gene, mutation in the K
R. Khosravi
Department of Environmental Sciences, Faculty of Natural
Resources, Isfahan University of Technology, Isfahan, Iran
M. Asadi Aghbolaghi : E. Nourani : M. Kaboli (*)
Department of Environmental Sciences, Faculty of Natural
Resources, University of Tehran, Karaj, Iran
e-mail: mkaboli@ut.ac.ir
H. R. Rezaei
Department of Environmental Sciences, Faculty of Natural
Resources, Gorgan University of Agriculture and Natural Resources,
Gorgan, Iran
locus due to past hybridization with free-ranging dogs, or the
effect of ecological factors and adaption to habitat conditions.
Keywords Black wolf . Hybridization . mtDNA .
Microsatellite . Artificial neural networks
Introduction
One of the most documented variations among wolves that
occupy different habitat types is color pattern. Mech (1970)
described coat color in wolves ranging from white, buff,
tawny, reddish, and gray to black, with gray being the most
common pelage (Apollonio et al. 2004). Anderson et al.
(2009) showed that dark color in North American wolves
living in forest habitats is the result of apparent selection for
the melanistic KB allele due to past hybridization with the
domestic dog. Also, many authors have suggested that anomalous morphological traits in wolves, such as atypical color
patterns, dewclaw, body proportions, or dental anomalies,
might be reliable signs of hybridization with free-ranging dogs
(Boitani 1992; Ciucci et al. 2003). By surveying three hybrid
samples, Milenković et al. (2006) identified some atypical
malformations, such as incompletely developed permanent
teeth P1, spongy bony tissue in the foramen infraorbitale,
semicircular lines of the hind part of the forehead in adult
wolves, and atypical appearance of the sutura frontalis skulls,
which were unusual for typical wolves. Andersone et al.
(2002) found black coat color in a litter of seven mongrel
pups in northern Latvia, whose individual genotypes showed
that most of the alleles were common with dogs. Randi and
Lucchini (2002) analyzed two wolves with black coat color
and found that one had mixed ancestry in the dog gene pool.
On the other hand, Apollonio et al. (2004) showed that the
occurrence of the black coat color in wolves is not necessarily
a result of interbreeding with free-ranging dogs and can be due
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to the natural combination of wolf alleles in the gene that
determines coat color. The results of these studies showed that
coat color in wolves is a complex mechanism affected by both
ecological and genetic factors. Various studies showed that
ecological features are more significant than geographic differences in determining the genetic and morphometric variations in wolf populations (Geffen et al. 2004; Carmichael et al.
2007; Musiani et al. 2007; Bennett 2010).
Wolves and free-ranging dogs are isokaryotypic, fully interfertile, and have been shown to mate in the wild as well as in
captivity (Wayne et al. 1995; Vila and Wayne 1999). Therefore,
being able to detect hybrid individuals is important from a
management perspective (Vila et al. 2003). Polymorphic regions of the mitochondrial DNA (mtDNA) have successfully
been applied to examine genetic relationships between populations within and among closely related species (Tsuda et al.
1997). Since mtDNA markers have shown a small rate of wolf–
dog hybridization, the use of mtDNA alone cannot provide any
information about the introgression of hybrids of crosses between a female dog and a male wolf in wolf populations.
Recent studies involving nuclear markers have shown that
hybridization occasionally occurs in the wild (Andersone
et al. 2002; Randi and Lucchini 2002). Therefore, the combined use of biparent and autosomal markers can be more
helpful in detecting hybrid individuals in wolf populations.
The gray wolf (Canis lupus pallipes) is one of the most
important carnivores in Iran. This species has evolved to
survive in a variety of habitats, from arid deserts to mountainous habitats and woodlands (Ziaie 2008). Diverse habitats,
such as the Alborz and Zagros mountain ranges in the north
and west, central deserts, the Caspian Sea, and the Persian
Gulf coasts, cause a lot of variation in morphological traits in
wolves (Khosravi et al. 2013). The black phenotype is not
considered a typical characteristic and has never been observed in the past. Nevertheless, recently, in western Iran,
especially Hamadan and Zanjan provinces, few black individuals were observed. The presence of a black wolf could be a
possible sign of crossbreeding with domestic dogs or a natural
combination of wolf alleles (Apollonio et al. 2004).
This study presents an application of mtDNA, microsatellite, and morphological markers to describe genetically and
morphologically the occurrence of black wolves in western
Iran. The genetic composition of two black specimens at 15
microsatellite markers were compared with those expected in
pure specimens and in hybrids. The artificial neural networks
(ANNs) method was applied to microsatellite data for classification and assignment. The ANNs are universal approximations of functions and have been successfully used in various
fields (Ermis et al. 2007; Zangeneh et al. 2010; Azadeh et al.
2008), but less in ecology and population genetics (Cornuet
et al. 1996; Aurelle et al. 1999). An ANN is expected to be
capable of classifying individuals in populations belonging to
the same subspecies which are relatively similar genetically.
Materials and methods
Tissue collection
A total of 30 tissue samples was collected from roadkill and
illegally hunted wolves. Two of the specimens were completely dark. One (W13) was an adult female shot in July 2010 in
the region of Bahar, Hamadan province in western Iran (34′
46′ N; 48′ 35′ E). Another (W16) was a subadult male, shot in
September 2010 in Ghidar, Zanjan province (36′ 40′ N, 48′ 30′
E). Village and purebred dog tissue samples (28) were obtained from private owners, roadkill, and feral individuals. Moreover, samples were obtained for four wolf–dog hybrids (H38,
H39, H40, H41) from local people in western Iran. Finally, 16
wolf skulls (two black wolves, nine adults, and five subadults), from natural museums and private owners, were
examined.
Genetic analyses
DNA extraction
Total DNA was extracted from tissues using the AccuPrep®
Genomic DNA Extraction Kit (Bioneer, South Korea).
Microsatellite
Polymerase chain reaction (PCR) for 15 microsatellite loci
(Andersone et al. 2002; Randi et al. 2000; Verardi et al. 2006)
was performed in 12-μl volumes with the AccuPower® PCR
PreMix kit (Bioneer, South Korea) using a Perkin Elmer 9600
thermal cycler. PCR products were separated on 8 % polyacrylamide gels, visualized with silver staining, and
photographed with the Molecular Imager® Gel Doc™ XR
system. Images were analyzed by Gel-Pro Analyzer 6.3.
mtDNA sequencing
The 544 base pairs (bp) of the inner mtDNA control region
was PCR amplified in all samples with the external primers Lpro (5′-CGTCAGTCTCACCATCAACCCCCAAAGC-3′)
and H-Phe (5′-GGGAGACTCATCTAGGCATTTTCAGTG3′) (Douzery and Randi 1997). The PCR mix (AccuPower®
PCR PreMix kit, Bioneer) in the volume of 25 μl included
20 ng DNA, 1U of Euro Top DNA polymerase, 1.5 μM
MgCl2, and distilled water. The PCR was carried out on an
Applied Biosystems thermocycler with an initial step of denaturation at 95 °C for 10 min, followed by 35 cycles of 94 °C
for 30 s, 58 °C for 30 s, and 72 °C for 60 s, and, finally, by a
further extension step of 72 °C for 10 min. Amplification
products were purified from low-melting agarose gel with
the AccuPrep kit (Bioneer). Double-strand cycle sequencing
was performed by the BigDye Terminator Cycle Sequencing
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Kit version 3.1 (Applied Biosystems) according to the manufacturer’s instructions, using the external primer L-pro and
internal primer H-576 (5′-TTTGACTGCATTAGGGCCGC
GACGG-3′) (Randi et al. 2000). The results of sequencing
were registered in GenBank (KC540917–KC540944)
(Aghbolaghi et al. 2014).
Microsatellite data analyses
Deviations from Hardy–Weinberg equilibrium (HWE) for
each locus per sample and linkage equilibrium (LE) between
pairs of loci using the Markov chain method (Guo and
Thompson 1992) were computed separately for wolves and
dogs using GENEPOP 4.1 (Raymond and Rousset 1995).
Scoring errors, large allele dropouts, and null alleles were
checked using the program Micro-Checker version 2.2.3
(Van Oosterhout et al. 2004). Significance levels were adjusted using the sequential Bonferroni method to apply multiple
tests on the same dataset (Rice 1989).
The classification of individuals of different populations is
a prerequisite for the study of genetic interactions (Aurelle
et al. 1999). The genetic structure and classification of the
wolf and dog samples were investigated using ANNs.
Khosravi et al. (2013) showed that, although there are numerous shared alleles between wolf and dog populations, microsatellite markers are variable enough to separate the two
species.
An ANN consists of interconnected identical simple processing units called neurons. Each neuron is connected with
the neighboring neurons by synapses. Each synapse can have
a different level of weight of the connection (Heidari et al.
2011). Each neuron integrates the signals received from the
former neurons and sends a new signal to the next ones. This
network of neurons and synapses stores the knowledge in a
“distributed” manner: the information is coded as an electrical
impulse in the neurons and is stored by changing the weight
(i.e., the conductivity) of the connections. A classical multilayer feedforward network (MLFN) consists of three layers:
an input layer, one or more hidden layers, and an output layer.
An MLFN model that consists of a single hidden layer can be
formulated as:
!!
H
I
X
X
yk ¼ f 2 wk0 þ
wkjf 1 w j0 þ
wji xi
;
j¼1
i¼1
where xi is the input value to node i of the input layer, Hj is the
hidden value to node j of the hidden layer, and yk is the output
at node k of the output layer (O). An input layer bias term I0 =1
with bias weights wj0 and an output layer bias term H0 =1 with
bias weights wk0 are included to permit adjustments of the
mean level at each stage (Heidari et al. 2011; Omid et al.
2009).
In our study, incoming signals in the input layer
corresponded to the code of samples based on 187 alleles.
Each individual was scored as 0.0 (the allele was not observed), 0.5 (the individual was a heterozygote), or 1.0 (the
individual was a homozygote). The outgoing neuron of the
output layer corresponded to the category where the studied
individual was assigned by the network. Based on direct
observation and morphological traits, the data were grouped
into three categories, including wolf, dog, and known hybrids.
Therefore, the output layer consisted of three neurons. The
expected scores for the three output neurons for the three
predefined classes were (1, 0, 0), (0, 1, 0), and (0, 0, 0),
respectively. We run an ANN with these layers to train the
network. In this study, an MLFN was trained based on an error
backpropagation (BP) algorithm and gradient descent momentum (GDM) for error minimization using NeuroSolutions
5.07. We used a holdout procedure (Kohavi 1995) to test the
validity of the network. Therefore, a dataset with known
categories was divided into two parts. The first part was used
for training the network and the second part was used for
testing. To test and evaluate the model, we used data that were
not used for learning. We used the holdout procedure, despite
the small dataset, because we were confident in the
preclassification of samples, the composition of samples was
well known, and there was no possibly of heterogeneity. The
performance of the network was evaluated using the mean
squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2):
2
1X
Y Estimated −Y T arget R2
n i¼1
n
MSE ¼
n
X
i¼1
¼X
n
Y Estimated −Y T arget
Y Estimated −Y T arget
2
2
MAE ¼
1 Xn
jO −Pi j;
i¼1 1
n
i¼2
where YTarget and YEstimated are the actual and estimated output
signals for the test dataset and n is the number of test samples.
When the best model was determined and the network was
verified as being well suited with no over-fitting of the learning data, it was applied to unknown data for correct determination (black samples).
After the best model was determined, each individual
was assigned to a category based on the scores in the
three output neurons. For example, individuals with an
observed score of one in a group (for example 0, 1, 0)
could be considered as quite accurately classified. However, the interpretation of individuals with intermediate
scores (0.5, for example) was not as easy as individuals
with a score of zero to 0.1, which were considered to be
incorrectly grouped.
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mtDNA data analyses
SeqScape 2.7 (Applied Biosystems) was used to reconcile
chromatograms of complementary fragments and to align
sequences, using the ClustalW algorithm. The sequence data
were analyzed using the maximum composite likelihood model with the MEGA 5 program (Tamura et al. 2007). Haplotypes were obtained and FST were calculated in Arlequin 3.5
(Excoffier et al. 2005). The phylogenetic tree, based on the
obtained mtDNA sequences, was constructed in the MEGA 5
program by the neighbor-joining (NJ) method. As an
outgroup, we used the corresponding control region sequence
of a jackal (AY289997).
Morphometric analyses
Based on del Zorro Rojo (2005), Milenković et al. (2006),
Milenkovic et al. (2010), and Khosravi et al. (2012), 18 cranial
and two mandible characters of two gray wolf skulls were
measured (Fig. 1). Craniometric measurements were taken
with a digital caliper with 0.01 mm accuracy. The cranial
Fig. 1 The wolf cranium and mandible dimensions employed in this
study: 1, cranial length; 2, greatest length of the nasals; 3, least length of
the nasals; 4, maximum zygomatic width; 5, cranial width; 6, postorbital
constriction; 7, frontal breadth; 8, distance between holes in the under
socket; 9, rostrum width; 10, basal length; 11, maximum width of
characters of black wolves were compared to 14 other adult
and subadult skulls.
Results
Genetic analysis
Microsatellite
Some of the microsatellite loci showed a deviation from
HWE. LE between pairs of loci after sequential Bonferroni
correction showed that all comparisons were at LE in both
sample groups (except for one comparison in dogs and two in
wolves). The examination of genotyping errors using MicroChecker revealed no evidence for large allele dropout or
stutter band scoring at any of the 15 loci. A total of 187 alleles
were scored in dog, wolf, and known hybrid specimens. All
microsatellites were polymorphic, showing five (locus
CPH22 in dogs) and 17 (CPH8 in wolves) alleles per locus,
with an overall average of nine alleles per locus. The number
occipital condyles; 12, least diameter of the auditory bulla; 13, greatest
breadth of the palatine; 14, carnasil length; 15, height of the upper canine;
16, length of P2 to M2; 17, length of the cheektooth row; 18, length of the
upper tooth row; 19, mandible length; 20, length of P1 to M3
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of private alleles varied between the two populations (34 in
wolves and 14 in dogs).
Various MLFNs included different hidden layer neurons
and arrangements were trained to find the best model prediction for classifying wolf, dog, and hybrid samples. A total of
15 configurations with different numbers of hidden layers
(one or two), different numbers of neurons for each of the
hidden layers (2–15 for one hidden layer and 2–12 for two
hidden layers), and different interunit connection mechanisms
were designed and tested. Therefore, ANNs with 187 inputs
and three outputs have been trained to estimate the network
parameters. The results of the MLFN trained for different
networks showed that, among the trained networks, the
(187-8-16-3)-MLFN, a network having 187 input variables,
eight and 16 neurons in two hidden layers, and three output
neurons, resulted in the best-suited model classifying wolf,
dog, and hybrid specimens. The coefficient of determination
(R2) between the output of the ANN model (estimated) and
the actual (observed) value for the three outputs was 0.90,
0.50, and 0.20, respectively. For this configuration, the MSE
and MAE for class 1 were 0.04 and 0.16, for class 2 0.13 and
0.25, and for class 3 0.14 and 0.23, respectively.
The percentage of correctly classified individuals by holdout was 100 % in the global comparison between wolf and
dog samples. Based on the best network, the scores of W13 for
the three classes (dogs, wolves, and hybrids) were 0.05, 0.90,
and 0.05, respectively. This result showed that W13 was
correctly grouped in class 2 (wolf). The score of W16 for
the three classes was 0.20, 0.74, and 0.1, respectively. Based
on this score, W16 was also grouped in class 2. This finding
showed that the black wolves were genetically close to wolf
samples and individual assignments based on autosomal
microsatellites also indicated that the two black wolves were
located in the wolf cluster.
mtDNA
In this study, 544 bp of mtDNA control region sequences were
obtained for the wolf and dog samples. Overall, 25 haplotypes
were identified, including 12 in dogs, ten in wolves, and three
shared haplotypes between wolves and dogs. The phylogenetic tree, constructed as shown in Fig. 2, separated wolves and
dogs into two distinct groups. The two black individuals (W13
and W16) were positioned in the wolf haplotype.
Morphological analysis
At first glance, the subadult black male skull (W16) looked
like a typical dog skull. We observed an added tooth in the
lower tooth row and completely dark general shade of the fur,
especially on the head. The rostrum width (36 mm) and
greatest breadth of the palatine (32.1 mm) were clearly smaller
than even the minimum values for subadult male wolves.
However, other craniometric characters such as the length of
the M2, zygomatic width, and postorbital width fell in the
range of wolf skull measurements, suggesting a close relationship with wolf samples.
Contrary to the subadult male skull, the appearance of the
black female (W13) specimen did not deviate from a typical
wolf and was not craniologically different from pure wolves in
the region. This specimen had a large skull with completely
dark fur, especially on the head and sides. The maximum
width of occipital condyles (47.9 mm) and the least diameter
of the auditory bulla (27.9 mm) were slightly larger than the
maximum values for adult female wolves. However, other
craniometric characters fell in the range of measurements for
this species (Table 1; Fig. 3).
We did not observe a typical malformation, such as incompletely developed permanent teeth P1, spongy bony tissue in
the foramen infraorbitale, semicircular lines of the hind part of
the forehead, and atypical appearance of the sutura frontalis, in
either of the black wolves. These findings showed that the two
skulls, both in appearance and in craniometric parameters, did
not deviate from the typical wolves; therefore, they cannot be
identified as hybrids.
Discussion
The existence of wolf–dog hybrids is rarely reported [see
Andersone et al. (2002), Randi and Lucchini (2002), Ciucci
et al. (2003), and Verardi et al. (2006)]. While some traits like
dewclaw, color pattern, and long tail can be considered as the
signs of hybridization between wolves and free-ranging dogs
(Apollonio et al. 2004), wolf–dog morphological traits are not
predictable. Color pattern is one of the most commonly documented variations among wolves. North American wolf
populations, for instance, show different color patterns, ranging from white in the Arctic regions to black coats observed in
the northwest USA (Brewster and Fritts 1995).
In Iran however, melanism is not considered a typical
characteristic. To understand whether two black wolves identified in western Iran were hybrids or purebred, we analyzed
them both genetically and morphologically. ANNs provided
important information about the genetic structure and classification of wolf and dog populations. When samples were
defined well based on morphological traits and direct observation, the results of ANN analyses showed that, when applied to microsatellite data, neural networks give reliable
results. ANNs based on allele frequency and holdout procedures showed a clear distinction between wolf and dog populations. Based on the ANN results, individuals with intermediate scores could be hybrids, and when such individuals are
present in a population, the network is able to recognize them.
The scores of two black specimens predicted by the best
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Fig. 2 Phylogenetic tree of mtDNA haplotypes. The dark specimens
(W13 and W16) and the four probably hybrid individuals (H38, H39,
H40, H41) are shown in ellipses and rectangles, respectively
network grouped them into the wolf population with a high
score. ANNs showed that black wolves were genetically close
to wolf samples and individual assignments based on autosomal microsatellites showed that the two black wolves
belonged to the wolf cluster.
Based on the results of the mtDNA analysis, although
wolves and dogs share certain haplotypes, wolf and dog
groups could be well discriminated. Some shared haplotypes
between the two groups might be the result of interbreeding
during dog domestication or interspecific hybridization in
previous generations (Ardalan et al. 2011). Four individuals
(H38, H39, H40, H41) identified as hybrids based on the
results of microsatellite analyses had three different haplotypes and belonged to the same haplogroup composed of
dog and wolf sequences. Two samples, H38 and H41, shared
a common haplotype. The results of analyzing mtDNA
markers, which are maternally inherited, points to the possibility that the hybrids were mothered by dogs and fathered by
purebred or hybrid wolves. Based on observations in the study
area and interviews with local people, these four individuals
(H38, H39, H40, H41) were the results of hybridization.
Behavioral and genetic studies show that mating among various genera of canid species is asymmetrical and differs
among species based on the direction and intensity of gene
flow (Vila and Wayne 1999). Since mtDNA markers determine hybrid individuals, it is not possible to use them in
determining the exact intensity of the presence of hybrids
within wolf populations in nature (Vila and Wayne 1999).
The mtDNA results were in concordance with the results of
microsatellite analysis. The two dark wolves (W13 and W16)
showed two separate haplotypes and were placed in two
distinct haplogroups. The results of mtDNA sequencing, a
method commonly used to trace maternal lineage in domestic
and wild populations (Freeland 2005), showed that the black
coat color does not indicate recent genetic flow between
wolves and dogs.
Moreover, the morphometric results showed that unusual
black wolves, both in appearance and in craniometric parameters, do not deviate from the typical wolf characters. Our
results support evidence of no inevitable direct relationship
between the presence of coats darker than usual in wolves and
recent hybridization with dogs.
Our results support Randi and Lucchini (2002), who stated
that dark color in Italian wolves could have been fostered by
the past demographic decline and expansion after a
bottleneck. Apollonio et al. (2004) reported that 22 % of
observed and 23 % of all dead wolves in a 3,300-km2 area
were completely black. Their analyses showed no evidence of
hybridization in ancestry and suggested that the occurrence of
the black phenotype in this area may be derived from a natural
combination of wolf alleles in coat-color-determining genes,
and not necessarily from crossbreeding with the domestic dog.
There are many factors that determine coat color in dogs, a
mechanism which is quite complex (Sponenberg and Rothschild 2001). Apollonio et al. (2004) suggested that it is
unlikely that a single event of hybridization with dogs in
recent years in any case would produce a black wolf and
that black color in wolves is likely to result from a
combination of dominant alleles. On the other hand,
Anderson et al. (2009) compared the genes of wolves from
Yellowstone National Park and the Canadian Arctic to those
of domestic dogs and coyotes. They found that, in each
species, the black individuals had the same mutation, which
first arose around 45,000 years ago, and molecular analysis
showed that the oldest mutation happened in dogs, suggesting
it originated in dogs and was then introduced to wolves and
coyotes through interspecific hybridization. Anderson et al.
(2009) showed that the KB allele which codes for black coat
color in wolves is more common in packs that inhabit forests
than those occupying the tundra. These findings show that
traits selected in domesticated species can influence the morphologic diversity of their wild relatives.
Environmental factors are more important than geographic
distance in determining the genetic and morphometric variations in wolf populations (Bennett 2010). Environmental gradients such as vegetation type and vegetation cover in a
habitat or type of prey available can influence the genetic
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Table 1 Minimum and maximum values (mm) of 20 cranial and dental characters in ten purebred wolves and two black wolf specimens (female from
Bahar in Hamadan province, male from Ghidar in Zanjan province, both in western Iran)
Measurements
Subadult male (5)
Female (9)
Min
Max
Min
W13
W16
Max
Male
Female
1
Cranial length
100.64
130.24
121.22
140.24
110.9
139.36
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Greatest length of the nasals
Least length of the nasals
Maximum zygomatic width
Cranial width
Postorbital constriction
Frontal breadth
Distance between holes in the under socket
Rostrum width
Basal length
Maximum width of occipital condyles
Least diameter of the auditory bulla
Greatest breadth of the palatine
Carnasil length
Height of the upper canine
Length of P2 to M2
Length of the cheektooth row
Length of the upper tooth row
Mandible length
71.32
64.3
95.1
66.28
33.42
49.9
38.46
39.08
161.54
35
20.22
32.6
20.1
19.14
54.72
65
79.4
143.72
82.22
72.48
128
75.22
43.94
61.98
47.46
43.64
190.12
40.22
24.9
41.38
23.4
22.2
70.14
78.74
96.2
177.86
82.98
72
118.7
69.54
40.7
54.62
45.78
39.22
210.86
39
21.98
36.4
23.4
18.64
58.36
74.68
91.62
182.56
100.46
88.9
138.24
86.55
51.24
70.34
55.86
55.86
240.24
47.34
27.0
42
26.5
25.3
75.24
85.12
110.2
225.16
77.2
65.9
110.5
72.72
38.26
55.36
45.04
36
187.96
36.12
22.4
32.1
22.6
22.64
64.1
73.16
88.34
154.56
89.32
77.56
136.36
78.9
48.98
66.88
49.5
48.46
220.1
47.9
27.9
41.74
25.5
25.2
72.76
80.12
101.84
182.18
20
Length of P1 to M3
71.66
87.44
78.58
96.26
78.3
82.82
makeup of local wolf populations. Musiani et al. (2007) found
that 93 % of wolves from tundra populations exhibited light
coloration, whereas only 38 % of boreal coniferous forest
wolves had this type of coloration. These findings showed
that genetic and phenotypic differentiations among wolves
can be caused by prey–habitat specialization rather than distance or topographic barriers. Pilot et al. (2006) examined the
effect of ecological factors on the genetic structure of European gray wolves. They found that the genetic differentiation
among local populations was correlated with climate, habitat
type, and wolf diet composition. This result indicates that
ecological processes may strongly influence the amount of
gene flow among populations. Carmichael et al. (2007) stated
that the genetic structure in wolves correlates strongly with
habitat type, and is probably determined by natal habitatbiased dispersal.
Although this study showed that the two black wolves did
not have a sign of hybridization in first or second past generations, this result is not accurate enough to exclude the possibility of more ancient hybridization. Anderson et al. (2009)
found no evidence of hybridization in black wolves using over
48,000 single nucleotide polymorphisms (SNPs) and suggested that hybridization occurred at least hundreds of years
ago.
Wolves in Iran occupy a wide range of habitats and are
absent only in the central deserts and Dasht-e-Lut. Variation in
Fig. 3 Lateral view of the skull of two black wolves. Subadult male (W16; a) and adult female (W13; b)
Author's personal copy
J Appl Genetics
color patterns might be due to the great diversity of habitats
which is caused by the existence of two large water bodies in
the north and south and the vast mountain ranges expanding in
the north and west. In western Iran, due to the existence of the
Zagros mountain range, variation in color can most probably
be resulted from ecological traits.
In conclusion, the results of this study showed that black
coat in wolves in western Iran is not necessarily the result of
genetic pollution by domestic genes in recent generations and
could be caused by habitat variation, local adaptation, and
natural combination of wolf alleles and introgression of the
KB allele from dogs into wolves due to past hybridization with
free-ranging dogs. Considering the limited sampling, however, the obtained results should be interpreted and generalized
with caution. Further investigations (SNP data) are needed for
assessing more ancient hybridization between wolf and dog
populations in Iran.
Acknowledgments This research was supported financially by the Iran
Department of Environment, Hamedan Provincial Office. We thank Ali
Shaabani, Vahid Nouri, and Shahabaddin Montazami for their help. The
authors also thank the anonymous referees for their valuable comments
on an earlier version of this article.
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