Genetic structuring of the common shrew, Sorex ... - Wiley Online Library

Hereditas 149: 197–206 (2012)

Genetic structuring of the common shrew, Sorex araneus (Soricomorpha: Soricidae) in the Polish Sudetes may suggest ways of northwards colonization MAGDALENA MOSKA1, HELIODOR WIERZBICKI1, TOMASZ STRZAŁA1, ANNA MUCHA1 and TADEUSZ DOBOSZ2 1 2

Department of Genetics, Wrocław University of Environmental and Life Sciences, Wrocław, Poland Institute of Molecular Techniques, Medical University, Wrocław, Poland

Moska, M., Wierzbicki, H., Strzała, T., Mucha, A. and Dobosz, T. 2012. Genetic structuring of the common shrew, Sorex araneus (Soricomorpha: Soricidae) in the Polish Sudetes may suggest ways of northwards colonization. – Hereditas 149: 197–206. Lund, Sweden. eISSN 1601-5223. Received 23 March 2012. Accepted 27 November 2012. An effect of geographical barriers on the level of population structuring and ways of northwards colonization in the common shrew Sorex araneus was investigated by contrasting gene flow inferred by one Y-linked and eight autosomal microsatellites. A total . of 102 shrews trapped at eight localities separated by mountain ridges of the Sńieznik Massif (East Sudetes, Poland) were studied. The genetic structure of populations was estimated using the standard analysis of molecular variance based on F-statistic, as well as two clustering methods implemented in Structure and Geneland. In pair-wise population comparisons both FST and RST were estimated. A Mantel-test was used to investigate the patterns and causes of structuring. No significant correlation between genetic differentiation and geographical distance was found for autosomal loci and for the Y-linked locus. Significant genetic structuring was found in four out of six pairs of populations. Studying autosomal loci we found nonsignificant correlations between pair-wise matrices of FST and RST and the presence of the barrier. On the other hand, for the Y-linked locus these correlations were significant, both for FST and RST, suggesting reduced gene flow between populations for males. Patterns of genetic structuring in the common . shrew of the Massif of Sńieznik may suggest two possible ways of northwards colonization, which promoted genetic distinction of shrews migrating different routes. Magdalena Moska, Department of Genetics, Wrocław University of Environmental and Life Sciences, Koz.uchowska 7, PL-51-631 Wrocław, Poland. E-mail: [email protected]

The role of geographical barriers, such as mountain ridges, in processes of genetic isolation and speciation, is one of the fundamental aspects of evolutionary biology. Heterogeneous habitats may influence gene flow among populations, shaping their genetic structure. Unlimited and high gene flow prevents the fixation of alleles, slowing down local adaptations and as a consequence the process of speciation (BARTON and HEWITT 1985). New polymorphisms, however, which can be brought about by gene flow, generates new gene combinations enriching the genetic potential of the population. Thus, geographically structured populations provide insight into the processes that affect their evolutionary ability (BALLOUX and LUGON-MOULIN 2002). The contribution of natural barriers to gene flow among populations of the common shrew Sorex araneus (Linnaeus, 1758) and their genetic structuring has mainly been investigated in mountain ranges. The influence of the alpine topography on speciation in Sorex araneus group and on the genetic differentiation of populations, living in isolated valleys, has been analysed in the French and Swiss Alps (WYTTENBACH et al. 1999; LUGON-MOULIN et al. 2000; LUGON-MOULIN and HAUSSER 2002). Results of © 2013 The Authors. This is an Open Access article.

these studies indicate that high mountain ridges form geographical barriers, shaping the genetic structure of isolated populations (LUGON-MOULIN et al. 2000). In Poland, the possible effect of mountain ridges on gene flow levels among common shrew populations, has not yet been studied. To our knowledge, only the anthropogenic barrier – a railway embankment (JADWISZCZAK et al. 2006) or aquatic barriers (WIERZBICKI et al. 2011) and their influence on the exchange of genes between subpopulations inhabiting fragmented area has been investigated. . The Sńieznik Massif is one of the places located in the mountains (East Sudetes, southwest Poland), where such processes can be studied. It consists of five main mountain ranges, dispersing from the one point – the top . of Sńieznik (Fig. 1). All ridges of the massif are separated . by valleys with steep slopes. The Massif of Sńieznik is distinctly different from the Alps, because of the presence of much lower mountain ridges (the highest peak is . Sńieznik – 1425 m a.s.l.). Therefore, the question arises if gene flow is unconstrained among geographically isolated populations of S. araneus given the topographic condi. tions of the Sńieznik Massif. DOI: 10.1111/j.1601-5223.2012.02266.x

198

M. Moska et al.

Hereditas 149 (2012)

. Fig. 1. Map of the study area showing topography of the Sńieznik Massif and location of sampling sites (full circles, n 8) separated by mountain ridges (thick lines); triangles indicate tops of mountains (meters).

The aims of the present study were: 1) to investigate . the influence of selected mountain ridges of the Sńieznik Massif on gene flow between populations of S. araneus living in the neighbouring valleys, 2) to assess the level of genetic differentiation among populations under investigation, and 3) to suggest possible ways of northwards colonization. To achieve these objectives we used genetic markers: biparentally inherited (autosomal) microsatellites and a uniparentally inherited (Y-linked) microsatellite. MATERIAL and METHODS Study area . The study area was located in the Sńieznik Massif (East Sudetes) in southwest part of Poland. Occurrence of . local forms of air circulation in the Sńieznik Massif is one of the important factors contributing to differentiation of the thermal conditions throughout the area. Temperately warm climatic floor, with average annual temperatute 6–8°C, reaches the altitude of 550 m, while cool floor, with 2–4°C, stretches the altitude of 950–1280 m. Length . of vegetation period in the Sńieznik Massif fluctuates from 131 days at the top to 213 days at the foot (WISZNIOWSKA and STEFANIAK 1996). All individuals of common shrew were collected from eight different mountain sites (Fig. 1, Table 1). The sites were separated from each other by different mountain

ridges: Nowa Morawa (NM) and Kamienica (KA) by Stromy Mt (808 m a.s.l.), Kletno (K) and Kamienica (KA) by Młyn´ sko Mt (991 m a.s.l.), Mie˛dzygórze I (MI) and Kletno (K) as well as Mie˛dzygórze III (MIII) . and Kletno (K) by Zmijowiec Mt (1153 m a.s.l.), Mie˛dzygórze I (MI) and Mie˛dzygórze II (MII) as well as Mie˛dzygórze I (MI) and Mie˛dzygórze IV (MIV) by Table 1. Localities of trapping sites and number of individuals (n) used in the study (number of males is given in brackets). No. 1. 2. 3. 4. 5. 6. 7. 8. Total

Locality

Coordinates

Altitude (m)

n

Kletno (K) Jodłów (J) Nowa Morawa (NM) Kamienica (KA) Mie˛dzygórze I (MI) Mie˛dzygórze II (MII) Mie˛dzygórze III (MIII) Mie˛dzygórze IV (MIV)

50°14′N, 16°51′E 50°10′N, 16°46′E 50°14′N, 16°54′E

740 820 680

15 (8) 27 (16) 12 (7)

50°14′N, 16°53′E

640

10 (7)

50°14′N, 16°47′E

840

7 (5)

50°13′N, 16°47′E

900

7 (2)

50°13′N, 16°46′E

700

12 (10)

50°13′N, 16°47′E

780

12 (11) 102 (66)


Gene flow of Sorex araneus in the Polish Sudetes

199

Statistical analysis

Fig. 2. Possible ways (“east” and “west”) of northwards . migration of Sorex araneus in the Massif of Sńieznik; thick lines indicate mountain ridges; triangles represent tops of mountains (meters).

Smrekowiec Mt (1124 m a.s.l.), Jodłów (J) and Mie˛dzygórze II (MII), and Jodłów (J) and Mie˛dzygórze IV (MIV) by Wysoczka (1185 m a.s.l.) (Fig. 2). The distance between the sites ranged from 0.7 km to 12 km with an average of 6 km. Sampling and molecular methods For the microsatellite analysis, 102 individuals of the Drnholec chromosome race were collected. This race occurs in central and southwest Poland and it is the only race in the studied area (WÓJCIK 1993; FEDYK et al. 2008). Sampling was performed from July to September of the years 2008–2009. The number of males evaluated by the Y-chromosome microsatellite was 66 (65% of the total sample). The end of the tail from each individual was preserved in 75% ethanol. Nine polymorphic autosomal microsatellites (L9, L13, L14, L33, L45, L67, L68, L92, L97) and a Y-linked microsatellite (L8Y) were used for the analysis. Amplification conditions for loci L9 and L45 were carried out according to WYTTENBACH et al. (1997), for loci L14, L33, L67, L68, L92, L97 according to BALLOUX et al. (1998), for the locus L13 according to LUGON-MOULIN et al. (2000), whereas for the locus L8Y according to BALLOUX et al. (2000). In order to improve the estimation of the size of the amplification product, one primer of each primer pair was labeled with a fluorescent dye (FAM or JOE) on the 5′-end, which allowed analyses with the use of an ABI 3100 Avant automated sequencer (Applied Biosystems).

For the studied localities (samples), the number of alleles (na), the allelic richness (A), the observed (HO) and expected (HE) heterozygosity were computed for all loci, using ARLEQUIN ver. 3.1 (EXCOFFIER et al. 2005). Inbreeding coefficients (FIS) were estimated to indicate the within population heterozygote deficiency due to non random mating. FIS was calculated per locus, over all loci and per population for the studied localities. Fixation index (FST) was estimated per locus and for overall microsatellite loci. Permutations were used for testing the values of FIS and FST for significant departure from zero. These tests were done using 10 000 permutations of alleles within samples (FIS) and 10 000 permutations of genotypes among samples for FST (ARLEQUIN ver. 3.1) (WRIGHT 1951; EXCOFFIER et al. 2005). To estimate gene flow between localities, conventional F-statistics were used (WEIR and COCKERHAM 1984). In pair-wise population comparisons both FST and RST were estimated. RST which is an FST analogue assuming a stepwise mutation model (SMM) (KIMURA and OTHA 1978), is thought to reflect more accurately the mutation pattern of microsatellites. The main problem affecting F-statistics when working with microsatellites, is their sensitivity to the mutation rate when migration is low. Conversely under a strict SMM (according to BALLOUX et al. 2000, L8Y locus appears to follow a fairly strict stepwise mutation pattern), RST is independent of the mutation rate. However, RST can be less accurate at reflecting population differentiation than FST due to its high associated variance. Under a SMM, RST will therefore benefit more than FST from reducing the sampling variance, for instance through increasing the number of populations sampled, the number of individuals per population or the number of loci scored (GAGGIOTTI et al. 1999; BALLOUX and GOUDET 2002). A hierarchical approach using F- and R-statistics was used to estimate the number of migrants (Nm) successfully entering a population per generation. This method uses gene frequency data to estimate Nm in natural populations indirectly (WRIGHT 1951). The genetic structure of populations was calculated using the standard analysis of molecular variance (AMOVA) based on F-statistics (implemented in ARLEQUIN ver. 3.5) as well as with Bayesian approach implemented in Structure ver. 2.3.3 (PRITCHARD et al. 2000; FALUSH et al. 2003, 2007). Correlated allele frequencies with an admixture model, varying K from 1 to 9 and performing five replicates for each K with 100 000 burn-ins and 100 000 replicates were used. To analyze Structure results Delta K method (EVANNO et al. 2005) combined with standard prediction of K based on plotted mean ln probability of K (L(K)) was applied. Both plots (Delta K and L(K)) were calculated using Structure Harvester

200

M. Moska et al.

(DENT and VON HOLDT 2011). Furthermore, to confirm Structure results we used a second clustering method implemented in Geneland (GUILLOT et al. 2005a, 2005b). Structure and Geneland group individuals in clusters in which HWE and LD are maximized. Main difference between these two Bayesian approach methods is that Geneland gives possibility to use spatial coordinates. In Geneland analysis we used uncorrelated model with 10 runs of K 1 to K 10. We performed 200 000 iteration for each run with a thinning of 10 and 20 000 burnins. Runs were sorted according to their mean posterior probability and the best runs were chosen. A hierarchical analysis of variance was conducted to partition variance into covariance components due to differences among populations, among individuals within populations and within individuals. Covariance components were then used to calculate fixation indices (EXCOFFIER et al. 1992; EXCOFFIER 2000). The relationships between genetic and geographical distances and the barriers (mountains ridges) were investigated using a simple Mantel-test (MANTEL 1967). The test determined significance of correlation between the two matrices. The matrix of genetic differentiation comprised the pair-wise FST- or RST-matrices. Genetic differentiation between pairs of localities was calculated. The barrier matrix contained 1 if the samples were separated by mountain ridge and 0 if did not. The matrix of geographic distance gave distances (in km) between localities. The correlation between FST/(1 FST) and natural logarithm of the distance was tested. A Manteltest was performed to investigate the association between the genetic distances matrix and the two other matrices. Potential barriers to gene flow were also investigated with Barrier ver. 2.2 (MANNI et al. 2004). Barrier uses geographic coordinates connected by Delaunay triangulation and associates them with genetic distances such as FST. Monmonier’s algorithm implemented in Barrier identifies barriers between areas where differences between pairs of populations are largest. The barrier starts and extends across directly adjacent edges associated with the largest genetic distance. Barrier is extended until it reaches outer edge of the network or meets another barrier. Analyses were performed with pair-wise FST -values for each eight loci separately and average pair-wise FST for all loci. Number of barriers from one to nine were checked to find those which are based on the highest number of loci supporting them. RESULTS Polymorphism, genetic variability of loci and heterozygote deficit within population Nine out of ten microsatellite loci were polymorphic. Only locus L13 was monomorphic and was thus excluded in

Hereditas 149 (2012) Table 2. Average allelic richness (A), observed (HO) and expected (HE ) heterozygosity and inbreeding coefficient (FIS) for each of the localities (localities orderd from north to south). Locality KA NM K MI MIII MII MIV J Total

A

HO

HE

FIS

10.2 9.7 11.5 5.8 10.3 6.2 10.0 13.8 –

0.70 0.71 0.77 0.59 0.70 0.73 0.67 0.71 –

0.87 0.85 0.83 0.74 0.87 0.81 0.85 0.89 –

0.22∗ 0.15∗ 0.08∗ 0.29∗ 0.23∗ 0.17∗ 0.18∗ 0.21∗ 0.19∗

∗P 0.05.

our study. The average allelic richness (A) of each sample ranged from 5.8 to 13.8 (Table 2). One hundred and sixty two alleles were found at eight autosomal microsatellite loci (Table 3). Seventeen alleles were found among the 66 males screened for the L8Y locus. Observed heterozygosity (HO) per locus ranged from 0.48 to 0.91, and over all loci amounted to 0.70. Expected heterozygosity (HE) ranged from 0.58 to 0.95 with an average value of 0.84 (Table 3). The average HO in different localities ranged from 0.59 to 0.77, whereas HE fluctuated from 0.74 to 0.89 (Table 2). FIS observed in each locality ranged from 0.08 (K) to 0.29 (M I), and departed significantly from HWE in five populations: J, KA, MI, MIII and MIV (P 0.001; Table 2). The other three localities showed no FIS-values significantly different from zero. FIS-values for each locus (over all populations) ranged from 0.04 (L9) to 0.37 (L45). Results from the permutation procedure indicated that five out of eight loci significantly

Table 3. Number of alleles (na), observed (Ho) and expected (HE) heterozygosity, inbreeding coefficient (FIS), and fixation index (FST) values, per locus and over all microsatellite loci. Locus

na

Ho

HE

FIS

FST

RST

L9 L14 L33 L45 L67 L68 L92 L97 All

34 19 28 16 12 14 8 31 162

0.91 0.80 0.69 0.49 0.80 0.83 0.48 0.62 0.70

0.95 0.90 0.91 0.76 0.82 0.88 0.58 0.91 0.84

0.05 0.11*** 0.25*** 0.37*** 0.04 0.06 0.24*** 0.34*** 0.18***

0.007 0.018 0.011 0.048 0.031 0.009 0.078** 0.016 0.027

0.005 0.009 0.021 0.027 0.009 0.048** 0.042∗ 0.035 0.010

∗P 0.05, ∗∗P 0.01, ∗∗∗P 0.001.



201

Table 4. Estimates of population genetic differentiation (pair-wise FST, below diagonal) and number of migrants per generation (Nm, above diagonal). Populations K J NM KA MI MII MIII MIV

K X 0.040*** 0.036** 0.014 0.015 0.026* 0.048*** 0.042***

J

NM

KA

MI

MII

MIII

MIV

X 0.012 0.013 0.069*** 0.042** 0.017 0.022*

6.7 20.6 X 0.010 0.058** 0.035* 0.030* 0.031*

17.6 18.9 24.7 X 0.024 0.039* 0.015 0.013

16.4 3.37 4.0 10.2 X 0.061* 0.076*** 0.069**

9.4 5.7 6.9 6.2 3.8 X 0.045* 0.053**

4.9 14.4 8.1 16.4 3.0 5.3 X 0.025

5.7 11.1 7.8 18.9 3.4 4.5 9.7 X

6.0

∗P 0.05, ∗∗P 0.01, ∗∗∗P 0.001.

differed from zero (L14, L33, L45, L92 and L97) (Table 1). FIS estimated over all loci revealed significant departure from HWE expectations (FIS 0.18, P 0.05). A significant heterozygote deficiency may suggest the presence of null alleles. Indeed, the Micro-Checker program detected the presence of null alleles at two loci: L45 and L97. Gene flow and population structure FST over all loci was small and not significant (FST 0.027; Table 3). Locus-specific FST ranged from 0.007 (L9) to 0.078 (L92), and significant genetic differentiation was found only at locus L92 (Table 3). Overall RST reached 0.010, and was smaller than FST. The estimates of RST ranged from 0.027 (L45) to 0.048 (L68) and for the loci L68 and L92, the results were significantly higher than zero (Table 3). Pair-wise estimates of FST varied from 0.010 to 0.076 and most of them were statistically significant, indicating differentiation between pairs of populations (Table 4). In five out of eight pairs of populations, which could be potentially isolated by mountain ridges, significant genetic structuring was evident. The highest and significant value of FST was estimated between MI and MIV (0.069, P 0.01). Weaker, but significant differentiation was found between MI and MII (0.061, P 0.05), MIII and K (0.048, P 0.001), J and MII (0.042, P 0.01), and J and MIV (0.022, P 0.05) (Table 5). An application of R-statistics revealed significant genetic differentiation only between two populations – MI and MII (0.155, P 0.05) (Table 5). Comparing of FST-values in both sexes separately indicated that females had higher FST than males (0.053 and 0.030, respectively), and in both cases the values were significantly higher than zero (P 0.01) (data not shown). The AMOVA analyses based on FST detected genetic differentiation among localities for both autosomal

and L8Y microsatellite loci. The genetic structuring was weak, but significant (P 0.001 for autosomal markers and P 0.05 for the Y-linked marker; data not shown). Partitioning of the total variation into components revealed that 3.23% (for autosomal loci) and 4.48% (for the Y-linked microsatellite) was caused by variability between populations. Both the results of Structure and Geneland seem to confirm our earlier findings indicating one admixed population as genetic structure of analyzed populations. The Delta K plot presented highest peak at K 2. On the other hand, the L(K)-plot showed break in linearity and lowest standard deviation for K 1 (plots not shown). With obvious reasons, the Delta K method is unable to analyze K 1 (it predicts the most likely K-value in a data set based on rates of change between successive K-values – there is no K 0 so there will be no Delta K1–K0). With this limitation it is common that K 2 is chosen with Delta K instead of K 1. Furthermore the probability of assignment of individuals for K 2 is rather intermediate. Individuals with probability higher than 0.8 do barely exceed 14% of all individuals. The results of Geneland analysis clearly indicate and confirm Structure results. Maximum a posteriori estimate of K was assessed for one population (Fig. 3). Table 5. FST and RST for pairs of populations separated by mountain ridges. Pairs of populations K/KA NM/KA MI/K MI/MII MI/MIV J/MIV J/MII MIII/K

Distances (km)

FST

RST

2.3 1.9 3.9 1.5 2.0 5.6 6.0 6.3

0.014 0.010 0.026* 0.061* 0.069** 0.022* 0.042** 0.048***

0.019 0.056 0.017 0.155* 0.019 0.025 0.060 0.007

∗P 0.05, ∗∗P 0.01, ∗∗∗P 0.001.

M. Moska et al.

202


10

0.5

6

Density

Number of classes

8

Number of populations along the chain after burn-in

Y-linked locus

r 0.077 0.316

r 0.379∗ 0.143 0.337∗ 0.123

0.3

∗P 0.05.

0.2

between MII and MIV, MI and MIII as well as between MII and MIII. Barriers between MII/MIV, MI/MII and MII/MIII were supported by five, five and six out of nine matrices, respectively. Connected together they divided analyzed population into two separate subpopulations, between which gene flow was limited.

0.1

0.0

Index of MCMC iteration Whole chain

Autosomal loci

0.4

2

150000

Matrices compared FST barrier FST geographical distance RST barrier RST geographical distance

4

0 50000

Table 6. Results of the Mantel-test over sampling sites.

2

4

6

8 10

No. of pop. along the chain (after a burn-in of 200x10i t.)

Fig. 3. Number of populations simulated from posterior distribution of analyzed data.

Migration, isolation by distance and barrier effect Estimates of the migration rate reflected high levels of exchange among studied localities (Table 4). Values of Nm were calculated only from FST for both types of genetic markers. We could not estimate Nm among populations for L8Y using RST, because its value was zero. The highest number of migrants (Nm 17.6 and Nm 24.7) was estimated for two pairs of localities (K/ KA and NM/KA, respectively) separated by the ridges lower than 1000 m a.s.l. The lowest migration rate (Nm 3.4 and Nm 3.8) was noted for the pairs of populations (MI/MIV and MI/MII, respectively) separated by Smrekowiec Mt (1124 m a.s.l.), whereas a little higher migration rate was found for the localities J/MII (Nm 5.7) and J/MIV (Nm 11.1) separated by Wysoczka Mt (1185 m a.s.l.). The average number of migrants per generation between all samples reached 7.6 shrews for autosomal microsatellites and 5.4 individuals for L8Y. The standard Mantel-test done for autosomal loci indicated nonsignificant correlation between genetic differentiation and both, geographical distance and presence of barriers (mountain ridges from 808 m a.s.l. to 1185 m a.s.l.) (Table 6). For the Y-linked locus we did not find a relationship between genetic differentiation and geographical distance. However, for the Y-linked locus correlations between pair-wise FST- and RST-matrices against barrier matrix were significant (r 0.379, P 0.01 for FST and r 0.337, P 0.01 for RST) (Table 6). Testing for presence of barrier indicated one significant barrier (Fig. 4). Genetic discontinuities were found

DISCUSSION Mountain ranges represent an important physical barrier to the dispersal of the common shrew. To date, detailed studies of the influence of the alpine topography on gene flow and genetic differentiation of this species have been carried out only in the French and Swiss Alps (WYTTENBACH et al. 1999; LUGON-MOULIN et al. 2000; LUGON-MOULIN and HAUSSER 2002; YANNIC et al. 2008). Studies, carried on by LUGON-MOULIN et al. (2000) showed, that high mountain ridges ( 2400 m) can significantly reduce gene flow, shaping genetic structure of isolated populations. The main goal of the present study was to investigate if . landscape features of the Sńieznik Massif, which is much lower than the Alps, affect gene flow among the common shrew populations and shape their genetic structuring. The studied populations of the common shrew, revealed small but significant genetic differentiation among most of them (FST ranged from 0.010 to 0.076). These findings were congruent with results reported by LUGON-MOULIN et al. (2000), who performed the pair-wise sample comparisons for the populations of S. araneus inhabiting the Swiss Alps. They estimated values of FST ranging from 0.007 to 0.120. When we analyzed the genetic differentiation between pairs of populations separated by mountain ridges, using FST- and RST-statistics, FST-values varied from 0.010 to 0.069 and RST-values ranging from 0.012 to 0.155 were found. Comparable values of FST were found in between-valley analysis in the Swiss Alps (FST: 0.013–0.083, RST: 0.018–0.148; LUGON-MOULIN et al. 2000). In the Alps, significant genetic differentiation in five out of six pairs of valleypopulations was found (LUGON-MOULIN et al. 2000). Our results, obtained in much lower the Polish Sudetes, indicate significant genetic differentiation between the following pairs of samples: MI/MII, MI/MIV, J/MII,



KA K 5

MIII

MI 3

6

203

NM

2 3

MII5 MIV 1 2 1

J

Fig. 4. Map presenting barriers localization and their significance (number near barrier represents the number of loci supporting the barrier).

J/MIV and MIII/K. The first two pairs were separated by the ridge of 1124 m a.s.l. (Smrekowiec Mt), J/MII and J/MIV by the ridge of 1185 m a.s.l. (Wysoczka Mt) and . the last pair by the ridge of 1153 m a.s.l. (Zmijowiec Mt). Significant genetic differentiation between MI and MII samples, separated by Smrekowiec Mt was also confirmed by RST-statistics (RST: 0.155, P 0.05). This suggests that these two populations are isolated from each other. Geographical distance did not influence genetic differentiation in the studied populations, as indicated by the Mantel-test. Neither for autosomal loci nor for the Y-linked locus significant correlation between genetic differentiation and geographical distance was found. These results suggest that in the common shrew populations inhabiting the Polish Sudetes, mountain ridges more than geographical distance, may prevent the dispersal of shrews. However, the pair-wise comparison of FST (Table 4) showed significant genetic differentiation between 14 out of 21 pairs of populations that were not separated by mountain ridges. These patterns were not consistent with divergence by distance. Cryptic barriers . to dispersal appear to exist in the area of Sńieznik Massif, allowing for small-scale genetic differentiation. Environmental gradients such as vegetation or climate can function as cryptic barriers to gene flow (YOSHIO et al. 2009). This may reduce migration between coexisting or nearby populations, leading to genetic divergence. According to BERGEK and BJÖRKLUND (2007) cryptic barriers to dispersal exist even in small, apparently homogenous environments.

. In the Massif of Sńieznik, the climate is very severe with long periods of snow cover and short vegetation. Milder climatic conditions and longer vegetation occur in the valleys that separate the ridges of the massif. Thus, factors such as distribution of vegetation and climatic conditions (there are no rivers and lakes in the massif which could be obstacles to gene flow) may have helped genetic structuring of the studied common shrew populations. Analyzes of genetic differentiation between populations revealed, that two most remote populations (J and NM) which were separated by a few mountain ridges showed nonsignificant genetic differentiation (Table 4). In order to elucidate this we investigated the associations between . topographic conditions of the Massif of Sńieznik and the neighbouring Králicky Sneˇžnik (situated in the Czech Republic), and values of FST estimated for each of the studied populations. The results of the study may suggest that shrews from the areas located south of Králicky Sneˇžnik (these individuals may have created a founder population for further spread into Poland) migrated northwards, colonizing the studied area using two ways which bypassed the highest ridges (Fig. 2). First way of migration (“west route”) may have bypassed the . Massif of Sńieznik and the neighbouring ridges from the west, leading through J to MIII. Second way of migration (“east route”) may have led through the Płoszczyna Pass and NM towards MI, bypassing the massif from the east. This hypothesis explains 1) lack of significant genetic differentiation between J and NM (both populations originate from the common ancestors living in the

204

M. Moska et al.

area located south of Králicky Sneˇžnik), 2) genetic similarity between NM and KA, KA and K, KA and MI (“east route” of migration), 3) genetic distinction of J and MIII (“west route” of migration) from NM, KA, K, MI (“east route” of migration). Significant genetic differentiation observed between MI and MIII, would indicate that these samples were taken from populations migrating using different ways. Geographic separation (shown in Fig. 4) of shrews migrating different routes (“east” and “west”) may have promoted their genetic distinction. In our study, values of FIS estimated over all populations and over all loci were very high and significantly different from zero (FIS 0.19 and FIS 0.18, respectively, P 0.001). Five out of eight populations: J, KA, MI, MIII and MIV showed a high and significant heterozygote deficit (Table 2). Potential reasons explaining high values of FIS, i.e. inbreeding, selection or small sample size were probably not important in our study. Previous studies did not show inbreeding of S. araneus (BENGTSSON and FRYKMAN 1990). In the common shrew, an effect of inbreeding is reduced by a special strategy of females, which are highly promiscuous and regularly mate to close relatives (STOCKLEY et al. 1993). The selection also does not seem a good explanation for a significant FIS in our study. All specimens represented the Drnholec chromosome race. It means that among studied individuals, there were no hybrids and possibly only the small number of Robertsonian heterozygotes. In the common shrew, selection against simple heterozygotes is very weak and negligible (SEARLE and WÓJCIK 1998). The deficit of heterozygotes, observed in our study, cannot rather be explained by the small sample sizes (number of individuals ranged from 7 to 27). It is noteworthy that a high FIS can be caused by more than one factor, and sometimes it is difficult to decide which factor is causative (FREELAND 2008). The findings on the relationship between genetic differentiation of the studied populations and presence of barriers were revealing. We found non-significant correlations between pair-wise matrices of FST and RST and the presence of the barrier. This indicates that the ridges . of the Sńieznik Massif have no significant impact on genetic structure of the populations of S. araneus. Low altitudes biotopes seem well connected. On the other hand, for the Y-linked locus, these correlations were significant, both for FST and RST, suggesting restricted malemediated gene flow between the populations (FST 0.379, P 0.01; RST 0.337, P 0.01). Our findings are comparable with results previously reported by different authors, who observed sex-biased dispersal in the common shrew (BALLOUX et al. 2000; LUGON-MOULIN and HAUSSER 2002; ANDERSSON 2004; YANNIC et al. 2008). However, in these studies, the reduced gene flow of males

Hereditas 149 (2012) was observed in hybrid zones, and was interpreted as a classic example of Haldane’s rule (male sterility in F1 hybrids). In our study, all specimens belonged to the same chromosome race (Drnholec), thus the Haldane rule cannot be applied. Thus, there must be another explanation for the barrier effect of relatively low mountain ranges to male-mediated gene flow. Dispersal of individuals often goes along with high costs due to predation, natural death or reduced resources. One of the models describing patterns of dispersal is the Greenwood’s model of competition for resources (GREENWOOD 1980). According to this model, philopatry (the ability to exploit resources and reproduce close to the birthplace) benefits the sex which is responsible for reproductive success. In monogamous species, males benefit more, while in polygynous species, females benefit. It is known that philopatry may promote genetic divergence among populations by limiting gene flow (PIERTNEY et al. 1998). Evidence for multiple paternity in the common shrew was reported by SEARLE (1990). This suggests that dispersal in the shrews is male-biased, in contrast to femalebiased dispersal in monogamous species. Contrary to Searle’s study, our results, suggest that in the Massif of . Sńieznik, females disperse more than males. A similar observation was reported by FIVAZ et al. (2003) who studied postglacial recolonization of the Valais by the Sorex antinorii. The analysis of the Y-chromosome microsatellite showed a nearly complete absence of male gene flow between populations from the Simplon Pass and the St. Bernard Pass. The authors speculated that the long isolation of S. antinorii in Italy may have changed the mating behaviour of this species to monogamy, as observed in Crocidura russula (FAVRE et al. 1997). . The shrews inhabiting the Massif of Sńieznik have not been isolated for a very long time (the Pleistocene glaciers did not reach this area, BIERON´SKI et al. 2007). Thus it is unlikely that the limited male gene flow between studied localities can be attributed to the C. russula-like mating behaviour. When we used autosomal loci to study genetic differentiation in both sexes separately we found that male’s FST was nearly two-fold lower as compared with the female’s one. This shows that males play a leading role in dispersal. Higher mobility of males in the genus Sorex was reported by CANTONI (1993) and CHURCHFIELD et al. (1995). According to these authors, females are considered to be territorial through most of their lifespan. However, the simple relation between FST and migration generally does not hold because of the non realistic assumptions of the underlying island model of migration (BALLOUX and LUGON-MOULIN 2002). One of the explanations for incongruence between autosomal and the Y-linked markers may be a high variance in

Hereditas 149 (2012) male reproductive success. According to LUGON-MOULIN and HAUSSER (2002), this variance may alter the genetic structure of Y-specific markers because of the Ychromosome effective population size (NE). With a balanced sex ratio, the effective population size of the Y-chromosome is fourfold lower than for autosomes so that the effect of genetic drift is more important. The estimated number of migrants per generation (Nm) does not reflect the geographical distance among the studied populations (Table 4). We did not find associations between remoteness of the populations and the migration rate among them. The number of migrants, however, seems to be affected by physical barriers. The lowest number of migrants between the localities separated by Smrekowiec Mt may be the outcome of deforestation of the top parts of the mountain during ecological disaster. In consequence, unsuitable habitats (open fields and dry slopes) for the dispersal of the common shrew have come into being. The limited gene flow among localities separated by Smrekowiec Mt (MI/MII and MI/MIV), confirmed by statistically significant values of pair-wise FST (0.069, P 0.05 and 0.061, P 0.01, respectively) seems to support this hypothesis. In conclusion, the present study revealed that most of the studied populations of the common shrew . inhabiting the Massif of Sńieznik showed little, but significant genetic structuring. We found that malemediated exchange of genes between the populations was restricted by the ridges of the Polish Sudetes. Besides historical processes and life histories, heterogeneous habitats may also alter genetic structure of the populations. Patterns of genetic structuring in the common shrew of . the Massif of Sńieznik may suggest two possible ways of northwards colonization, which promoted genetic distinction of shrews migrating different routes. If dispersal is geographically restricted, we can expect important implications for evolutionary processes in the isolated populations. Acknowledgements – We are very grateful to Patrick Basset for helpful comments on a first version of the manuscript. We also wish to thank A. Jonkisz for laboratory help.

REFERENCES Andersson, A.-C. 2004. Postglacial population history of the common shrew (Sorex araneus) in Fennoscandia. – Acta Univ. Upsaliensis, Uppsala, 2004. Balloux, F. and Goudet, J. 2002. Statistical properties of differentiation estimators under stepwise mutations in a finite island model. – Mol. Ecol. 11: 771–783. Balloux, F. and Lugon-Moulin, N. 2002. The estimation of population differentiation with microsatellite markers. – Mol. Ecol. 11: 155–165.


205

Balloux, F., Ecoffey, E., Fumagalli, L. et al. 1998. Microsatellite conservation, polymorphism, and GC content in shrews of the genus Sorex (Insectivora, Mammalia). – Mol. Biol. Evol. 15: 473–475. Balloux, F., Brünner, H., Lugon-Moulin, N. et al. 2000. Microsatellites can be misleading: an empirical and simulation study. – Evolution 54: 1414–1422. Barton, N. H. and Hewitt, G. M. 1985. Analysis of hybrid zones. – Annu. Rev. Ecol. Syst. 16: 113–148. Bengtsson, B. O. and Frykman, I. 1990. Karyotype evolution: evidence from the common shrew (Sorex araneus L.). – J. Evol. Biol. 3: 85–101. Bergek, S. and Björklund, M. 2007. Cryptic barriers to dispersal within a lake allow genetic differentiation of Eurasian perch. – Evolution 61: 2035–2041. Bieron´ ski, J., Socha, P. and Stefaniak, K. 2007. Deposits and fauna of the Sudetic caves – the state of research. – Karst and Cryocarst, pp. 183–201. Cantoni, D. 1993. Social and spatial organization of freeranging shrews, Sorex coronatus and Neomys fodiens (Insectivora, Mammalia). – Anim. Behav. 45: 975–995. Churchfield, S. C., Hollier, J. and Brown, V. K. 1995. Population dynamics and survivorship patterns in the common shrew Sorex araneus in southern England. – Acta Theriol. 40: 53–68. Dent, E. A. and von Holdt, B. M. 2011. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. – Conserv. Genet. Res. DOI: 10.1007/s12686-011-9548-7 Ver. 0.6.8 Oct 2011. Evanno, G., Regnaut, S. and Goudet, J. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. – Mol. Ecol. 14: 2611–2620. Excoffier, L. 2000. Analysis of population subdivision. – In: Balding, D., Bishop, M. and Cannings, C. (eds), Handbook of statistical genetics. Wiley and Sons, pp. 636–661. Excoffier, L., Smouse, P. and Quattro, J. 1992. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial restriction date. – Genetics 131: 479–491. Excoffier, L., Laval, G. and Schneider, S. 2005. Arlequin ver. 3.0: an integrated software package for population genetics data analysis. – Evol. Bioinfo. Online 1: 47–50. Falush, D., Stephens, M. and Pritchard, J. K. 2003. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. – Genetics 164: 1567–1587. Falush, D., Stephens, M. and Pritchard, J. K. 2007. Inference of population structure using multilocus genotype data: dominant markers and null alleles. – Mol. Ecol. Notes 7: 574–578. Favre, L., Balloux, F., Goudet, J. et al. 1997. Female-biased dispersal in the monogamous mammal Crocidura russula: evidence from field data and microsatellite patterns. – Proc. R. Soc. B 264: 127–132. Fedyk, S., Wójcik, J. M., Che˛tnicki, W. et al. 2008. Invalidation of Stobnica chromosome race of the common shrew, Sorex araneus. – Acta Theriol. 53: 375–380. Fivaz, F., Basset, P., Lugon-Moulin, N. et al. 2003. Postglacial recolonization of the Valais (Switzerland) by the shrew Sorex antinorii: is dispersal sex-biased? A preliminary study. – Mammalia 67: 253–262.

206

M. Moska et al.

Freeland, J. R. 2008. Molecular ecology. – Wydawnictwo Naukowe PWN SA, Warszawa, Poland, in Polish. Gaggiotti, O. E., Lange, O., Rassmann, K. et al. 1999. A comparison of two indirect methods for estimating average levels of gene flow using microsatellites data. – Mol. Ecol. 8: 1513–1520. Greenwood, P. J. 1980. Mating systems, philopatry and dispersal in birds and mammals. – Anim. Behav. 28: 1140–1162. Guillot, G., Mortier, F., Estoup, A. 2005a. Geneland: a program for landscape genetics. – Mol. Ecol. Notes 5: 712–715. Guillot, G., Estoup, A., Mortier, F. et al. 2005b. A spatial statistical model for landscape genetics. – Genetics 170: 1261–1280. Jadwiszczak, K. A., Ratkiewicz, M. and Banaszek, A. 2006. Analysis of molecular differentiation in a hybrid zone between chromosomally distinct races of the common shrew Sorex araneus (Insectivora: Soricidae) suggests their common ancestry. – Biol. J. Linn. Soc. 89: 79–90. Kimura, M. and Otha, T. 1978. Stepwise mutation model and distribution of allelic frequencies in a finite populations. – Proc. Natl Acad. Sci. USA 75: 2868–2872. Lugon-Moulin, N. and Hausser, J. 2002. Phylogenetical structure, postglacial recolonization and barriers to gene flow in the distinctive Valais chromosome race of the common shrew (Sorex araneus). – Mol. Ecol. 11: 785–794. Lugon-Moulin, N., Balloux, F. and Hausser, J. 2000. Genetic differentiation of common shrew Sorex araneus populations among different alpine valleys revealed by microsatellites. – Acta Theriol. 45 (Suppl. 1): 103–117. Manni, F., Guérard, E., Heyer, E. 2004. Geographic patterns of (genetic, morphologic, linguistic) variation: how barriers can be detected by “Monmonier’s algorithm”. – Human Biol. 76: 173–190. Mantel, N. 1967. The detection of disease clustering and generalized regression approch. – Cancer Res. 27: 209–220. Piertney, S. B., MacColl, A. D., Bacon, P. J. et al. 1998. Local genetic structure in red grouse (Lagopus lagopus scoticus): evidence from microsatellite DNA markers. – Mol. Ecol. 7: 1645–1654. Pritchard, J. K., Stephens, M. and Donnelly, P. 2000. Inference of population structure using multilocus genotype data. – Genetics 155: 945–959.

Hereditas 149 (2012) Searle, J. B. 1990. Evidence for multiple paternity in the common shrew (Sorex araneus). – J. Mammal. 71: 139–144. Searle, J. B. and Wójcik, J. M. 1998. Chromosomal evolution: the case of Sorex araneus. – In: Wójcik, J. M. and Wolsan, M. (eds), Evolution of shrews. Mammal Res. Inst., Polish Acad. . Sci., Białowieza, pp. 219–268. Stockley, P., Searle, J. B., Macdonald, D. W. et al. 1993. Female multiple mating behaviour in the common shrew as strategy to reduce inbreeding. – Proc. R. Soc. B 254: 173–179. Weir, B. C. and Cockerham, C. C. 1984. Estimating F-statistics for the analysis of population structure. – Evolution 38: 1358–1370. Wierzbicki, H., Moska, M., Strzała, T. et al. 2011. Do aquatic barriers reduce male-mediated gene flow in a hybrid zone of the common shrew (Sorex araneus)? – Hereditas 148: 114–117. Wiszniowska, T. and Stefaniak, K. 1996. Mammals. – In: Jahn, A., Kozłowski, S. and Pulina, M. (eds), The Massif of . Sńieznik, changes in the natural environment. Polish Ecol. Agency, Warsaw, (in Polish). Wójcik, J. M. 1993. Chromosome races of the common shrew Sorex araneus in Poland: a model of karyotype evolution. – Acta Theriol. 38: 315–338. Wright, S. 1951. The genetical structure of populations. – Ann. Eugen. 15: 323–354. Wyttenbach, A., Favre, L. and Hausser, J. 1997. Characterisation of simple sequence repeats in the genome of the common shrew. – Mol. Ecol. 6: 797–800. Wyttenbach, A., Narain, Y. and Fredga, K. 1999. Genetic structuring and gene flow in a hybrid zone between two chromosome races of the common shrew (Sorex araneus, Insectivora) revealed by microsatellites. – Heredity 82: 79–88. Yannic, G., Basset, P. and Hausser, J. 2008. A new perspective on the evolutionary history of western European Sorex araneus group revealed by paternal and maternal molecular markers. – Mol. Phylogenet. Evol. 47: 237–250. Yoshio, M., Asada, M., Ochiai, K. et al. 2009. Evidence for cryptic genetic discontinuity in a recently expanded sika deer population on the Boso Peninsula, central Japan. – Zool. Sci. 26: 48–53.