Molecular Ecology (2005)

14

, 469–481 doi: 10.1111/j.1365-294X.2005.02428.x

© 2005 Blackwell Publishing Ltd

Blackwell Publishing, Ltd.

Geographical distance and physical barriers shape the genetic structure of Eurasian red squirrels (

Sciurus vulgaris

) in the Italian Alps

I . TRIZIO,

*

B . CRESTANELLO,

†

P . GALBUSERA,

‡

L . A . WAUTERS,

*‡

G . TOSI ,

*

E . MATTHYSEN

‡

and H. C . HAUFFE

†

*

Department ‘Environment-Health-Security’, University of Insubria, Varese, Via J.H. Dunant 3, I-21100 Varese, Italy,

†

Centre for Alpine Ecology, 38040 Viote del Monte Bondone, Trento, Italy,

‡

Research Group of Animal Ecology, Department of Biology, University of Antwerp, Universiteitsplein 1, B-2610 Antwerp, Belgium

Abstract

Red squirrels (

Sciurus vulgaris

) are widely distributed throughout Eurasia, occurring inmany types of coniferous and mixed-deciduous forests. In fragmented landscapes, smalland partly isolated populations with low immigration rates show reduced genetic diver-sity, but reforestation can increase gene flow and restore levels of genetic variation in a fewdecades. No studies have so far investigated the genetic structure of red squirrel in large,continuous forests. The Italian Alps are presently characterized by almost continuous,recently reconnected forest habitats, that were affected by deep landscape changes duringlast glaciations but remained mostly unchanged between 10 000 and 200 years

BP

, whenforest cover was heavily reduced. In this study we analyse patterns of genetic variability ofred squirrels in and between seven sites distributed over 250 km of Alpine habitat, usingmitochondrial DNA (mtDNA) and microsatellites. We use isolation-by-distance (IBD)models to investigate the relative importance that past (Pleistocene glaciations) and recent(fragmentation, bottlenecks) events had on the present genetic situation. Both nuclear andmtDNA data indicate a significant differentiation among study sites and a significant cor-relation between genetic and geographical distance only over a large scale. No recent bottle-necks are recorded through microsatellites and demographic models strongly supportequilibrium between gene flow and drift; however, mtDNA suggests that there may havebeen local demographic crashes, probably in correspondence with the 19th-century forestfragmentation. These findings indicate that local landscape factors other than geographicaldistance per se, such as barriers of unsuitable habitat, affect gene flow and determinedifferentiation.

Keywords:

gene flow, isolation-by-distance, microsatellites, mitochondrial DNA, population struc-ture, red squirrel

Received 26 July 2004; revision received 04 November 2004; accepted 04 November 2004

Introduction

It is now well accepted that genetic diversity is a prerequisitefor adaptive potential and evolution. Since diversity isfound both within and between subpopulations, estimatesof both of these variance components are necessary forunderstanding and conserving this potential (Reed &Frankham 2003). In a population at equilibrium, these

estimates will reflect the current balance between geneticdrift and gene flow, both of which will shape the geneticstructure of a population. Although the importance of driftwill largely depend on effective population size, gene flowis affected by recent and historical values of various habitatparameters such as geographical distance and physicalbarriers.

Commonly, isolation-by-distance (IBD), a correlationbetween genetic differentiation and geographical distance(Wright 1943), has been invoked to suggest that geograph-ical distance alone could explain genetic structure. Slatkin

Correspondence: Luc A. Wauters, Fax: + 39-0332-421554; E-mail:l.wauters@libero.it (and l.wauters@uninsubria.it)

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(1993) theoretically showed that, in a species expanding itsrange to a new habitat, IBD should be low at first andattained at short geographical distances, then increaseprogressively over larger geographical distances whensufficient time has elapsed for patterns to be establishedand stabilized. However, IBD may depend on the scale ofanalysis (Rousset 1997) and recently it has been shown thatthis method may not exclude the effect of physical barriers(Garnier

et al

. 2004). For nonflying terrestrial vertebrates,barriers may be natural, such as valleys, fast-flowing rivers,and hostile habitats, but can be also induced throughdestruction or fragmentation of suitable habitat by humansor both (Roach

et al

. 2001; Geffen

et al

. 2004; Neumann

et al

.2004). So, depending on the timescale covered by thegenetic marker, the scale of analysis and the presence ofpotential barriers, different outcomes can be expected.

Alpine populations of the Eurasian red squirrels (

Sciurusvulgaris

) are suitable for evaluating this conditionality ofthe IBD test because this species is highly dependent onparticular habitat types (montane and prealpine forests),and alpine populations are or have been isolated by phys-ical barriers or deforested areas. Over most of the ItalianAlps, these forests were nearly continuous from the end ofthe last ice age until the 19th century, when two-thirdswere removed for industrial purposes. This deforestationmay have caused partial isolation and decrease of geneflow between local squirrel populations. After 1945, mostof the remaining forests were protected and new ones wereplanted; Alpine pastures were largely abandoned and nat-ural regrowth of montane and sub-Alpine conifer forestshad increased cover at higher elevations. The recovery ofthis species has been hampered by hunting, which contin-ued in Italy until 1977.

In the present study we investigate the impact of recentand historical events on the present level and patterns ofneutral genetic variation in populations of the red squirrelinhabiting conifer forests in the western and centralItalian Alps, using 10 microsatellite markers, and controlregion (D-loop) sequences. The simultaneous analysis ofthese two types of genetic markers has already been usedsuccessfully for investigating the effects of both historicprocesses (e.g. Pleistocene ice ages), and more recentevents (e.g. habitat fragmentation, bottlenecks), on geneticvariation within and between geographically distinct popu-lations (e.g. Arbogast 1999; Hewitt 2000). We hypothesizethat if the pattern of present genetic diversity is mainlydetermined by postglacial colonization of the Alps from arefuge on the Italian Peninsula (as hypothesized by Taberlet

et al

. 1998), IBD measured at both mitochondrial DNA(mtDNA) and nuclear level is expected to be strong andsignificant over local and regional geographical scales. If,in contrast, recent bottlenecks and disruption to gene flowhave had an additional impact on the present genetic struc-ture of squirrel populations, a complex distribution of

genetic variability is expected and IBD will not fully explainthe population structure.

Materials and methods

Study areas

Seven study areas were chosen within mature, secondarymontane and sub-Alpine mixed conifer forests of theItalian Alps, with altitudes ranging from 1100 to 2100 ma.s.l. (the upper tree-line). These areas are distributed overtwo geographical regions: COG and RHE, are located in theCogne and Rhemes Valleys of the Gran Paradiso NationalPark, in the Western Alps, while CED, OGA, SAN, VAFand BOR, are in the Valtellina Valley in the Central Alps(Fig. 1). Currently, the forests between Gran Paradiso andValtellina are virtually uninterrupted; however, deforestedvalley floors, rivers, and mountain peaks over 3000 m maypose potential barriers to squirrel dispersal between thetwo geographical regions (Fig. 1). Distances between studyareas are presented in Table 3.

Trapping and sampling techniques

In each study area, squirrels were live-trapped bimonthlyfrom April to October 2000 to 2003, using ground-placedTomahawk ‘squirrel’ traps (Tomahawk Live Trap Co.).Traps were prebaited with sunflower seeds and hazelnutsfour to six times over a 30-day period, and then baited andset for 8 to 12 days, until no new, unmarked squirrelswere trapped for at least two consecutive days. For eachindividual, 30–50 hairs with bulbs were plucked from thetail and/or dorsal area and stored at room temperature inpure ethanol until DNA extraction. Alternatively, one ortwo tissue samples per individual were taken from the earpinna using a 3 mm biopsy punch and placed in a 0.2 mLeppendorf vial containing 90% denaturated ethanol andstored at 4

°

C until DNA extraction.

DNA extraction

For mtDNA analysis, DNA was extracted from hair bulbsof 70 individuals (30 hairs per individual) from five studyareas (Table 1a), while DNA for microsatellite analysis wasextracted from biopsy punches of the ear pinna and hairsamples of 289 individuals from six study areas (Table 2).DNA was obtained using either a standard phenol–chloroform (Sambrook

et al

. 1989) or chelex (Walsh

et al

.1991) protocol. The former procedure was applied to hairsamples collected for mtDNA sequencing because itallowed us to obtain the high-purity DNA required by thesequencer, and included overnight incubation with 100

µ

gproteinase K, followed by two phenol–chloroform washes andDNA precipitation using 100% isopropanol. This procedure

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was also used for tissue samples collected for microsatelliteanalysis. Instead, hair samples for microsatellite analyseswere subjected to a chelex-based procedure because weneeded to obtain as much DNA as possible, regardless ofpurity, in order to analyse such a high number of loci. DNAwas obtained by boiling 30 hairs in a 5% chelex solution after

incubation for 90 min at 55

°

with 100

µ

g proteinase K. AllDNA extraction and polymerase chain reaction (PCR)procedures were tested and optimized by using trialsamples from dead specimens. Definitive extraction protocolswere standardized for all samples, so that the quantity ofDNA from each extraction was comparable for all samples.

mtDNA sequencing

A 378-bp fragment of the D-loop of the mtDNA wasamplified by PCR with the primers SQL (Lpro-SQL-ACTAATCCATCGTGATGTCTTATTTA) and SQR SQR-CTTACTTGACCAATCCCTCACT designed by one of us(BC) using the published sequence of the complete mtDNAgenome of

Sciurus vulgaris

(nucleotide positions 15548–15926; GenBank Accession no. AJ238588; Reyes

et al

. 2000).PCR was performed in a reaction volume of 20

µ

L containing1.5 U of

Taq

DNA polymerase (Polymed), buffer 1

×

(Polymed), 2.5 m

m

MgCl

2

, 100

µ

m

deoxynucleosidetriphosphate (dNTPs), 20 pmol each of primers SQL andSQR, and 2

µ

L of template DNA. PCR was performed in anMJ Research PTC150 thermal cycler with a denaturation stepof 1 min at 94

°

C, followed by 40 amplification cycles (94

°

Cfor 30 s, 52

°

C for 30 s, and 72

°

C for 1 min). The double-stranded PCR products were purified by centrifugationwith the QIAquick PCR purification Kit (QIAGEN) priorto sequencing. 2

µ

L of PCR product was used in thesequencing reaction mix which was prepared with an ABIBigDye Terminator Cycle Sequencing chemistry (AppliedBiosystems) in a final volume of 20

µ

L. The DNA dye-labelledfragments were purified with Centri-sep centrifuge columns

Fig. 1 Location of the seven study sites inthe Italian Alps. Grey areas represent for-ests. See Table 4 for linear pairwise distances(km).

Table 1 (a) Standard indices for five study areas calculated from70 D-loop sequences. (b) Number of shared haplotypes betweenstudy sites (above diagonal) and conventional FST values betweenstudy areas calculated from mtDNA haplotype frequencies(below diagonal)

(a) Population (n) CED OGA SAN COG RHE

Mean number of pairwise differences*

6.25 5.89 6.85 5.91 5.63

Gene diversity† 0.82 0.90 0.90 0.91 0.79Nucleotide diversity* 0.02 0.02 0.02 0.01 0.02Max. pairwise distances between haplotypes‡

2.6% 2.6% 2.4% 2.9% 2.6%

(b) Population CED OGA SAN COG RHE

CED — 2 1 0 1OGA 0.118 — 3 1 1SAN 0.063 0.028 — 0 0COG 0.131 0.094 0.091 — 4RHE 0.189 0.148 0.160 −0.021 —

Significant amova values (P < 0.05) are shown in bold.*as calculated by arlequin version 2.000, Schneider et al. 2000; †Nei 1987; ‡as calculated by paup*.

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(Princeton Separations) and sequenced on an ABI Prism310 automated sequencer.

Statistical analyses of mtDNA sequences

In addition to the 70 D-loop sequences generated bythis study, five sequences of

S. vulgaris

from the UK, partof the complete

S. vulgaris

mitochondrial genome, and theequivalent sequence from

Sciurus carolinensis

were selectedfrom the GenBank database and added to the analysis asreference (GenBank Accession nos AF111011, AF111016,AF111019, AF111021, AF111022, AF111027, Barratt

et al

. 1999;AJ238588, Reyes

et al

. 2000). Haplotypes were identifiedusing

collapse

version 1.1 (Posada 1999). Phylogenies wereinferred using

phylip

(Phylogeny Inference Package) version3.5c (Felsenstein 2004). For comparison, a network estimationof gene genealogies was generated with

tcs

version 1.13(Clement

et al

. 2000).

S. carolinensis

was used as outgroup.Uncorrected

p

pairwise differences between haplotypeswere calculated using

paup

* version 4.0 beta 10 (Swofford1998). Standard indices and an analysis of molecular vari-ance (

amova

) using conventional

F

-statistics calculatedfrom haplotype frequencies between study areas were esti-mated using

arlequin

version 2.000 (Schneider

et al

. 2000).A neighbour-joining (NJ) tree was generated with

phylip

version 3.5c using the average pairwise differences betweenstudy sites (calculated using

arlequin

version 2.000).Because this tree and the matrix of

F

ST

values indicated thatthe populations at COG and RHE are more closely related toeach other than to OGA, CED and SAN, the sequences fromthese two geographical areas (Gran Paradiso and Valtellina)were pooled before calculating

F

ST

between them.In order to investigate IBD, the natural logarithm of

minimum geographical distance in km was compared to

F

ST

using Mantel tests (Mantel 1967) in

genetix

using

r

, anormalization of the

Z

-statistic defined by Mantel (Smouse

et al

. 1986).

Microsatellite analyses

Individuals were genotyped at 10 microsatellite loci (SCV4(GT)

23

, SCV9 (ACAG)

3

(AC)

14

, SCV12 (TG)

15

(CACG)

2

, SCV13

(GA)

15

, SCV19 (TG)

11

(Hale

et al

. 2001b); RS

µ

1 (GGAT)

13

,RS

µ

3 (GA)

9

(GACA)

9

, RS

µ

4 (ATCC)

12

, RS

µ

5 (GT)

10

, RS

µ

6(GTT)

10

(Todd 2000). PCR amplifications were carried outin 10

µ

L reaction volumes containing 1

×

buffer (10 m

m

Tris-HCl, pH 8.8, 1.5 m

m

MgCl

2

, 50 m

m

KCl and 0.1% TritonX-100), 200

µ

m

dNTPs, 0.33–0.4 m

m

of each primer, 2.5–3 m

m

MgCl

2

, 0.6 U

Taq

polymerase (FINNZYMES) and100–300 ng genomic DNA. PCR amplifications wereperformed following protocols in Todd (2000) and Hale

et al

. (2001b). PCR products were analysed by an ALFexpressII DNA analyser (Amersham Pharmacia Biotech) and

allelelinks

version 1.02 software (Amersham PharmaciaBiotech). Internal and external standards were used andreference samples with known genotype for each locuswere included in each run to ensure reliable scoring ofgenotypes across different gels.

Microsatellite data analyses

Various indices of genetic diversity were measured, as themean number of alleles per locus, allele frequencies, allelicrichness, and observed (

HO) and expected (HE) heterozygosities(Nei 1978) using fstat version 2.9.3 (Goudet 2001). fstatwas also used to test for significant differences in averagevalues of these parameters among the two groups of studysites (one sided test using 1000 permutations). genepop 3.3(Raymond & Rousset 1995a) was used to measure deviationsfrom Hardy–Weinberg equilibrium (HWE) and linkagedisequilibrium (LD) across pairs of loci; all probability testswere based on Markov chain Monte Carlo simulation(MCMC, Guo & Thompson 1992; Raymond & Rousset1995b) using default values and a sequential Bonferronicorrection (Rice 1989) where appropriate. The frequency ofpossible null alleles was estimated using cervus 2.0(Marshall et al. 1998). To study the differentiation betweenstudy sites, exact probability tests with Fisher’s combinedprobability method for genic differences in allelic frequenciesamong sites were performed using genepop, and overallpairwise Wright’s FST (Weir & Cockerham 1984) and Nei’sstandard genetic distance, DS (Nei 1978) were estimatedusing genetix (Belkhir 1999). Confidence limits were obtainedby generating 1000 bootstrap replicates. A graphical

Table 2 Measures of genetic diversity among the six populations of red squirrel at ten microsatellite loci

CED OGA VAF BOR COG RHE Valtellina Gran Paradiso

Number of individuals 28 43 64 35 59 60 170 119Mean number of alleles 4.7 5.0 5.4 4.8 4.6 4.8 5.6 5.8allelic richness 4.65 4.66 4.85 4.65 4.25 4.31 4.70 4.48Gene diversity 0.548 0.598 0.615 0.564 0.493 0.562 0.590 0.558HO 0.532 0.588 0.539 0.589 0.490 0.477 0.561 0.494FIS 0.03 NS 0.02 NS 0.12** −0.04 NS 0.01 NS 0.15** 0.05 NS 0.11**

NS = nonsignificant, **P < 0.01. (obtained by fstat).

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representation of the distance matrix was constructed usingthe upgma algorithm of phylip version 3.5c (1000 bootstrapreplicates; Felsenstein 2004). For comparison, graphicalplotting of population differentiation was obtainedthrough principal component analysis implemented inpcagen (available at http://www.unil.ch/izea/softwares/pcagen.html). A hierarchical amova was performed usingarlequin (version 2.000), by grouping study sites by region(Gran Paradiso or Valtellina).

All study sites were tested for a recent reduction of theireffective population size using bottleneck (Cornuet &Luikart 1996). The program also calculates the modal allelefrequency class to detect deviations from the L-shapeddistribution. IBD was investigated with the Mantel test usingthe same procedure as that used for mtDNA.

The genetic structure of populations was investigatedusing structure (Pritchard et al. 2000). The number ofsubpopulations (K) was calculated assuming no priorknowledge of population of origin of individuals. MCMCparameters were set on burn-in period of 30 000 and runlength of 106 iterations under the admixture model; eachrun was repeated five times to ensure convergence amongestimated parameters. The second step of the analysisinvolved assigning individuals to each of the K subpopulationsto estimate direct rates of migration among them. Study siteswere grouped into the obtained K clusters and individualmembership likelihoods were calculated to evaluate theextent of ancestry in the respective subpopulation.

The demographic history of squirrels in the different studysites and the probability of common ancestry (through theestimate of F parameter) among individuals were investigatedusing 2mod (Beaumont 2001). We used these models to detectif the genealogical history of alleles in the given populationscould be explained by one of two alternative demographicscenarios: gene flow model (an equilibrium between geneflow and drift) or (pure) drift model. Likelihoods of the geneflow and drift models were computed in four independentruns choosing 100 000 iterations. As suggested from theauthor, the first 10% of output calculations were discardedto avoid dependence on initial starting values.

Results

mtDNA sequences

A high number of haplotypes (23) are found in our sampleof 70 squirrels from five study sites (CED, OGA, SAN,RHE, COG) in two geographical regions (Gran Paradisoand Valtellina); however, these haplotypes are evolutionarilyvery similar, since out of 378 bp, only 34 sites are variable andincluded one deletion, 32 transitions and two transversions(ratio 16 : 1). The high level of haplotype diversity isreflected in the standard indices (Table 1a) which indicatethat the number of pairwise differences and gene diversity

are correspondingly high within each study area. However,the maximum pairwise distances between haplotypeswithin study sites are low (up to 2.9%; Table 1a), but verysimilar between haplotypes unique to study sites (2.9%,N = 15), between unique haplotypes in each of thegeographical areas (Gran Paradiso and Valtellina) (3.2%,N = 22) and between the Alpine and UK haplotypes (3.2%,N = 30). The values shown in Appendix 1 indicate that thefive study areas appear to be highly differentiated on thebasis of haplotype frequency, both between study sites andgeographical areas. Only two haplotypes occur in bothgeographical areas (CED310, OGA14; Appendix 1) and allstudy areas except RHE have at least two unique haplotypes.Populations within the same region share more haplotypesthan they do with populations of different regions(Appendix 1); however, the pattern of haplotype sharingbetween study sites within regions is different in GranParadiso and Valtellina. For example, in Gran Paradiso,RHE and COG share the highest number of haplotypes(four), while only one haplotype is common to all threestudy sites in Valtellina (SAN16), and very few haplotypesare shared between pairs of these sites (Table 1b, Appendix 1).And while populations in the Central Alps have differentdominant haplotypes (CED: ced49l OGA: san17; SAN:san16), RHE and COG have the same dominant haplotype(rhe24). The overall fixation index between study areas(calculated on the basis of haplotype frequency) is low butsignificant (FST = 0.10, P < 0.001). Pairwise FST valuesbetween populations range from only −0.02–0.189, butseven of the 10 comparisons are significant (P < 0.05,Table 1b). The FST value between Gran Paradiso and Valtellinais also moderate, but significant (FST = 0.11, P < 0.001), and,in agreement with pairwise distances, is also very similarto that between study areas. Most of the 23 Alpinehaplotypes and six UK haplotypes cluster randomly withrespect to geographical origin, as is shown in the NJphylogenetic tree generated by phylip version 3.5c (Fig. 2).There is no genetic affinity of haplotypes at the regionallevel, as even the UK haplotypes cluster a random withrespect to the Alpine samples. At the local level, only fivebootstrap values can be considered significant (> 70%),and even two of these clusters include haplotypes fromdistant study areas (SAN and CED). tcs generated verysimilar results (not shown).

Correlation between ln(geographical distance) and FSTvalues among all populations is statistically significant(Mantel test r = 0.80; P = 0.029; Fig. 3a). Because of the lownumber of pairwise comparisons, it was not possible toperform further Mantel test on a smaller geographical scale.

Microsatellites

Red squirrels from all seven study sites show high levels ofvariability at the 10 microsatellite loci, with allele numbers

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ranging from four to 10 per locus (mean 5.8). All 58 allelesare present in the Valtellina sites and 56 (97%) in the GranParadiso ones. Mean number of alleles per study site rangefrom 4.6 (COG) to 5.4 (VAF) (Table 3). HE is 0.56 and 0.59in Gran Paradiso and Valtellina populations, respectively,ranging from 0.49 (COG) to 0.61 (VAF). None of theseparameters differ significantly between Valtellina and GranParadiso (all comparisons, P > 0.05).

No LD is apparent among the 270 locus pair/study sitecomparisons (P > 0.0001 in all cases). However, tests forHW disequilibrium are significant for VAF and RHE sites.In particular, RHE displays significant heterozygote deficitacross three loci (Rsµ1, Rsµ4, Rsµ5; P < 0.005) and VAFacross two loci (Rsµ1, Scv19; P < 0.005). The HWE test overloci shows a weak indication for heterozygote deficit atlocus Rsµ1 (0.005 > P > 0.01), thus, we repeated the testsdropping this locus. Results for both RHE and VAF did notdiffer and heterozygote deficit was still significant. Theestimated frequencies of null alleles at all loci are < 0.05,suggesting there are no null alleles.

Overall differentiation among study sites is quite low(FST = 0.041) but statistically significant (95% confidencelimits = 0.027–0.055; P = 0.007). FST values are lower betweenGran Paradiso and Valtellina study sites (Gran ParadisoFST = 0.025, 95% confidence interval 0.007–0.046, P < 0.001;Valtellina: FST = 0.028, 95% confidence limits = 0.018–0.038,P = 0.005). All differences in pairwise allelic frequenciesare highly significant (all P < 0.001). FST pairwise compari-sons (Table 3) are also highly significant in all cases (P <0.0001), the level of absolute values being lower between

geographically neighbouring sites. FST between the twomain geographical regions is comparable to those observedat the previous level (FST = 0.034, 95% confidence limits =0.01463–0.05341; P = 0.01).

The results of the hierarchical amova confirm that thehighest percentage of variation is found within study areas(99.5%, P < 0.001), while differentiation among sites withingroups is low but significant (0.3%, P < 0.001), and variancebetween geographical regions is not significant (0.2%,P = 0.069).

Population differentiation was further investigatedthrough genotype assignment approach implemented instructure program. We found a population substructur-ing (K = 4) as most probable (P (K|X) = 0.99) with fourgroups as follows: group 1, OGA; group 2, BOR, CED,VAF; group 3, RHE; and group 4, COG. As suggested byPritchard et al. (2000), however, K should be intended as aguide, and results should be interpreted in light of biologicalcriteria. The upgma tree (Fig. 4a) reveals the existence of twomain clusters (Gran Paradiso and Valtellina), and a thirdminor cluster represented by OGA. Despite fairly low sta-tistical support for nodes, the topology of the tree seems tobe confirmed by the structure results, suggesting sub-structuration into at least three groups, and by the PCA plotshowing separation of the two distinct geographical regionson the first axis, significant according to the broken-stick (43.7%inertia, P = 0.02; second axis: 19.6% inertia, P = 0.9; Fig. 4b).

Correlation between ln (geographical distance) and FSTvalues among all populations is statistically significant(Mantel test r = 0.80; P = 0.036; Fig. 3b). No significant

Fig. 2 Neighbour-Joining tree of Sciurusvulgaris haplotypes obtained using phylip(Phylogeny Inference Package) version 3.5cusing seqboot (to generate 1000 bootstrapreplicates), dnadist (to calculate geneticdistances using a maximum likelihoodalgorithm), and neighbour (to compute theneighbour-joining tree) (Felsenstein 2004).treeedit version 1.0a10 (2001, AndrewRambaut & Mike Charleston) was used tovisualize the tree.

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correlation persists if only the Valtellina populations areconsidered (r = 0.74; P = 0.24).

The 2mod analyses on the entire data set indicates thegene flow model has a much higher likelihood than thepure drift model (P gene flow model = 0.99; P drift = 0.02;Bayes Factor = 499 gene flow model; Table 4), implyingthat overall gene frequencies, and thus observed level ofdifferences, are determined by recurrent immigration froma stable base source population. When the Gran Paradisoand the Valtellina study sites are considered separately,the results show a slightly different situation. For GranParadiso, the drift model seems to be more likely, thoughnot strongly statistically supported by Bayes factor (P driftmodel = 0.63; P gene flow model = 0.36; Bayes Factor =2.59 drift model), the drift effect being largely attributableto COG (P drift model = 0.75; P gene flow model = 0.24;Bayes factor = 3.08; Table 4). Under the gene flow modelthe estimated deviations are FGran Paradiso = 0.074, FValtellina =0.044. In particular, the highest value is detected for COG(F = 0.080). For Valtellina sites, the gene flow model is

still the most probable (P gene flow model = 0.85; P driftmodel = 0.14; Bayes Factor = 5.8; Table 4).

No indications for recent bottlenecks are found across thestudied populations, and all allele frequency distributions

Fig. 3 Isolation by distance. Logarithmof geographical distance is plotted againstpairwise FST values measured by: (a) mtDNA;and (b) microsatellite data.

Table 3 Pairwise FST (lower diagonal) between six sampling areasbased on microsatellite data

CED OGA VAF BOR (SAN) COG RHE

CED — 58 61 60 (30) 196 217OGA 0.038 — 9.3 8.2 (53) 250 271VAF 0.024 0.032 — 1.1 (47) 255 276BOR 0.036 0.030 0.018 — (48) 253 274(SAN) — — — — — (225) (238)COG 0.057 0.060 0.057 0.050 — — 21RHE 0.062 0.043 0.043 0.042 — 0.025 —

All comparisons obtained after 1000 permutation by Fisher’s exact test were highly significant (all P < 0.001).Geographical distances (km) in the upper diagonal (also shown for SAN, between brackets).

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result L-shaped (data not shown). The Wilcoxon test did notdetect significant HO excess under any of the three modelsinvestigated (all P > 0.05).

To estimate direct gene flow, we ran structure byselecting the ‘prior knowledge of population’ option andgrouping sampling locations into K = 4 clusters (see previous).Overall, 89.6% of the samples are assigned with a highdegree of certainty (membership likelihood for the popu-

lation of origin: P > 0.9). Among the remaining individualsdisplaying lower likelihood values, only three present anassignment score lower than 0.7. Since this P value is stillquite high, these individuals can not be defined as migrants,but only as individuals with an uncertain ancestry (P c. 0.6).Although a low number of loci were used in this studycompared to that recommended (Pritchard et al. 2000), thevery low number of ‘wrong’ assignments can be an indica-tion of low level of gene flow among the four clusters.

Discussion

Being a habitat specialist, the red squirrel is an ideal modelfor studying the effects of forest fragmentation on populationgenetics and demography (Wauters et al. 1994; Hale et al.2001a), and a useful indicator of past biogeographical changesin forest cover (Hale et al. 2004). In this study, we analysedpatterns of genetic variability of Alpine red squirrel in orderto obtain an insight on the possible scenarios that shapedthe present status of populations.

Genetic variability

The Alpine squirrel populations studied here display highgenetic variability both at the nuclear and mtDNA level(Tables 1a and 2). Despite the small geographical scale ofthe investigation, 23 mtDNA haplotypes were detected inonly 70 individuals. This number is even higher than thatfound in the study by Barratt et al. (1999) over a much largergeographical area (25 haplotypes in about 200 individualsfrom the UK and mainland Europe), already consideredby these authors to be significant. However, as noted byBarratt et al. (1999; and references therein), rodents areexpected to have high numbers of haplotypes; in fact, sucha number is not unusual for studies of small mammals ona similar scale (e.g. 19 haplotypes in 44 Madeiran housemice, Mus domesticus, Gunduz et al. 2001; 20 haplotypes infive populations of Calomys muscilinus, Gonzalez Ittig &Gardenal 2002), even in those considered rare or endangered(e.g. Dipodomys stephensi, Metcalf et al. 2001).

For microsatellite loci, mean number of alleles (MNA)was slightly higher in Italian (MNA = 4.6; this study) thanin British populations (MNA = 4.0; Hale et al. 2001b), butlower than in German (MNA = 7.2; Todd 2000) and Bel-gian populations (MNA = 6.6; Todd 2000). Observed andexpected heterozygosity values did not differ stronglyfrom those recorded in the previously cited studies. AverageHO and HE for squirrels from Belgium and Germany were53.8% and 56.9%, respectively, compared to HO = 55.6%and HE = 55% for Italian populations. For the four micro-satellites used both in this study and by Hale et al. (2001a),British populations had slightly lower values for bothHO = 49.5% (52.3% in Italian populations) and HE = 56.9%(65% in Italian populations). In general, heterozygosity

Fig. 4 (a) Unrooted upgma tree of Nei (1978) genetic distanceobtained using phylip package version 3.5 (1000 bootstraps); and(b) PCA plot of allele frequencies.

Table 4 Summary statistics obtained from 2mod; a Bayes factor> 3 is conventionally referred to as a substantial support for modellikelihood (Kass & Raftery 1995)

Study areaSupport for gene flow

Support for drift

Bayes factor

F (gene flow)

CED 68.40% 32.6% 2.1 0.039OGA 86.60% 13.40% 6.4 0.043VAF 44.37% 55.63% 1.18 0.027BOR 92.45% 7.55% 12.24 0.041COG 24.50% 75.50% 3.08 0.080RHE 72.00% 28.00% 2.57 0.068Valtellina 85.40% 14.60% 5.8 0.044Gran Paradiso 36.40% 63.60% 2.59 0.074Overall 99.80% 0.20% 499 0.050

Values of significant models in bold.

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values in red squirrels were similar to those displayed inother sciurid species (Alpine marmot, Marmota marmota,HE = 0.30–0.65, Kruckenhauser & Pinsker 2004 and HE =0.57–0.76, Goossens et al. 2001; Siberian flying squirrel,Pteromys volans, HE = 0.48–0.86, Painter et al. 2004; yellow-pinechipmunk Tamias amoenus, HE = 0.28–0.79, Schulte-Hosteddeet al. 2001). Expected heterozygosity was also similar tovalues measured for 48 species of mammals (0.58 ± 0.25,DeWoody & Avise 2000). However, these comparisonsrequire caution, since results from monomorphic loci arerarely published, affecting the actual levels of heterozygosity.

Bottlenecks

Microsatellite data suggest that none of our study populationshas suffered a bottleneck in the recent past. This may not besurprising since heterozygosity excess and deviations ofmicrosatellite allelic frequencies from equilibrium can berestored in as quickly as three generations (Keller et al. 2001)or about 15–20 years for squirrels (Wauters & Dhondt1995). In contrast, analyses of mtDNA sequences revealhigh levels of haplotype diversity and very low nucleotidediversity values at all study sites (see Table 2a). Thiscondition is usually attributed to expansion after a periodof low effective population size (Avise et al. 1984). Therefore,present mtDNA diversity could reflect expansion after areduction in population size caused by the severe forestfragmentation in the early 19th century, whereas the lackof evidence for a bottleneck at the nuclear level couldreflect recently restored gene flow (last 50 years). The factthat populations in Gran Paradiso and Valtellina onlyshare two haplotypes, both at a low frequency (Appendix 1)and the significant differentiation between these two regions,would suggest that post-1945 expansion originated fromdifferent fragments of sub-Alpine conifer forest, althoughfurther analysis of neighbouring populations is necessaryto confirm the location of these source populations.

Hardy–Weinberg equilibrium and population differentiation

HWE was found in all but two study sites: RHE, in GranParadiso, and VAF, in Valtellina. Since we can exclude thepresence of null alleles because of the obtained low frequencyestimates, HW disequilibrium in RHE and VAF could bean effect of local population substructuring. Trapping andradio-tracking data indicated that these sampling areashad more dispersing individuals than recorded in theremaining areas (Bertolino & Wauters, unpublished),suggesting that these two sites may represent part of wider,substructured populations, displaying heterozygosity deficitreflecting a weak Wahlund effect. However, no furthersubstructure could be detected when RHE and VAF wereanalysed separately in structure (K = 1).

FST-based population differentiation is significant atboth local and regional scale. These data are confirmedby both molecular markers employed (Tables 2b and 3).mtDNA data indicate differentiation among sites, particu-larly at the regional scale, while at the local scale, the patternof differentiation appears to be random. Differentiationamong study sites is also indicated by high numbers ofunique haplotypes per study site (Appendix 1), althoughthe phylogenetic tree (Fig. 2) does not suggest any geo-graphical distribution pattern. These results are concordantwith analysis of control region sequences of red squirrelsfrom the UK, Germany, Belgium, the Netherlands, Spainand northern Italy, which also showed strong differentia-tion among sites for this species (Barratt et al. 1999; Haleet al. 2004). All haplotypes have less than 3% pairwise dif-ferences (Table 1a), indicating that study sites were notever isolated from one another for long periods of time;moreover, very low nucleotide diversity values (average0.018) indicate a relatively recent differentiation.

Analyses of microsatellite data suggest that populationsfrom the six study sites are well differentiated from oneanother (Table 3), and presently divided into at least threemain groups: Gran Paradiso, the three Valtellina sitessouth of the Adda river (BOR-CED-VAF), and OGA. Thissubstructuring is confirmed from both upgma analysis ofgenetic distances and Bayesian clustering approach.

Isolation by distance

According to Slatkin (1993), during the evolution of a geneflow–drift equilibrium system, IBD should be evident firstover small geographical distances, then over larger ones. Ifthis is true, results obtained by Mantel tests seem toindicate the trace of a near-equilibrium system betweenpopulations 250 km apart: in effect IBD was significantwhen all sites were included, but not when consideringonly four Valtellina sites. This hypothesis is also confirmedby the 2mod demographic model, which strongly supportsthe existence of an equilibrium between gene flow anddrift in the alpine squirrel population. Thus, in Valtellina,the absence of correlation between geographical andgenetic distance at the local scale (Mantel test), suggeststhat other factors potentially shaped the postglacial geneticscenario. Moreover, both the degree of differentiation amongsites and indications of low levels of gene flow over shortgeographical distances are consistent with the existence ofbarriers affecting genetic structure of populations at thisscale. Patterns of isolation by distance have been widelystudied at comparable scales in other small and medium-sized mammals such as the white-toothed shrew (Crocidularussula, Favre et al. 1997), Alpine marmot (M. marmota,Goossens et al. 2001), Tasmanian devils (Sarcophilus laniarius,Jones et al. 2004), wolverine (Gulo gulo, Kyle & Strobeck2001), American marten (Martes americana, Kyle et al. 2000),

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northern Idaho ground squirrel (Spermophilus brunneusbrunneus Gavin et al. 1999), and house mouse (Mus musculus,Dallas et al. 1995). In several rodent species, IBD modelexpectations and sensitivity to habitat fragmentation areoften in disagreement with observed data (see for instanceRoach et al. 2001; Hinten et al. 2003). This is probably relatedto species-specific habitat use and requirements (e.g. sizeof high-quality habitat patches, hostility of matrix habitats),and thus the scale at which they perceive the structure ofthe landscape (With et al. 1997). In the common hamsterCricetus cricetus, for example, lack of correlation betweengenetic and linear geographical distance was explained bypoor dispersal abilities and the absence of high-densityperiods that normally trigger large distance migration,exacerbated by the presence of human-constructed barriers(Neumann et al. 2004). Up to now, IBD has never beenspecifically studied in red squirrels but the sensitivity ofthis species to potential barriers, such as cultivated andurbanized areas, that reduce dispersal has been illustrated(e.g. Verboom & Van Apeldoorn 1990; Verbeylen et al. 2003).Our results confirm that barriers could have an effect ongene flow in red squirrels on a local scale.

In Valtellina, microsatellite analysis indicates differenti-ation between OGA and VAF-BOR-CED sites, confirmedby FST values, despite small geographical distances betweenOGA and BOR (Table 3). This could indicate that the defor-ested valley floor and/or the Adda River impede geneticadmixture between OGA and neighbouring sites. The highlyaltered habitat of urbanized and cultivated land in therelatively wide Adda River valley (1.8 km) is likely to rep-resent a barrier for dispersing squirrels between the forestsnorth and south of the valley, thus allowing differentiationof the two groups (see also Wauters et al. 1994; Hale et al.2001a). However, microsatellite data of heterozygosities,allelic richness and HWE in OGA suggest that this site isnot genetically isolated; in fact, the woodland at OGA isonly about 70 years old, and is probably connected withmixed conifer forests on its northern side (from which welack samples) where gene flow could persist. Despite the60 km linear distance between CED and VAL-BOR, we havestatistical evidence from both microsatellite substructuringand FST values, that these study sites actually representa single population. In fact, at the landscape level, thesestudy areas are part of large, continuous conifer forests, ahabitat structure which promotes squirrel dispersal andthus genetic admixture. However, from about 200 toonly 50 bp, these forests had been reduced to small,isolated blocks, suggesting that a few decades, or about fiveto maximum 10 generations for squirrels, are sufficient tobring the distribution of microsatellite alleles to equilibrium.Similar results were found in northern England wherepopulations displayed genetic exchange and equilibriumrestore 20–30 years after creating a corridor among habitatpatches (Hale et al. 2001a).

Also the pattern of mtDNA haplotypes suggests thatthe 19th-century forest fragmentation in Valtellina hadprofound effect on gene flow. Firstly, the presence of onlytwo high frequency haplotypes in CED suggests that popu-lation reductions (bottleneck events) may have occurredin the recent past. Secondly, the random pattern of uniquehaplotypes in each site indicates fragmentation followedby random drift. Finally, the distribution and frequencyof shared and unique haplotypes does not seem to displayany clear genetic pattern (Appendix 1) or any correlationwith geographical distance.

In Gran Paradiso, microsatellites indicate that RHE andCOG are genetically differentiated despite the low geo-graphical distance (see Table 3) and the presence of otherforests between them. In addition, genetic diversityparameters show lower genetic variability in COG and, toa lesser extent, RHE (Table 2) compared to the remainingstudy sites, suggesting the presence of a barrier affectinggene flow between them which precludes equilibrium. Thepartially cultivated valley floor and some high mountainridges (> 2500 m) of the Valsavaranche Valley, that separ-ates the two study sites, are likely to reduce gene flow, aconclusion confirmed by movements of radio-taggedsquirrels (Bertolino & Wauters unpublished). The fact thatRHE and COG share four mtDNA haplotypes suggests thatthe limitation to gene flow is a recent phenomenon, prob-ably linked with increased urbanization and cultivation ofthe valley floor. Thus, differentiation between the two sitescould arise from a partial isolation of COG, while RHE, couldrepresent an area hosting an expanding squirrel population.

In conclusion, the pattern of shared and unique mtDNAhaplotypes suggests that the ancient, postglacial panmicticpopulation of alpine red squirrels probably suffered localdemographic declines in the recent past followed by a slow,ongoing expansion and differentiation shaped by forestmanagement and exploitation. Genetic variation of micro-satellites indicates that nuclear genetic variability has beenrestored in the past 40–60 years and is mainly shaped bybarriers of unsuitable habitat. Further sampling and radio-tracking is required to exclude the possibility that patterns ofgenetic differentiation can also be affected by habitat prefer-ences of dispersing animals (see Haughland & Larsen 2004for the North American red squirrel, Tamiasciurus hudsonicus).

Implications for conservation and management

Studies of the Eurasian red squirrel in fragmented habitatshave revealed loss of genetic variation in small populationslinked to rates of dispersal along corridors and/or woodlandsfunctioning as stepping stones (Wauters et al. 1994; Haleet al. 2001a). This is the first study of the species inlandscapes dominated by continuous forests. Our studysuggests that both historical and recent habitat alterationsshape genetic diversity and gene flow in Alpine populations

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of the red squirrel and confirms that habitat connectivitycan restore genetic variability relatively quickly. Reforestationpolicy and the creation of natural corridors appear to befundamental to the conservation management of redsquirrels. This agrees with previous studies on other forest-dwelling mammals for which population persistence and/or population size are positively affected by the existenceof woodland corridors between habitat patches of suitablequality (Laurance & Laurance 1999; Bentley et al. 2000;Ramanamanjato & Ganzhorn 2001).

These results also form a particularly useful database forthe genetic monitoring of Alpine populations of red squir-rel, which are expected to suffer the same dramatic declineas populations in the UK and parts of Piedmont, Italy,upon the apparently inevitable expansion of the compet-ing grey squirrel into the Alps. Field research is ongoinginto the possibility that Alpine populations of the redsquirrel may find refuge from the grey squirrel at higheraltitudes. If this is confirmed, it may be possible to con-serve or plan a network of corridors to ensure the long-term survival of the species in this area. Comparison of thepresent results with future genetic monitoring could detectthe efficiency of these corridors in preserving the genetic‘health’ of these populations.

Acknowledgements

We wish to thank S. Bertolino, A. Molinari, and M. Zaninetti forassistance with fieldwork, and T. Schenck for help with geneticanalyses. M. Casiraghi and two anonymous referees gave usefulcomments on an earlier version of the manuscript. The study wasfinanced by grants awarded to L.A.W. from the Gran ParadisoNational Park, Italy (DGE 25–2000), and the Committee forResearch and Exploration of the National Geographic Society,Washington DC, USA (grant no. 6997–01). Further financial sup-port was given by the Centre for Alpine Ecology, Istituto Oikos,the Wildlife Service of the Province of Sondrio, and the ParcoRegionale Orobie Valtellinese. Logistic support was provided bythe Stelvio National Park.

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Appendix 1

Mitochondrial DNA haplotype diversity in five study areas of the red squirrel from the Italian Alps

Haplotype

Study area (sample size)

CED (14) OGA (16) SAN (7) RHE(16) COG (17)

ced123 0.07 — — — —ced309 0.07 0.06 — — —ced310 0.07 — — 0.06 —ced49 0.36 — — — —ced50 0.07 — — — —ced52 0.07 — — — —cog22 — — — — 0.12cog37 — — — — 0.06cog44 — — — — 0.06cog81 — — — — 0.06cog96 — — — 0.19 0.18cog98 — — — — 0.06oga10 — 0.19 — — —oga14 — 0.06 — 0.13 0.12oga3 — 0.13 — — —oga302 — 0.13 — — —rhe24 — — — 0.38 0.24rhe26 — — — 0.25 0.12san16 0.29 0.06 0.29 — —san17 — 0.25 0.14 — —san18 — 0.13 0.14 — —san19 — — 0.28 — —san54 — — 0.14 — —

Number of samples in brackets.