Conserv Genet (2014) 15:75–86 DOI 10.1007/s10592-013-0522-7
RESEARCH ARTICLE
High genetic diversity in isolated populations of Thesium ebracteatum at the edge of its distribution range Toma´sˇ Dosta´lek • Zuzana Mu¨nzbergova´ Ivana Placˇkova´
•
Received: 18 July 2012 / Accepted: 7 August 2013 / Published online: 21 August 2013 Ó Springer Science+Business Media Dordrecht 2013
Abstract The aim of this study was to estimate the degree and distribution of genetic diversity within CentralEuropean populations of Thesium ebracteatum—one of the most endangered plant species in Europe. By analyzing allozymes from 17 populations, we estimated the distribution of genetic diversity and suggest the most valuable populations for conservation. Analysis of molecular variance results showed the highest variance existed between populations (54 %), whereas the mean variance within populations was 46 %. A surprisingly low degree of variance (less than 1 %) was found between the six studied regions. We also observed no correlation between geographical and genetic distance, which supports the idea that individual populations are strongly isolated. T. ebracteatum undergoes extensive clonal growth and may survive for very long periods of time without generative reproduction. Consistent with this, we found a strong and significant relationship between genetic diversity and population size. All populations occupying an area greater than 300 m2 showed high genetic diversity, whereas small populations contained less genetic diversity. Therefore, conservation priorities could generally be decided based on population size. Because this species is a weak competitor, existing localities should also be managed to prevent species loss from habitat degradation, by mowing or from time to time otherwise disturbing population areas to create open areas for growth. T. Dosta´lek (&) Z. Mu¨nzbergova´ I. Placˇkova´ Institute of Botany, Academy of Sciences of the Czech Republic, Za´mek 1, 252 43 Pru˚honice, Czech Republic e-mail:
[email protected] T. Dosta´lek Z. Mu¨nzbergova´ Department of Botany, Faculty of Science, Charles University, Bena´tska´ 2, 128 01 Prague 2, Czech Republic
Keywords AMOVA Isozymes NATURA 2000 Santalaceae
Introduction Reductions in the current loss of biodiversity often require active management strategies (Global strategy for plant conservation; http://www.cbd.int/gspc/). Resources are often limited during conservation efforts, and the same levels of funding and effort cannot be applied to all areas. To actively protect species over larger regions, it is necessary to identify and focus specifically on the most endangered species and the most valuable populations within the species. The later can be accomplished by quantifying the organization of genetic variation within a species and selecting those populations that contain the majority of the existing variation (Neel and Ellstrand 2003). For example, highly diverse or differentiated populations could be targeted for protection while genetically poor populations might be targeted for management strategies to restore or increase diversity (e.g., Godt et al. 1996; Petit et al. 1998). However, we often lack information concerning the distribution of genetic diversity for many high-priority species, making such classifications difficult or impossible (European Commission 1992). Sufficient amounts of genetic diversity within populations of rare species are also important for their adaptability to future changes in habitat (Ellstrand and Elam 1993; Reed and Frankham 2003; Johansson et al. 2007). Furthermore, genetic diversity is very often correlated with plant fitness and more genetically diverse populations are thus also more fit (e.g., Reed and Frankham 2003; Leimu et al. 2006; Mu¨nzbergova´ and Placˇkova´ 2010; Ilves et al. 2013).
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Understanding the relative importance of processes that affect genetic diversity within and between populations (e.g., inbreeding, gene flow, genetic drift, and selection) can help predict future risks to loss of diversity and aid in the design of effective conservation strategies for rare taxa. For example, if genetic diversity is primarily contained within populations, fewer populations would need to be conserved to maintain the overall range of variation within a taxon. If this were true, genetic diversity might become a less important criterion for selecting particular populations for conservation compared with other criteria. Alternatively, a taxa with the majority of its variation spread among different populations would require the protection of a larger proportion of existing populations to maintain overall genetic variation (Neel and Ellstrand 2003). Furthermore, the structure of genetic diversity within and between populations has important implications for the development of sampling strategies for species restoration and reintroduction (e.g., Godefroid et al. 2011). An increasing number of studies are attempting to describe the distribution of genetic variation within specific plant species and to use this information to prioritize sites and management strategies for capturing and maintaining maximum genetic variation (e.g., Neel and Cummings 2003; Neel and Ellstrand 2003; Bonin et al. 2007; Dosta´lek et al. 2010; Mandel 2010; Sˇmı´dova´ et al. 2011; Bruetting et al. 2012; Bucharova´ and Mu¨nzbergova´ 2012; Nicoletti et al. 2012). However, there remain many endangered species for which this information is not available. The aim of this study is to assess the nature and distribution of genetic diversity within Central Europe for populations of Thesium ebracteatum Hayne (Santalaceae), a species that is endangered throughout the entire continent (Bilz et al. 2011). More specifically, we address two main questions: (1) What is the distribution of genetic diversity within and between populations of T. ebracteatum in various regions of Central Europe? (2) What are the most valuable populations for conservation within Central Europe?
Materials and methods Study species Thesium ebracteatum Hayne is a perennial and stoloniferous herb with erect stems 10–15 (-25) cm in length (Hendrych 1980). This species is found in various habitats, including fen-like meadows (Grulich 1997, 2002), semiwet or dry meadows (Janchen 1966–1975), termophilous grasslands on acidic sandy soils, heaths on acidic soils (Oberdorfer 2001) and light termophilous oak (or mixed) forests, often along roadsides (Zaluski 2004; Dosta´lek and Mu¨nzbergova´ 2010; Pawlikowski 2011).
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The distribution of this species is continuous from Poland to Russia. In Central Europe (i.e., the Czech Republic, Austria and Germany), T. ebracteatum populations are sparse, with only a few remaining in each country (Hendrych 1969), and the number of localities hosting this species is continuously decreasing. In Germany, there were 79 localities of T. ebracteatum prior to 1950; for some time after 1950, 21 localities were known, of which only 3 exist today (BfN, FloraWeb 2012; Dosta´lek and Mu¨nzbergova´ 2010). In the Czech Republic, at least eight localities of T. ebracteatum were recorded in the twentieth century, today only two localities exist in the Czech Republic. In addition one population used to exist in Slovakia where the species is already extinct (Cˇerˇovsky´ 1999; Prach and Zajı´cˇkova´ 2009). In Poland and Austria, detailed information concerning earlier species distributions are lacking, but it can be concluded from available sources that the number of T. ebracteatum localities is continuously decreasing in both countries (Schratt-Ehrendorfer and Schmiderer 2005; Pawlikowski 2011). Species decline is caused by the drainage of wet habitats and the abandonment of traditional management processes (grazing, in particular) that leads to higher and thicker layers of herbs and shrubs in these habitats, which is not suitable for growth of this species (Jakubowska-Gabara 1993; Herbich 1974; Załuski 2004; Załuski et al. 2009; Schratt-Ehrendorfer and Schmiderer 2005; Pawlikowski 2011). Therefore, the conservation of T. ebracteatum is of high interest in Europe (NATURA 2000, European Commission 1992; Bilz et al. 2011). Sample collection and genetic analyses Genetic diversity of the populations was assessed using allozymes. Allozyme variation was analyzed in 454 T. ebracteatum individuals sampled from 17 populations in 4 countries of Central Europe (Fig. 1; Table 1); nearly all the remaining T. ebracteatum populations in Central Europe were sampled. In Germany, we sampled all 3 remaining populations: Scho¨nwalde and Bredower Forst in Brandenburg and at Bo¨tersheime Heide in Niedersachsen. In the Czech Republic, we sampled the last two remaining populations: Velenka (Cˇerˇovsky´ 1999) and Beˇsˇtı´n (Prach and Zajı´cˇkova´ 2009) in central Bohemia. In Austria, we sampled 2 populations near Seedo¨rfl at Achau, close to Wien (Melzer and Barta 1994). In Poland, we sampled 10 populations in two regions: three in northeast Poland (Biebrza National Park, Bialystok and Podozierany) and seven in central Poland (three localities in Go´rzno-Lidzbark Landscape Park (Go´rzno 1, Go´rzno 2 and Go´rzno 3), Włoclawek, Barbarka, Cierpice and Gajtowo near Torun´). According to a recent Polish national report to the EU (http://bd.eionet.europa.eu/article17/chapter1), 78 total localities of T. ebracteatum should exist in Poland. The
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Fig. 1 A map showing the sampled populations of T. ebracteatum. Code names are given in Table 1
Table 1 A list of the sampled T. ebracteatum populations, including their code names and GPS coordinates (WGS 84) Region
Population
Code
GPS coordinates N
E
AU
Seedorfl 1
AU_SEE1
48.058792
16.387162
AU_SEE2
48.068317
16.396703
CZ
Seedorfl 2 Beˇsˇtı´n Velenka
CZ_BES CZ_VEL
49.806439 50.152019
14.022031 14.902707
Bredower Forst Scho¨nwalde
GE1_BF
52.569158
13.010114
GE1_SCH
51.994612
13.772408
GE1 GE2
Harburg
GE2_HAR
53.338801
9.721889
PL1
Białystok
PL1_BIA
53.174386
23.151777
PL2
Biebrza
PL1_BIE
53.568351
22.738684
Podozierany
PL1_POD
53.019653
23.744443
Barbarka
PL2_BAR
53.055586
18.55572
Cierpice
PL2_CIE
52.995021
18.43802
Gajtowo Go´rzno 1
PL2_GAJ
53.022683
18.422527
PL2_GOR1
53.185876
19.732862
Go´rzno 2 Go´rzno 3
PL2_GOR2
53.149705
19.746165
PL2_GOR3
53.155958
19.779382
Włocławek
PL2_WLO
52.591669
19.017581
AU Austria, CZ Czech Republic, GE Germany, PL Poland
distance between the two furthest populations in Poland is 640 km, whereas in our study, the distance between the two furthest populations in Poland was 356 km. Although we attempted to sample a greater number of populations, we were unable to locate the other remaining localities. Nevertheless, we believe that our samples from Poland are
representative enough for comparison. All sampled populations contained at least 50 shoots of T. ebracteatum with no other occurrences of the species within 1 km. A ‘‘region’’ was defined as a group of populations separated from other populations by at least 150 km. Because T. ebracteatum reproduces clonally and its populations are usually composed of hundreds or thousands of shoots, population size was recorded as the number of m2 covered by at least three shoots of T. ebracteatum; therefore, population size is reported in m2. During June to August in 2006–2009, 30 individuals were sampled per population when possible (Table 2). Plants were randomly chosen on transects across populations (only adult plants were sampled). For populations larger than 50 9 50 m, several transects were established. The minimum distance between samples was 0.5 m for small populations. Samples collected in the field were stored on ice for 24–48 h until allozyme extraction in the laboratory. For each sample, *60–70 mg leaf tissue was mechanically ground with Dowex-Cl (1-X8) and homogenized on ice in 0.6–0.7 ml extraction buffer (0.1 M Tris–HCl pH 8.0, 70 mM 2-mercaptoethanol, 26 mM sodium metabisulfite, 11 mM ascorbic acid, and 4 % (w/v) polyvinylpyrrolidon). Further extraction and electrophoresis was carried out according to the protocols described in Chrtek et al. (2007). The following fourteen enzymes were tested: leucyl aminopeptidase (LAP, E.C. 3.4.11.1), superoxide dismutase (SOD, E.C. 1.15.1.1), aspartate aminotransferase (AAT, E.C. 2.6.1.1), 6-phosphogluconate dehydrogenase (6PGDH, E.C. 1.1.1.44), alcohol dehydrogenase (ADH, E.C.
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1.1.1.1), shikimate dehydrogenase (SKDH, E.C 1.1.1.25), phosphoglucomutase (PGM, E.C. 5.4.2.2), malic enzyme (ME, E.C. 1.1.1.40), esterase (EST, E.C. 3.1.1.-), isocitrate dehydrogenase (IDH, E.C 1.1.1.42), diaphorase (DIA, E.C. 1.6.99.3), phosphoglucoisomerase (PGI, E.C. 5.3.1.9), malate dehydrogenase (MDH, E.C. 1.1.1.37), and glucose6-phosphate dehydrogenase (G-6-PDH, E.C. 1.1.1.49). Six enzyme systems showing high variation and interpretable bands were selected for further analyses, including AAT, 6-PGDH, ADH, SKDH, PGM and DIA. The staining procedures are described in Appendix 1. Data analysis At the species level, we assayed the following 9 polymorphic loci and identified 38 total alleles: Aat-1 (6 alleles), Aat-2 (6 alleles), 6-pgdh-1 (4 alleles), 6-pgdh-2 (3 alleles), Adh (3 alleles), Skdh (4 alleles), Pgm (3 alleles), Dia-1 (3 alleles), and Dia-2 (6 alleles). Because T. ebracteatum is a tetraploid species (Dvorˇa´k 2010), it was rarely possible to determine the exact ratio of alleles based solely on band intensity. Therefore, only the presence or absence of alleles was recorded, and data points were treated as dominant markers (for a similar approach, see Dias et al. 2008). Because of this dominant-data approach, we considered each allele to be a separate locus. In this manner, we analyzed 38 total loci. Using the software program GENALEX 6.2 (Peakall and Smouse 2006), we calculated the following for each population and region: total number of alleles (TA), Shannon’s information index (I), diversity (h), unbiased diversity (uh), proportion of polymorphic loci (P) and mean genetic distance (mean pair-wise phiPT) to other populations (regions). Further genotypic diversity (DG) was calculated using the approach of Godt and Hamrick (1999). DG is maximized (i.e., equal to 1) when each individual has a unique multilocus genotype and minimized (i.e., equal to 0) when the same multilocus genotype is detected within all samples (Manda´k et al. 2005). We also performed a hierarchical AMOVA with populations nested within regions to examine the distribution of variation and connectivity between populations (PhiPT), regions (PhiRT), and populations within regions (PhiPR), which was carried out using GENALEX 6.2 (Peakall and Smouse 2006). The significance levels of variance components were calculated by conducting 999 permutations of the data. Population and regional pairwise phiPT values (an analogue of FST, i.e., genetic diversity among populations) were also estimated using GENALEX 6.2 AMOVA (Breinholt et al. 2009). We also attempted to analyze our data using the STRUCTURE 2.3.4 software program (Pritchard et al. 2000). However, it was very difficult to assign samples or populations to clusters, as there were only very small
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differences in LnP(D) over the K values 1–20, and no clear assignments were compatible with any structures between or within populations or regions. Therefore, we do not discuss these results further in this paper. Principal Coordinate Analysis (PCoA) based on Euclidean distances was carried out using the software program PCO (Anderson 2003) to visualize the distribution of samples based on the presence of absence of alleles in different regions and in different populations within each region. We also determined the number of unique alleles (i.e., alleles detected in only one population or region). A Mantel test (Mantel 1967) was used to test whether the matrix of genetic distances was correlated with the matrix of geographical distances (Poptools 3.2.5; Hood 2011, 10,000 permutations). Correlations between measures of genetic diversity (TA, I, h, uh, P) and population size were tested using linear regression in S-Plus (S-Plus 2000). We used a logarithmic transformation of population size to achieve a better fit with the genetic diversity measurements; log transformations of population size (or area) is often used during correlations with genetic diversity (e.g., Frankham 1996; Hornemann et al. 2012).
Results At the species level, 36 out of the 38 loci that we examined were polymorphic according to the dominant-data approach. All the results are summarized in Table 2. The proportion of loci that were polymorphic within the 17 sampled populations ranged from 0 (Beˇsˇtı´n CZ, Włoclavek PL2) to 0.66 (Velenka CZ). The number of alleles per population ranged from 15 (Beˇsˇtı´n CZ, Włoclavek PL2) to 31 (Velenka CZ). Shannon’s diversity index and the Nei indices of genetic diversity were strongly correlated with the proportion of polymorphic loci. DG was equal to 0 in three populations (Beˇsˇtı´n CZ, Włoclavek PL2, Go´rzno 1 PL2), suggesting that these populations likely consist of a single genetic clone. On the other hand, the populations with the highest DG values were Velenka CZ (0.988), Scho¨nwalde GE1 (0.987) and Biebrza PL1 (0.961); in these populations, most of the sampled individuals belonged to different genetic clones. The populations with the highest mean genetic differentiation from all the other populations (PhiPT) are the two Austrian populations (Seedorfl 1 AU, Seedorfl 2 AU) and Go´rzno 1 PL2 (Table 2). One unique allele was found in each of the three populations with the highest genotypic diversity values (Velenka CZ, Scho¨nwalde GE1 and Biebrza PL1). Analysis of molecular variance (AMOVA) showed highest variance among populations (54 %), whereas variance within populations was 46 %. Surprisingly low
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Table 2 Population characteristics and genetic diversity statistics for 38 loci in 17 populations of Thesium ebracteatum Region
Altitude (m)
Austria (AU) Seedorfl 1 Seedorfl 2 Czech Republic (CZ) Beˇsˇtı´n Velenka
Sample size
TA
I
h
uh
P
DG
phiPT
Habitat type
300
60
21
0.159
0.111
0.113
0.29
0.703
0.273
169
150
30
17
0.025
0.015
0.016
0.05
0.545
0.731
Meadow fragment surrounded by shrubs
173
150 2,004
30 65
17 31
0.016 0.219
0.008 0.133
0.008 0.135
0.08 0.66
0.246 0.788
0.799 0.171
Mown wet meadow
384
4
30
15
0.000
0.000
0.000
0.00
0.000
0.520
Mown wet meadow
184
2,000
35
31
0.258
0.163
0.168
0.66
0.988
0.401
Mown wet meadow
1,150
59
28
0.230
0.144
0.146
0.63
0.973
0.201
35
150
26
22
0.156
0.106
0.110
0.29
0.905
0.363
Unmanaged meadow edge
52
1,000
33
24
0.195
0.126
0.130
0.47
0.987
0.414
Open mixed forest (birch, pine, oak, aspen) next to railway
Germany 1 (GE1) Bredower Forst Scho¨nwalde
Pop. size (m2)
Germany 2 (GE2)
200
30
21
0.079
0.045
0.047
0.24
0.733
0.347
25
200
30
21
0.079
0.045
0.047
0.24
0.733
0.596
780
90
30
0.187
0.114
0.115
0.61
0.930
0.200
Białystok
137
300
30
24
0.174
0.110
0.114
0.45
0.926
0.464
Pine forest on sand next to a road
Biebrza
110
430
30
26
0.167
0.105
0.108
0.39
0.961
0.399
Unmanaged wet meadow
Podozierany
167
50
30
17
0.035
0.022
0.023
0.08
0.467
0.528
Pine forest on sand next to a road
436
150
27
0.155
0.092
0.093
0.50
0.927
0.237
75
300
30
23
0.159
0.104
0.107
0.34
0.954
0.407
Pine and aspen forest next to a railway
Cierpice
62
20
20
17
0.010
0.005
0.005
0.05
0.100
0.611
Road surroundings with oak
Gajtowo
58
8
20
19
0.071
0.051
0.053
0.11
0.674
0.523
Railway surroundings with pine and aspen on sand
Go´rzno 1
153
8
20
16
0.000
0.000
0.000
0.00
0.000
0.640
Go´rzno 2
156
50
20
19
0.054
0.034
0.036
0.13
0.553
0.527
Road surroundings in light forest with oak and birch Road surroundings in light forest with birch and oak
Go´rzno 3
157
20
20
19
0.087
0.059
0.062
0.16
0.711
0.459
Road surroundings in light forest with pine and oak
69
30
20
15
0.000
0.000
0.000
0.00
0.000
0.601
Road surroundings with oak and pine
26.7
36
0.087
0.056
0.058
0.21
0.971
Harburg Poland 1 (PL1)
Poland 2 (PL2) Barbarka
Włoclawek Species mean
Small meadow surrounded by heath
TA total number of alleles, I Shannon’s information index, h diversity, uh unbiased diversity, P proportion of polymorphic loci, DG genotypic diversity, phiPT population (region) mean genetic distance to remaining populations (regions)
variance was observed over the six studied regions (less than 1 %, Table 3). Very low variation among regions and high variation between populations indicate that the genetic differences between closely situated populations are often the same as between distant regions, implying very low gene flow between populations. PCoA showed tendency of samples from some regions to cluster together. Closer inspection, however, always revealed samples from one region mixed with samples from the other regions (Fig. 2). Similarly, populations within regions seem to be partly separated, but some mixing was always present (with the exception of two populations within the Austrian region; Fig. 3).
Genetic differentiation (PhiPT) for all pairs of populations ranged from 0.000 to 0.976. Nearly all (with the exception of 3 low values) of the 136 paired comparisons were significant (Table 4). PhiPT values between populations were not significantly correlated with geographical distance (r = 0.146, P = 0.838). We observed a strong significant correlation between population size and genetic diversity (r = 0.854 for proportion of polymorphic loci (P; see Fig. 4), r = 0.823 for total number of alleles (TA), r = 0.803 for Shannon’s information index (I), r = 0.782 for diversity (h) and r = 0.7793 for unbiased diversity (uh); P \ 0.001 in all cases). Df = 16 in all cases.
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Table 3 A summary of the analysis of molecular variance (AMOVA) results within and between populations of Thesium ebracteatum from Central Europe Source Between regions Among populations within regions
d.f.
SS
MS
Estimated variance
Percentage
Stat
Value
P
5
216.786
43.357
0.011
0%
PhiRT
0.004
0.121
11
406.950
36.995
1.409
54 %
PhiPR
0.538
0.001
Within populations
437
528.749
1.210
1.210
46 %
PhiPT
0.540
0.001
Total
453
1,152.485
2.629
100 %
d.f. degrees of freedom, SS sum of squared observations, MS mean of squared observations, PhiRT proportion of the total genetic variance between regions, PhiPR proportion of the total genetic variance between populations within a region, PhiPT proportion of the total genetic variance between individuals within a population
Fig. 2 Principal Coordinate Analysis (PCoA) of the genetic data based on Euclidean distances given for axes 1 and 2. Symbols indicate regions of origin (see Table 1 for abbreviations)
Discussion This study uncovered a wide range of genetic diversity within populations of T. ebracteatum, suggesting that certain populations still host significant genetic diversity while others are genetically poor. We found a strong correlation between genetic diversity and population size, which is frequently observed in other studies (see Frankham 1996; Leimu et al. 2006 for reviews). Small populations covering only a few square meters often consist of a single genetic clone. On the other hand, all populations covering an area larger than 300 m2 have substantially higher values of genetic diversity, and the most genetically diverse population in the data set was also the largest (Velenka CZ). This is a common pattern for clonally reproducing plant species in which small populations are often formed from a single colonizing genotype. Another reason for low genetic
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diversity is a reduction in population size due to habitat fragmentation, which can lead to strong genetic drift in a population. Leimu et al. (2006) describes two main reasons for the positive relationship between population size and genetic variation. First, this correlation may indicate an extinction vortex, whereby reductions in population size decrease genetic variation. If this reduction leads to inbreeding depression or reduced mate availability, it will consequently reduce plant fitness and lead to a further decrease in population size (Ellstrand and Elam 1993). The negative effects of inbreeding could explain the small population sizes at certain T. ebracteatum localities, which decreased due to land-use changes in these areas. However, at least a few of the very small populations were thriving and did not appear to suffer from inbreeding; for example, the very small population Beˇsˇtı´n CZ consisted of a dense layer of T. ebracteatum shoots (several hundred per square meter). It should be noted that the health of this population was likely positively affected by recent regular mowing at this particular meadow, which demonstrates that, in some cases, appropriate management techniques can play a greater role than the genetic diversity of a population (as shown also by other studies, e.g., de Vere et al. 2009). Another explanation for the low genetic diversity observed in small populations is that these populations consist of only a few genotypes particularly well adapted to local habitat conditions (Hereford 2009; Raabova´ et al. 2011). This can occur for T. ebracteatum because this species shows extensive clonal growth and can survive for very long periods without generative reproduction. The second main reason for the positive relationship between population size and genetic diversity, according to Leimu et al. (2006), is that plant fitness differs between populations due to differences in habitat quality. Such differences will result in variations in population size and may consequently influence the level and distribution of genetic variation (Vergeer et al. 2003; de Vere et al. 2009). In our dataset, there appeared to be no relationship between genetic diversity and habitat characteristics. Both small and large populations occurred in managed meadows and light forests, often next to railways or roads. T. ebracteatum
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Fig. 3 Principal Coordinate Analysis (PCoA) of genetic data from six regions based on Euclidean distances given for axes 1 and 2. Symbols indicate populations of origin (see Table 1 for abbreviations)
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0.805
0.651
0.901
0.759
0.943
0.821
0.726
0.893
Cierpice
Gajtowo
Gorzno 1.
Gorzno 2.
Gorzno 3.
Włoclawek
0.632
Biebrza
Barbarka
0.616
Białystok
Podozierany
0.632
0.606
0.515
Bredower Forst
Harburg
0.342
Velenka
Scho¨nwalde
0.969
0.910
0.973
0.754
0.883
0.976
0.830
0.956
0.643
0.874
0.658
0.626
0.864
0.577
0.703
0.548
–
0.942
1
–
Seedorfl 2 Beˇsˇtı´n
Seedorfl 2
Seedorfl 1
Seedorfl 1
0.000
0.516
0.670
0.000
0.502
0.927
0.534
0.185
0.495
0.544
0.728
0.452
0.404
0.493
–
260
260
Beˇsˇtı´n
0.356
0.335
0.444
0.565
0.412
0.487
0.344
0.447
0.373
0.377
0.258
0.385
0.254
–
74
256
257
Velenka
0.433
0.295
0.308
0.586
0.224
0.355
0.251
0.306
0.233
0.336
0.315
0.293
–
299
315
555
556
Bredower Forst
0.546
0.289
0.390
0.556
0.377
0.386
0.287
0.383
0.327
0.194
0.542
–
82
220
244
475
476
Scho¨nwalde
0.681
0.607
0.595
0.826
0.560
0.745
0.524
0.641
0.524
0.523
–
311
236
503
492
751
751
Harburg
0.576
0.424
0.448
0.628
0.450
0.479
0.406
0.457
0.339
–
892
647
683
660
734
741
742
Białystok
0.459
0.270
0.316
0.534
0.360
0.303
0.209
0.359
–
52
861
627
659
658
731
756
757
Biebrza
0.869
0.366
0.502
0.849
0.349
0.614
0.436
–
91
43
934
684
723
688
762
756
757
Podozierany
0.475
0.167
0.274
0.546
0.426
0.338
–
347
284
307
589
344
377
410
479
575
576
Barbarka
0.968
0.377
0.374
0.952
0.617
–
10
355
293
315
582
335
368
399
468
566
568
Cierpice
0.826
0.446
0.443
0.789
–
3
10
356
293
316
580
335
367
401
470
569
570
Gajtowo
0.000
0.702
0.785
–
89
89
80
268
204
228
665
424
456
474
545
616
617
Gorzno 1
0.837
0.348
–
4
90
89
80
267
204
227
667
423
457
472
543
612
614
Gorzno 2
0.717
–
2
5
92
91
82
265
202
225
669
426
459
474
545
614
615
Gorzno 3
–
81
79
82
62
59
60
321
271
285
628
363
406
394
466
536
537
Włoclawek
Table 4 A matrix of geographical distances (km; above diagonal) and genetic distances (pairwise PhiPT values with probability values based on 999 permutations; below diagonal). All PhiPT values were significant (P \ 0.001), with the exception of the Beˇsˇtı´n/Go´rzno 1, Beˇsˇtı´n/Włoclawek, and Go´rzno 1/Włoclawek pairs
82 Conserv Genet (2014) 15:75–86
Conserv Genet (2014) 15:75–86
Fig. 4 The relationship between population size (log area in m2) and the proportion of polymorphic loci in Thesium ebracteatum (r = 0.854, P \ 0.001)
appears able to grow in a wide variety of habitats, which are most often locations with open spaces maintained by land-use management (e.g., mowing) or created by traffic disturbances. Some studies have also shown differences in genetic diversity between populations from different areas of a species distribution range (see Eckert et al. 2008 for review). Peripheral populations, compared with central populations, are often genetically depauperate, which is caused by chronic genetic drift and low gene flow. On the other hand, peripheral populations can maintain substantial genetic variation and may therefore play a role in the maintenance and generation of biological diversity (Eckert et al. 2008). However, we found no such relationship between genetic diversity and population position within the distribution range of T. ebracteatum. It is possible that all of our sampled populations derive from a previously fragmented region of the overall species distribution range, and we did not include populations located more toward the center of the range in Eastern Europe (for example from Russia). Moreover, we believe that all of the studied populations have likely been isolated for a long period of time and that gene flow is reduced. The weak connection between populations is demonstrated by the fact that T. ebracteatum is pollinated by insects (horseflies) and its seeds are dispersed via autochory (Zaluski 2004; Schratt-Ehrendorfer and Schmiderer 2005), making interactions between populations separated by more than a few hundreds of meters difficult (Wide´n and Wide´n 1990; Darvill et al. 2004). A reduction in gene flow is supported by the strong differentiation between populations within regions, but not between regions, which was shown using analyses of molecular variance (AMOVA). In agreement with the results of AMOVA and in contrast to our expectations, we did not find substantial genetic differentiation between the six studied regions. And
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although there was a tendency of samples from some regions to cluster together in a PCoA analysis, closer inspection revealed no region completely cluster together or is completely separated. In addition, a STRUCTURE analysis completely failed to identify any sensible structure in the data, further supporting the fact that neither regions, nor populations are clearly separated. There was also no correlation between genetic and geographical distance in this species. These findings indicate that one would not expect there to be greater genetic diversity between two populations from different regions compared with two populations from the same region. This pattern is also consistent with a model of strong isolation for individual populations of T. ebracteatum. The relatively small variation within populations was consistent with other studies of clonally reproducing selfcompatible species (e.g., Hensen et al. 2010). However, in contrast with the other studies (e.g., Bucharova´ and Mu¨nzbergova´ 2012), despite the relatively long distance between sampled populations, we found very low genetic differentiation between regions (see e.g., Ilves et al. 2013 and Mu¨nzbergova´ et al. 2013a for a similar pattern). One explanation for this observation could be reduced gene flow over a long period of time as well as relatively high population differentiation within each region. Another explanation could be related to strong founder effects in the populations and their slow dynamics (e.g., Pedersen et al. 2012; Mu¨nzbergova´ et al. 2013b). Final explanation could simply be a lower resolution of our data due to the specific allozyme markers used in this study. Conservation implications In this study, we explored the genetic diversity of populations of T. ebracteatum in Central Europe—the western part of the overall distribution range of this species. While it would be useful to assess the genetic diversity from the central and eastern parts of the T. ebracteatum distribution range (i.e., Russia), conservation strategies are still mainly carried out within individual countries or within the EU. Therefore, measuring genetic diversity for this species within four countries in Central Europe will provide useful background information for determining conservation priorities in this region. All populations occupying an area greater than 300 m2 showed high genetic diversity, indicating that these five populations (Velenka CZ, Scho¨nwalde GE1, Bialystok PL1, Biebrza PL1 and Barbarka PL2) should receive the highest conservation priority. Moreover, three of these populations (Velenka CZ, Scho¨nwalde GE1 and Biebrza PL1) contain unique alleles that were not found in any of other studied populations. These three populations are the largest and the most genetically diverse within Central
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Europe. By maintaining these three populations, we will help to ensure that all of the alleles identified in our analyses are preserved. It was also found that as populations decreased in size, they contained lower amounts of genetic diversity. Therefore, one might conclude that decisions concerning conservation priorities should be decided based largely on population size. However, special efforts to conserve small populations should be carried out in regions where different populations of T. ebracteatum are situated in close proximity (in particular, regions PL2 and AU), as it may be possible to restore gene flow between nearby populations. Currently existing localities should be managed to prevent habitat degradation. Previously published reasons for species decline were the drainage of wet habitats and the abandonment of traditional management techniques (especially grazing), leading to taller and thicker layers of herbs and shrubs within forests, which is not suitable for growth of T. ebracteatum (Jakubowska-Gabara 1993; Herbich 1974; Załuski 2004; Załuski et al. 2009; Schratt-Ehrendorfer and Schmiderer 2005; Pawlikowski 2011). Populations of T. ebracteatum also appear to benefit from land-use practices, such as mowing, occasional traffic, or the use of agriculture machines, which maintain open areas in the localities where T. ebracteatum can establish and grow. Acknowledgments The authors wish to thank all those who provided useful information concerning the species in the field, especially A. Wrobłewska, L. Rutkowski and T. Załuski from Poland; H. Illig, A. Herrmann and D. Gumz from Germany; and D. Prehsler and L. Schratt-Ehrendorfer from Austria. Ade´la Mackova´ and Karin Kottova´ were also a great help in the isozyme laboratory. This study was supported by GAUK 148/2006, FRVSˇ 1897/2006, MSˇMT 2B06178, and partly by institutional projects RVO 67985939 and MSˇMT. We thank the two anonymous reviewers for their useful comments on a previous version of this manuscript.
Appendix 1 The staining procedures were carried out according to Vallejos (1983) for AAT, ADH, DIA, and PGM, and according to Wendel & Weeden (1989) for 6-PGDH and SKDH, with certain modifications. Two staining solutions were prepared for AAT: solution A (20 ml of 0.1 M Tris– HCl, pH 8.4, 240 mg of aspartic acid, and 40 mg of aketoglutaric acid) and solution B (20 ml of 0.1 M Tris– HCl, pH 8.4, 50 mg of Fast Blue BB Salt, 50 mg of Fast Violet B, and 25 mg of pyridoxal-5-phosphate). Solution A was prepared at least 15 min in advance, as the acids are not readily soluble. The gel was rinsed in water and then in Tris–HCl pH 7 buffer. The solutions were then mixed and poured on the gel. The gel was incubated in the dark at 35 °C until bands appeared and then rinsed and fixed with a 1:1:3:5 solution of glycerine, acetic acid, H2O and
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methanol, respectively. To stain for ADH, 30 mg of NAD, 20 mg of MTT and 2 mg of PMS were dissolved in 40 ml of Tris–HCl buffer, pH 7.5, and poured on the gel. Next, 10 ml of cold ethanol was added after 20 min and again after 1 h (20 ml in total). DIA was stained using the following solution: 100 ml of Tris–HCl buffer pH 8, 26 mg of NADH, 10 mg of MTT and 4 mg of 2,6-dichlorophenolindophenol. PGM was stained using the following solution: 50 ml of 0.05 M Tris–HCl, pH 8.5, 24 mg of MgCl2, 100 mg of glucose-1-phosphate, 10 mg of NADP, and 20 units of glucose-6-phosphate dehydrogenase (NADP). The enzyme 6-PGDH was stained using the following solution: 30 ml of 0.1 M Tris–HCl, pH 8.4, 10 mg of 6-phosphogluconate, 5 mg of NADP, 5 mg of MTT, 2 mg of PMS and 30 mg of MgCl2. SKDH was stained using the following ingredients: 30 ml of 0.1 M Tris–HCl, pH 8.4, 30 mg of shikimic acid, 5 mg of NADP, 6 mg of MTT, and 2 mg of PMS. All gels were incubated in the dark at 35 °C until bands appeared. Afterward, all gels were thoroughly rinsed in distilled water, dried between two pieces of cellophane, and stored for later use.
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