Conservation Genetics 4: 83–93, 2003. © 2003 Kluwer Academic Publishers. Printed in the Netherlands.
83
Allozyme variability in central, peripheral and isolated populations of the scarce heath (Coenonympha hero: Lepidoptera, Nymphalidae): Implications for conservation Anna Cassel1,∗ & Toomas Tammaru2 1 Evolutionary
Biology Centre, Department of Conservation Biology and Genetics, Uppsala University, Norbyvägen 18D, S-752 36 Uppsala, Sweden; 2 Institute of Zoology and Hydrobiology, University of Tartu, Vanemuise 46, EE-51014 Tartu, Estonia (∗ Author for correspondence:
[email protected])
Received 6 December 2001; accepted 23 March 2002
Key words: allozymes, Coenonympha hero, conservation priority, low variation, peripheral
Abstract Genetic drift tends to lower genetic variability in peripheral and isolated populations. These populations also tend to diverge from more central populations if the degree of isolation is high enough. These processes could have opposite effects on the value of the respective populations in the species conservation context. On the basis of allozyme polymorphism data, we compare genetic variability and differentiation between core, peripheral and isolated populations of the scarce heath, a butterfly endangered in Northern and Central Europe. Genetic variation was lowest in populations that were both peripheral and isolated (P = 16.5%, Hobs = 0.017), and highest in the central populations (P = 35%, Hobs = 0.052). However, overall variability was low also in the core area compared to that of closely related butterfly species. The peripheral region was more differentiated from the other regions than the isolated region (FPC = 0.118, FPI = 0.257, FIC = 0.068). This study indicated that isolation in combination with marginality have caused an erosion of the gene pool. The observed patterns may be caused both by the contemporary population structure of the species, as well as by the colonisation history. Both genetic and ecological evidence suggests that the species is likely to follow the stepping-stone model of dispersal.
Introduction Genetic variation is expected to be lower in peripheral and isolated populations than in central ones. The most obvious reason is the influence of genetic drift in these typically small populations, which is combined with limited gene flow (Nei et al. 1975; Hartl and Clark 1997). Genetic drift may have occurred in the colonisation phase as a consequence of repeated founder events (Sjögren 1991; Stone and Sunnucks 1993; Ibrahim et al. 1996; Malacrida et al. 1998; Hewitt 1999). A similar effect can, however, also be caused by contemporary phenomena such as long-term fluctuations in population size (Motro and Thomson 1982; Sjögren 1991) as well as smaller average population sizes (Lande and Barrowclough 1987; Sjögren-Gulve and Berg 1999).
Genetic effects of isolation are not necessarily limited to the erosion of variability. Peripheral and isolated populations also tend to diverge from more central populations as a consequence of genetic drift and selection. Lesica and Allendorf (1995) have emphasized that these populations may constitute valuable units for conservation because of their unique genetic features. Such populations can thus contribute significantly to intra specific genetic diversity, and may act as units for adaptive responses to environmental changes, and ultimately, for future speciation events (Avise 1994 and references therein). The peripheral and isolated populations may thus have undergone genetic changes that lead to different predictions regarding their value for species conservation. We are not yet able to make a priori judgements about when the ‘positive’ or ‘negative’ effects of isolation
84
Figure 1. Location of populations sampled for allozyme studies in core (Ural), peripheral (Estonia) and isolated (Sweden) parts of C. hero distribution area. The larger dots in Sweden represents two populations, each separated by 150 metres and 3.5 km, respectively. Shaded area denotes total range of the species.
will dominate. There is thus a practical need for descriptive studies on particular endangered species. Such case studies will eventually form the necessary basis for generalizations about the conservation value of populations with a different degree of isolation, or a different colonisation history. The scarce heath butterfly is a species with a wide Pale-arctic distribution. It is found from Japan in the east, through central Russia to Scandinavia and central Europe in the west (Korshunov and Gorbunov 1995; van Swaay and Warren 1999). The distribution is, however, fragmentary in Western and Northern Europe and the species is considered threatened in most countries (Van Swaay and Warren 1999). The populations in Scandinavia are isolated from the main body of the species range (Figure 1), and the species is classified as ‘near threatened’ on the Swedish Red list (Gärdenfors 2000). A low level of allozyme variability was recently documented in two studies of Swedish and Norwegian populations of the scarce heath (Hindar et al. 1997; Cassel unpublished data) Genetic diversity was considerably lower than typically found in closely related species in Scandinavia and other parts of Europe
(Hindar et al. 1997; Porter et al. 1995; Weimers 1994). These findings prompted the present study of allozyme variability, in which we compare the genetic composition of the marginal Scandinavian populations to that of populations representing the core area of the species. Further, the geographically close populations in Estonia (Kesküla 1992) offered a possibility to study if the patterns of genetic variability in these differ from the isolated Swedish populations. The Estonian populations are similarly peripheral but apparently not isolated, since they lie on the margin of a wide continuous distribution of the species. The results of this study allow us to discuss the effect of geographical position on a population’s conservation value. Moreover, the data collected provides insights concerning dispersal patterns in the studied butterfly.
Material and methods Study species The scarce heath (Coenonympha hero Linneaus 1761; Nymphalidae: Satyrinae) is a small (wing span 27–
85 32 mm), slow-flying butterfly that is nowadays predominantly found in a mosaic landscape of forest and small-scale agriculture. The species primarily occurs in managed hay fields or abandoned arable land. Suitable habitats are typically moist, rich in grasses and herbs, and mostly surrounded by forest. Accordingly, each subpopulation is usually well defined, having a semi-open population structure, i.e. a restricted gene flow among subpopulations (A. Cassel, unpublished observations). Rather than actual species composition of the vegetation, microclimatic conditions and the structure of the patch appear to be critical for the persistence of the species (see Berglind 1996 for further details). The scarce heath is a univoltine early summer species, on the wing from the middle of June until the middle of July (central Sweden). Eggs are laid singly on dry vegetation close to the ground and scattered throughout the patch (A. Cassel, personal observations). Larvae feed on various species of grass. They have been successfully raised in captivity on Festuca ovina, Agrostis capillaris as well as Dactylis glomerata. Collection of samples Adult butterflies were collected for genetic analyses from the field during 1996–1999. The three regions studied were central Sweden, Estonia and the surroundings of the city of Yekaterinburg in the Ural Mountains, Russia (Figure 1). In total, thirteen populations were sampled; six from Sweden, three from Estonia, and four from the Ural region. Distances between populations within each region were on average 62 km (ranging from 0.2 to 200 km). A population was defined as a group of individuals inhabiting one easily delimited patch of suitable habitat surrounded by unsuitable matrix, generally forest. The Swedish populations, except the Mora population, were sampled throughout the flight period during two or three seasons. Male butterflies were preferred in order to reduce the negative impact on these vulnerable populations. The Estonian and Ural samples were collected at the peak of the flight period in one season. Sampled individuals were frozen alive and initially stored at –20 ◦ C and subsequently in –70 ◦ C until electrophoresis. Sample sizes ranged from 20 to 43 per population (Table 1). Enzyme analysis All sampled individuals were homogenised in PPS buffer (modified from Nakanishi et al. 1969) and ana-
lysed with standard starch-gel electrophoresis using procedures described by Pasteur et al. (1987) with minor modifications. After an initial screening of 26 enzyme systems, 10 loci were easily scorable and used for screening the total sample. These included malate dehydrogenase (2 loci: MDH, E.C. 1.1.1.37), aspartate aminotransferase (AAT, E.C. 2.6.1.1), phosphoglucose isomerase (PGI, E.C. 5.3.1.9), phosphoglucomutase (PGM, E.C. 2.7.5.1), esterase (2 loci: EST, E.C. 3.1.1.1.), isocitrate dehydrogenase (2 loci: IDH, E.C. 1.1.1.42) and mannose phosphate isomerase (MPI, E.C. 5.3.8.1). Three buffer systems were used; tri-lithium/citrate-borate pH 8.5, tris-citrate pH 6.0 and N- (3-aminopropyl) morpholine/citrate pH 6.1, all modified from Murphy et al. (1990). Histochemical staining recipes were also modified from Murphy et al. (1990). All polymorphic and some monomorphic samples were run at least twice in order to confirm identified electro morphs. Statistical analyses Samples originating from the same population but different years were analysed for differences in allele frequencies. As no significant difference (p = 0.42– 0.95) in allele frequencies between these temporal sub-samples were found, they were pooled. Basic population genetic analyses (heterozygosity, HardyWeinberg segregation, linkage among loci) were done using the softwares Genepop 3.1d (described in Raymond and Rousset 1995) and Biosys-2 (Swofford and Selander, updated release from 1997). Heterozygosity estimates are based on the 10 loci. Weighted Fstatistics (Weir and Cockerham 1984) were calculated to describe variation among populations and hierarchical F-statistics (Wright 1978) to describe genetic sub-structuring among regions (Biosys-2). Pair-wise comparisons of allelic richness, observed heterozygosity and differentiation among regions were tested for significance using FSTAT (Goudet 1995). Test procedure is described therein. A principal component analysis (PCA) on allele frequencies was performed to visualise genetic differences among populations (Rohlf 1998). For comparison genetic distances were also estimated (Nei 1978) and a summary UPGMA phenogram was produced, using PHYLIP (Felsenstein 1989). Level of genetic differentiation was further correlated to geographic distance to assess dispersal pattern. Because distances between regions were of several magnitudes larger than actual dispersal distances and because of low number of populations
86 Table 1. Allele frequencies for six allozyme loci in 13 populations of Coenonympha hero. Deviations from Hardy-Weinberg expectations are included in the table. Populations and loci showing significant heterozygote deficiency are indicated with D (∗ = p < 0.05, ∗∗ = p < 0.01, ∗∗∗ = p < 0.001) population Locus/ allele
Drögsnäs Rönningen Djupdalen Fallet Hagge Mora Sonda
Laelatu Tähtvere Sportivnaya Il’movka Krasnoyar Merkitasikha
MDH (n) 43 1 0.000 2 1.000
33 0.000 1.000
42 0.000 1.000
31 25 20 26 0.000 0.000 0.050 0.000 1.000 1.000 0.950 1.000
30 0.000 1.000
32 0.000 1.000
23 0.000 1.000
29 0.017 0.983
36 0.000 1.000
38 0.000 1.000
AAT (n) 1 2 3
43 0.000 1.000 0.000
33 0.000 1.000 0.000
43 0.000 1.000 0.000
31 0.000 1.000 0.000
25 0.000 1.000 0.000
20 0.000 1.000 0.000
26 0.000 0.981 0.019
30 0.000 1.000 0.000
32 0.000 1.000 0.000
21 0.024 0.976 0.000
29 0.017 0.983 0.000
36 0.014 0.986 0.000
38 0.000 1.000 0.000
PGM (n) 1 2 3
43 0.023 0.977 0.000
33 0.015 0.985 0.000
43 0.012 0.988 0.000
31 0.000 1.000 0.000
25 0.100 0.900 0.000
20D∗ 0.150 0.850 0.000
26 0.500 0.481 0.019
30 0.183 0.817 0.000
28 0.375 0.482 0.143
26 0.135 0.865 0.000
30 0.133 0.867 0.000
36D∗ 0.125 0.875 0.000
37D∗ 0.203 0.770 0.027
PGI (n) 1 2
44 0.886 0.114
33 0.864 0.136
43 0.895 0.105
31 25 20 26 0.984 1.000 1.000 1.000 0.016 0.000 0.000 0.000
30 1.000 0.000
32 1.000 0.000
23 1.000 0.000
29 0.983 0.017
36 1.000 0.000
38 1.000 0.000
EST (n) 27 1 0.000 2 1.000
31 0.000 1.000
26 0.000 1.000
31 22 15 25D∗ 0.000 0.000 0.000 0.080 1.000 1.000 1.000 0.920
21 0.000 1.000
28D∗∗∗ 21D∗∗∗ 0.214 0.119 0.786 0.881
28D∗∗ 0.286 0.714
32 0.156 0.844
30 0.167 0.833
IDH (n) 43 1 0.000 2 1.000
33 0.000 1.000
40 0.000 1.000
31 25 20 26D∗∗∗ 30 0.000 0.000 0.000 0.038 0.000 1.000 1.000 1.000 0.962 1.000
32 0.000 1.000
29 0.017 0.983
36 0.069 0.931
38D∗∗∗ 0.079 0.921
represented in Estonia and Ural, we only present data from the Swedish region. To test for isolation by distance we performed Mantel tests according to Sokal and Rohlf (1998) with 1000 random permutations (Genepop 3.1d). Significance was tested with Spearman Rank correlation.
Results Genetic variability Five of the 10 scorable loci were polymorphic in at least one population, using the p < 95% frequency criterion for the most common allele (Table 1). All populations shared the same predominating allele at all loci (Table 1) and the mean number of alleles per population ranged from 1.1 to 1.6 (averaged over all loci, Table 2). Allelic richness was significantly
21 0.095 0.905
lower in Sweden compared to both Estonia and Ural (p = 0.048 and 0.001, respectively. One-sided test, 1000 permutations). There was no difference between Estonia and Ural (p = 0.22). Two private alleles (alleles unique for populations within a region) were found in the AAT locus. One allele was found exclusively in the Sonda population in Estonia and one in three of the populations in the Ural region (Table 1). The Ural populations had the highest level of polymorphism with an average of 35% polymorphic loci. The lowest levels, 16.7%, were found in the six Swedish populations (Table 2). The three Estonian populations were intermediate with on average 20% polymorphic loci. Expected heterozygosity was 0.073 (± 0.004), 0.068 (± 0.020) and 0.022 (± 0.004) for the Ural, Estonian and Swedish populations, respectively. The observed heterozygosity was 0.052 (± 0.002) for Ural, 0.051 (± 0.011) for Estonia and 0.018 (± 0.004) for Sweden. Observed heterozygosity was significantly higher in
87 Table 2. Mean sample sizes, number of alleles and percentage polymorphic loci of ten allozyme loci in 13 populations of Coenonympha hero. Observed and expected heterozygosity are shown in the last columns together with asterixes indicating significant heterozygote deficiency, ∗ = p < 0.05, ∗∗ = p < 0.01, ∗∗∗ = p < 0.001. Values in parentheses are standard deviations of the mean estimates. Populations belong to the regions Sw = Sweden, Es = Estonia and Ur = Ural, respectively. ◦ A locus is considered polymorphic if the frequency of the most common allele does not exceed 0.95. ◦◦ Unbiased estimate (Nei 1978) Population
Mean sample size per locus
Mean no. of alleles per locus
Percentage of loci polymorphic◦
Mean heterozygosity Direct-HdyWbg count expected◦◦
Drögsnäs, Sw
40.4 (1.7)
1.2 (0.1)
20.0
0.027 (0.023)
0.025 (0.020)
Rönningen, Sw
30.9 (1.5)
1.2 (0.1)
20.0
0.024 (0.021)
0.027 (0.024)
Djupdalen, Sw
37.5 (2.3)
1.2 (0.1)
20.0
0.019 (0.016)
0.021 (0.019)
Fallet, Sw
30.9 (0.1)
1.1 (0.1)
10.0
0.003 (0.003)
0.003 (0.003)
Hagge, Sw
23.6 (0.9)
1.1 (0.1)
10.0
0.012 (0.012)
0.018 (0.018)
Mora, Sw
18.9 (0.7)
1.2 (0.1)
20.0
0.020 (0.013)
0.036∗ (0.027)
Sonda, Es
23.9 (1.5)
1.5 (0.2)
30.0
0.054 (0.042)
0.079∗∗∗ (0.052)
Laelatu, Es
28.2 (1.2)
1.1 (0.1)
10.0
0.030 (0.030)
0.030 (0.030)
Tähtvere, Es
28.0 (1.9)
1.3 (0.2)
20.0
0.068 (0.060)
0.096∗∗ (0.067)
Sportivnaya, Ur
21.3 (1.2)
1.4 (0.2)
30.0
0.048 (0.025)
0.068∗ (0.032)
Il’movka, Ur
28.0 (0.9)
1.6 (0.2)
50.0
0.055 (0.026)
0.079∗ (0.044)
Krasnoyar, Ur
33.5 (1.4)
1.4 (0.2)
30.0
0.049 (0.024)
0.065∗ (0.033)
Merkitasikha, Ur
34.5 (2.1)
1.4 (0.2)
30.0
0.056 (0.034)
0.080∗∗∗ (0.044)
Estonia and Ural compared to the Swedish regions (p = 0.006 and p = 0.002, respectively, Figure 2). However, there were no significant difference in heterozygosity between Estonia and Ural (p = 0.53, Figure 2). The populations in Estonia showed larger differences in variability than the populations in the other areas. Laelatu, on the west coast of Estonia had the lowest level of variability of the three Estonian populations.
Deviations from Hardy-Weinberg segregation and linkage equilibrium There was a significant deficiency of heterozygous individuals when tested over all loci and all populations (multi-sample score test p < 0.0001, Raymond and Rousset 1995). When single populations were tested over all loci, none of the Swedish popu-
88
Figure 2. Observed heterozygosity (± S.E.) for 10 allozyme loci plotted against longitudinal position for 13 populations of C. hero. Populations originate from three parts of the species distribution; one core (Ural), one peripheral (Estonia) and one isolated (Sweden). ∗∗ = p < 0.01 indicating that Sweden had significantly lower heterozygosity than both Estonia and Ural.
lations deviated from Hardy-Weinberg expectations. All Ural populations and two of the three Estonian populations showed significant heterozygote deficiency when tested over all polymorphic loci. The proportion of significant departure from HW distribution was larger than expected by chance at the 5% significance level. Excess of heterozygosity was never observed. One should not, however, conclude that the departure from the HW equilibrium was different in different regions: the lower levels of statistical significance in Swedish populations are explainable by a weaker statistical power in the case of lower genetic diversity. Consistently, the population-specific Hobs/Hexp ratios did not differ between the regions (Kruskall-Wallis test, df = 2, χ 2 = 0.73, p = 0.64). MDH and IDH showed significant linkage disequilibria when tested across all populations. Since IDH showed higher level of polymorphism, and thus carries more information, MDH was excluded from further analyses. The other loci segregated independently. Population and regional differentiation Estimates of Wrights F-statistics showed high deviations from random mating (FIT = 0.360 ± 0.071, Jack-knife estimates over loci with S.D.), which was due to both differentiation between populations (FST =
0.141 ± 0.040) and heterozygote deficiency within local populations (FIS = 0.258 ± 0.093; Table 4). More variation was found between regions (FRT = 0.086) than between populations within regions (FSR = 0.043). Fixation index was also calculated for region pairs (Table 5). Largest differentiation was found between Sweden and Estonia and lowest between Estonia and Ural. Differentiation was not in accordance with geographic expectations. Further, a principal component analysis (PCA) generated five principal components that explained 99% of the allelic variation. PC1 explained 73% being primarily correlated positively with the frequencies of the common allele in PGM and the rare allele in PGI (Figure 3). PC2 explained 20% and correlated primarily positively with the frequency of the common allele in EST. The PCA also showed that not all populations clustered in agreement with geographic expectations. The Tähtvere and Sonda populations from Estonia deviated from all other populations (Figure 3). The third Estonian population, Laelatu, on the other hand showed a strong genetic resemblance with the populations from Dalarna in Sweden (Figure 1). This is also confirmed by low FST values (Table 3) and the UPGMA phenogram (Figure 4). The four populations from the Värmland County in Sweden appeared in a clearly separate cluster, largely due to separation in
89 Table 3. Pair wise weighted Fst’s (Weir and Cockerham 1984) for 13 populations of Coenonympha hero, measured over five polymorphic allozyme loci
Rönningen Djupdalen Fallet Hagge Mora Sonda Laelatu Tähtvere Sportivnaya Il’movka Krasnoyar Merkitasikha
Dr.
Rö.
Dj.
Fa.
Ha.
Mo.
So.
La.
Tä.
Sp.
Il’
Kr.
–0.012 –0.011 0.050 0.064 0.086 0.357 0.114 0.308 0.095 0.170 0.098 0.132
–0.011 0.072 0.082 0.100 0.350 0.128 0.302 0.100 0.170 0.103 0.134
0.041 0.068 0.097 0.372 0.128 0.314 0.095 0.167 0.096 0.134
0.073 0.133 0.420 0.158 0.346 0.108 0.201 0.101 0.143
–0.023 0.246 0.007 0.214 0.030 0.123 0.041 0.061
0.177 –0.022 0.153 0.015 0.102 0.030 0.034
0.162 0.016 0.156 0.184 0.171 0.089
0.149 0.030 0.117 0.042 0.039
0.121 0.106 0.126 0.057
0.023 –0.020 –0.011
0.009 0.012
–0.006
Table 4. Summary of F-statistics using five polymorphic loci in 13 populations of C. hero. S.E. for each locus are shown within parentheses. S.D. was calculated for the estimated values over all loci Locus
FIS
AAT –0.003(0.000) PGM 0.199(0.063) PGI 0.038(0.092) EST 0.468(0.129) IDH 0.303(0.253) Jackknife estimate over loci: Mean (S.D.) 0.258(0.093)
FST
FIT
–0.001(0.003) 0.179(0.068) 0.065(0.016) 0.107(0.040) 0.032(0.012)
–0.004(0.002) 0.340(0.055) 0.100(0.086) 0.526(0.119) 0.324(0.239)
0.141(0.040)
0.360(0.071)
PC1. The Ural populations were also separate from the other populations, though not as clearly. There was a significant positive correlation between genetic and geographic distance within the Swedish region (p = 0.016, Figure 5). Discussion The present study confirmed the pattern of low genetic variability found in earlier studies of the Scandinavian populations of the scarce heath (Hindar et al. 1997; A Cassel unpublished data). The levels of allozyme polymorphism are considerably lower than typically found in other butterfly species in Europe (Brookes et al. 1997; Hindar et al. 1997; Johannesen et al. 1996; Porter et al. 1995; Schmitt and Seitz 2001; Vandewoestijne et al. 1999; Wiemers 1994). The level
Table 5. F-statistics describing differentiation among three regions of C. hero. ∗∗ indicate significant differentiation at the 1% level after Bonferroni correction Regions populations
No. of component
Variance
Fregion−total
Sweden/Estonia Sweden/Ural Estonia/Ural
6/3 6/4 3/4
0.129 0.055 0.052
0.257∗∗ 0.118∗∗ 0.068∗∗
of genetic variability was higher in the peripheral Estonian and the core Ural region. This pattern was apparent in terms of the number of polymorphic loci, number of alleles, as well as the level of heterozygosity. However, genetic diversity was also low in the species’ core area compared to that which has been documented in related butterfly species (Hindar et al. 1997; Porter and Geiger 1988; Porter et al. 1995; Wiemers 1998; Wiernasz 1989). Thus, the low genetic variability of the Scandinavian populations appears to result only partly from their geographically peripheral and isolated situation. The reasons for the overall low genetic variability in the scarce heath remain unclear but they may be related to the post-glacial colonization history. Although central with respect to the contemporary distribution of the species, the sites in the Ural mountains could not have been inhabited during the ice age (Hewitt 1996, 1999), and subsequent (re) colonisation may have eroded the genetic variance (see Pamilo 1999, for a review of the effects of post-glacial colonisation on genetic variability).
90
Figure 3. First and second principal components based on allele frequencies from five allozyme loci for 13 populations of C. hero explained 93% of the variation found among populations. Amount variation explained by each component is shown in parentheses.
Figure 4. UPGMA phenogram based on unbiased genetic distances (Nei 1978) of 13 populations of C.hero.
91 Regarding level of differentiation, the peripheral and isolated Swedish populations showed low divergence from the core populations in the Ural region. At all loci studied, the Swedish populations shared the same predominating alleles with the Ural populations. Furthermore, no private allele was found in the isolated populations (Table 1). Quite unexpectedly, differentiation was more apparent between Sweden and Estonia (Table 5). There were further indications that the situation is somewhat different with the peripheral but non-isolated Estonian populations compared to the Swedish populations. In particular, the Estonian populations were more scattered in the PCA analysis (Figure 3), suggesting that the intermediate position (peripheral but non-isolated) creates a potential for differentiation in different directions. The populations of Estonia may occasionally receive immigrants from more central and variable parts, but may still be isolated enough to allow populations to diverge. These results may be seen as consistent with the hypothesis suggesting that peripheral populations may have a higher conservation value due to a higher degree of genetic differentiation (Lesica and Allendorf 1995). However, our results cannot be regarded as strict proof of this hypothesis. This is because the limited distances between the populations do not allow them to be treated as true replicates. It is tempting, however, to speculate that the ‘positive’ effects of the peripheral positions were overridden by the ‘negative’ ones in the isolated Swedish populations. We cannot, however, exclude the possibility that the observed lack of uniqueness in Sweden may be a consequence of the choice of genetic marker. Allozymes have a slow mutation rate (of the order of 10−6 per generation, Voelker et al. 1980) and the Swedish populations are also rather young because of the glaciation 10 000 years ago (Vasari 1986). More rapidly evolving parts of the genome, and parts subjected to strong selection, may very well show more uniqueness than the allozymes studied here (Bossart and Scriber 1995; Tregenza and Butlin 1999). For example, Karhu et al. (1996) found significant genetic differentiation in the date of bud set among Finnish populations of Scots pine (Pinus sylvestris L.). However, this differentiation was not reflected by the variability in various molecular markers. Corresponding microsatellite and morphological studies are currently underway to obtain a more complete picture of interpopulation differentiation in the scarce heath. In a recent study, Cassel et al. (2001) found that the species is sensitive to inbreeding in this region.
Offspring of females from large populations demonstrated high survival and growth rates, while offspring from small and isolated populations had significantly reduced rates of survival and growth. These results indicate that large sub populations carry sufficient genetic variability to buffer against deleterious alleles (Allendorf and Leary 1986), while the smallest populations suffer from increased homozygosity expression of recessive deleterious alleles (Wright 1977). It is thought that populations that have gone through bottlenecks should be purged of lethal alleles though mildly deleterious alleles can be fixed (Hedrick 1994). The high survival and growth rates in the large Swedish populations of the scarce heath, in combination with the obvious reduction in fitness in the isolated populations do not support the possibility that the Swedish populations have gone through such severe bottlenecks. The correlation between geographic and genetic distance in the Swedish populations suggests a stepping-stone like dispersal pattern rather than irregular long-distance dispersal (Ibrahim et al. 1996). The stepping-stone model is also supported by the general distribution pattern found in butterflies of the genus Coenonympha. In particular, the genus is characterized by notably sharp distribution boundaries in northern Europe (Marttila et al. 1990; Kesküla 1992). For example, the scarce heath reaches the northern coast of Estonia as a moderately abundant species, while only one single stray individual has been taken in Finland (Marttila et al. 1990). Most other butterfly genera have much less distinct, fluctuating distribution boundaries, and are characterized by the frequent occurrence of stray individuals far outside their main range. The absence of special genetic features in the isolated populations of the scarce heath may be a consequence of the colonisation history. The arguments presented above suggest that the post-glacial dispersal in the scarce heath did not rely mainly on longdistance dispersers, which would have implied the generation of severe bottlenecks. We have not found isolated populations with radical changes in allele frequencies, but instead a pattern consistent with a stepwise colonization history (Stone and Sunnucks 1993, Ibrahim et al. 1996; Keyghobadi et al. 1999; Schmitt et al. 2000). Rather, the apparent isolation may be a recent secondary consequence of habitat destruction and fragmentation related to reforestation and intensified agricultural land use in the last centuries (Swedish National Atlas 1992).
92 This study also revealed that there was a significant deficiency of heterozygotes compared to HardyWeinberg expectations in all the Ural populations, in two of the Estonian populations, and one of the Swedish populations. Homozygote excess can be a consequence of subdivision within the patch (causing Wahlund effect), biased sampling of certain broods, or selection pressure. We believe that subdivision of the population within the patch is the most likely explanation. This is indicated by the fact that different loci deviate from H-W in different populations, seemingly at random. Even if selection may be an additional force, the seemingly random pattern suggests that it is not the main cause. The differences in level of homozygote excess between the regions do not necessarily indicate differences in species ecology between regions, but should rather be ascribed to the lower statistical power associated with low genetic diversity. In conclusion, genetic variability at the allozyme level differed between C. hero populations from a central, peripheral and isolated part of the species distribution. Variability was highest in the core area and lowest in the isolated populations. Overall variability was lower than that found in related butterfly species. Further, the populations in the peripheral region were more clearly differentiated from the other regions than the isolated populations in Sweden, and might thus have a higher value for conservation purposes. It also indicated that isolation in combination with marginality has caused further erosion of the gene pool. Future studies using markers with higher mutation rates, and morphological measures, will determine whether this pattern is consistent on other genetic levels.
Acknowledgements We wish to thank Alexey Makhanek, Erki Õunap, Göran Ripler, Björn Cederberg, Jörgen Gulve and Dag Alfredsson for valuable help with collection of the material. We also want to thank Adam Porter, Diane Wiernasz and colleagues at the Department of Conservation Biology and Genetics for guidance and tips concerning the electrophoresis analyses. We are further very grateful to Per Sjögren-Gulve, Thomas Schmitt, Pekka Pamilo and two anonymous reviewers for constructive comments on the manuscript. This work was supported by WWF (World Wildlife Fund for Nature), the Swedish Environmental Protection Agency and Ebba and Sven Schwartz Foundation.
T. Tammaru was supported by the Estonian Science Foundation grant 4076.
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