Conservation Genetics

, Volume 15, Issue 1, pp 187–199 | Cite as

Genetic structure and expansion of golden jackals (Canis aureus) in the north-western distribution range (Croatia and eastern Italian Alps)

  • Elena Fabbri
  • Romolo Caniglia
  • Ana Galov
  • Haidi Arbanasić
  • Luca Lapini
  • Ivica Bošković
  • Tihomir Florijančić
  • Albena Vlasseva
  • Atidzhe Ahmed
  • Rossen L. Mirchev
  • Ettore Randi
Research Article

Abstract

The golden jackal, widely distributed in Europe, Asia and Africa, is one of the less studied carnivores in the world and the genetic structure of the European populations is unknown. In the last century jackals strongly declined mainly due to human persecution, but recently they expanded again in eastern Europe. With the aim to determine the genetic structure and the origin of expanding jackals, we analyzed population samples obtained from Bulgaria, Serbia, Croatia (Dalmatia and Slavonia) and individuals sampled in north-eastern Italy. Samples were typed at the hypervariable part of the mitochondrial DNA control-region (mtDNA CR1) and at 15 canine autosomal microsatellite loci (STR), and analyzed using multivariate, Bayesian and landscape genetic methods. The mtDNA CR1 was monomorphic, showing a single haplotype shared among all the populations. The STR loci were variable, with 2–14 alleles and intermediate values of heterozygosity (Ho = 0.47; He = 0.51). Genetic diversity was significantly partitioned (θST = 0.07; P < 0.001) and the populations were partially distinct, perhaps in consequence of recent fragmentations. Jackals from Dalmatia were the most genetically differentiated. Assignment testing and gene flow analyses suggested that jackals colonizing Italy have admixed origins from Dalmatian and Slavonian populations. They are not first generation migrants, suggesting that dispersal towards north-eastern Italy is a stepping-stone process. Golden jackal and wolf colonization patterns might be different, with prevalent short-distance dispersal in jackals versus prevalent long distance dispersal in wolves. The admixed origin of jackals in the Alps ensures abundant genetic variability, which may enhance adaptive fitness and expectancy of population growth. The intersections between Dinaric–Balkan and Eastern Alps are areas of population expansion and admixture, highlighting their conservation, ecological and evolutionary values.

Keywords

Admixture analysis Assignment testing Autosomal microsatellites Canis aureus Colonization genetics Mitochondrial DNA control-region Population structure 

Introduction

Climate warming and the spread of forests in mountain areas of Europe are determining deep changes in the composition of animal communities (Mawdsley et al. 2008). In the last few decades carnivore populations expanded in central and eastern Europe (Trouwborst 2010). The lowlands around the north Adriatic basin and subalpine areas of the eastern Alps have also been affected by waves of species expansion. Dinaric–Balkan populations of carnivores such as the brown bear (Ursus arctos) and the wolf (Canis lupus) expanded towards the Alps, entering in north-eastern Italy (Lapini et al. 2010; Fabbri et al. 2013), followed by lynx (Lynx lynx; Lapini et al. 1996), otter (Lutra lutra; Lapini and Bonesi 2011), raccoon dog (Nyctereutes procyonoides; Lapini 2006) and golden jackal (Canis aureus; Lapini et al. 2011). The expansion of wolves and other carnivores might follow a propagule model (Ibrahim et al. 1996), characterized by: (1) the early dispersal of vagrants, usually young males in search of new suitable territories (Valière et al. 2003; Fabbri et al. 2007, 2013); (2) the settlement of stable reproductive familial units, which may rapidly expand and saturate all suitable areas (Mech 1970). The genetic diversity of the new colonies is determined by the rates of long-range dispersal and by the consequences of founder effects (Fabbri et al. 2007). Isolated colonies might experience losses of genetic variation if population settlement and expansion are delayed by years (Vila et al. 2003). In the second phase of colonization, variable combinations of short-range dispersal, inbreeding avoidance and the turnover rates of breeding adults will determine the evolution of genetic variation (vonHoldt et al. 2010). This model well fits with wolf expansion dynamics, but the colonization genetics of other species is less known.

The golden jackal, a highly adaptable canid widespread from Africa to the Arabian peninsula, reaching central Asia, India and Indochina, is a historic invasive species in Europe (Sillero-Zubiri et al. 2004). Golden jackal populations in Europe are fragmented, particularly towards the north-west periphery of the range. The main populations occur in the Balkans, Hungary and Ukraine, and northward they reach Slovenia and Austria (Humer et al. 2007). Smaller isolated populations are distributed along the Adriatic coast of Albania, Montenegro and Croatia, and in the Black Sea coast of the Balkan peninsula (Arnold et al. 2012). Golden jackals in Europe declined until 30–40 years ago due to human persecution and overhunting and were sometimes treated as pests and eradicated (Spassov 1989). Golden jackals and wolves are competitors, and the recent waves of wolf expansion in Europe might have contributed to worsen the decline of golden jackals (Genov and Wassilev 1989; Kryštufek and Tvrtković 1990). Negative demographic trends are, however, reversing and golden jackals are currently expanding again in eastern Europe, particularly in Bulgaria (Kryštufek et al. 1997). Climate and habitat changes, and partial legal protection are favouring the expansion in areas from where the species has been absent till recent. Vagrant or reproductive individuals were recently observed in Slovenia, Austria and north-eastern Italy, probably pushed by the ongoing expansion from Bulgaria (Kryštufek et al. 1997).

Golden jackals were reported for the first time in Croatia in 1491 (Kryštufek and Tvrtković 1990), in southern Dalmatia, where they have been continually occurring. A permanent population was established in northern Dalmatia during the 20th century, and since the 1980s they have been spreading northwest populating Istria (Kryštufek and Tvrtković 1990). Since 1903, jackals occurred also in the continental eastern part of Croatia (Slavonia). This population expanded in the last 15 years, presumably due to immigration from Bulgaria, Romania (Banea et al. 2012) and Serbia (Selanec et al. 2011). Golden jackals recently entered north-eastern Italy (Friuli Venezia Giulia and Veneto), slowly expanding towards the Italian Alps in Trentino Alto Adige (Lapini et al. 2009, 2011) and Switzerland. Since the 1980s the presence of golden jackals has been regular, and now three to eight family-groups (15–40 jackals) might reproduce in north-eastern Italy (Lapini et al. 2011; Fig. 1).
Fig. 1

Sampled populations: Bulgaria 2008–2009 (BG-2), Serbia (SRB), Slavonia (SLA) and Dalmatia (DA) in Croatia and vagrant individuals from north-eastern Italy (IT). Geneland modal population for pixel of the study area and the spatial distribution of cluster membership coefficient for each individual according to Structure (locprior model K = 3). The insert map is the Canis aureus range distribution in Europe obtained from IUCN (http://www.iucnredlist.org/)

However, the genetic structure of golden jackal populations still remains unknown (Zachos et al. 2009). Here we report results of genetic analyses of DNA samples obtained from four geographic regions (Bulgaria, Serbia, Slavonia and Dalmatian Adriatic coast in Croatia), and individuals from north-eastern Italy (Friuli Venezia Giulia, Veneto, Alto Adige; Fig. 1; Table 1), which were typed at the hypervariable part of the mitochondrial DNA control-region (mtDNA CR1) and at 15 autosomal microsatellite loci (STRs). Multivariate, Bayesian and landscape genetic methods were used to determine genetic diversity and differentiation of the studied populations and the origin of golden jackals colonizing eastern Italy.
Table 1

Golden jackal (Canis aureus) samples analysed in this study

Country: region (acronym)

N

Sample kind

Year

Collector

Bulgaria (BG-1)

40

Skins

1980–1990

P. Genov

Bulgaria (BG-2)

15

Tissues

2008–2009

A. Vlasseva, A. Ahmed

Serbia: East Serbia (SRB)

8

Tissues

2010

I. Bošković

Croatia: Slavonia (SLA)

34

Tissues

2008–2010

I. Bošković

Croatia: Dalmatia (DA)

16

Tissues

2009–2010

I. Bošković, A. Galov

Italy: Friuli Venezia Giulia, Trentino Alto Adige, Veneto (IT)

7

Tissues, skins, teeth, hair from museum samples

1985–2011

L. Lapini

Total

120

 

Country and region of origin, sample size (N), sample kind, sampling year and name of collectors are indicated

Materials and methods

Sampling and DNA extraction

A total of 120 golden jackal samples were collected from Bulgaria (n = 55), eastern Serbia (close to the Bulgarian border; n = 8), Croatia (n = 50) and north-eastern Italy (n = 7; Fig. 1; Table 1). Samples from Bulgaria were obtained from animals legally shot or road-killed from 1980 to 1990 (n = 40 skins; BG-1) and in 2008–2009 (n = 15, muscle tissues collected near the city of Plovdiv and the village of Garbino c. 234 km far from Plovdiv; BG-2). Golden jackals in Croatia were legally shot or road-killed in 2008–2010 (three Dalmatian specimens collected in 2002) in two regions: continental Slavonia (n = 34) and coastal Dalmatia (n = 16). The seven samples from north-eastern Italy were collected from the regions Friuli Venezia Giulia, Veneto and Alto Adige.

Genomic DNA was extracted using a Wizard Genomic DNA Purification Kit (Promega, USA) and a Quick-gDNA™ MiniPrep kit (Zymo Research, USA). DNA was eluted in 100 μl of purified DNA/RNA-free water and stored at −20 °C until subsequent handlings.

Mitochondrial DNA control-region sequencing

We amplified 450 bp of the hypervariable left domain of the mitochondrial DNA control-region (mtDNA CR1) using PCR and sequencing primers WDLOOPL 5′-TCCCTGACACCCCTACATTC-3′, H519 5′-CGTTGCGGTCATAGGTGAG-3′ (designed on wolf mtDNA CR; Caniglia et al. 2013), in a 10 μl total PCR volume with: 2μl of 20–40 ng/μl DNA, 0.3 μM of primer mix (forward and reverse), 10x PCR buffer with 2.5 mM Mg2+ and 0.25 units of Taq polymerase (5 PRIME Inc., Gaithersburg, USA). The PCR profile was: 94 °C/2 min, 94 °C/15 s, 55 °C/15 s, 72 °C/30 s, 72 °C/5 min of final extension, for 40 cycles. The amplification products were purified using ExoSAP-IT (Affimetrix, Inc., Cleveland, Ohio USA) and sequenced in both directions using an Applied Biosystems 3130XL DNA sequencer (Life Technology). The sequences were visualized and corrected in SeqScape v.2.5 (Life Technology) and successively aligned in BioEdit (Hall 1999) with a 392 bp-long Serbian jackal sequence downloaded from GenBank (GU936689; Zachos et al. 2009). Negative controls (no DNA) were always used to check for PCR contaminations, which never occurred.

Microsatellite loci and PCR amplifications

PCR-amplification rates in 21 unlinked autosomal canine microsatellites (http://research.nhgri.nih.gov/dog_genome/) were determined in a subset of 13 golden jackal samples. PCRs were performed in 10 μl of final volume including: 20–40 ng/μl of DNA, 0.2 or 0.3 μM of each primer; 0.4 μM of dNTPs, 2 mg/ml of bovine serum albumin (BSA), 10x PCR buffer with 2.5 mM Mg2+ and 0.25 U of Taq polymerase (5 PRIME Inc., Gaithersburg, USA) and purified water. The STRs were amplified in multiplexing sets of two or three loci (Table 2) using the same thermal profile composed by an initial denaturation cycle of 94 °C for 2 min followed by 35 cycles of 94 °C for 30 s, 57 °C for 90 s, and 72 °C for 60 s and with a final extension of 72 °C for 10 min. PCR products were analysed in an Applied Biosystems 3130XL DNA sequencer (Life Technology) and allele sizes were estimated using the software GeneMapper 4.0 (Life Technology). Positive (known genotypes) and negative (no DNA) controls were used to check for laboratory contaminations, which never occurred. All skin and museum samples and a 10 % randomly selected subset of the other samples were PCR-replicated two times to check for allelic drop-out and false alleles. Each locus was checked for null alleles using Micro-Checker (Van Oosterhout et al. 2004).
Table 2

Variability at 15 canine microsatellite loci selected to genotype the golden jackal samples used in this study

Locus

Na

PIC

Ho

Range

M

CPH6a

4

0.51

0.53

120–126

A

FH2004b

8

0.67

0.62

162–192

A

FH2088b

2

0.38

0.44

93–97

A

FH2140b

5

0.59

0.61

112–168

B

C20.253c

6

0.65

0.63

90–106

B

CPH8a

4

0.35

0.27

195–201

C

FH2096b

4

0.50

0.49

80–104

C

CXX.213c

3

0.31

0.31

158–162

D

C09.250c

6

0.63

0.61

117–135

D

CPH4a

4

0.61

0.61

137–150

E

CPH5a

3

0.46

0.51

110–118

E

CPH9a

4

0.52

0.57

138–154

F

CPH12a

4

0.19

0.14

186–202

F

CPH22a

5

0.35

0.35

110–120

G

FH2137b

14

0.77

0.71

148–204

G

Average

5.07 (SE = 0.74)

0.50 (SE = 0.04)

0.46 (SE = 0.03)

 

Na number of alleles, PIC polymorphic information content; Ho observed heterozygosity and allelic molecular weight range, M multiplex identification

Microsatellites were originally isolated by: a Fredholm and Wintero (1995), b Francisco et al. (1996), c Ostrander et al. (1993)

At a 99 % confidence interval and after Bonferroni correction, locus CPH2 showed evidence of null alleles and it did not amplify in 20 % of samples; thus it was discarded. Three loci were monomorphic (CPH3, CPH7 and FH2079), two (FH2010 and FH2054) did not consistently amplify (amplification rate ≤0.6), while the others 15 reliably amplified, were polymorphic (Ho = 0.14–0.71; PIC = 0.19–0.77; Table 2) and were used in this study.

Analysis of genetic diversity

Allele frequencies, average number of observed (Na) and private (Np) alleles, observed and expected heterozygosity (Ho, He), probability-of-identity (Waits et al. 2001) among unrelated individuals (PID) and among full sibs (PIDsibs) and AMOVA (Excoffier et al. 1992) were computed with GenAlEx 6.4 (Peakall and Smouse 2006). Fstat 2.9.3.2 (Goudet 1995) was used to compute the allelic richness (Nar), the number of alleles standardized for the smallest number of individuals in a sample (n = 15; excluding the smallest sample of seven admixed jackals that were collected in Italy). Genetix 4.03 (Belkhir et al. 1996–2001) was used to estimate FIS, θST and Nm (Weir and Cockerham 1984) and to test for Hardy–Weinberg equilibrium (HWE). Pair-wise linkage equilibrium (LE) among loci was estimated using the Guo and Thompson’s (1992) Markov chain method in Genepop 4.0 (Rousset 2008). The sequential Bonferroni correction test for multiple comparisons was used to adjust the significance levels at α = 0.05 (Rice 1989).

Population structure and landscape genetic analyses

We used Stucture 2.3.3 (Pritchard et al. 2000) to identify the optimal number of K genetically distinct clusters (assuming HWE and LE) in the samples and to assign the individuals to the clusters. We applied the admixture and correlatedallele frequencies (F) models (Falush et al. 2003), with K from 1 to 10, running five replicates each of 4 × 105 iterations following a burn-in of 4 × 104 iterations. We also used the locprior model, assuming that sampling locations are informative priors to assist the clustering procedure (Hubisz et al. 2009). To evaluate eventual substructure in the Bulgarian samples we considered BG-1 and BG-2 groups as two distinctive locpriors. The statistic ΔK was used to identify the major increase in the posterior probability, Ln P(D), between each successive K (Evanno et al. 2005). At optimal K we assessed the average proportion of membership (Qi) of the sampled populations and the individual proportion membership (qi) to the clusters, using an arbitrary threshold qi = 0.80 (which has been used in other carnivore studies: Oliveira et al. 2008; Randi 2008; vonHoldt et al. 2010). The spatial locations of genetic clusters were reconstructed by landscape genetic analyses in Geneland 4.0.3 (Guillot et al. 2005), with 105 MCMC iterations, (thinning = 103 and post-process burn-in = 102 iterations), the correlatedallele frequency model and coordinates uncertainty = 0.01 corresponding to 1.6 km (radius of a territory of 8 km2; Taryannikov 1977). In addition we used a spatial principal component analysis in Adegenet (sPCA, Jombart et al. 2008), which describes the spatial pattern of population structuring independently on any assumption on population genetic equilibrium. The geographic coordinates of samples collected in Bulgaria from 1980 to 1990 were not available, thus these samples were not used in Geneland and Adegenet analyses. Current rates of gene flow between the populations were estimated by Bayesass (Wilson and Rannala 2003), with 3 × 106 iterations, a thinning interval of 2000 and a burn-in of 105. We compared the results obtained by Bayesian methodologies with Paetkau et al.’s (2004) frequency-based assignment test implemented in GenAlEx.

Results

Genetic variability

We obtained a fragment of 450 bp of the hypervariable domain of the mtDNA CR1 from 120 golden jackals collected in Bulgaria, Croatia, Serbia and Italy. The aligned sequences were identical: all jackals shared the same haplotype and, consequently, haplotype and nucleotide variability were zero. This unique haplotype was identical to the one described in Serbian jackals (Zachos et al. 2009; GenBank accession number KF588364). In contrast, 15 of the 21 STRs were polymorphic. We obtained complete multilocus genotypes at the 15 polymorphic STRs in 114 samples, whereas six individuals (5 %) showed from 1 to 5 loci with missing data: four at one locus, one at two loci and one at five loci. The number of alleles per locus varied from Na = 2 (in locus FH2088) to Na = 14 (FH2137); the observed heterozygosity varied from Ho = 0.14 (CPH12) to Ho = 0.71 (FH2137; Table 2). Overall there were 8/15 (53 %) loci not in HWE (P < 0.05) due to presence of genetic substructure (see results below), with average FIS = 0.11, significantly different from 0 (P < 0.001). The loci were not in linkage disequilibrium (at P = 0.05 after Bonferroni correction).

Population structure and landscape genetic analyses

The genetic differentiation among the populations was described by Bayesian clustering in Structure. The admixture and locprior models yield optimal clustering values at K = 3 (Table 3; Fig. 2). All Bulgarian jackals clustered together in all the tested assignment models, although the oldest ones (BG-1) showed slightly deeper signals of admixture both with the admixture and locprior models (Fig. 2). Thus, we excluded any sub-structuring depending on genetic changes through time collection.
Table 3

Average proportion of membership (Qi) computed from five replicated runs of Structure of golden jackal genotypes grouped in five predefined populations (the samples from Bulgaria are in the same group; sample size in parenthesis), and assigned to three (K = 3) clusters (90 % credibility intervals in parenthesis)

Population (n)

Admixture model

Locprior model

QI

QII

QIII

QI

QII

QIII

BG (55)

0.56 (0.15–0.95)

0.37 (0.03–0.81)

0.07 (0.00–0.31)

0.76 (0.22–1.00)

0.22 (0.00–0.77)

0.01 (0.00–0.11)

SLA (34)

0.34 (0.06–0.75)

0.54 (0.17–0.92)

0.12 (0.00–0.39)

0.07 (0.00–0.24)

0.90 (0.68–1.00)

0.03 (0.00–0.18)

DA (16)

0.02 (0.00–0.14)

0.03 (0.00–0.16)

0.95 (0.85–0.98)

0.01 (0.00–0.05)

0.01 (0.00–0.10)

0.98 (0.87–1.00)

SRB (8)

0.32 (0.00–0.73)

0.63 (0.19–1.00)

0.05 (0.00–0.28)

0.15 (0.00–0.39)

0.82 (0.57–1.00)

0.03 (0.00–0.14)

IT (7)

0.19 (0.00–0.66)

0.22 (0.00–0.77)

0.59 (0.19–0.90)

0.18 (0.00–0.50)

0.25 (0.00–0.68)

0.57 (0.25–0.85)

Values larger than the threshold qi = 0.80 are in bold. Population acronyms: Bulgaria (BG), Slavonia (SLA), Serbia (SRB), Dalmatia (DA), north-eastern Italy (IT)

Fig. 2

Bayesian clustering of golden jackal samples obtained by Structure using admixture (a) and locprior (b) models. Each individual is represented by a vertical bar fragmented in K sections of different length, according to their membership proportion in the inferred genetic clusters. The sampled regions: Bulgaria (BG-1; BG-2), Serbia (SRB), Slavonia (SLA) Dalmatia (DA) and Italy (IT), are indicated

Only jackals from Dalmatia clustered separately from the other samples, independently of the model, with average proportion of membership QIII > 0.90 and individual proportion of membership qi = 0.82–0.96 (admixture model) and 0.96–0.99 (locprior model). The other populations were not assigned to any single cluster with the admixture model (Table 3), whereas a sharper sub-structuring was detected using the locprior model. The Slavonian and Serbian jackals were assigned to same genetic cluster with QII = 0.90 and 0.82, respectively, while the samples from Bulgaria showed signals of admixture (Table 3). Independently of the model, golden jackals sampled in Italy were genetically admixed (Fig. 2; Table 6). With the admixture model, three samples (1216, 1219 and 1220) were assigned to the Dalmatian cluster with qi = 0.96, 0.79 and 0.90, respectively. The other four samples showed genetic components shared with the Dalmatian and Slavonian jackal clusters (Table 6). The assignments of the Italian samples with the locprior model were weaker (Table 6).

The use of prior sampling location information in Geneland yield four geographical clusters (mean of log posterior density of the model = −963.11, the best values over five independent runs), confirming the genetic distinction of jackals from Dalmatia, Slavonia–Serbia and Bulgaria, and adding a separate cluster formed by jackals sampled in the Italian Alps (Fig. 1). The first axis of the sPCA explained 58 % of the total variance (first axis eigenvalue = 0.41), which was considerably larger than variance proportions explained by the other axes (Fig. 3a). The scores on the first principal component showed an east–west variation, with jackals sampled in the eastern regions (Bulgaria and Serbia) that had negative values (Fig. 3b), indicated by larger white squares in Fig. 3c, while samples collected in Dalmatia had positive values (Fig. 3b), indicated by larger black squares in Fig. 3c. Samples from Slavonia were assigned to the eastern populations, although the scores were lighter and eight individuals were partially associated to the Dalmatian population (Fig. 3b). Three Italian samples (1219M, 1220F and 1221F) were assigned to the Dalmatian cluster, while the other four samples showed lower scores to both the main eastern and western clusters (Table 6).
Fig. 3

Assignment of individual golden jackals to their population of origin in Italy, Croatia, Serbia and Bulgaria, as obtained by spatial Principal Component Analysis (sPCA in Adegenet): (a) variance explained by each spatial PCA eigenvalues, positive eigenvalues (on the left) correspond to global structures, while negative eigenvalues (on the right) indicate local patterns; (b) loadings of individual jackal genotypes to the first spatial principal component, explaining 58 % of the total genetic variability, sample are linearly ordered according to their East–West (form right to left) sampling locations; (c) black and white squares represent individual genotype scores on the first principal component of the spatial PCA. Large white squares indicate individuals with high negative scores; large black squares indicate individuals with high positive local scores; square dimension is proportional to the degree of differentiation (high for large squares, low for small squares). The Italian golden jackal samples are identified as in Table 6

Genetic variation in the subpopulations

We computed summary genetic statistics in the three main clusters identified by Structure and concordant with the geographic origins of the samples: golden jackals from Bulgaria (n = 55), Slavonia–Serbia (n = 42) and Dalmatia (n = 16). The 15 STRs were polymorphic in the three clusters, except for locus CPH12 that was monomorphic in the Dalmatian samples. The average number of alleles was smaller in Dalmatia (Na = 2.8) than in the other populations (Na = 3.7–4.7), and the values of allelic richness varied concordantly (Table 4). The samples from Bulgaria and Slavonia–Serbia showed 10 and 4 private alleles, respectively, while there were only two private alleles in the Dalmatian jackals. The observed heterozygosity (Ho) was always lower than expected (He), and the FIS values were significantly positive (P < 0.05) in all clusters (Table 4). The AMOVA showed that a significant 11 % of the total genetic diversity was partitioned among the three groups (P = 0.001), and the average θST = 0.07 was also significant (P = 0.001), reflecting a significant partition of genetic diversity among the sampled geographic regions. The highest pair-wise θST values were between the Dalmatian and the other groups (Fig. 3). The probability-of-identity were PID = 7.1−10 (Bulgaria), 1.1−9 (Slavonia–Serbia), 4.2−7 (Dalmatia), and PIDsibs = 9.3−5 (Bulgaria), 1.0−4 (Slavonia–Serbia), 9.0−4 (Dalmatia). The genetic diversity of the two Bulgarian samples that were collected in two successive periods were not significantly different (P > 0.5; t test), and the two groups were in HWE (Table 4). The admixed samples from Italy did not show private alleles and, in apparent contrast with the recent origin and very small size of this population, their values of genetic variability (Na = 3.07, Ho = 0.50, He = 0.52; Table 4) were comparable to the other populations. Their PID = 4.2−9 and PIDsibs = 1.5−4 values supported reliable individual genotype identifications. The microsatellite data are deposited in Dryad: http://doi.org/10.5061/dryad.1529d.
Table 4

Genetic variability in golden jackals sampled in Bulgaria 1980–1990 (BG-1), Bulgaria 2008–2009 (BG-2), Slavonia (SLA) plus Serbia (SRB), Dalmatia (DA), and in north-eastern Italy (IT), and genotyped at 15 autosomal STRs ( = sample size)

Region (N)

Na

Nar

Np

Ho

He

FIS

P

PID

PIDsibs

BG-1 (40)

4.5 (0.6)

3.8 (0.4)

12

0.50 (0.0)

0.50 (0.0)

0.016

0.270

1.1−9

1.1−4

BG-2 (15)

3.5 (0.4)

3.5 (0.4)

2

0.50 (0.0)

0.50 (0.0)

0.063

0.109

1.9−9

1.2−4

BG-tot (55)

4.7 (0.6)

3.8 (0.4)

10

0.53 (0.04)

0.54 (0.04)

0.04

0.047*

7.1−10

9.3−5

SLA-SRB (42)

4.0 (0.4)

3.6 (0.3)

4

0.49 (0.05)

0.53 (0.05)

0.09

0.001*

1.1−9

1.0−4

DA (16)

2.8 (0.2)

2.8 (0.2)

2

0.37 (0.05)

0.43 (0.05)

0.16

0.001*

4.2−7

9.0−4

IT (7)

3.7 (0.4)

0

0.50 (0.07)

0.52 (0.05)

0.13

0.043*

4.2−9

1.5−5

Na number of alleles, Nar allelic richness based on 15 individuals, Np number of private alleles per population, Ho observed heterozygosity, He expected heterozygosity, FIS fixation index (Weir and Cockerham, 1984), P probability to obtain FIS values higher than observed after 10,000 random permutations (*P < 0.05); standard errors in parentheses

Gene flow and origin of jackals in Italy

The rates of historical gene flow estimated by θST ranged from Nm = 1.6 (between Dalmatia and Bulgaria) to Nm = 15.5 (between Bulgaria and Slavonia–Serbia; Fig. 3). The means (averaged over posterior probabilities) of the recent migration rates (m) between populations, computed with Bayesass are shown in Table 5. Jackal populations appear quite isolated from each other, exchanging less than 2 % effective migrants per generation (m < 0.019 from Bulgaria to Serbia). Exceptions are the Bulgarian and Serbian populations, which apparently received c. 25 % immigrants per generation from Slavonia, this pattern of gene flow being extremely asymmetric because the Slavonian population apparently did not receive immigrants from Bulgaria (m = 0.008) or from Serbia (m = 0.007; Table 5). However, these values of migration rates (m) could be inflated, because θST values among SLA, BG and SRB are <0.05 and populations are not differentiated enough to ensure reliable estimations of m (Table 5; see also Faubet et al. 2007).
Table 5

a Matrix of inter-population θST values (lower left) and the corresponding estimates of historical gene flow (Nm; upper right) among the sampled golden jackal populations; in parenthesis probability values based on 999 permutations. b Mean (standard deviation) posterior probabilities of migration rates m into each jackal population computed in Bayesass (Wilson and Rannala 2003). The populations from which individuals were sampled are listed in the rows; the populations from which they migrated are listed in the columns. Values along the diagonal are the proportions of individuals originated from the source population (not migrants). Es: 73.8 % individuals sampled in BG are from BG (source population), 24.6 % are from SLA, 0.6 % from DA, etc.

 

BG

SLA

DA

SRB

IT

(a) θST\Nm

BG

14.34

1.60

15.27

3.96

SLA

0.017 (0.001)

1.72

23.12

4.74

DA

0.135 (0.001)

0.127 (0.001)

1.45

2.94

SRB

0.016 (0.083)

0.011 (0.161)

0.147 (0.001)

4.67

IT

0.059 (0.001)

0.050 (0.003)

0.078 (0.002)

0.051 (0.014)

(b) Into\From

BG

0.738 (0.047)

0.246 (0.047)

0.006 (0.008)

0.005 (0.006)

0.004 (0.005)

SLA

0.008 (0.012)

0.970 (0.023)

0.008 (0.011)

0.007 (0.001)

0.007 (0.009)

DA

0.004 (0.009)

0.006 (0.009)

0.981 (0.019)

0.005 (0.009)

0.005 (0.008)

SRB

0.019 (0.023)

0.246 (0.046)

0.018 (0.023)

0.699 (0.029)

0.018 (0.024)

IT

0.025 (0.029)

0.094 (0.053)

0.149 (0.063)

0.022 (0.025)

0.709 (0.037)

The m values >0.10 are in bold. Standard deviations are in parenthesis

The assignment test in GenAlEx indicated that 65 % (Bulgaria), 71 % (Slavonia–Serbia) and 81 % (Dalmatia) of the samples were assigned to their own population of origin. In Structure (K = 3) computed with the admixture and F models, only the Dalmatian samples were assigned to their own cluster with Qi = 0.95 and individual qi ranging from 0.85 to 0.98 (Table 3). Alternative models (locprior and I) performed similarly. Assignment analyses concordantly indicated that jackals colonizing Italy have admixed genotypes originating mainly from the Dalmatian population (Figs. 1, 2, 3). In particular, Structure (admixture model; K = 3) assigned three individuals to the Dalmatian population (1216, 1219 and 1220 with qi > 0.79; Table 6). The other four samples showed admixed origins. The assignment test in Bayesass suggested that golden jackals sampled in Italy derived mainly from Dalmatia (m = 0.149) and in part from Slavonia (m = 0.094; Table 5; individual assignments are shown in Table 6). According to Structure results, samples 1216 and 1220 (plus 1215) were identified as migrants from Dalmatia. The other jackals originated from Slavonia (1217) or were admixed. Interestingly, all the genotypes sampled in Italy were not first generation migrants, but were likely generated one generation back, mostly from Dalmatian or from admixed Dalmatian x Slavonian parentals.
Table 6

Identification (ID), sampling year and locality of golden jackals collected in north-eastern Italy (see: Fig. 1)

ID

Year

Locality

STRU admixture

STRU locprior

BASS Time 1

BASS Time 2

sPCA I

1211F

1985

Udine

0.37 DA

0.51 DA

0.45 SLA

0.004 DA

0.18 SLA

0.31 DA

−0.40

1215M

1994

Gorizia

0.59 DA

0.59 DA

0.81 DA

0.004 DA

−0.07

1216M

1994

Belluno

0.96 DA

0.72 DA

0.92 DA

0.07 DA

0.26

1217M

2009

Trieste

0.07 DA

0.38 DA

0.95 SLA

0.008 SLA

−0.17

1219M

2009

Bolzano

0.79 DA

0.61 DA

0.66 DA

0.005 DA

0.46

1220F

2009

Udine

0.90 DA

0.64 DA

0.96 DA

0.004 DA

0.57

1221F

2011

Gorizia

0.46 DA

0.57 DA

0.49 SLA

0.48 DA

0.006 SLA

0.020 DA

0.53

The samples were assigned to their putative population of origin by clustering and assignment testing as implemented in Structure (STRU), with K = 3 (concordantly with the geographic origin of the samples), the admixture and locprior models; in Bayeass (BASS) at time 1 = migrant of first generation, or 2 = offspring of a migrant (second generation migrant); and in sPCA in Adegenet. Individual scores on the first (sPCA I) spatial component of the sPCA analysis

DA Dalmatia, SLA Slavonia

Discussion

Despite its large range distribution, the golden jackal has been little investigated and few genetic studies have been published so far. Zachos et al. (2009) genotyped 121 individuals from Serbia using eight STRs, finding a very low genetic variability. Cohen et al. (2013) analysed 88 jackals from Israel using 14 STRs, and found a high level of genetic diversity and no evidence of bottleneck, even if the species was near-completely eradicated in this region. In our study the golden jackal populations sampled in Bulgaria, Serbia and Croatia share the same mtDNA CR1 haplotype, but they have polymorphic STRs with 2–14 alleles and intermediate values of heterozygosity (Ho = 0.47; He = 0.51). Although more empirical data are needed, these first results may suggest that Europe was colonized a few centuries ago by small numbers of founders, which carried a limited portion of the total genetic diversity of the southern golden jackal source populations.

The monomorphic mtDNA CR1 sequence consistently found in this study and in Zachos et al. (2009) suggests a scenario of historical population contraction to restricted refuge areas, followed by recent re-expansion waves and rapid demographic expansion. The number of sampled populations and the empirical evidence available so far are not enough to allow reconstructing a reliable global phylogeographic framework for the golden jackal in Eurasia. Thus, at the moment it is not possible to further speculate on the phylogeographical relationships of extant golden jackal populations, and identify the causes of the observed mtDNA monomorphism. It is well known that the control-region includes the most variable sequences in the mtDNA genome of the vertebrates (Simon 1991). There are some described exceptions, and among them, notably, a carnivore, the otter (L. lutra) that showed very conserved mtDNA CR sequences in all the populations studied so far (Mucci et al. 2010). The two competing hypotheses, that the golden jackal mtDNA CR monomorphism derives: (1) from an exceptionally conserved molecular dynamics, or (2) from a recent origin of the extant European jackal populations, may be tested by sequencing other mtDNA and nuclear DNA genes (e.g., polymorphic genes from the major histocompatibility complex, or olfactory receptors; Aguilar et al. 2004; Quignon et al. 2012) and comparing their mutation rates, or by genotyping golden jackal samples collected throughout the entire distribution range in Eurasia and Africa.

The STRs allele distributions in the sampled golden jackal populations were significantly different (θST = 0.07; P = 0.001), yielding consistent pictures of genetic structuring, using multivariate, Bayesian or landscape population genetic methods. These three statistical methods are based on different assumptions. In particular, Structure assumes that an admixed sample of unknown subpopulations can be split in distinct genetic clusters maximizing both HWE and LE within each of them (Pritchard et al. 2000). Obviously, empirical multilocus datasets (including our jackals) might not include subpopulations in genetic equilibrium, because of recent founder effects, ongoing admixture or biased sampling. The consequences of assumption violations on Structure results are, however, poorly known (Kaeuffer et al. 2007; Kalinowski 2011), and it is wise to compare the results obtained with different methods. Landscape genetic models assume that spatial proximity should be a priori related to genetic similarity, and that the use of spatial information could improve clustering in populations that, as in the golden jackal case-study, are not deeply divergent (θST < 0.10; Guillot 2008).

Bayesian and landscape genetics models showed a weak population subdivision, but golden jackals sampled in Dalmatia were clearly separated from all the other samples. In Structure jackals from Dalmatia clustered separately from the other samples assuming K = 3. Jackals from Slavonia and Serbia were assigned to the same genetic cluster, while samples from Bulgaria remained distinct, showing strong signals of admixture. The spatial genetic analyses (Structure with the locprior model; sPCA) confirmed these results, showing a major subdivision between jackals from Dalmatia versus all the other sampled groups. Geneland confirmed this pattern, showing sharp subdivisions among jackals sampled in Dalmatia, Slavonia–Serbia and Bulgaria. These results are in agreement with the available historical information. The golden jackal is a historical invasive species in Europe, and its permanent presence has been reported at least since the Middle Ages in southern Dalmatia (Kryštufek and Tvrtković 1990). Permanent populations in northern Dalmatia were established much later, during the 20th century. Further north-western expansions towards Istria were more recent (in the 1980s; Kryštufek and Tvrtković 1990). The coastal golden jackal populations might have originated centuries ago from unknown source populations, and might have survived in isolation for hundreds of generations. The empirical genetic evidence support this hypothesis. In fact, the 15 selected STRs were polymorphic in all the sampled golden jackal populations, except for locus CPH12 that was monomorphic in the Dalmatian samples. Moreover, allele richness and heterozygosity were smaller in Dalmatia than in the other populations. Reduced genetic diversity suggests that jackals in Dalmatia might have experienced a demographic bottleneck in the past (a test designed to detect recent bottlenecks did not yield significant results; Cornuet and Luikart 1996) and have survived in isolation for centuries. Once again, historical information and genetic evidences are in agreement. The expansion of jackals in Slavonia, in the continental eastern part of Croatia, occurred only from 1903 onwards, and the more recent expansion wave occurring in the last 15 years is most probably due to immigration of jackals from Bulgaria, Romania and Serbia (Giannatos 2004).

This pattern of population clustering yields a reference framework for efficient assignments of individuals to the populations. Although only jackals from Dalmatia are always consistently assigned to their population of origin, at least a portion (71 %) of the Slavonian–Serbian samples was correctly assigned using genetic information only. All three geographic groups (Dalmatia, Slavonia–Serbia and Bulgaria) were identified and the individuals were correctly assigned by integrating the geographical information in Geneland, which also assigned the admixed Italian samples to a fourth group (Fig. 1). These results indicate that weakly differentiated populations (θST < 0.10) can be identified and individuals assigned if multilocus genetic and geographical information are integrated in landscape genetics models. The results obtained from landscape analyses allowed drafting a preliminary reconstruction of jackal expansion in eastern Europe.

Jackals in Bulgaria, Croatia and Slavonia–Serbia showed low rates of both historical and current gene flow (Table 5), meaning that they were and are substantially isolated from one other. The only exception is the jackal population in Bulgaria, which appears strongly admixed and historically connected with jackals in Slavonia–Serbia (Nm = 15.5). These findings are in agreement with demographic data suggesting that the expansion of the more abundant and widespread jackal populations in Romania and Bulgaria increased local admixtures and pushed migration towards north-western areas. Current rates of gene flow estimated in this study, though they should be evaluated with caution because of partial differentiation among populations (Faubet et al. 2007), suggest that Slavonia is the main source of jackals migrating both eastwards (towards Bulgaria) and westwards (towards Dalmatia and Italy). Therefore, as the reliability of gene flow estimates based on Fst or assignment procedures is conditioned by a number of assumptions and sampling design (Whitlock and Maccauley 1999), this hypothetical scenario should be tested indirectly by genotyping additional samples, or directly by reconstructing the dispersal movements of radio-tagged jackals. The historical invasion of golden jackals in Europe could have been sustained by anthropogenic habitat changes, which may favour opportunistic mesocarnivores, and by global climate changes, which may favour the expansion of species of southern origins. These dynamics could modify the ecological interaction in prey–predator communities in southern Europe, with implications for conservation of both carnivores and their prey.

Population structure and assignment tests concordantly indicated that jackals colonizing Italy have admixed genotypes that originated from the Dalmatian and Slavonian populations (Figs. 1, 2, 3). Admixed origins, further supported by the absence of any private allele, and evidence that the genotypes sampled in Italy are not first generation migrants but might have originated one generation back, suggest that jackal dispersal from Dalmatia and Slavonia towards north-eastern Italy is a stepping-stone process. Jackals migrating from their source populations probably met and mated in some (still un-sampled) intermediate areas. In this perspective, golden jackal and wolf colonization patterns might be significantly different, due to the prevalent short-distance dispersal with intermediate admixture in jackals versus prevalent long distance dispersal in wolves. This hypothesis might be tested with additional genetic, demographic and behavioural data. The admixed origin ensures abundant genetic diversity in the golden jackal colonizers, which may enhance their adaptive fitness and expectancy of population growth. These findings and the recent expansion of wolf (Fabbri et al. 2013), brown bear, lynx and otter (Lapini et al. 1996; Lapini and Bonesi 2011) confirm that the intersections between the Dinaric–Balkan and Italian Alps are areas of population expansion and admixture, highlighting their conservation, ecological and evolutionary values. Monitoring the ongoing jackal colonization process in the Dinaric–Italian Alps might take advantage of improved non-invasive genetic sampling and molecular identification procedures (Waits and Paetkau 2005). Jackals colonizing Italy show enough genetic diversity and low probability-of-identity to allow reliable individual genotype identifications also from DNA extracted from non-invasive samples.

Notes

Acknowledgments

We warmly thank everybody who made it possible to realize this research project, and that contributed to obtain samples used in this study. This project was supported by ISPRA, by the Italian Ministry of Environment, Direction of Nature Protection, and it was partly supported by the European Social Fund through the Ministry of Education, Youth and Science of Bulgaria (BG051PO001-3.3.04/41). We are particularly grateful to Aritz Ruiz-Gonzalez for his useful suggestions and help in landscape genetic analysis and for his comments on a preliminary version of this manuscript.

References

  1. Aguilar A, Roemer G, Debenham S, Binns M, Garcelon D, Wayne RK (2004) High MHC diversity maintained by balancing selection in an otherwise genetically monomorphic mammal. Proc Natl Acad Sci USA 101:3490–3494PubMedCrossRefGoogle Scholar
  2. Arnold J, Humer A, Heltai M, Murariu D, Spassov N, Hackländer K (2012) Current status and distribution of golden jackals Canis aureus in Europe. Mamm Rev 42:1–11CrossRefGoogle Scholar
  3. Banea OC, Krofel M, Červinka J, Gargarea P, Szabó L (2012) New records, first estimates of densities and questions of applied ecology for jackals in Danube Delta Biosphere Reserve and hunting terrains from Romania. Acta Zool Bulg 64:353–366Google Scholar
  4. Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F (1996–2004) Genetix 4.05, logiciel sous Windows TM pour la génétique des Populations. Laboratoire Génome, Populations, Interactions, CNRS UMR 5000, Université de Montpellier II, Montpellier, France Google Scholar
  5. Caniglia R, Fabbri E, Mastrogiuseppe L, Randi E (2013) Who is who? Identification of livestock predators using forensic genetic approaches. Forensic Sci Int Genet 7:397–404PubMedCrossRefGoogle Scholar
  6. Cohen TM, King R, Dolev A, Boldo A, Lichter-Peled A, Bar-Gal GK (2013) Genetic characterization of populations of the golden jackal and the red fox in Israel. Con Gen 14:55–63CrossRefGoogle Scholar
  7. Cornuet JM, Luikart G (1996) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144:2001–2014PubMedGoogle Scholar
  8. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Ecol 14:611–2620Google Scholar
  9. Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131:479–491PubMedGoogle Scholar
  10. Fabbri E, Miquel C, Luchini V, Santini A, Caniglia R, Duchamp C, Weber J-M, Lequette B, Marucco F, Boitani L, Fumagalli L, Taberlet P, Randi E (2007) From the Apennines to the Alps: colonization genetics of the naturally expanding Italian wolf (Canis lupus) population. Mol Ecol 16:1661–1671PubMedCrossRefGoogle Scholar
  11. Fabbri E, Caniglia R, Kusak J, Galov A, Gomerčić T, Arbanasić H, Huber D, Randi E (2013) Genetic structure of expanding wolf (Canis lupus) populations in Italy and Croatia, and the early steps of the recolonization of the Eastern Alps. Mamm Biol (in press)Google Scholar
  12. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–1587PubMedGoogle Scholar
  13. Faubet P, Waples RS, Gaggiotti OE (2007) Evaluating the performance of a multilocus Bayesian method for the estimation of migration rates. Mol Ecol 16:1149–1166PubMedCrossRefGoogle Scholar
  14. Francisco LV, Langston AA, Mellersh CS, Neal CL, Ostrander EA (1996) A class of highly polymorphic tetranucleotide repeats for canine genetic mapping. Mamm Genome 7:359–362PubMedCrossRefGoogle Scholar
  15. Fredholm M, Wintero AK (1995) Variation of short tandem repeats within and between species belonging to the Canidae family. Mamm Genome 6:11–18PubMedCrossRefGoogle Scholar
  16. Genov P, Wassilev S (1989) Der Schakal (Canis aureus L.) in Bulgarien. Ein Beitrag zu seiner Verbreitung und Biologie. Z Jagdwiss 35:145–150Google Scholar
  17. Giannatos G (2004) Conservation action plan for the golden jackal Canis aureus L. in Greece. WWF Greece, p 47Google Scholar
  18. Goudet J (1995) Fstat (Version 1.2): a computer program to calculate F-statistics. J Hered 86:485–486Google Scholar
  19. Guillot G (2008) Inference of structure in subdivided populations at low levels of genetic differentiation. The correlated allele frequencies model revisited. Bioinformatics 24:2222–2228PubMedCrossRefGoogle Scholar
  20. Guillot G, Mortier F, Estoup A (2005) Geneland: a computer package for landscape genetics. Mol Ecol Notes 5:708–711CrossRefGoogle Scholar
  21. Guo SW, Thompson EA (1992) Performing the exact test of Hardy–Weinberg proportion for multiple alleles. Biometrics 48:361–372PubMedCrossRefGoogle Scholar
  22. Hall TA (1999) BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/97/NT. Nucleic Acids Symp Ser 41:95–98Google Scholar
  23. Hubisz M, Falush D, Stephens M, Pritchard J (2009) Inferring weak population structure with the assistance of sample group information. Mol Ecol Resour 9:1322–1332PubMedCentralPubMedCrossRefGoogle Scholar
  24. Humer A, Heltai M, Murariu D, Spassov N, Häcklander K (2007) Current status and distribution of golden jackals (Canis aureus) in Europe. In: Sjöberg K, Tuulikki R (eds) Abstracts XXVII Congress IUGB, Uppsala, Sweden. Swedish University of Agricultural Sciences, Umeå, p 272Google Scholar
  25. Ibrahim KM, Nichols RA, Hewitt GM (1996) Spatial patterns of genetic variation generated by different forms of dispersal during range expansion. Heredity 77:282–291CrossRefGoogle Scholar
  26. Jombart T, Devillard S, Dufour A-B, Pontier D (2008) Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Heredity 101:92–103PubMedCrossRefGoogle Scholar
  27. Kaeuffer R, Réale D, Coltman DW, Pontier D (2007) Detecting population structure using STRUCTURE software: effect of background linkage disequilibrium. Heredity 99:374–380PubMedCrossRefGoogle Scholar
  28. Kalinowski ST (2011) The computer program STRUCTURE does not reliably identify the main genetic clusters within species: simulations and implications for human population structure. Heredity 106:625–632PubMedCrossRefGoogle Scholar
  29. Kryštufek B, Tvrtković N (1990) Range expansion by Dalmatian jackal population in the 20th century (Canis aureus L. 1758). Folia Zool 39:291–296Google Scholar
  30. Kryštufek B, Murariu D, Kurtonur C (1997) Present distribution of the Golden Jackal Canis aureus in the Balkans and adjacent regions. Mamm Review 27:109–114CrossRefGoogle Scholar
  31. Lapini L (2006) Il cane viverrino Nyctereutes procyonoides ussuriensis Matschie, 1908 in Italia: segnalazioni 1980–2005 (Mammalia: Carnivora: Canidae). Boll Mus Civ St Nat Venezia 57:235–239Google Scholar
  32. Lapini L, Bonesi L (2011) Evidence of a natural recovery of the Eurasian otter in northeast Italy. In: Proceedings of the 29th European Mustelid Colloquium, Southampton, UKGoogle Scholar
  33. Lapini L, Dall’Asta A, Dublo L, Spoto M, Vernier E (1996) Materials for the theriofauna in north-eastern Italy (Mammalia, Friuli-Venezia Giulia) Gortania. In: Proceeding of the museum of the natural history of friuli 17, pp 149–248 (in Italian)Google Scholar
  34. Lapini L, Molinari P, Dorigo L, Are G, Beraldo P (2009) Reproduction of the golden jackal (Canis aureus moreoticus I. Geoffroy Saint Hilaire, 1835) in Julian pre-Alps, with new data on its range-expansion in the high-adriatic Hinterland (Mammalia, Carnivora, Canidae). Boll Mus Civ St Nat Venezia 60:169–186Google Scholar
  35. Lapini L, Brugnoli S, Krofel M, Kranz A, Molinari P (2010) A grey wolf (Canis lupus Linné, 1758) from Fiemme Valley (Mammalia: Canidae: north-eastern Italy). Boll Mus Civ St Nat Venezia 61:117–129Google Scholar
  36. Lapini L, Conte D, Zupan M, Kozlan L (2011) Italian jackals 1984–2011. An updated review (Canis aureus: Carnivora, Canidae). Boll Mus Civ St Nat Venezia 62:219–232Google Scholar
  37. Mawdsley JR, O’Malley R, Ojima DS (2008) A review of climate-change adaptation strategies for wildlife management and biodiversity. Conserv Biol 23:1080–1089CrossRefGoogle Scholar
  38. Mech LD (1970) The wolf: the ecology and behavior of an endangered species. Natural History Press, Garden City, NYGoogle Scholar
  39. Mucci N, Arrendal J, Ansorge H et al (2010) Genetic diversity and landscape genetic structure of otter (Lutra lutra) populations in Europe. Conserv Genet 11:583–599CrossRefGoogle Scholar
  40. Oliveira R, Godinho R, Randi E, Alves PC (2008) Hybridization versus conservation: are domestic cats threatening the genetic integrity of wildcats (Felis silvestris silvestris) in Iberian Peninsula? Philos Trans R Soc B 363:2953–2961CrossRefGoogle Scholar
  41. Ostrander EA, Sprague GF, Rine J (1993) Identification and characterization of dinucleotide repeat (CA)n markers for genetic mapping in dog. Genomics 16:207–213PubMedCrossRefGoogle Scholar
  42. Paetkau D, Slade R, Burdens M, Estoup A (2004) Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation based exploration of accuracy and power. Mol Ecol 13:55–65PubMedCrossRefGoogle Scholar
  43. Peakall R, Smouse PE (2006) GenAlEx 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes 6:288–295CrossRefGoogle Scholar
  44. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedGoogle Scholar
  45. Quignon P, Rimbault M, Robin S, Galibert F (2012) Genetics of canine olfaction and receptor diversity. Mamm Genome 23:132–143PubMedCrossRefGoogle Scholar
  46. Randi E (2008) Detecting hybridization between wild species and their domesticated relatives. Mol Ecol 17:285–293PubMedCrossRefGoogle Scholar
  47. Rice WR (1989) Analyzing tables of statistical tests. Evolution 43:223–225Google Scholar
  48. Rousset F (2008) GENEPOP’007: a complete re-implementation of the GENEPOP software for Windows and Linux. Mol Ecol Resour 8:103–106PubMedCrossRefGoogle Scholar
  49. Selanec J, Lauš B, Sindičić M (2011) Golden jackal (Canis aureus) distribution in Croatia. In: Abstract of VI European Congress of mammalogy, Paris, France, p 60Google Scholar
  50. Sillero-Zubiri C, Hoffmann M, Macdonald DW (2004) Canids: foxes, wolves, jackals and dogs: status survey and conservation action plan, 2nd edn. IUCN Canid Specialist Group, CambridgeGoogle Scholar
  51. Simon C (1991) Molecular systematics at the species boundary: exploiting conserved and variable regions of the mitochondrial genome of animals via direct sequencing from amplified DNA. In: Hewitt GM, Johnston AWB, Young JPW (eds) Molecular techniques in taxonomy. Springer, Berlin, pp 33–71CrossRefGoogle Scholar
  52. Spassov N (1989) The position of jackals in the Canis genus and life-history of the golden jackal (Canis aureus L.) in Bulgaria and on Balkans. Hist Nat Bulg 1:44–56Google Scholar
  53. Taryannikov VI (1977) On the population structure and dynamics of Canis aureus aureus in the Syrdarja river valley. Zool Z Mosca 71:1423–1425Google Scholar
  54. Trouwborst A (2010) Managing the carnivore comeback: international and EU species protection law and the return of lynx, wolf and bear to western Europe. J Environ Law 22:347–372CrossRefGoogle Scholar
  55. Valière N, Fumagalli L, Gielly L, Miquel C, Lequette B, Poulle M-L, Weber J-M, Arlettaz R, Taberlet P (2003) Long-distance wolf recolonization of France and Switzerland inferred from non-invasive genetic sampling over a period of 10 years. Animal Conserv 6:83–92CrossRefGoogle Scholar
  56. Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) Microchecker: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes 4:535–538CrossRefGoogle Scholar
  57. Vila C, Sundqvist AK, Flagstad O et al (2003) Rescue of a severely bottlenecked wolf (Canis lupus) population by a single immigrant. Proc R Soc London B 270:91–97CrossRefGoogle Scholar
  58. vonHoldt B, Stahler D, Pollinger J, Smith D, Bangs E et al (2010) A novel assessment of population structure and gene flow in gray wolf populations of the Northern Rocky Mountains of the United States. Mol Ecol 19:4412–4427PubMedCrossRefGoogle Scholar
  59. Waits LP, Paetkau D (2005) Noninvasive genetic sampling tools for wildlife biologists: a review of applications and recommendations for accurate data collection. J Wildl Manag 69:1419–1433CrossRefGoogle Scholar
  60. Waits LP, Luikart G, Taberlet P (2001) Estimating the probability of identity among genotypes in natural populations: cautions and guidelines. Mol Ecol 10:249–256PubMedCrossRefGoogle Scholar
  61. Weir BS, Cockerham CC (1984) Estimating F-statistic for the analysis of population structure. Evolution 6:1358–1370CrossRefGoogle Scholar
  62. Whitlock MC, Maccauley DE (1999) Indirect measures of gene flow and migration: Fst =/1/(4Nm + 1). Heredity 82:117–125PubMedCrossRefGoogle Scholar
  63. Wilson GA, Rannala B (2003) Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163:1177–1191PubMedGoogle Scholar
  64. Zachos FE, Cirovic D, Kirschning J, Otto M, Hartl GB, Petersen B, Honnen A (2009) Genetic variability, differentiation, and founder effect in golden jackals (Canis aureus) from Serbia as revealed by mitochondrial DNA and nuclear microsatellite loci. Biochem Genet 47:241–250PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Elena Fabbri
    • 1
  • Romolo Caniglia
    • 1
  • Ana Galov
    • 2
  • Haidi Arbanasić
    • 2
  • Luca Lapini
    • 3
  • Ivica Bošković
    • 4
  • Tihomir Florijančić
    • 4
  • Albena Vlasseva
    • 5
  • Atidzhe Ahmed
    • 5
  • Rossen L. Mirchev
    • 6
  • Ettore Randi
    • 1
    • 7
  1. 1.Laboratorio di GeneticaIstituto Superiore per la Protezione e Ricerca Ambientale (ISPRA)Ozzano dell’EmiliaItaly
  2. 2.Department of Biology, Faculty of ScienceUniversity of ZagrebZagrebCroatia
  3. 3.Museo Friulano di Storia Naturale, UdineUdineItaly
  4. 4.Department for Hunting, Fishery and BeekeepingFaculty of Agriculture in OsijekOsijekCroatia
  5. 5.Institute of Biodiversity and Ecosystem ResearchBulgarian Academy of SciencesSofiaBulgaria
  6. 6.National Research Station of Wildlife Management, Biology and PathologySofiaBulgaria
  7. 7.Department 18/Section of Environmental EngineeringAalborg UniversityÅlborgDenmark

Personalised recommendations