ecological factors and gene flow in wolves

Molecular Ecology (2006)

15

, 4533–4553 doi: 10.1111/j.1365-294X.2006.03110.x

© 2006 The AuthorsJournal compilation © 2006 Blackwell Publishing Ltd

Blackwell Publishing Ltd

Ecological factors influence population genetic structure of European grey wolves

MA

L

GORZATA PILOT,

*

W

L

ODZIMIERZ J

E

DRZEJEWSKI ,

†

WOJCIECH BRANICKI ,

‡

VADIM E. S IDOROVICH,

§

BOGUMI

L

A J

E

DRZEJEWSKA,

†

KRYSTYNA STACHURA

†

and STEPHAN M. FUNK

¶**

*

Museum and Institute of Zoology, Polish Academy of Sciences, Ul Wilcza 64, 00-679 Warszawa, Poland,

†

Mammal Research Institute, Polish Academy of Sciences, 17-230 Bia

l

owie

*

a, Poland,

‡

Institute of Forensic Research, Ul Westerplatte 9, 31-033 Kraków, Poland,

§

Institute of Zoology, National Academy of Sciences of Belarus, Akademicheskaya Str 27, 220072 Minsk, Belarus,

¶

Institute of Zoology, Zoological Society of London, London RW1 4RY, UK

Abstract

Although the mechanisms controlling gene flow among populations are particularly impor-tant for evolutionary processes, they are still poorly understood, especially in the case oflarge carnivoran mammals with extensive continuous distributions. We studied the questionof factors affecting population genetic structure in the grey wolf,

Canis lupus

, one of themost mobile terrestrial carnivores. We analysed variability in mitochondrial DNA and 14microsatellite loci for a sample of 643 individuals from 59 localities representing most of thecontinuous wolf range in Eastern Europe. We tested an array of geographical, historical andecological factors to check whether they may explain genetic differentiation among localwolf populations. We showed that wolf populations in Eastern Europe displayed nonrandomspatial genetic structure in the absence of obvious physical barriers to movement. Neithertopographic barriers nor past fragmentation could explain spatial genetic structure. However,we found that the genetic differentiation among local populations was correlated with climate,habitat types, and wolf diet composition. This result shows that ecological processes maystrongly influence the amount of gene flow among populations. We suggest natal-habitat-biased dispersal as an underlying mechanism linking population ecology with populationgenetic structure.

Keywords

: cryptic genetic structure, gene flow, genetic diversification, grey wolf, natal-habitat-biased dispersal, predator–prey interaction

Received 4 April 2006; revision received 29 June 2006; accepted 25 July 2006

Introduction

Understanding the micro evolutionary process thatgenerates population genetic structure of large and highlymobile carnivoran mammals is crucial for improving ourknowledge of the mechanisms of their adaptive divergenceand speciation. Classical population genetics explains thepopulation genetic structure by species behavioural traits(forming herds, flocks or colonies), geographical featureslimiting gene flow, such as spatial distance and topographic

barriers (Hartl & Clark 1997), or historical factors such aspast colonization, range expansion or isolation in differentglacial refugia (Hewitt 1996, 2000; Taberlet

et al

. 1998;Templeton 1998). However, besides geographical limitationsand historical events, complex ecological processes mayinfluence the amount of gene flow among populations.

Indeed, an increasing number of studies indicate crypticgenetic structures that cannot be explained either bygeographical or historical factors (e.g. Sponer & Roy 2002;Spinks & Shaffer 2005). Strikingly, many of these studiesconcern large and medium-sized carnivoran mammals withextensive continuous distributions: grey wolf

Canis lupus

(Carmichael

et al

. 2001; Geffen

et al

. 2004), coyote

Canislatrans

(Sacks

et al

. 2004), lynx

Lynx lynx

and

Lynx canadensis

(Rueness

et al

. 2003a, b), puma

Puma concolor

(McRae

et al

.2005), and arctic fox

Alopex lagopus

(Dalén

et al

. 2005). High

Correspondence: Ma

L

gorzata Pilot, Fax: +48-22-6296302; E-mail: [email protected].**Present address: Nature Heritage Ltd., 145-157 St. John Street,London, UK, and Durrell Wildlife Conservation Trust, Les AugresManor, Jersey JE3 5BP, UK.

4534

M . P I L O T

E T A L

.


mobility of these animals and their ability to cross most ofpotential topographic barriers (such as rivers or mountainranges) minimize the influence of geographical factors ongene flow and reduce the effects of historical events, so thatthe effect of ecological factors may be more prominent.

The grey wolf is one of the most mobile terrestrialmammals that disperse rapidly over distances up to 900 km(Fritts 1983; Mech & Boitani 2003). Dispersing individualswere reported to successfully cross four-lane highways andcircumvent large lakes and cities (Mech

et al

. 1995; Merrill& Mech 2000; Wabakken

et al

. 2001). The historical range ofwolves covered nearly the entire Holarctic, from tundra tograsslands and deserts (Nowak 2003). Long-distance dis-persal capabilities combined with the ability to occupy avariety of habitats imply high rates of gene flow that reducegenetic differentiation among local populations. Indeed, astudy based on mitochondrial DNA (mtDNA) control regionsequence data from a worldwide sample of grey wolvessuggested an absence of a large-scale genetic structure, andindicated local, small-grained structure probably causedby the recent restricted gene flow (Vil

à

et al

. 1999).On the other hand, considerable morphological differenti-

ation, that may be a result of genetic divergence, is observedwithin the species (Nowak 2003). Moreover, two studies onNorth American grey wolves reported nonrandom patternsof gene flow that may result from ecology and behaviourof the species. A study on microsatellite variability of greywolves from the Canadian Northwest (Carmichael

et al

.2001) revealed population genetic structure that correspondswith migration patterns of caribou

Rangifer tarandus

, themain prey of wolf in this region. On a larger scale, it wasshown that vegetation types and climate influence geneticdissimilarities among grey wolf populations in NorthAmerica (Geffen

et al

. 2004). Results of that study may havebeen restricted by small sample size. Therefore, we furtherinvestigated the problem of the effect of environmental andecological factors on population genetic structure in largecarnivores, based on an extensive sample of grey wolvesfrom Eastern Europe. We analysed mtDNA control regionsequences and 14 microsatellite loci for 643 individualsfrom 59 localities, distributed across a diversity of habitatsand climatic zones. The analysis of both types of markersrevealed nonrandom population genetic structure. We testedits dependence on historical, geographical and ecologicalfactors, aiming to identify underlying mechanisms of geneticdifferentiation among wolf populations.

Materials and methods

Samples

We analysed 643 tissue samples of wolves from 59 localitiessituated in 10 countries: Poland, Lithuania, Latvia, Belarus,Ukraine, the European part of Russia, Slovakia, Bulgaria,

Greece, and the European part of Turkey. These localitiesrepresent most of the area within the continuous range ofthe species in Europe (see Fig. 4 in Results). The number ofsamples in a locality varied from 2 to 37, with an averageof 11. Most samples (97%) dated from the years 1995–2004.Older samples (dated from the years 1958–1994) were peltsof wolves killed by hunters in Poland. A group of wolvesfrom one locality will be referred to as a local population(we did not assume that a discrete population occurred ineach locality; however, the definition of sample groupswas necessary for population-based analyses).

Laboratory methods

DNA extraction from soft tissues was performed using A& A Biotechnology extraction kit. QIAamp DNA Mini Kit(QIAGEN) was used for DNA extraction from pelts. DNAextraction from teeth was performed following the protocolof Yang

et al

. (1998) modified by Wandeler

et al

. (2003).Amplification of 257 bp of the HV1 domain of the mtDNAcontrol region was performed using the primers from Vil

r

et al

. (1997). The polymerase chain reaction (PCR) mixturewas made up of 1 U

Taq

polymerase, 200

µ

m

dNTP, 2.0

µ

L10

×

concentrated PCR buffer, 1.5 m

m

MgCl

2

, 0.1 m

m

ofprimers and 4

µ

L of DNA for 20

µ

L reactions. The reactionconditions were as follows: 2 min at 94

°

C of initial denatura-tion, 36–40 cycles of 20 s at 94

°

C, 30 s at 69

°

C, 40 s at 72

°

C,and the final elongation step for 10 min at 72

°

C. Negativecontrols were added to each set of samples during extractionas well as during PCR amplification to control for contami-nation. PCR products were purified using the QIAquick PCRPurification Kit (QIAGEN). Sequencing reactions wereperformed using BigDye Terminator Cycle Sequencing Kit(PerkinElmer) and detection of sequencing reaction productswas carried out on ABI PRISM 3100 genetic analyser (AppliedBiosystems). Sequencing results were analysed with ABIPRISM DNA Sequencing Analysis software, version 3.0, andalignments were performed using

sequence navigator

2.0.We also analysed 14 microsatellite loci: FH2001, FH2010,

FH2017, FH2054, FH2079, FH2088, FH2096 (Francisco

et al

.1996), C213, C250, C253, C466, C642 (Ostrander

et al

. 1993),AHT130 (Holmes

et al

. 1995) and VWF (Shibuya

et al

. 1994).Microsatellites were amplified in five multiplexes, usingMultiplex PCR Kit (QIAGEN) and the PCR conditionsdescribed in manufacturer’s instruction (with the annealingtemperature 58

°

C). PCR products were analysed on ABIPRISM 3100 genetic analyser. Allele lengths were deter-mined using

genescan

3.7 and

genotyper

3.7 software.

Estimation of the total number of mtDNA haplotypes

The fact that the number of haplotypes increases with thenumber of analysed samples was used to estimate the totalnumber of wolf haplotypes in the study area and compare

E C O L O G I C A L F A C T O R S A N D G E N E F L O W I N W O L V E S

4535


it with the number of actually found haplotypes. We con-structed a rarefaction curve plotting the cumulative numberof haplotypes found with increasing sample size. The totalnumber of haplotypes was estimated as the asymptoteof this curve (Kohn

et al

. 1999; Leonard

et al

. 2005). As thesampling order affects the shape of the curve, the dataset was randomised 1000 times (without replacement ofhaplotypes) using

gimlet

(Valière 2002) and 1000 rarefactioncurves were generated using the

r

package (Ihaka &Gentleman 1996) and a script file produced by

gimlet

. Theasymptote (

a

) for each curve was calculated from theequation:

y

=

ax

/(

b + x

), where

y

is the cumulative numberof haplotypes,

x

is the number of sampled individuals, and

b

is the rate of decline in the slope of the curve (Kohn

et al

.1999). The number of haplotypes was estimated as themean value of the asymptote

a

for all iterations.

Analysis of mtDNA variability: phylogenetic analysis

To test phylogenetic relationships among the haplotypes, weconstructed phylogenetic trees in

paup

4.0b10 (Swofford1998) using a HKY +

Γ

model of nucleotide substitutionwith a shape parameter of the gamma distribution

α

= 0.0736,as estimated in

modeltest

3.6 (Posada & Crandall 1998).The phylogenies were rooted with two coyote sequencesfrom GenBank (Accession nos AF008158, AF020700).We constructed trees using neighbour-joining, minimum-evolution, maximum-likelihood, and maximum-parsimonyalgorithms. Confidence in estimated relationships wasdetermined by calculating bootstrap values, which wereobtained through 1000 replicates, using the heuristic searchalgorithm implemented in

paup

. Additionally, we con-structed a Bayesian tree in

mrbayes

3.1 (Huelsenbeck &Ronquist 2001) using the HKY +

Γ

model of nucleotidesubstitution, as estimated in

mrmodeltest

2.2 (Nylander2004). In the Markov chain Monte Carlo simulation, fourchains were run simultaneously for 1 million generations.Trees were sampled every 10 generations for a total of 100 000trees in the initial sample. Stationarity of the process wasdetermined to have occurred by the 10 000th trees andtherefore ‘burn-in’ was completed by this stage. Thus, thetree and clade credibility values were obtained from 90 000trees. The heterogeneity of mutation rates among lineageswas tested by comparing the log-likelihoods of maximum-likelihood trees obtained with and without enforcingmolecular clock, using the likelihood-ratio test of Shimodaira& Hagesawa (1999).

In order to estimate the coalescence time of EasternEuropean wolves, we calculated mean sequence divergencewithin wolves and net sequence divergence (corrected forancestral within-species polymorphism) between wolvesand coyotes, using the program

mega

3.1 (Kumar

et al

.2004). The standard error of these estimates was calculatedwith 1000 bootstrap pseudo-replicates. Because the HKY

model of nucleotide substitution is not implemented in

mega

, and because this model is a special case of the Tamura–Nei model (Nei & Kumar 2000), we used the Tamura–Neimodel with a shape parameter of the gamma distribution

α

= 0.10, as estimated in

tree

-

puzzle

(Schmidt

et al

. 2000).The minimum evolution tree constructed in

mega

usingthe Tamura–Nei model had similar topology as the treesconstructed in

paup

using the HKY +

Γ

model.The nested clade analysis (NCA) (Templeton 1998, 2004)

of the geographical distribution of mtDNA haplotypeswas performed to separate effects of a recurrent gene flowand historical factors, such as past fragmentation, colon-ization or range expansion. Statistical parsimony approachimplemented in the software

tcs

(Clement

et al

. 2000) wasused to construct the minimum spanning network, whichwas nested according to the rules described in Templeton

et al

. (1992) and Templeton & Sing (1993). The hypothesisof no geographical association of nested clades was testedusing the program

geodis

(Posada

et al

. 2000). Results wereinterpreted using the inference key from Templeton (2004).

Additionally, we compared grey wolf haplotypes foundin our study with those reported in previous studies (Vil

r

et al

. 1999; Randi

et al

. 2000). This allowed us to identifyhaplotypes that have been previously found and to relatehaplotypes from Eastern Europe to previously publishedphylogenetic trees of worldwide grey wolf haplotypes(Vil

r

et al

. 1999; Leonard

et al

. 2005).

Analysis of mtDNA variability: frequency-based analysis

To analyse population genetic structure, we used the spatialanalysis of molecular variance implemented in the

samova

software (Dupanloup

et al

. 2002). This method defines groupsof local populations that are geographically homogenousand maximally differentiated from each other. The methodis based on a simulated annealing procedure that aims tomaximize the proportion of total genetic variance due todifferences between groups of populations, measuredby

Φ

CT coefficient of the amova Φ-statistics (Excoffier et al.1992). In contrast to classical tests of genetic structure (suchas amova), in which groups of populations are defined apriori, the samova procedure finds a structure based solelyon genetic data and geographical location of populations.However, this approach requires the a priori definition of thenumber (K) of groups. Thus, we ran samova successivelyon our data set with different K, ranging from 2 to 20. Ananalysis with each K-value was performed twice to checkwhether results are consistent between runs. In each run,100 simulated annealing processes were performed. Theidentification of the most probable number of groups wasbased on the pattern of changes in values of Φ-statisticsparameters with K.

Although the idea of the samova procedure is to ensure thatthe inferred groups are composed of adjacent populations,

4536 M . P I L O T E T A L .


it can sometimes lead to the definition of groups in which allthe populations are not geographically adjacent (Dupanloupet al. 2002). Thus, after identifying the most probablepopulation genetic structure with samova, we modified itso as to receive geographically homogenous groups. Thesegroups will be referred to as subpopulations of the totalpopulation of Eastern European wolves. We used amovaprocedure implemented in the arlequin software (Schneideret al. 2000) to calculate Φ-statistics for the inferred popula-tion genetic structure and test its significance. For acomparison, we also calculated Φ-statistics for a randomgrouping of local populations into 10 groups. Using theprogram contrib (Petit et al. 1998), we calculated haplotypediversity and allelic richness for the inferred subpopulations.Allelic richness employs rarefaction method to control forthe effect of sample size on the number of haplotypes, andthus allows comparing genetic diversity among groups ofdifferent size.

Additionally, we used the software spagedi (Hardy &Vekemans 2002) to calculate pairwise ΦST between localpopulations (an analogue of FST for haplotypic data). Next,we performed the Mantel test to check for the correlationbetween genetic distances (measured as linearized pairwiseΦST) and log-transformed geographical distances betweenlocal populations.

Analysis of genetic variability in microsatellite loci

Population genetic structure in microsatellite loci wasinvestigated using the geneland 1.0.5 software (Guillot et al.2005b). geneland provides a Bayesian clustering method thatallows making use of georeferenced individual multilocusgenotypes for the inference of the number (K) and spatialdistribution of subpopulations. In this software, all unknownparameters are inferred through MCMC computations. Inour inference, we used a similar procedure as described byCoulon et al. (2006). At first, we ran the MCMC 10 times,allowing K to vary, with the following parameters: 200 000MCMC iterations, maximum rate of Poisson process fixedto 500, uncertainty attached to spatial coordinates fixed to0.5° (i.e. the minimal precision of our sample locations),minimum K fixed to 1, maximum K fixed to 20, maximumnumber of nuclei in the Poisson-Voronoi tessellation fixedto 200, and the Dirichlet model as a model for allelicfrequencies. Next, we inferred the number of subpopulationsfrom the modal K of these 10 runs, and ran MCMC 20times with K fixed to this number and other parametersunchanged. We computed the posterior probability ofsubpopulation membership for each pixel of the spatialdomain and the modal subpopulation for each individualfor each of the 20 runs (with a burn-in of 20 000 iterations).We also calculated the mean logarithm of posterior prob-ability for each run. Finally, we checked the consistency ofthe results across these 20 runs. For each of these runs, we

tested the significance of the inferred structure by performinga two-level amova (among and within subpopulations)with arlequin.

For population-based analyses of genetic variability inmicrosatellite loci, spatial units (sample groups) larger thanlocal populations were desired to avoid potential biases(e.g. resulting from the presence of closely related individualsin the sample). Thus, we grouped local populations into16 regions (Fig. 1), based on their geographical proximity,similarity of habitats, and genetic discontinuities revealedfrom the analysis of population genetic structure. Using thesoftware spadedi (Hardy & Vekemans 2002), we calculatedNei’s standard genetic distance (DS) and pairwise FST betweenthe regions. Next, we performed the Mantel test to check for

Fig. 1 (a) Map of Europe indicating the study area. (b) Schematicmap of 16 regions — spatial units used in the Mantel test anddistance-based redundancy analysis for microsatellite data. Eachcircle represents one region and is situated in its centroid. Circlesize reflects sample size. Darker grey area denotes the continuouswolf range in Europe, based on Sulkava & Pulliainen (1999),Jedrzejewski et al. (2002) and Boitani (2003), modified.

E C O L O G I C A L F A C T O R S A N D G E N E F L O W I N W O L V E S 4537


the correlation between genetic distances (measured eitheras DS or linearized pairwise FST) and log-transformedgeographical distances between the regions.

Analysis of a dependence of genetic diversification on environmental variables

To examine whether environmental factors may explaingenetic differentiation among local wolf populations, weused a distance-based redundancy analysis, which is a formof multivariate multiple regression that can be performeddirectly on a genetic distance response matrix (Legendre& Anderson 1999; McArdle & Anderson 2001). Althoughpartial Mantel test (Smouse et al. 1986) is the most commonmethod of performing partial regression analyses for geneticdistances (e.g. Carmichael et al. 2001; Sacks et al. 2004), thevalidity of this approach has been questioned (Raufaste &Rousset 2001; Rousset 2002). Therefore, following Geffenet al. (2004), the distance-based multivariate approach ofMcArdle & Anderson (2001) was used here instead of partialMantel test.

In case of mtDNA, we used 59 localities as spatial unitsand pairwise ΦST as a measure of genetic differentiation.In case of microsatellite markers, we used 16 regions asspatial units and Nei’s DS distances and pairwise FSTvalues as measures of genetic differentiation. We tested fordependence of genetic differentiation among the samplegroups on an array of predictor variables, grouped intofive sets: (i) geographical distance (latitude and longitude);(ii) types of potential vegetation (lowland deciduousforests, lowland coniferous and mixed forests, mountainconiferous forests, forest-steppe, steppe, Mediterraneanvegetation); (iii) temperature (mean annual temperature,mean January temperature and mean July temperature);(iv) mean annual rainfall; and (v) wolf diet composition(moose, Alces alces; red deer, Cervus elaphus; roe deer, Capreoluscapreolus; and wild boar, Sus scrofa). All predictor variablesexcept vegetation types were continuous. Vegetation typeswere presented as categorical variables, with two states: 1 ifa sample group was located in a given vegetation category,and 0 if it was located in another vegetation category. Thus,each vegetation type was presented as a vector with values0 and 1, and there were six such vectors corresponding tosix vegetation types. All vegetation types were analysed asa set, i.e. they were combined in a single test.

Environmental variables were taken from the databases:WWF Terrestrial Ecoregions (data set provided by ESRI,www.esri.com) and WorldClimate (www.worldclimate.com).The information about wolf diet composition (measured as afrequency of a given species in the total number of ungulateskilled by wolves) in different localities of the study area wasderived from published studies (Kerechun 1979; Vatolin 1979;Filonov 1989; Andersone 1998; Jedrzejewska & Jedrzejewski1998; Sidorovich et al. 2003; Gula 2004; Nowak et al. 2005)

and unpublished master degree theses (Koniuch 2002;Kloch 2003; Nedzynska 2003; Wojtulewicz 2004) super-vised by W. Jedrzejewski and J. Goszczynski. Only nativeand common ungulate species were considered.

Using the program distlm version 5 (Anderson 2004),we performed the marginal tests on individual sets ofpredictor variables, aiming to identify those variables thatwere correlated with genetic distance. The P values in theseanalyses were obtained using 9999 unrestricted, simultane-ous permutations of the rows and columns of the distancematrix. Next, we performed the conditional tests, wherelatitude and longitude were included as covariables toindividual sets of predictor variables or to multiple setsof predictor variables. The conditional tests allowed us toexamine the extent to which any of the sets of predictorvariables (or their combination) explains genetic diversifica-tion among wolf populations over and above that explainedby geographical distance alone. The P values in these analyseswere obtained using 9999 permutations of the rows andcolumns of the multivariate residual matrix under thereduced model (Anderson & Legendre 1999).

To examine which subset of predictor variables will pro-vide the best model explaining genetic differences amongwolf populations, we performed the forward selectionprocedure on all sets of variables, using the program distlmforward (Anderson 2003). The forward selection procedureconsists of sequential tests, fitting each set of variables oneat a time, conditional on the variables that were alreadyincluded in the model. Most pairs of the tested predictorvariables were correlated; for example, habitat types werecorrelated with temperature. However, the forward selec-tion procedure allowed us to control for the correlationsbetween the predictor variables. Similarly, as in the previousanalysis, the P values were obtained using 9999 permuta-tions of the rows and columns of the multivariate residualmatrix under the reduced model. Results of the above testsallowed us to identify sets of variables that were importantin explaining the genetic differentiation among wolfpopulations, while controlling for effects of geographicaldistance and other analysed variables.

Results

Genetic variability of Eastern European wolves

We found 21 haplotypes of mtDNA control region amonganalysed samples. Fourteen of these haplotypes have beenknown from previous studies (Vilr et al. 1999; Randi et al.2000; Jedrzejewski et al. 2005) and seven were found for thefirst time (see Table 1 for GenBank Accession numbers).Most localities (83%) had more than one haplotype andneighbouring localities frequently had the same haplotypes.The number of haplotypes found in a locality was correlatedwith the number of analysed samples (r = 0.60, P < 0.001).

4538 M . P I L O T E T A L .


The total number of haplotypes within the study area wasestimated from the rarefaction curve at 23 (mean estimate:23.3 ± 1.4, median: 23.1, the range: 20.0–29.9).

Genetic variability in nuclear markers was assessed for545 samples that were successfully genotyped in at least 11of 14 analysed loci (most of samples for which the geno-typing failed were tanned pelts). Mean number of alleles perlocus in the total population was 11 (range 5–18). Observedheterozygosity estimated at 0.71 (SD = 0.10) was lowerthan expected heterozygosity estimated at 0.78 (SD = 0.08),and heterozygote deficiency was significant (P < 0.0001;see Appendix I).

Phylogenetic relationships among mtDNA haplotypes

Nucleotide diversity among grey wolf haplotypes was 0.017(SD = 0.009), and mean within-species sequence divergencewas 0.032 (SE = 0.013). The net sequence divergence betweenwolves and their closest wild relatives, coyotes, was 0.334

(SE = 0.209). The likelihood-ratio test of Shimodaira &Hagesawa (1999) failed to reject the hypothesis of clock-likeevolution of analysed sequences (P = 0.11). The phylogeneticrelationships among haplotypes revealed the presenceof two main clades (Fig. 2a). Most individuals (87%) hadhaplotypes from the clade 4-1. Bootstrap support values werelow, most likely due to the small number (15) of parsimony-informative sites between wolf sequences. However, allmethods of tree construction provided similar topologiesand supported these two main clades. Moreover, theminimum-spanning network approach that is consideredto reflect intraspecific phylogenetic relationships better thanphylogenetic trees (Crandall et al. 2000) also supported thesubdivision of haplotypes into two main clades (Fig. 2b).

There was no clear geographical pattern in the distributionof haplotypes: the ranges of both clades extended over mostof the study area. It indicates that the Eastern European wolfpopulation does not have geographically distinct subunitsthat would be reciprocally monophyletic for mtDNA

Table 1 Percentage frequencies of mtDNA haplotypes among subpopulations of wolves in Eastern Europe. Numbers of samples, differenthaplotypes, and unique haplotypes, as well as haplotype diversity and allelic richness are indicated for each subpopulation. The mostcommon haplotypes in each subpopulation are marked in bold. For haplotypes found in earlier studies, ID numbers previously assignedare given: ‘lu’ denotes haplotype names from Vilr et al. (1999), and ‘RW’ from Randi et al. (2000). AF344300 and AF098123 are GenBankAccession numbers of haplotypes from Jedrzejewski et al. (2005) and an unpublished study of B. F. Koop and coworkers, respectively.Accession numbers of new haplotypes found in this study are denoted by asterisks

Haplotype

Subpopulations

ID S1 S2 S3 S4 S5 S6 S7 S8 S9 S10

w1 67.1 20.2 6.5 20.0 18.2 4.9 lu12, RW8w2 11.3 5.6 20.3 2.5 AF344300w3 10.3 15.7 64.0 9.1 lu8w4 1.1 2.3 2.0 15.0 63.6 11.1 27.8 lu7, RW4w5 0.7 2.3 2.0 10.0 lu13, RW13w6 1.1 85.4 25.0 5.6 RW16w7 6.4 44.9 4.6 5.0 100 lu17w8 1.1 3.4 AY842293*w9 0.3 AF098123w10 0.3 50.0 16.7 lu3, RW9w11 1.1 9.1 RW17w12 0.3 3.4 DQ421802*w13 22.2 lu10, RW5w14 75.0 lu6w15 88.9 DQ421803*w16 2.4 5.5 RW1w17 22.2 DQ421804*w18 2.4 DQ421805*w19 0.6 RW18w20 2.4 DQ421806*w21 1.1 DQ421807*N samples 283 89 153 20 11 11 9 41 8 18N different haplotypes 11 10 7 5 1 4 2 6 2 6N unique haplotypes 2 0 1 0 0 0 1 2 1 2Haplotype diversity 0.52 0.73 0.55 0.71 0 0.60 0.22 0.27 0.43 0.84Allelic richness 2.01 3.01 1.94 2.76 0 2.40 0.89 1.14 1.00 3.58



haplotypes, suggesting there are no evolutionarily significantunits (sensu Moritz 1994b). Haplotypes previously reportedfrom Western Europe fall into the two main clades of EasternEuropean haplotypes: a haplotype from the ApenninePeninsula (lu5 in Vilr et al. 1999; W14 in Randi et al. 2000) fallinto the clade 4-2, and haplotypes from the Iberian Peninsula(lu1, lu3, lu4 in Vilr et al. 1999; W19, W20 in Randi et al.2000) fall into the clade 4-1. The Scandinavian haplotype(lu12 in Vilr et al. 1999) is identical with our haplotype w1(see Table 1). Thus, also in the scale of the entire Europe,there are no evolutionarily significant units. In the trees ofworldwide wolf haplotypes (Vilr et al. 1999; Leonard et al.2005), haplotypes from both clades of Eastern European

wolves do not form monophyletic branches, but are inter-mixed with haplotypes from Asia and North America.

Although the differentiation of the Eastern Europeanwolf population is not strong enough to form reciprocallymonophyletic subunits, haplotype frequencies substantiallydiffer between local populations. A permutation categoricalcontingency analysis of the whole network rejected the nullhypothesis of no association with geographical location(P < 0.0001), which indicated population differentiation.For three clades, the NCA indicated restricted gene flowwith isolation by distance. Of the remaining significantresults, the NCA showed past events of range expansion infive clades of different levels (Fig. 2b; Appendix II).

Fig. 2 Phylogenetic relationships among mtDNA haplotypes of Eastern European wolves, based on 257 bp of control region sequence. (a)Minimum-evolution tree with maximum-likelihood distances. Bootstrap support is indicated at nodes if found in more than 50% of 1000bootstrap trees. Additionally, bootstrap support is indicated for two main clades, which are named the same as the respective clades fromthe minimum-spanning network. (b) Minimum-spanning network of the haplotypes. Big circles represent the haplotypes and small circlesindicate interior nodes that were absent from the sample because of insufficient sampling or extinct haplotypes. Each line represents a singlemutational change. Similar haplotypes are grouped into nested clades, which are denoted by rectangles. The clades for which restrictedgene flow with isolation by distance (RGF) has been indicated are marked in light grey, and the clades for which past events of the rangeexpansion (RE) have been indicated are marked in dark grey.

4540 M . P I L O T E T A L .


Population genetic structure inferred from mtDNA

The results of the spatial analysis of molecular variance(samova) indicated significant population genetic structurefor each assumed number of groups, from 2 to 20 (P <0.00001 in each case). In a graph of changes in values ofΦ-statistics parameters with K, the highest increase in ΦCTvalue occurred between K = 9 and K = 10, and all parametersof Φ-statistics stabilized beginning from K = 10 (Fig. 3).Thus, we identified K = 10 as the most probable number ofgroups. In the subdivision into 10 groups inferred by thesamova procedure, some groups were not geographicallyhomogenous, as some single localities were placed withinthe area of other groups. However, after modifying thissubdivision so as to receive geographically homogenoussubpopulations, we still received a highly significant sub-division (ΦCT = 0.37, P < 0.00001; Table 2 and Fig. 4) thatwas assumed to be the most probable population geneticstructure. The subpopulations identified in this way

substantially differed in haplotype composition (Table 1)and in environmental characteristics, as indicated by meanvalues of analysed environmental variables for localitieswithin each subpopulation (Table 3).

Population genetic structure inferred from microsatellite loci

Out of 10 geneland runs with varying K, seven gave a modalnumber of 3 subpopulations, and three gave a modal numberof 4 subpopulations. We then preformed 20 runs with K = 3and compared the distribution of subpopulations inferredin subsequent runs. The results of these runs showed globallygood consistency. In five independent runs, individuals wereassigned in the same way: two subpopulations (A and B)were modal subpopulations for 298 and 245 individuals,respectively (Fig. 5a). The third subpopulation (C) wasmodal for two individuals only and the majority of the areaof this subpopulation corresponded to the part of the studyarea with no sampled individuals (Appendix IIIa). In otherfive runs, subpopulation C was modal for none of theindividuals. In the remaining 10 runs it was modal for 1–21 individuals (depending on the run) from nine locationsthat did not constitute a geographically homogenous group(Fig. 5b). This suggests that subpopulation C is a ‘ghostpopulation’, as defined by Guillot et al. (2005a), rather thana real subpopulation.

The subpopulations inferred in the five independent runswere separated by narrow border zones, indicating steepgenetic discontinuities (Fig. 5a and Appendix IIa). Otherruns differed from this modal result in the assignmentof individuals situated near the border zones betweensubpopulations (Fig. 5b), and — as a result — the bordersbetween subpopulations were less straight (AppendixIIIb). The runs that inferred the most complicated patternof the distribution of subpopulations, with subpopulationC that was not spatially homogenous, had the highestmean posterior probability. However, repeatability of the

Fig. 3 The pattern of changes in values of Φ-statistics parameterswith the assumed number of groups (K), revealed using samova. ΦSCmeasures the proportion of the variance among local populationswithin groups. ΦST measures the proportion of the varianceamong local populations within the total population. ΦCT denotesthe fraction of the total variance that is explained by the grouping.

Table 2 Φ-statistics parameters for different groupings of localwolf populations in Eastern Europe based on mtDNA: (1) Thegrouping revealed in samova with 10 groups assumed; (2) Thegrouping (1) modified so as to obtain spatially homogenoussubpopulations (see Fig. 4); (3) The grouping (2) modified bypooling subpopulations S1 and S2; (4) Random grouping of localpopulations into 10 groups. For definitions of parameters ΦSC, ΦST,ΦCT, see Fig. 3

Subdivision ΦSC ΦST ΦCT P

(1) samova, 10 groups 0.071 0.440 0.398 < 0.00001(2) Homogenous, 10 groups 0.117 0.441 0.367 < 0.00001(3) Homogenous, 9 groups 0.181 0.463 0.344 < 0.00001(4) 10 random groups 0.397 0.381 −0.021 0.659



Fig. 4 (a) Subpopulations of wolves in Eastern Europe delimited based on frequencies of mtDNA haplotypes, against the background ofthe continuous wolf range (darker grey area). Each symbol represents one local population and is situated in its centroid. The size of asymbol reflects sample size. Different symbols represent local populations assigned to different groups by the samova procedure. Arrowsindicate local populations for which the original samova assignment has been changed to receive geographically homogenous groups. (b)Frequency of haplotypes belonging to different 2-step clades of the minimum-spanning network (see Fig. 2) in each subpopulation.

Table 3 Climate, potential vegetation and mean wolf diet composition in the areas of wolf subpopulations S1–S10. Climatic variables(temperature and rainfall) were calculated as means from the studied localities within respective subpopulations. Similarly, mean wolf dietcomposition in a subpopulation was calculated as a mean from localities where wolf diet composition was known (see the main text forreferences). Only common ungulate prey species were considered: moose (A.a.), red deer (C.e.), roe deer (C.c.), and wild boar (S.s.). Symbolsof potential vegetation are as follows: BF, boreal forest; TF, temperate deciduous and mixed forest; FS, forest-steppe; ST, steppe; MF,temperate mountain forest; MW, Mediterranean woodlands and shrubs

Sub-population

Mean annual temperature (°C)

Mean temperature of January (°C)

Mean temperature of July (°C)

Mean annual rainfall (mm)

Potential vegetation

Mean wolf diet composition (%)

A.a. C.e. C.c. S.s.

S1 5.4 −6.8 17.4 610 BF, TF 39 21 24 16S2 4.4 −8.2 16.5 604 BF, TF 26 0 14 60S3 6.7 −5.9 18.3 625 TF, FS 3 41 43 13S4 1.8 −14.2 18.1 563 BF, TFS5 4.7 −13.6 22.5 363 STS6 7.7 −6.5 21.0 472 STS7 9.8 −1.5 21.6 450 STS8 9.2 −1.7 19.3 663 MF 0 47 41 12S9 8.1 −3.5 18.1 687 MF 0 46 50 4S10 13.0 1.9 21.3 517 MW

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result among runs may be a better indicator of the trueassignment to subpopulations than the mean posteriorprobability (see Coulon et al. 2006). The general location oftwo main subpopulations was consistent among all 20 runs,and 453 (83%) individuals were assigned in the same wayin all runs (259 individuals to subpopulation A and 194individuals to subpopulation B). Out of the remaining 92individuals, 67 were assigned either to subpopulation A orB, 23 either to subpopulation A or C, and two individualswere assigned to subpopulations A, B or C, depending onthe run (Fig. 5b).

Because of the high consistency among the 20 performedruns, we decided that performing more runs with K = 3is unnecessary. However, we performed three additionalruns with K = 4. These runs indicated two subpopulationscorresponding to subpopulations A and B revealed fromruns with K = 3. The third subpopulation, correspondingto subpopulation C, was modal for none of the individuals(one run) or for 13 individuals from four locations thatwere not spatially grouped (two runs). Fourth subpopula-tion overlapped spatially with the third one and was notmodal for any individual. It confirmed that K = 3 was theproper number of subpopulations.

For the genetic structure inferred from each of the 20runs with K = 3, we performed the analysis of molecularvariance (amova). amova confirmed the significance of thestructure inferred by geneland (P < 0.00001 in each case),although genetic differentiation among subpopulations waslow (FST ranged from 0.014 to 0.024 depending on the run).

Isolation by distance: mtDNA and microsatellites

Spatial differentiation in haplotype frequencies, measuredas linearized pairwise ΦST between 59 localities, was signi-ficantly higher than expected for a panmictic populationand followed isolation by distance (Mantel test, r = 0.149,P = 0.007). Spatial differentiation in frequencies of micro-satellite alleles, measured as Nei’s standard genetic distancebetween 16 regions, also followed isolation by distance(r = 0.241, P = 0.036). However, when genetic distance wasmeasured as linearized pairwise FST, its dependence on geo-graphical distance was insignificant (r = 0.186, P = 0.078).

Dependence of genetic diversification on environmental variables: mtDNA

A test on the influence of latitude and longitude (treated ascovariables) on genetic differentiation among local wolfpopulations showed that pairwise ΦST measures betweenlocalities strongly depended on latitude (P = 0.0001), but noton longitude (P = 0.48). As in Europe many environmentalfactors change along the north–south axis, we analysed anarray of environmental variables to evaluate whetherthey may explain genetic differentiation among local wolfpopulations over and above the influence of geographicaldistance. In marginal tests, two sets of environmental vari-ables were significantly correlated with genetic distance:vegetation types and temperature (Table 4a). When geo-graphical coordinates were taken into account in a form of

Fig. 5 Subpopulations of wolves in Eastern Europe, delimited based on allele frequencies of microsatellite loci, against the background ofthe continuous wolf range (darker grey area). (a) Modal assignment of individuals, i.e. assignment that was inferred in five independentruns using the software geneland. (b) Recapitulation of the assignment results from all 20 runs. The maps are based on 545 individualsthat were successfully genotyped. Each point represents a wolf or several wolves from the same location, and different symbols representsubpopulations.



covariables in the multiple regression analysis, vegetationtypes were still significantly correlated with genetic distance,but temperature was not (Table 4a). However, when acombined influence of vegetation types and temperaturewas taken into account, these variables were significantlycorrelated with genetic distance (P = 0.01) and explained43% of the genetic variation over and above the influenceof geographical distance. Additionally, the forward selectionprocedure (Anderson 2003) that classifies variables accordingto the proportion of explained variation, fitted vegetation

types and temperature prior to geographical distance inthe multiple regression model (Table 4a).

For 17 localities situated in the northwestern part of thestudy area (within subpopulations S1–S3, S8 and S9, seeFig. 4), we were able to analyse the influence of yet anotherfactor: wolf diet composition. For these 17 localities, foursets of environmental variables were correlated with geneticdistance: geographical coordinates, temperature, rainfall,and vegetation types (Table 4a). Wolf diet composition wasnot significantly correlated with genetic distance. However,

Table 4 Effects of environmental factors on genetic differentiation of Eastern European wolves based on mtDNA and microsatelliteanalysis. Marginal and conditional tests of individual variable sets as well as sequential tests of the forward selection procedure are reported(see Materials and methods for the description of the tests). P indicates probability values and ‘%var’ the percentage of the genetic variationexplained by the particular variable. In the case of sequential tests, ‘%var’ indicates the percentage of the genetic variation explained by acumulative effect of variables. The top-down sequence of variables corresponds to the sequence that was indicated by the forward selectionprocedure

Variable set

Marginal tests Conditional tests Sequential tests

P %var P %var P %var

(a) Tests for mtDNA and genetic distances measured as pairwise ΦSTAll local populations, without considering the prey composition

Vegetation 0.002 22.5 0.038 24.0 0.002 22.5Temperature 0.029 11.7 0.379 4.6 0.112 29.4Coordinates < 0.001 17.1 — — 0.995 100.0Rainfall 0.196 3.1 0.159 2.8 0.036 —

17 local populations, for which the prey composition was knownTemperature 0.023 47.5 0.553 9.5 0.023 47.5Rainfall 0.011 28.8 0.138 9.8 0.134 60.9Prey 0.057 20.1 0.417 2.9 0.026 63.5Vegetation 0.011 46.9 0.018 33.8 0.208 84.3Coordinates 0.013 42.6 — — 0.013 85.5

(b) Tests for microsatellite loci and Nei’s standard genetic distanceAll regions, without considering the prey composition

Vegetation 0.091 52.2 0.005 53.2 0.091 52.2Coordinates 0.002 43.1 — — 0.126 67.5Temperature 0.109 33.0 1.000 0.6 0.402 68.1Rainfall 0.058 17.7 0.323 4.8 0.003 75.9

Eight regions, for which the prey composition was knownTemperature 0.006 82.8 0.481 26.9 0.006 82.8Prey 0.099 53.7 0.393 19.8 0.034 98.4Vegetation 0.179 51.8 0.249 24.1 1.000 100.0Rainfall 0.165 25.4 0.472 6.2 — —Coordinates 0.058 57.2 — — — —

(c) Tests for microsatellite loci and genetic distances measured as pairwise FSTAll regions, without considering the prey composition

Coordinates 0.004 36.4 — — 0.004 36.4Vegetation 0.247 43.2 0.042 47.1 0.042 83.5Temperature 0.089 33.1 0.676 10.3 0.492 93.4Rainfall 0.023 20.8 0.099 11.7 0.924 93.7

Eight regions, for which the prey composition was knownTemperature 0.010 82.1 0.518 26.0 0.010 82.1Prey 0.158 49.6 0.402 19.5 0.021 98.8Vegetation 0.326 18.3 0.228 25.2 0.866 100.0Rainfall 0.148 27.9 0.495 5.8 — —Coordinates 0.062 56.8 — — — —

4544 M . P I L O T E T A L .


when only the frequency of one prey species, red deer, inthe wolf diet was taken into account, the correlation wasmarginally significant (P = 0.057) and 20% of genetic vari-ation was explained by this factor. When geographicalcoordinates were taken into account in a form of covariablesin the multiple regression analysis, only vegetation typeswere significantly correlated with genetic distance (Table 4a).However, we found that a combination of temperature,rainfall, vegetation types and a frequency of red deer in thewolf diet explained 54% of the genetic variation amongthese localities (P = 0.008), when considered together inthe conditional test where geographical coordinates wereincluded as covariables. Moreover, all of the above variableswere classified as more important than geographical distanceby the forward selection procedure (Table 4a).

Dependence of genetic diversification on environmental variables: microsatellite loci

Similarly as in the case of mtDNA, pairwise genetic distancebetween regions calculated for microsatellite loci dependedon latitude (P = 0.002 for DS distance and P = 0.003 for FST),but not on longitude (P = 0.236 and 0.156, respectively). Inmarginal tests, only rainfall was significantly correlated withgenetic distance measured as pairwise FST (P = 0.023). Itwas also marginally correlated with Nei’s genetic distance(P = 0.058), while other variables were not correlated withit (Table 4b, c). When geographical distance was taken intoaccount in a form of a covariable in the multiple regressionanalysis, vegetation types were correlated with Nei’sgenetic distance and with pairwise FST (P = 0.005 and 0.042,respectively), but other variables were not correlated withany genetic distance measure (Table 4b, c). An influence ofvegetation types explained 53% of the genetic variationmeasured as Nei’s genetic distance and 47% of the geneticvariation measured as pairwise FST, over and above theinfluence of genetic distance. As opposite to genetic variationmeasured for mtDNA, if vegetation types and temperaturewere analysed together, they were not correlated withgenetic distance.

In the case of Nei’s genetic distance, the forward selec-tion procedure fitted vegetation types before geographicaldistance and other variables in the multiple regression model(Table 4b, c). In this test, neither geographical distance nortemperature was significant, when the influence of vegeta-tion types was taken into account. Both the results of theconditional test and the forward selection procedureindicated that the influence of vegetation types explainsubstantial proportion of genetic variation over and abovethe influence of genetic distance. In the case of pairwise FST,geographical distance was fitted before vegetation types(Table 4b, c). However, in both cases the influence ofvegetation types explained more genetic variation than theinfluence of geographical distance.

For eight regions from the northwestern part of the studyarea (see Fig. 1), we also analysed the correlation of geneticdistance with wolf’s diet composition. For these eight regions,only temperature was correlated with genetic distancein a marginal test (P = 0.006 for Nei’s genetic distance andP = 0.01 for FST). Wolf diet composition was not significantlycorrelated with genetic distance in a marginal test, but theforward selection procedure fitted it just after temperaturein the multiple regression model both in the case of Nei’sgenetic distance (Table 4b) and pairwise FST (Table 4c). Inthis case, both temperature and wolf diet composition weresignificantly correlated with genetic distance and explainedtogether 98% and 99% of genetic variation, respectively,while other variables were insignificant.

Discussion

We found that most local wolf populations in Eastern Europehad more than one mtDNA haplotype and most haplotypeswere widely distributed. This result is contradictory toprevious studies, based on less extensive sampling, whichsuggested that the majority of extant populations in Eurasiahave unique haplotypes (Wayne et al. 1992; Vilr et al. 1999).This discrepancy can be explained by the fact that thenumber of haplotypes found in a locality depends on thenumber of analysed samples. It may be also important thatsamples from strongly fragmented western populationsprevailed within the data analysed by Wayne et al. (1992)and Vilr et al. (1999), whereas those from the continuousspecies range in Eastern Europe were limited. Indeed, Randiet al. (2000) showed that wolves from southeastern andnortheastern Europe are more differentiated (9 haplotypeswere found among 29 individuals from Bulgaria and 3haplotypes among five individuals from Finland), which isin agreement with our results. In our study, the number ofdetected haplotypes is close to the expected total numberof haplotypes, estimated from the rarefaction curve. Thisresult indicates that our sample is representative for thestudied population, as most haplotypes were detected.

The distribution of haplotypes may result from bothcurrent and historical processes. Thus, an insight intopopulation history is needed for the proper inference on thecontemporary factors shaping population genetic structure.According to fossil records, grey wolves and coyotesdiverged about 2 million years ago (Nowak 2003). Giventhis divergence date, a net sequence divergence betweenthese species of 33.4% and a mean sequence divergence inEastern European grey wolves of 3.2%, a coalescence ofEastern European wolf haplotypes can be roughly esti-mated at about 200 000 years ago. Such coalescence time isconsistent with that estimated for all known wolf lineagesat 290 000 years ago (Vilr et al. 1999), taking into account thatEuropean wolves represent most of these lineages (Vilret al. 1999). As this coalescence time substantially pre-dates



the Last Glacial Maximum (17 000–21 000 years ago), itimplies that most haplotypes diverged prior to the lastglaciation. Thus, the current genetic pattern may potentiallyresult from processes that occurred during and after thePleistocene glaciations. One of the most important historicalprocesses that strongly influenced patterns of geneticdifferentiation of many species was isolation in differentglacial refugia (Hewitt 1996, 2000; Taberlet et al. 1998).However, the NCA provided no evidence for any pastfragmentation events within the Eastern European wolfpopulation, and indicated past events of range expansionas the main historical factor that influence current distribu-tion of haplotypes. It was impossible to indicate the directionof this range expansion, as the results for different nestedclades indicated different directions and some of them wereopposite one another. It suggests nondirectional spreadof haplotypes throughout the continent. Thus, it can beconcluded that the differentiation of the wolf populationin Eastern Europe is a result of past admixture (i.e. rangeexpansions of different haplotypes in different directions)and present restricted gene flow. As current restrictions ingene flow were identified as a factor shaping the distributionof haplotypes for the clade 4-1 that included haplotypes ofmost (87%) Eastern European wolves, this indicates the abilityof mtDNA to reveal contemporary patterns in this case.

The restrictions in gene flow are reflected in the distinctpopulation structure revealed from the frequencies ofmtDNA haplotypes by the samova analysis. This structurehas two unusual features. First, subpopulation S8, connectedwith the mountains, is presented as having the noncontin-uous range (see Fig. 4). This is due to the lack of samplesfrom the Romanian Carpathian Mountains located betweenthe two parts of this subpopulation. Second, subpopulationS2 is located within the range of subpopulation S1, whichis an unusual pattern. The general pattern of subdivisioninto subpopulations can be explained by the influence ofecological factors, but significant ecological differencesbetween areas of subpopulations S1 and S2 are not obvious.However, there may be other causes of the existence of adistinct subpopulation S2 within subpopulation S1. Forexample, a recent appearance of a haplotype that is new fora given area (e.g. as a result of immigration) may result in atemporary appearance of an ‘island’ of its high concentrationthat may be recognized as a separate subpopulation. Thefact that subpopulation S2 was not recognized as a distinctsubpopulation based on microsatellite analysis is consistentwith this explanation. If subpopulation S2 was not distin-guished within subpopulation S1, the population structurewas still highly significant (ΦCT = 0.34, P < 0.00001; seeTable 2).

Such distinct population genetic structure points torestrictions in gene flow, despite long dispersal rangesof wolves. Extensive and overlapping ranges of the mainclades of the minimum-spanning network indicate the

absence of geographical features in the landscape thatwould constitute efficient barriers for wolf dispersal. Thus,the restrictions in gene flow must be caused by factorsother than geographical barriers. High distinctiveness ofsubpopulations may be to some extent an artefact of theanalysis method. Changes in haplotype frequencies may bemore clinal, not as discrete as the samova analysis suggests.However, the results show explicitly that the population isnot panmictic. Moreover, it is unlikely that the populationdifferentiation results from the isolation by distance alone,as genetic differentiation between wolves from differentlocalities was correlated with latitude, but not with longitude.This reflects the influence of environmental factors, asin Europe the north–south axis is a direction of the mostprominent changes in many environmental variables, suchas vegetation types, temperature and depth of snow cover.Variation in morphology, size and colour of Old Worldwolves also is the greatest along the north–south axis(Bibikov 1985; Nowak 2003). The dependence of geneticdifferentiation on clinally changing environmental variablesis in agreement with the theoretical model of Doebeli &Dieckmann (2003), showing that processes of evolutionarydiversification may lead to sharp geographical differentia-tion along environmental gradients. As the environmentalgradients that we consider have existed for an extendedperiod of time, they are likely to be reflected in mtDNAdespite its relatively low variability.

Importantly, the same result — correlation of geneticdistance between wolf populations with latitude and withenvironmental variables — was obtained based on micro-satellite loci analysis. It indicates that this pattern isindependent on the type of markers and on the way ofdelimiting sample groups. However, the population geneticstructure in microsatellite loci was less pronounced andfewer subpopulations were delimited than in the case ofmtDNA analysis. Differences in the degree of structuringin mitochondrial and nuclear DNA were also observed inNorth American grey wolf populations, which were groupedinto clusters when restriction fragment length polymorphism(RFLP) profiles of mtDNA were taken into account, but didnot show similar groupings based on microsatellite analysis(Geffen et al. 2004). The contrasting mitochondrial andnuclear DNA patterns were also reported for other animals,e.g. green turtle, Chelonia mydas (Karl et al. 1992), Oregonslender salamander, Batrachoseps wrighti (Miller et al. 2005),brown bear, Ursus arctos (Waits et al. 2000), and wolverine,Gulo gulo (Chappell et al. 2004). They are usually explainedby differences in male and female dispersal (Karl et al. 1992;Avise 1994; Moritz 1994a): male-biased gene flow implieslow introgression of mtDNA haplotypes from neighbouringpopulations, and therefore greater structuring in mtDNAas compared with nuclear markers. It is also a possibleexplanation for the results obtained in our study, as somedata bring evidence that long-distance dispersal may be

4546 M . P I L O T E T A L .


male-biased in wolves (Wabakken et al. 2001; Flagstad et al.2003; Jedrzejewski et al. 2005). Differences in mitochondrialand nuclear DNA patterns may also result from the factthat the effective population size of mtDNA is four timessmaller than that of nuclear DNA, and therefore mtDNAvariability is more sensitive to random drift than variabilityof nuclear DNA (Avise et al. 1984). Thus, genetic diversifica-tion resulting from a limited, but not totally prevented geneflow may be more pronounced in frequencies of mtDNAhaplotypes than in the variability of microsatellite loci.

It is also possible that small number of samples fromsouthern and eastern part of the study area preventeddetection of distinct subpopulations there (Fig. 5). On thecontrary, the northwestern part of the study area wasextensively sampled, and thus the genetic discontinuitybetween subpopulations A and B was highly supported bythe data. It is important that the location of this border zoneis similar to the location of the border between two biggestsubpopulations, S1 and S3, delimited based on mtDNAanalysis. In the area of this genetic discontinuity, there areno obvious barriers to gene flow, and the fact that it issituated in the horizontal axis suggests that it is caused byenvironmental variables changing along the south-northgradient. The influence of gradually changing environ-mental variables would also explain why the location ofthe border zones inferred from mtDNA and microsatelliteanalyses is not exactly the same: in the absence of anabsolute barrier some level of admixture must occur, andthe real discontinuity is probably not as sharp as the resultsof both analyses indicate.

The dependence of genetic differentiation on climatic andecological variables suggests a link between the ecologicaland genetic spatio-temporal processes, which was previouslysuggested for another large, mobile carnivore, Canadianlynx (Rueness et al. 2003b; Stenseth et al. 2004a, b). It hasbeen shown that both demographic and population geneticpatterns may be influenced by the interaction between acarnivore and its prey (wolf: Carmichael et al. 2001; lynx:Stenseth et al. 2004b). A possible mechanism that wouldexplain how the composition of ungulate community mayinfluence wolf dispersal (and therefore patterns of geneflow) is differential prey selection. In multiprey systems,certain prey species may be preferred to others (Carbyn1983; Potvin et al. 1988; Dale et al. 1994; Kunkel et al. 2004).In northeastern Europe, three cervids — red deer, moose,and roe deer — dominate wolf diet (Okarma 1995). A positiveselectivity for red deer and strong functional responseto an increase in red deer densities have been observed(Okarma 1995; Jedrzejewski et al. 2000), and the abundanceof this species in wolf diet was correlated with geneticstructure in our tests. This suggests that differences inhunting strategy for prey of different sizes may lead tolocal prey specialization, and thus to genetic differentia-tion in wolves.

A connection between population genetic structure andprey specialization was previously suggested for wolves innorth-western Canada (Carmichael et al. 2001). Accordingto that study, each wolf pack is connected with a particularherd of caribou, and thus migratory routes of caribou arereflected in population genetic structure of wolves. Anothercase of a connection between genetic differentiation andprey specialization is known for the arctic fox. Populationgenetic structure of this species is consistent with thesubdivision into two ecotypes: ‘lemming’ ecotype thatfeeds mainly on lemmings and ‘coastal’ ecotype that feedsmainly on eggs, birds and carrion from the coastal ecosystem(Dalén et al. 2005). Similarly, significant genetic differenti-ation was found between two groups of killer whales Orcinusorca that occurred in the same area, but specialized in for-aging on fish or on marine mammals (Hoelzel et al. 1998).

As the composition of the ungulate community stronglydepends on the habitat type, the same mechanism may leadto the dependence of genetic differentiation of wolves onthese two factors. As suggested by Geffen et al. (2004), wolfdispersal may be habitat-biased. Young individuals oftenstay in their natal packs for a long time (Mech & Boitani2003), learning to prey on animals characteristic for thehabitat where they live. The fact that young wolves observedor assisted in hunting of particular species may result intheir subsequent willingness to choose these species forprey and to choose habitats where these particular speciesare abundant, which will increase the wolves’ chances forsurvival (Gese & Mech 1991). Indeed, some authors suggestthat experience and learning help wolves to successfullyattack their prey and to avoid being harmed (Mech &Peterson 2003; Peterson & Ciucci 2003). Similarly, studieson Canadian lynx suggested that individuals familiar withprey conditions in a certain area would stay within an areaof similar conditions when dispersing, because there isa cost to the process of learning how to use a new habitat,which reduces the probability of reproductive success(O’Donoghue et al. 2001). Differences in reproduction andmortality in areas with known vs. unknown prey speciesmay lead to the connection between habitat types, dietcomposition and dispersal patterns.

The hypothesis that wolf dispersal may be habitat-biasedis additionally supported by the fact that one of the mostcommon processes of pack formation known as ‘budding’,which results in the establishment of a new pack close tothe parental pack (Mech & Boitani 2003; Jedrzejewski et al.2004), promotes a selection of similar habitats by relatedindividuals. Natal-habitat-biased dispersal was alsosuggested for coyotes as the most probable explanation ofpopulation genetic structure that corresponded to habitat-specific breaks (Sacks et al. 2004). Thus, natal-habitat-biaseddispersal together with prey specialization may induce anassociation of habitat types and diet composition withpopulation genetic structure.



Additionally, as studies on Canadian lynx showed, snowconditions are the climatic factor that directly influencespredator–prey interactions and thus may influencepopulation genetic patterns through nonrandom dispersal(Stenseth et al. 2004b). Snow conditions may play the samerole in genetic differentiation of other large predators foundin habitats with long, snowy winter seasons (Stenseth et al.2004b). Indeed, our study showed that besides habitat typesand prey composition, wolf population genetic structure inEastern Europe is influenced by temperature, which is oneof the main factors regulating snow conditions. Althoughnone of these environmental and ecological factors constituteabsolute barrier to gene flow, their combined influence maylead to population differentiation.

Based on an example of a highly mobile and widelydistributed carnivore species, we showed that a distinctgenetic structure may occur in regions where environmentalchanges are gradual and do not prevent gene flow. Ourresults support the growing body of literature that demon-strates the influence of ecological factors such as habitattypes and diet composition on geographical patterns ofgenetic variation within the species. It indicates the impor-tance of further studies aimed at understanding the directmechanism that links population ecology and populationgenetic structure.

Acknowledgements

We are grateful to Y. Iliopoulos from the Greek Society for the Pro-tection and Management of Wildlife ARCTUROS, Z. Andersone,A. N. Bunevich, O. Buzbas, I. Dikiy, V. Dumenko, J. Goszczynski,T. KaKamarz, A. Kloch, M. Nedzynska, S. Nowak, A. Olczyk, M.Shkvirya, W. )mietana, E. Tsingarskaya, V. Tokarskiy, M. Woj-tulewicz and S. Zhyla, who helped in collecting the samples. Wethank G. Guillot for the consultation on the use of the genelandsoftware. We are grateful to J. Goszczynski, O. Liberg, B. N. Sacks,R. Van Den Bussche, R. K. Wayne, and two anonymous reviewersfor helpful comments on earlier drafts of the manuscript. Thisproject was funded by the former Polish State Committee for Sci-entific Research (Grant no. 6P04F 09421), the budget of MammalResearch Institute, European Nature Heritage Fund — Euronatur,and the UK Wolf Conservation Trust.

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Appendix I

Heterozygosity deficit in microsatellite genotypes of Eastern European wolves and its effect on the results of the analysis of populationgenetic structure

We analysed deviations from Hardy–Weinberg equilibrium (HWE) and their direction (heterozygote deficit or excess) using the exact test of Guo & Thompson (1992) implemented in genepop (Raymond & Rousset 1995). In the total population of Eastern European wolves, we found a deficit in average observed heterozygosity relative to HWE (P < 0.0001), and positive FIS (0.09). This deviation from HWE was due to 11 loci. We also found significant linkage disequilibrium for 59 of 91 pairs of loci (P < 0.05, after correcting for multiple tests). Among analysed loci, only two pairs were located on the same chromosomes and in both cases the loci were located in distant parts of these chromosomes (Breen et al. 2001), so disequilibrium was unlikely to be due to physical linkage.We also tested for HWE in two main subpopulations, A and B, presented in Fig. 5a (two individuals assigned by geneland to subpopulation C were included into subpopulation B). In both subpopulations, heterozygote deficit was significant (P < 0.0001 in each case), and FIS was positive (0.10 in subpopulation A and 0.06 in subpopulation B).Additionally, we performed multipopulation tests for HWE for each analysed locus, using 16 regions as spatial units that defined sample groups (see Fig. 1). Out of 14 analysed microsatellite loci, seven showed significant heterozygote deficit (P < 0.001 in each case, after correcting for multiple tests). For these seven loci, the number of sample groups showing significant heterozygote deficit was as follows: 2 at the loci FH2079, FH2088, C253, and AHT130; 5 at the locus FH2017; 9 at the locus C213, and 10 at the locus C642. At the remaining loci, significant heterozygote deficit was observed in at most one sample group. None of the analysed loci showed significant heterozygote excess. FIS was positive in 15 out of the 16 sample groups and ranged from 0.01 to 0.20.The observed deficit of heterozygotes may due to several reasons: (i) the presence of null alleles; (ii) the existence of an underlying genetic structure (Wahlund’s effect); (iii) inbreeding in local wolf populations; (iv) the presence of closely related individuals (members of the same packs) in the sample. Null alleles are likely to occur at the loci showing significant heterozygote deficit, especially at the loci C213 and C642 that show heterozygote deficit in more than half of the sample groups. However, other population genetic studies on Eurasian and North American wolves (that used different sets of microsatellite loci) also showed significant heterozygote deficit and positive values of FIS, which was explained by moderate inbreeding, the presence of closely related individuals in the analysed sample, or the presence of genetic structuring (Roy et al. 1994; Forbes & Boyd 1997; Lucchini et al. 2004).Although we cannot exclude the presence of null alleles in our data, they are unlikely to be a problem for the analysis of population genetic structure, as simulations have shown that geneland is robust with regard to null alleles (unpublished data of G. Guillot reported in Coulon et al. 2006). The Wahlund’s effect cannot be excluded, either. However, it would not negate the existence of the genetic structure detected but only imply the existence of an additional, undetected structure. The presence of closely related individuals in the sample is unlikely to lead to the improper inference of population genetic structure by geneland as well (see Coulon et al. 2006). On the other hand, the departure from the model assumptions (HWE and linkage equilibrium) may create ghost populations (Coulon et al. 2006). Ghost populations, as defined by Guillot et al. (2005a), are not modal for any individual and thus can be easily identified. Subpopulation C inferred in our study in most geneland runs might be a ghost population, because it was modal for only several individuals (1–21 depending on the run), and these individuals did not form spatially homogenous groups (see Results section). The inference of this subpopulation may be the result of the departure from the model assumptions.

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Appendix II

Results of nested clade analysis of phylogenetic relationships among mtDNA haplotypes of wolves from Eastern Europe

Clade distances (Dc) and nested clade distances (Dn) are calculated for each clade within the nested group. In the row labelled I-T the average differences in distances between interior and tip clades are given. Interior clades are shaded. At the bottom of those boxes in which one or more of the geographical distance measures for nested clades was significantly large (L) or small (S) is a line with the biological inference. The numbers refer to the sequence of questions in the inference key (Templeton 2004) that the pattern generated, followed by the answer to the final question in the key. RE, range expansion; CRE, continuous range expansion; LDC, long distance colonization; RGF, recurrent restricted gene flow with isolation by distance; LDD, long distance dispersal.



Appendix III

Results of the analysis of population genetic structure using geneland: maps of the posterior probability to belong to each subpopulation(a) for the geneland assignment presented on Fig. 5a, (b) for the geneland assignment with the highest mean posterior probability. Lightercolours denote higher assignment probabilities to a given subpopulation. Units of axis are geographical coordinates.

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