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Population Biology |
2Institut für Umweltwissenschaften, Universität Zürich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland; 3Institut für Systematische Botanik, Universität Zürich, Zollikerstrasse 107, CH-8008 Zurich, Switzerland
Received for publication May 15, 2001. Accepted for publication December 11, 2001.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Key Words: calcareous fens • F statistics • genetic variability • Gentianaceae • habitat fragmentation • isozyme electrophoresis • phenotypic fitness measures • population viability • Swertia perennis • Switzerland
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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). Often the result of human activity, habitat fragmentation has two major consequences: populations become more isolated and they become smaller (Saunders, Hobbs, and Margules, 1991
). Isolation can result in strong genetic differentiation between populations, especially between more geographically isolated ones. At the same time, fragmented populations are likely to lose genetic variability by genetic drift, which can increase the inbreeding level of populations (Lacy, 1987
; Shaffer, 1987
; Levin, 1988
; Ellstrand and Elam, 1993
). Inbreeding levels within small populations can further increase (Ellstrand and Elam, 1993
) because of changed abundance or behavior of pollinators (Rathcke and Jules, 1993
; Olesen and Jain, 1994
). Higher inbreeding levels can reduce individual fitness and population viability via inbreeding depression and thus increase extinction risks (Young, Boyle, and Brown, 1996
). The strongest inbreeding depression is expected in outcrossing plant populations of recently reduced size, whereas plants with a longer history of inbreeding may have purged some of their genetic load (Byers and Waller, 1999
). Reduced genetic variability may also increase susceptibility to plant pathogens (Schmid, 1994
; Ouborg, Biere, and Mudde, 2000
). A positive feedback between genetic and demographic factors may even force small, isolated populations into an "extinction vortex" (Gilpin and Soulé, 1986
). Positive correlations between genetic variability, population size, and fitness measures have been reported for a number of rare plant species (Oostermeijer, van Eijck, and den Nijs, 1994
; Young, Boyle, and Brown, 1996
; Fischer and Matthies, 1998
; Fischer, van Kleunen, and Schmid, 2000
). However, little emphasis has been placed on the effects of habitat fragmentation on once widespread species.
We examined the consequences of habitat fragmentation on the population genetics of Swertia perennis L. (Gentianaceae), a specialist of calcareous pre-alpine fens (Caricion davallianae alliance; Ellenberg, 1978
). Wetlands are the habitat of many specialist species, and calcareous fens harbor nearly 50% of all endangered plant species in Switzerland (Landolt, 1991
). Fens belong to the few remaining seminatural ecosystems of central Europe and were traditionally mown in late summer or extensively grazed. In Switzerland wetlands have been largely destroyed and severely fragmented (Hintermann, 1992
); the overall wetland area has been reduced by 90% since 1800 (Broggi and Schlegel, 1989
). Wetlands, in general, and fens, in particular, are thus well suited to investigations of the effects of habitat fragmentation.
The destruction and fragmentation of Swiss wetlands has decreased the number and size of S. perennis populations and increased their spatial isolation. Swertia perennis still grows abundantly in many calcareous fens in northeastern Switzerland; however, we found that 15 of 63 populations of S. perennis in this region had disappeared since 1871. Most of the local extinctions could be attributed to habitat fragmentation and some to changed land use as well (Lienert, Fischer, and Diemer, 2002
). Moreover, we found that habitat fragmentation significantly reduced plant density and fitness (Lienert, Diemer, and Schmid, 2002
) and that S. perennis is highly susceptible to inbreeding depression (J. Lienert and M. Fischer, unpublished data).
In the study reported here, we investigated whether habitat fragmentation also affects the genetic variability and population viability of S. perennis. To clearly distinguish between the two elements of habitat fragmentation, habitat area and spatial isolation, we studied (1) large habitats; (2) small, barely isolated habitats; and (3) small, spatially isolated habitat remnants. We assessed the genetic structure of 17 populations of S. perennis in northeastern Switzerland with isozyme electrophoresis. Moreover, we tested whether measures of isozyme variability were correlated with fitness measures and with the magnitude of herbivory recorded in an earlier study (Lienert, Diemer, and Schmid, 2002
). We asked the following questions: (1) How large is the isozyme variability of S. perennis? (2) How large are population differentiation and gene flow between populations? (3) Is genetic variability reduced in small populations, especially in small, isolated populations? (4) Is genetic variability correlated with fitness measures of S. perennis?
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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]) has a wide but discontinuous distribution from central Europe through Asia to western North America, with a center in the European Alps (Hulten and Fries, 1986
). It can be locally abundant, but is considered endangered worldwide (Jäger and Hoffmann, 1997
). In Switzerland it occurs only in pre-alpine fens north of the Alps, in the region between the Alps and the Swiss lowlands (Fig. 1; Welten and Sutter, 1982
). The Red List classification (see International Union for Conservation of Nature and Natural Resources [IUCN]: www.redlist.org) of S. perennis in Switzerland ranges from "not endangered" to "highly endangered" and even "extinct" (Landolt, 1991
).
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). Vegetative adults have a rosette of ovate leaves and can develop daughter rosettes from lateral meristems, which are usually situated very close to the parent rosette. Reproductive shoots flower from July to August and have a single, 15–60 cm long stem with up to 30 star-shaped, protandrous flowers. The flowers are visited by various insects, including species of Coleoptera, Lepidoptera, Diptera (especially Syrphidae), and Hymenoptera (especially Bombus and Vespidae) (J. Lienert, personal observations). Up to 50 winged seeds develop in one ovate capsule in each flower (Hegi, 1906
). Aboveground plant parts emerge in April/May, and plants die back as soon as the ground is covered with snow, typically in November.
Study design
The hierarchical study design comprised 18 calcareous fens, which were equally divided between two Swiss cantons, St. Gallen (SG) and Schwyz (SZ) (Hooftman et al., 1999
; Fig. 1, Appendix). In each region we used three "fen systems." A fen system consisted of three fens: one large Main island (>5 ha), which was accompanied by two smaller islands (<0.5 ha). The small Near island was located 40–125 m from its Main island, and the small Distant island was separated by ca. 1000 m from both its Main island and another fen. Woodlands, cattle pastures, or intensively used agricultural land separated the individual fens (Appendix). Most fens have been fragmented since at least the 1930s, according to aerial photographs (Bundesamt für Landestopographie, Wabern, Switzerland). Our study design followed Levin's (1988)
assumption that the dispersal of pollen or seeds for more than 1000–2000 m is very unlikely, even over the same habitat type. Hence, the Distant islands were presumably completely isolated, whereas the Near populations were subject to sporadic gene flow. The altitude of all fens was between 900 and 1340 m above sea level, and all fens were mown annually after mid-September. Population sizes of S. perennis ranged from 758 to 118 500 flowering adults on the Main islands and from 7 (but with ca. 20 vegetative adults) to 6738 flowering adults on the Near and Distant islands (Appendix). One Distant fen did not contain any plants of S. perennis. In each fen we randomly chose five experimental plots, each 2 x 1 m, by using random numbers to determine the distance from the center of a fen to each of the plots. Hence, these plots represented similar areas in all populations.
Plant material and fitness measures
We collected leaves from 30 S. perennis plants in each population in August 1998. When possible, we sampled six plants per plot and preferentially selected those tagged for a study on the demography of S. perennis (Lienert, Diemer, and Schmid, 2002
). When this was not possible or when plants had already begun to senesce, we took additional samples from randomly selected plants near the plots. To avoid collecting clones, we made sure that individual plants were separated by at least several centimeters. We stored the leaf samples for a maximum of 4 d at 4°C until further processing.
For our demographic study we assessed the number of rosette leaves, the length of the longest rosette leaf, the occurrence of herbivory of any leaves (two classes: yes/no), the magnitude of herbivory of rosette leaves (amount of leaf loss in six classes), and the aggregation of individual S. perennis plants (two classes: aggregated [three or more plants per 100 cm2] or not aggregated). For flowering plants we also measured the number of stem leaves, the length of the longest stem leaf, the height of the flowering stem from the ground to the top of the plant (i.e., plant size), and the number of flowers per plant. We took the same measures for all plants in the study reported here, including those that were not part of the demographic study.
Isozyme electrophoresis
We ground each sample with 200 µL Tris-HCl grinding buffer-polyvinylpyrolidone (PVP) solution (Soltis et al., 1983
), to which we added 20 µL dimethylsulfoxide (DMSO). The ground material was adsorbed onto wicks of filter paper (2.5 x 19 mm), which we kept in Eppendorf tubes at –78°C for 4 mo at most.
We performed horizontal starch gel electrophoresis with 30 plants per population according to the protocols of Soltis et al. (1983)
and Wendel and Weeden (1989)
. We used a LiOH-borate, pH 8.3 (Soltis et al., 1983
) buffer system and a morpholine/citric acid, pH 6.4 (Werth, 1991
) buffer system. For the latter we used 11.1 mL morpholine instead of 13.4 mL. We prepared the starch gel (12% mass/volume) according to Wendel and Weeden's (1989)
protocol, but mixed the boiling buffer with an ordinary household mixer while adding the dissolved starch.
We chose the following enzyme systems for the LiOH-borate buffer: AAT, ME, NADPH, PGM, and TPI; and for the morpholine/citric acid buffer we chose: G6PDH, GDH, GPI, MDH, PGD, and SKD (Table 1). For AAT and NADPH we used the protocols of Wendel and Weeden (1989)
; for all others, those of Soltis et al. (1983)
. We modified some protocols: G6PDH without MgCl2; GDH with CaCl2·2H2O (Wendel and Weeden, 1989
); GPI and PGM with 7 µL glucose-6-phosphate dehydrogenase; PGM with 200 mg d-glucose phosphate. Staining temperature was always 40°C.
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We scored 17 loci for the 11 screened enzyme systems and found up to three alleles per locus (Table 1). Only Pgd2 had a complicated banding pattern, which was probably caused by a duplicated locus, each with three alleles. For the analyses of genetic variability we considered Pgd2 as two different loci; however, we did not consider Pgd2 in calculating heterozygosities and F statistics. We named loci and alleles according to their electrophoretic mobility (locus a = fast, b = slow).
Enzyme variability and F statistics
To calculate standard measures of genetic variability, we used the computer program BIOSYS-1 (release 1.7; Swofford and Selander, 1989
). We calculated genetic similarity between populations as Nei's (1978)
unbiased identity (I). Moreover, we tested for correlations between genetic and corresponding geographic distances between pairs of populations with a Mantel permutation test (Mantel, 1967
) in GENEPOP (version 1.2.; Raymond and Rousset, 1995
). We calculated summary F statistics (FST, FIS, FIT) and the estimated level of gene flow (Nem, under the assumptions of Wright's [1943]
island model) for each polymorphic locus over all populations, and used the formula Nem = (1 – FST)/4FST, where Ne = effective population size and m = migration rate. Then we calculated FST for each population compared with all 16 other pooled populations, as well as FIS, where FIS = 1 – (Ho/He), and FIT, where FIT = FIS + (1 – FIS)FST (Hartl and Clark, 1989
). We also calculated FST for every population relative to the other two pooled populations of the same fen system. Finally, with a chi-square test, we tested whether frequencies of homozygotes and heterozygotes per population deviated from those expected under Hardy-Weinberg equilibrium.
Within-population genetic variability
We analyzed among-population differences in the number of alleles per polymorphic locus (APp), percentage of polymorphic loci (Pp), observed heterozygosity (Ho), within-population inbreeding coefficient (FIS), and genetic differentiation of populations (FST for every population relative to the other two pooled populations of the same fen system) with the computer program GenStat 5 (release 3.2.; Payne et al., 1993
). Using hierarchical analyses of variance (ANOVAs), we tested for the effects of the two study regions, the six fen systems (three in each region), and the three island types (Main, Near, Distant) in each fen system (test A in Table 4). To test whether the effect of island type was due to spatial isolation (test B) or to small habitat area (test C), we split the two degrees of freedom for the three island types in both possible ways. In test B we first corrected for the effect of island area by testing the Main island against the two small Near and Distant islands. Then we tested the effect of isolation as the difference between the Near island and the Distant island. In test C we first corrected for the effect of spatial isolation by testing the Main and Near islands against the Distant island. Then we tested the effect of island area as the difference between the large Main island and the small Near island. We corrected the significance levels for Type 1 error with a sequential Bonferroni test (Dunn-
idák method; Sokal and Rohlf, 1995
).
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). Since we worked in nature reserves, we could not sample plant biomass. We therefore regarded density, herbivory, and these vegetative fitness traits as indicative of individual fitness. Usually, vegetative measures are closely correlated with reproductive fitness measures (Calvo, 1990
; Schmid et al., 1994
). To see whether effects of habitat fragmentation on fitness traits could have been due to effects on genetic variation, we correlated these traits with the observed heterozygosity of individual plants, with the percentage of polymorphic loci as a measure of the genetic variability of populations, and with FST of each population (against the other two populations of the same fen system) as a measure of genetic differentiation between populations. We tested the relationship between the isozyme measures and all fitness measures with multiple analyses of variance (MANOVA). We corrected the significance levels of the fitness traits for Type 1 error with a sequential Bonferroni test. Here, we used the statistical software SPSS 9.0 (SPSS, Chicago, Illinois, USA). |
RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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0.110, and against the other two populations of the same fen system, it was 0.076 (Table 3B, C). The mean fixation index, FIS, of individuals relative to their population was 0.076 (Table 3A), with a range of –0.194–0.216 (Table 3B). The mean overall inbreeding coefficient, FIT, was 0.194 and ranged from –0.182 to 0.272 among populations.
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Within-population genetic variability
None of the estimates of genetic variability differed between the two study regions or among the six fen systems (Table 4). The mean number of alleles per polymorphic locus (APp; P < 0.05; Fig. 3A) and the mean observed heterozygosity (Ho; P < 0.1; Fig. 3B) were lowest on Distant islands. Tests B and C, which tested for isolation or habitat area effects, indicated that this finding was not explained by small habitat area alone, but was also due to large isolation distance. Moreover, mean FIS was highest and positive on Distant islands (P < 0.1; Fig. 3C), whereas it was negative on Near islands and approximately zero on Main islands.
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10% loss of total leaf area), and 29% had >10% leaf-area loss. The occurrence of herbivory was highly correlated with the magnitude of herbivory (Pearson's chi-square value: 387.14, N = 540, P < 0.001); the length of the stem leaf was highly correlated with both the length of rosette leaf and the stem height (Pearson's correlation: N = 201 and 203, r = 0.409 and 0.577, respectively, P < 0.001); and the number of flowers was highly correlated with stem height (N = 203, r = 0.504, P < 0.001). Therefore, to avoid problems due to collinearity among variates, we dismissed the occurrence of herbivory, length of stem leaf, and number of flowers from our analysis. The observed heterozygosity of individuals (Ho) increased marginally significantly with increasing length of the longest rosette leaf (P < 0.1; Table 5A, Fig. 4A) and was significantly associated with all fitness traits (MANOVA; P < 0.01). On the population level, the percentage of polymorphic loci and FST were significantly associated with all fitness traits (MANOVA; both P < 0.05; Table 5B). The magnitude of herbivory decreased with increasing percentage of polymorphic loci (P < 0.05; Fig. 4B) and increased slightly with increasing differentiation between populations of the same fen system (FST; P < 0.1; Fig. 4C). Mean herbivory was low in all but one Main population, but increased with increasing FST in Near and Distant populations (Fig. 4C).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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, 172 outcrossing, animal-pollinated plant species had mean (±1 SE) A values of 2.0 (±0.07; S. perennis: 1.53), Pp was 50.1 (±2.0; S. perennis: 42.5), and He was 0.17 (±0.01; S. perennis: 0.14).
Genetic differentiation between populations of S. perennis (FST = 0.128) can be compared with the genetic differentiation of other plants measured as GST because FST approximately equals GST when there are only two alleles per locus (Hartl and Clark, 1989
; Swofford and Selander, 1989
). The case of S. perennis was similar to the one of other outcrossing plants: mean GST of 76 predominantly outcrossed plants was 0.118 (±0.036); of 146 sexually reproducing plants, 0.300 (±0.087; Loveless and Hamrick, 1984
), and of 124 outcrossing, animal-pollinated plants, 0.197 (Hamrick and Godt, 1990
). However, FST of S. perennis was high compared with other long-lived plants (mean GST of 48 long-lived plants = 0.077 [±0.027] and mean GST of 43 outcrossing, long-lived perennial plants = 0.068 [Loveless and Hamrick, 1984
]). Ellstrand and Elam (1993)
considered GST values of >0.1 to represent high among-population variation. Generally, FST values of 0.05–0.15 indicate moderate genetic differentiation (Hartl, 1988
). Given the perennial and outcrossing life history of S. perennis, we conclude that genetic differentiation between populations was at least as high as, if not higher than, expected.
Geographic and genetic distances between pairs of populations were not correlated, which follows the distribution of alleles (Table 1). Neither the populations of the two study regions nor those of the same fen system were grouped in clusters (but see Wildhaus Main/Near and Sattel Main/Distant in Fig. 2). Hence, gene flow between geographically close populations (i.e., within fen systems) was not higher than between geographically more distant ones.
The FST value of 0.128 corresponds to a gene flow (Nem) of 1.7 migrants per generation (under the assumptions of Wright's island model, but see Whitlock and McCauley, 1999
). However, even 5–20 migrants per generation may not prevent the loss of genetic diversity within and differentiation between populations (Lacy, 1987
). Furthermore, the measured FST values of the long-lived S. perennis may still partly reflect the situation of the prefragmentation landscape. Today, gene flow is presumably even smaller than our estimate. Hence, it is possible that gene flow between populations of S. perennis is insufficient to counteract the negative effects of habitat fragmentation on genetic diversity.
Genetic variability and inbreeding within populations
Our most important finding is the following: only the combination of geographic isolation and small population size reduced some genetic variability measures of S. perennis populations (Table 4, test B; Fig. 3A, B). Small population size alone did not significantly reduce genetic variability in Near islands, which are close to large islands, and therefore presumably experience much greater gene flow (Table 4, test C). Hence, rare long-distance dispersal to Distant islands is probably insufficient to counteract the negative effects of small population size.
Decreased isozyme variability in smaller populations has been reported in other studies. The number of alleles per locus (A) and the percentage of polymorphic loci (Pp) were most often positively correlated with population size in 10 rare or endemic plant species (Ellstrand and Elam, 1993
) and in 11 of 16 woody species and perennial herbs (Frankham, 1996
). The same relationship was found for Salvia pratensis L., Scabiosa columbaria L. (van Treuren et al., 1991
), Gentiana pneumonanthe L. (Raijmann et al., 1994
), Spiranthes sinensis (Pers.) Ames (Sun, 1996
), and Rutidosis leptorrhynchoides F. Muell. (Young, Brown, and Zich, 1999
). Like S. perennis, these species have become increasingly rare because of habitat fragmentation and destruction. However, population sizes in these studies were often much smaller than ours, the majority of populations often harboring 100 flowering adults (e.g., S. pratensis, G. pneumonanthe, S. sinensis). Moreover, these species are considered rare or endangered in the study regions, although this is not yet the case for S. perennis.
Reports of positive correlations between population size and heterozygosity (He or Ho) are much rarer than those for A or Pp (Ellstrand and Elam, 1993
; Frankham, 1996
; but see Godt, Johnson, and Hamrick, 1996
and Luijten et al., 2000
). In our study, observed heterozygosity (Ho) was also nearly significantly reduced on Distant islands, which indicates relatively pronounced effects of habitat fragmentation on the genetic variability of individual plants. Again, this is especially interesting because population sizes on Distant islands ranged from 7 to 4446 flowering adults (Appendix). Clonal reproduction of S. perennis cannot account for this reduced genetic variability, because the 6% of plants that could possibly have been clonal were evenly distributed among populations (see RESULTS). Hence, genetic variability is already reduced in even fairly large, isolated populations. The largest Distant population (Unteriberg) was larger than its Main population because of the high density of S. perennis (Appendix). Nevertheless, within the fen system of Unteriberg, genetic variability measures were still highest on the Main and Near islands (Table 2), although the Distant population had an excess of heterozygotes (see below).
Inbreeding was presumably responsible for the reduced genetic variability in S. perennis populations, because within-population inbreeding estimated over all populations (FIS = 0.076), and especially of individual populations (FIS = –0.194–0.216), was relatively high. The FIS of outcrossing plants with random mating within populations is expected to be near zero (Hartl and Clark, 1989
). Moreover, FIS was nearly significantly higher on Distant islands in the ANOVA (Table 4, Fig. 3C). Additionally, genetic drift or subsampling may have caused population differentiation (mean FST = 0.128) and contributed to the high total amount of inbreeding (FIT = 0.194). Further, Table 3B shows that all but one of the five Distant populations (the unusually large one in Unteriberg, with 4446 flowering adults) had a heterozygote deficit (FIS > 0), whereas all Near populations had a nonsignificant excess of heterozygotes (FIS < 0; Fig. 3C). Increased FIS values can be caused both by inbreeding (Charlesworth and Charlesworth, 1987
) and by structuring within sample units (Wahlund effect; e.g., Hartl and Clark, 1989
). However, since our sampling procedure was randomized, and since sample areas were similar in all island types, inbreeding in Distant islands is a more likely explanation. The negative FIS values on Near islands could still reflect their historical association with the corresponding Main islands. Inbreeding may also be attributed to less effective pollination in small habitats, which increases selfing mechanisms (e.g., attraction of fewer insects [Sih and Baltus, 1987
] and altered or ineffective pollinator behavior [Heinrich, 1979
; Groom, 1998
]). The Sattel Main island had an unexpectedly high FIS value (0.216). A possible explanation is that pollinator flight distances were shorter by chance, so that less migrant pollen contributed to the total pollen pool in this population (Richards, Church, and McCauley, 1999
).
Most previous studies have focused only on population size, implicitly assuming the same degree of isolation for all populations. As we did in our study, Hall, Walker, and Bawa (1996)
found a negative relationship between genetic variability and geographic isolation for the tree Pithecellobium elegans Ducke. This was not the case for Silene regia Sims (Dolan, 1994
), possibly because of effective long-distance pollen dispersal by hummingbirds. In the forest tree Acer saccharum Marsh. gene flow between populations actually increased after fragmentation, presumably because of enhanced wind flux after fragmentation (Foré et al., 1992
). Likewise, Rutidosis leptorrhynchoides did not show an isolation effect; the observed levels of genetic differentiation may represent the prefragmentation situation (Young, Brown, and Zich, 1999
). This could also be true for S. perennis; hence, the negative effects of fragmentation on genetic diversity could increase in the future. To obtain a more general picture of the relative importance of population size and isolation, we suggest that geographic isolation be considered in future studies of genetic variability of populations.
Correlations between genetic variability and fitness
Isozyme variability of S. perennis was positively correlated with all vegetative fitness traits (but only weakly with single fitness traits) and negatively correlated with the magnitude of herbivory (Table 5, Fig. 4). Because reduced leaf size or vegetative plant mass can lead to reduced reproductive success (e.g., Calvo, 1990
; Schmid et al., 1994
), long-term viability of populations with reduced genetic variability could become destabilized. Indications for positive relationships between population size and genetic variability on the one hand and/or between genetic variability and fitness on the other were found in Gentiana pneumonanthe (Oostermeijer, van Eijck, and den Nijs, 1994
; Oostermeijer et al., 1995
), Gentianella germanica (Willdenow) Börner (Fischer and Matthies, 1998
), Pedicularis palustris L. (Schmidt and Jensen, 2000
), Rutidosis leptorrhynchoides (Young et al., 2000
), and Ranunculus reptans L. (Fischer, van Kleunen, and Schmid, 2000
), but not in Salvia pratensis (Ouborg and van Treuren, 1994
), Lychnis flos-cuculi L. (Hauser and Loeschcke, 1994
), and Arnica montana L. (Luijten et al., 2000
). In L. flos-cuculi only one of four examined populations was small (300 ramets; Hauser and Loeschcke, 1994
), and the self-incompatibility system of A. montana may have reduced the importance of inbreeding depression (Luijten et al., 2000
). All of the above species except L. flos-cuculi are regarded as rare or endangered in the study regions, and their population sizes were often much smaller than those of S. perennis (exceptions are R. leptorrhynchoides and L. flos-cuculi). Therefore, it can be expected that inbreeding effects are stronger than in our fairly large study populations. Ouborg and van Treuren (1994)
suggest that small populations of S. pratensis could be in an early phase of genetic erosion, where the supposed selectively neutral variation of isozymes is already reduced in small populations, but selectively nonneutral variation is only slightly affected. Similarly, isozyme variability of the long-lived S. perennis may not be very representative for variation at fitness loci, which could account for the rather weak correlation of genetic variability with fitness measures in our study.
Increased herbivory in small and genetically less diverse populations may have genetic but also nongenetic causes. Small sites with species-rich vegetation could attract a greater diversity of invertebrates than the surrounding agricultural land (Wettstein and Schmid, 1999
). Moreover, higher plant fitness could be due to better habitat quality in large field sites than in small ones. It is possible that nutrient influx from the surrounding farmland into the smallest fens was increased because of an unfavorable edge-to-perimeter relationship. In contrast, nutrient influx in large fens may be buffered at the edges and not affect the center.
Nevertheless, while nutrient influx could have contributed to reduced fitness in smaller islands, it cannot explain the significant differences in genetic variability between the similarly small Near and Distant sites (Fig. 3) or the positive relationship between genetic differentiation and the magnitude of herbivory in all small sites (Fig. 4C). We conclude that reduced genetic variability may have contributed to reduced fitness of S. perennis in small and isolated populations, but that environmental influences presumably also adversely affect these populations. The observed genetic effects of small population size and isolation suggest that phenotypic effects of reduced genetic variability may become increasingly important for the long-term viability of this species.
Conservation implications
Swertia perennis, a habitat specialist, is negatively affected by habitat fragmentation, and spatial isolation of about 1000 m is sufficient to reduce the genetic variability of small populations of this species. So far, formerly common habitat specialists that, like S. perennis, are still growing abundantly on many sites have attracted far less conservation attention than rare species. However, if more common plant species decline in increasingly fragmented landscapes, rarer species that depend on common ones will inevitably become more threatened. We therefore recommend that future investigations and conservation activities consider the importance of more common plants in preserving biodiversity.
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FOOTNOTES |
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4 Author for reprint requests, present address: EAWAG (Swiss Federal Institute for Environmental Science and Technology), Ueberlandstrasse 133, P.O. Box 611, CH-8600 Duebendorf, Switzerland (judit.lienert{at}eawag.ch
)
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= in lowlands, frequent; 
= in lowlands, rare; 
= in mountains, frequent; 
= in mountains, rare; L = bibliographic reference
= small, isolated Distant island