• floral odour;
  • scent;
  • phenylacetaldehyde;
  • pollen transfer;
  • reproductive isolation;
  • Silene dioica;
  • Silene latifolia;
  • species boundaries


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References

Mechanisms preventing interspecific pollination are important in closely related plant species, in particular when post-zygotic barriers are weak or absent. We investigated the role of floral odour in reproductive isolation between the two closely related species Silene latifolia and S. dioica. First, we tested whether floral odour composition and emission differed between the species. We found significant odour differences, but contrary to expectations, both species showed a rhythmic emission of the same compounds between day and night. Second, in a field experiment, odour of the two species was made more similar by applying phenylacetaldehyde to flowers. This manipulation led to higher pollen-analogue transfer between species, revealing that floral odour differences are important for maintaining reproductive isolation. We conclude that differences in single key compounds can reduce pollen transfer across species boundaries by pollinators and demonstrate that odour differences are an important component of premating floral isolation between closely related plant species.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References

The evolution and maintenance of reproductive isolation is of central interest in evolutionary biology because it is essential for speciation and the maintenance of species integrity (Coyne & Orr, 2004). Reproductive barriers involve both pre- and post-zygotic mechanisms. Ecological habitat differences, pollinator-mediated reproductive isolation, and pollen competition are pre-zygotic barriers, whereas examples of post-zygotic barriers are genetic incompatibility, decreased hybrid viability and low hybrid fertility (Muller, 1942; Stebbins, 1950; Dobzhansky, 1951; Grant, 1981). In closely related plant species that grow in sympatry or parapatry and have weak intrinsic reproductive barriers, species integrity relies in particular on successful prevention of interspecific pollination, and thus on pre-zygotic barriers.

Interspecific pollination of closely related, insect-pollinated plant species can be prevented by floral isolation, which can be brought about by either morphological or ethological components (Grant, 1994). If the flower structure between two plant species differs, so that the pollinating insects of one species are unable to enter or pollinate the flowers of the second species, then this is known as morphological or mechanical isolation (Dobzhansky, 1951). Ethological isolation occurs when cross-pollination is prevented through pollinator behaviour (e.g. specific attraction through floral signals). Colour, shape, size and odour are such signals that can contribute to ethological reproductive isolation between species (reviewed by Grant, 1994).

Although the roles of floral colour, shape and size have been thoroughly investigated in the past, less is known about the role of floral odour in the evolution and maintenance of plant reproductive isolation. Floral odour is known to have many important roles in the relationship between flowers and their pollinators. Odour attracts pollinators and promotes floral constancy and thus foraging efficiency of insects, which increases plant fitness (Wells & Wells, 1985; Goulson, 1994; Grant, 1994; Chittka et al., 1999). Floral odour is often variable between or within populations of the same species (Knudsen & Tollsten, 1991; Schiestl et al., 1997; Ayasse et al., 2000; Knudsen, 2002) and typically differs among closely related species (Gregg, 1983; Knudsen & Mori, 1996; Dobson et al., 1997; Jürgens et al., 2002; Raguso et al., 2003). This suggests that odour is also of importance for plant reproductive isolation, but specific experimental data are lacking (Knudsen, 1994; Tollstein et al., 1994; Levin et al., 2001).

The genus Silene L. (Caryophyllaceae), with about 700 species worldwide, is one of the largest genera of the world’s flora (Greuter, 1995). Floral odour differences between several Silene species have been investigated (Jürgens et al., 2002; Jürgens, 2004), as well as odour variation among populations of the dioecious species Silene latifolia and changes in odour emission following pollination (Dötterl et al., 2005b; Muhlemann et al., 2006). These studies have shown that odour is a key trait for pollinator attraction in several species, and interspecific differences in floral odour can be pronounced, making the genus a suitable model system to test the role of odour in plant reproductive isolation.

Two species that are closely related but differ in floral odour and other floral traits are S. latifolia and S. dioica of Silene section Elisanthe (Fenzl). These two perennial, dioecious plant species are native and widespread in Europe (Baker, 1948; McNeill, 1978). Silene latifolia Poiret is often found in highly disturbed habitats such as gravel pits, roadsides and field margins (McNeill, 1977). Male and female plants produce flowers with white petals and are heavily scented during the night (Jürgens et al., 2002). Common pollinators are sphingid and noctuid moths (e.g. Hadena bicruris) (Brantjes, 1978; Meusel & Mühlberg, 1979; Shykoff & Bucheli, 1995; Jürgens et al., 1996; Altizer et al., 1998). Silene dioica (Linnaeus) Clairville grows in more stable and moist habitats and often occurs at higher altitudes (Baker, 1947; Richards & Baker, 1947). It has red flowers that emit odour during the day (Jürgens, 2004) and are primarily visited by day-active insects such as bumblebees, syrphids, butterflies and muscid flies (Westerbergh & Saura, 1994; Goulson & Jerrim, 1997; Carlsson-Graner et al., 1998). In Switzerland, the two species are often somewhat separated along an altitudinal gradient. Silene latifolia is found growing in lowland habitats and S. dioica predominantly occurs in montane and sub alpine habitats (Hess et al., 1972). Nevertheless, the two species co-occur where their habitats and altitudinal distribution ranges meet and often have overlapping flowering times (Hess et al., 1972). Because pollinators do not strictly discriminate between the two species and intrinsic reproductive barriers are weak, hybridization may occur (Baker, 1948; Goulson & Jerrim, 1997; Minder et al., 2007).

Here, we used this pair of closely related species to investigate the role of floral odour in plant reproductive isolation. For this purpose, we sampled odour from several populations of both species, quantified floral odour composition and compared emission during both day and night in order to identify odour differences between species. In a second step, we assessed the role of floral odour differences in reproductive isolation between the two species. We experimentally manipulated odour to decrease the species difference in a single ‘key’ odour compound and measured pollen transfer between species in experimental arrays.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References

Plant material

In 2002, plants were grown in a greenhouse from seeds collected from three S. latifolia populations (Leuk, Switzerland, n = 25; Ribes de Fraser, Spain, n = 4 and Lyon, France, n = 26) and one S. dioica (Davos, Switzerland, n = 52) population. As S. dioica needs a vernalization period to induce flowering in spring, all plants were placed outside during winter and returned to the greenhouse at the beginning of May 2003. Floral odour was collected in the greenhouse when the plants were in full flower. In total, we analysed 55 S. latifolia floral odour samples (27 day samples and 28 night samples) and 52 S. dioica samples (23 day samples and 29 night samples). We tested for differences among floral odour bouquets by including all samples in a multivariate analysis. To characterize floral odour chemistry of the two species and to compare rhythmic odour emission, we used only those individuals that were sampled both during day and night. To examine effects of artificial floral odour manipulation on reproductive isolation, plants from a single S. latifolia population (Leuk) were used in a field experiment. For S. dioica, plants derived from populations near Davos and the Gotthard Pass (Switzerland) were used. In total, 60 individually potted plants of each species grown in the greenhouse were used for the experiment.

Volatile collection

Floral odour was collected in the greenhouse using the dynamic headspace sorption method (Dobson, 1991). We collected odour at daytime from 8 am to 6 pm and from 9 pm to 7 am during the night. The entire inflorescence, with newly opened flowers, was enclosed within a polyethylene terephtalate oven bag (Nalo® Kalle GmbH, Wiesbaden, Germany). A filter, constructed from cut glass micropipettes (Blaubrand®, Brand Gmbh & Co., Wertheim, Germany) filled with 4 mg of Porapak Q (Mesh size 80/100; Alltech Associates Inc., Deerfield, IL, USA) between two plugs of glass beads (∼0.3 mm, Merck KGaA, Darmstadt, Germany) and quartz wool, was used as an odour trap. One adsorbent trap was placed inside each bag and connected to a battery-operated vacuum pump (Personal Air Sampler; SKC Inc., Eighty-Four, PA, USA), which drew air over the filter at a rate of approximately 150 mL min−1 throughout the sampling period. Before use, all the filters were cleaned with 100 μL dichloromethane and 100 μL hexane. Surrounding air samples were taken simultaneously as a control sample for ambient contaminants. After sampling, the trapped volatile compounds were eluted with 50 μL of a hexane and acetone (9:1) solvent mixture. All floral odour samples were stored in sealed glass vials at -20 °C for subsequent gas chromatograph (GC) analysis. Information about duration and volumes of sampling was used to calculate absolute amounts of each compound per litre sampled air and hour and plant or flower, respectively.

Chemical analysis

The headspace samples were analysed with an Agilent 6890 N gas chromatograph (GC; Agilent Technologies, Palo Alto, CA, USA) fitted with an HP5 column (5%-Phenyl-methylpolysiloxane, 30 m × 0.32 mm ∅ × 0.25 μm film thickness; Agilent Technologies) and a flame ionization detector (FID). Hydrogen served as carrier gas and nitrogen was used as make-up gas. The injector temperature was kept at 300 °C. For quantitative analysis, an internal standard was added to all samples (100 ng n-octadecane, purity 99.8%; Fluka, Buchs, Switzerland). One microlitre of the odour samples was injected splitless at a temperature of 50 °C (1 min) followed by heating to 150 °C at a rate of 5 °C min−1, and then to 300 °C at a rate of 10 °C min−1; the oven was then kept at 300 °C for 10 min. Chromatogram outputs were recorded by the Chemstation program (Agilent Technologies) for qualitative and quantitative analysis. The internal standard method was applied to calculate absolute amounts of odour compounds (Schomburg, 1990). To identify the floral odour compounds, peak retention times were compared with those of authentic standard compounds and confirmed by comparison of spectra obtained by gas chromatography–mass spectrometry (GC–MS). One micro litre aliquots of the odour samples were injected into a GC (HP G 1800 A; Hewlett Packard Inc., Palo Alto, CA, USA) with a mass selective detector using the oven and column parameters described above. Lilac aldehyde C and benzylacetate eluted together and could therefore not be listed individually. As some compounds in the flower odour samples could not be identified, we calculated the Kovàts retention index (Schomburg, 1990) to provide a means for comparing our data with future studies.

Manipulation of floral odour

In the field experiment, we used phenylacetaldehyde to artificially make the floral odour bouquets of the two species more similar in the treatment plots. The plants were placed in 20 plots, each comprising three S. dioica and three S. latifolia individuals that were positioned at random (Fig. 1). The distance between the plots was 3 m and within each plot plants were positioned with a distance of 0.5 m to the neighbouring plant. Ten plots were randomly assigned to either treatment or control groups. We coiled a rubber septum (Ø = 11 mm; Supelco Inc., Bellefonte, PA, USA) that was cut in quarters and beaded on thread, around each inflorescence for odour application. In the treatment plots, the septa were soaked for two hours in a phenylacetaldehyde/dichloromethane mixture (1:5) before they were applied to the inflorescence of each plant, resulting in an emission of approximately 400 ng h−1 during the first two hours, and in constant odour emission of about 50 ng h−1 for the remaining time period (46 h; data not shown). In control plots, septa solely soaked in the solvent dichloromethane (2 h) were used on the inflorescences. We used the biologically active benzenoid phenylacetaldehyde for odour manipulation because this compound is dominant in the odour of S. dioica and contributed strongly to species differentiation. An additional application of the monoterpenoids lilac aldehydes that were important odour compounds in S. latifolia was not possible, as the lilac aldehydes were not commercially available and are very elaborate to synthesize. All treatment and control septa were changed every 48 h. Detection of interplant visitation by pollinators was achieved by applying small amounts of fluorescent pigments (Radiant Color®, Magruder Color, Richmond, CA, USA) to flowers using a brush. Fluorescent dye powders are good pollen-analogues for studying carry-over and estimating pollen flow between flowers (Waser & Price, 1982; Adler & Irwin, 2006). Transfer of pigments was detected using a UV lantern (Super Mini Ultra Violet fluorescent lantern; Goulson & Jerrim, 1997). We used four different colours for the detection of visitation between plants by pollinators in this experiment. Silene latifolia flowers were brushed with blue and green pigments and S. dioica flowers with pink and orange. The same two colours per plot were applied on flowers, one for each species. We applied different colour combinations for the species in neighbouring plots in order to detect transfer of pigments between plots. Data were collected every day shortly after dawn. The transfer between species and between plots was counted by the presence of foreign colours. Flowers with foreign colours were removed and the total number of flowers was noted for every plant. The interspecific dye transfer rate was calculated as the proportion of flowers with dye from the other species. The experiment was run for four days.


Figure 1.  Design of experimental array. Circles represent potted S. latifolia (open circles) and S. dioica (filled circles) plants. Treatment plots are shown in grey. Each plot comprised six plants. The experiment was performed with 20 plots arranged in five columns and four rows. Distances are not shown to scale.

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Statistical analysis

All data were tested for homogeneity of variances (Levene’s test) and normality (Kolmogorov–Smirnov test) and Bonferroni correction was applied to correct for multiple testing. The relative amounts for each volatile compound were obtained by dividing the absolute amount of a single compound by the sum of all compounds. The differences in chemical classes and in single odour compounds between day and night within species were analysed by paired t-tests or by nonparametric Wilcoxon signed-rank tests for related samples when data distribution differed from normality. Species differences of single odour compounds were analysed on the basis of the mean between day and night emission using a Mann–Whitney U-test. Total amount of odour was compared between day and night within species by using log-transformed data in a paired t-test. We used principle components analysis (PCA) to reduce the numerous volatile compounds to fewer factors and to ensure independence of variables for the multivariate analysis. The extraction method included varimax rotation with 25 iterations and Kaiser Normalization for the volatile compounds. Only factors with an eigenvalue higher than one were considered in the further canonical discriminant analyses (CDA). We used the stepwise method with an F-value of 3.84 to enter an independent variable and an F-value of 2.71 to remove it in the CDA, and pairwise group comparisons were applied to describe which groups were different from each other.

In the floral odour manipulation experiment, the number of flowers and the number of interspecific visits were recorded for each plot and for each plant in the plot. For each plot, these numbers were summed up for the duration of the experiment. Then, the ratio of the total interspecific transfers/total number of flowers was calculated for each plot. A t-test was carried out to test for significant differences of mean total interspecific transfers within plots per total number of flower between the two treatments. Differences in the distance (intraplot/interplot) were compared with a Wilcoxon signed-rank test. The difference in direction (S. dioica to S. latifolia and reverse) of transfer was tested in the same way. All analyses were conducted using spss 11.0.4 for Mac OS X (SPSS Inc., Chicago, IL, USA).


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References

Floral volatiles

We identified 28 different odour compounds in the floral odour of both S. latifolia and S. dioica. Table 1 shows the relative amounts of the compounds found in the headspace samples for both species at day and night separately. Absolute amounts of the odour compounds are listed in Table 2. Five out of the 28 compounds could not be identified. Floral odour was qualitatively and quantitatively different between the two species (Tables 1 and 2). The three compounds 2-methoxyphenol, veratrole and benzyl benzoate were not found in the odour of S. dioica, whereas nonanal was absent in the odour of S. latifolia. Quantitative differences between species on the basis of mean relative amounts of day and night emission were found in 18 different compounds (Table 1).

Table 1.   Mean relative amounts of odour compounds (±SE) identified in headspace samples. Compounds belonging to the same chemical class are ordered according to retention times.
CompoundsSilene dioica (n = 16)Silene latifolia (n = 20)
Mean ± SEMean ± SEMean ± SEMean ± SE
  1. RI: Kovat’s retention index.

  2. †Significant differences between species on basis of mean amounts of day and night emission.

  3. *Significant differences between means comparing day and night amounts within species.

Fatty acid derivates1.88 ± 0.561.88 ± 0.565.67 ± 2.552.90 ± 0.92
Octanal1.86 ± 0.571.88 ± 0.565.67 ± 2.552.90 ± 0.92
Nonanal0.02 ± 0.020.00 ± 0.000.00 ± 0.000.00 ± 0.00
Benzenoids52.64 ± 4.9546.10 ± 5.5324.13 ± 3.6923.75 ± 4.24
Benzaldehyde†0.48 ± 0.190.89 ± 0.203.78 ± 0.81*1.70 ± 0.47
Phenylacetaldehyde†50.53 ± 4.9543.19 ± 5.4712.88 ± 3.1811.42 ± 2.86
2-Methoxy phenol†0.00 ± 0.000.00 ± 0.000.21 ± 0.160.07 ± 0.03
Methyl benzoate†1.08 ± 0.160.86 ± 0.141.17 ± 0.750.04 ± 0.02
2-Phenyletanol†0.18 ± 0.100.20 ± 0.161.25 ± 0.561.41 ± 0.47
Veratrole†0.00 ± 0.000.00 ± 0.003.17 ± 2.297.53 ± 2.95
Methyl salicylate†0.37 ± 0.130.96 ± 0.740.12 ± 0.101.18 ± 0.65
Benzyl benzoate†0.00 ± 0.000.00 ± 0.001.56 ± 1.330.40 ± 0.19
Monoterpenoids33.95 ± 3.70*42.93 ± 4.5244.81 ± 4.76*63.85 ± 4.17
α-pinene†5.42 ± 1.164.44 ± 0.8217.11 ± 3.91*8.15 ± 2.09
Camphene†0.84 ± 0.121.21 ± 0.333.62 ± 0.852.25 ± 0.59
β-pinene†4.12 ± 2.891.87 ± 0.960.99 ± 0.250.45 ± 0.18
Limonene†6.12 ± 0.97*2.94 ± 0.301.84 ± 0.57*0.86 ± 0.44
Eucalyptol0.53 ± 0.370.13 ± 0.130.34 ± 0.240.85 ± 0.48
Trans-β-Ocimene0.04 ± 0.030.13 ± 0.070.25 ± 0.200.05 ± 0.02
Linalool†0.13 ± 0.06*0.37 ± 0.120.14 ± 0.090.08 ± 0.07
Lilac aldehyde A7.32 ± 1.60*15.82 ± 2.347.45 ± 1.67*19.73 ± 2.43
Lilac aldehyde B†6.12 ± 1.33*10.14 ± 1.519.35 ± 2.17*24.23 ± 3.04
Lilac aldehyde C/Benzyl acetate1.39 ± 0.332.56 ± 1.161.89 ± 0.352.60 ± 0.43
Lilac alcohol1.92 ± 0.49*3.32 ± 0.631.83 ± 0.56*4.60 ± 0.84
Sesquiterpenoides0.21 ± 0.150.66 ± 0.431.10 ± 0.401.52 ± 0.78
β-farnesene†0.21 ± 0.150.66 ± 0.431.10 ± 0.401.52 ± 0.78
Irregular terpenes4.82 ± 0.92*1.72 ± 0.291.09 ± 0.351.88 ± 0.78
6-Methyl-5-hepten-2-one†4.82 ± 0.92*1.72 ± 0.291.09 ± 0.351.88 ± 0.78
Unknowns with Kovat’s retention index (RI)6.49 ± 1.706.69 ± 2.2923.19 ± 4.44*6.11 ± 1.66
Unknown 1 (978)†0.41 ± 0.150.63 ± 0.112.51 ± 0.62*1.02 ± 0.28
Unknown 2 (992)5.29 ± 1.715.34 ± 2.2718.23 ± 4.35*2.64 ± 1.75
Unknown 3 (1009)0.22 ± 0.130.05 ± 0.050.62 ± 0.300.11 ± 0.08
Unknown 4 (1112)0.01 ± 0.010.19 ± 0.120.00 ± 0.000.23 ± 0.18
Unknown 5 (1191)†0.56 ± 0.240.48 ± 0.101.83 ± 0.742.10 ± 0.59
Table 2.   Mean absolute amounts of odour compounds (ng h−1 ± SE) identified in headspace samples. Compounds belonging to the same chemical class are ordered according to retention times.
CompoundsSilene dioica (n = 16)Silene latifolia (n = 20)
Mean ± SEMean ± SEMean ± SEMean ± SE
  1. RI: Kovat’s retention index.

  2. †Significant differences between species on the basis of mean amounts of day and night emission.

  3. *Significant differences between means comparing day and night amounts within species.

Fatty acid derivates1.59 ± 0.361.97 ± 1.332.46 ± 1.161.21 ± 0.25
Octanal1.51 ± 0.351.97 ± 1.332.46 ± 1.161.21 ± 0.25
Nonanal0.08 ± 0.080.00 ± 0.000.00 ± 0.000.00 ± 0.00
Benzenoids53.81 ± 14.6540.92 ± 11.0225.82 ± 14.8538.76 ± 15.30
Benzaldehyde†0.73 ± 0.440.48 ± 0.131.87 ± 0.461.28 ± 0.49
Phenylacetaldehyde†51.64 ± 13.9339.09 ± 10.6910.31 ± 3.4813.56 ± 4.47
2-Methoxy phenol†0.00 ± 0.000.00 ± 0.000.05 ± 0.030.29 ± 0.14
Methyl benzoate†1.06 ± 0.290.58 ± 0.130.09 ± 0.060.08 ± 0.05
2-Phenyletanol0.15 ± 0.080.32 ± 0.270.52 ± 0.263.11 ± 1.75
Veratrole†0.00 ± 0.000.00 ± 0.0012.25 ± 11.9218.56 ± 11.94
Methyl salicylate†0.23 ± 0.110.45 ± 0.250.56 ± 0.541.49 ± 0.91
Benzyl benzoate†0.00 ± 0.000.00 ± 0.000.16 ± 0.120.38 ± 0.19
Monoterpenoids39.75 ± 12.2853.19 ± 18.4023.94 ± 10.10*131.25 ± 45.65
α-pinene†5.63 ± 1.603.80 ± 1.336.43 ± 2.194.07 ± 0.96
Camphene0.98 ± 0.290.61 ± 0.141.33 ± 0.431.09 ± 0.25
β-pinene3.57 ± 2.291.69 ± 0.830.48 ± 0.160.50 ± 0.18
Limonene†6.31 ± 1.872.94 ± 1.042.17 ± 1.030.58 ± 0.23
Eucalyptol1.09 ± 1.060.10 ± 0.100.13 ± 0.090.27 ± 0.14
Trans-β-Ocimene0.08 ± 0.070.05 ± 0.030.06 ± 0.040.09 ± 0.04
Linalool†0.11 ± 0.060.22 ± 0.100.04 ± 0.030.09 ± 0.07
Lilac aldehyde A10.08 ± 4.9818.63 ± 6.343.62 ± 1.77*42.07 ± 15.09
Lilac aldehyde B7.72 ± 3.4613.24 ± 4.986.82 ± 3.58*62.04 ± 22.62
Lilac aldehyde C/Benzyl acetate1.75 ± 0.636.90 ± 5.171.35 ± 0.516.95 ± 2.43
Lilac alcohol2.43 ± 1.085.02 ± 2.071.53 ± 0.97*13.50 ± 5.08
Sesquiterpenoides0.07 ± 0.050.25 ± 0.230.65 ± 0.240.57 ± 0.24
β-farnesene†0.07 ± 0.050.25 ± 0.230.65 ± 0.240.57 ± 0.24
Irregular terpenes4.13 ± 0.92*1.21 ± 0.340.48 ± 0.171.32 ± 0.42
6-Methyl-5-hepten-2-one†4.13 ± 0.92*1.21 ± 0.340.48 ± 0.171.32 ± 0.42
Unknowns with Kovat’s retention index (RI)9.78 ± 4.429.34 ± 5.1613.44 ± 3.9010.92 ± 4.67
Unknown 1 (978)†0.66 ± 0.400.40 ± 0.101.45 ± 0.401.02 ± 0.33
Unknown 2 (992)8.03 ± 4.058.12 ± 4.7310.87 ± 3.64*1.91 ± 0.90
Unknown 3 (1009)0.26 ± 0.140.18 ± 0.180.25 ± 0.090.13 ± 0.07
Unknown 4 (1112)0.01 ± 0.010.05 ± 0.030.00 ± 0.000.10 ± 0.07
Unknown 5 (1191)†0.83 ± 0.460.59 ± 0.240.88 ± 0.417.76 ± 4.44
Mean total amount of odour (ng h−1)109.13 ± 0.59106.89 ± 32.1066.78 ± 26.03184.03 ± 60.30

We found eight different compounds that showed significantly different emission between day and night in S. latifolia (Table 1). Five of these compounds were monoterpenoids, two were unknown compounds and one was a benzenoid. The lilac aldehydes A and B, and lilac alcohol, were emitted in significantly higher relative amounts during the night. Benzaldehyde, α-pinene, limonene, and the two unknown compounds 1 and 2 were found in significantly higher relative amounts during the day. In S. dioica, six compounds were found that showed significant differences in emission between day and night (Table 1). All compounds were monoterpenoids, except 6-methyl-5-hepten-2-one, an irregular terpene. Linalool, the lilac aldehydes A and B, and lilac alcohol were found in significantly higher relative amounts during the night, whereas limonene and 6-methyl-5-hepten-2-one were found in significantly higher relative amounts during the day. Altogether we found four compounds that showed the same rhythmic pattern of day–night emission in both species, namely limonene, the lilac aldehydes A and B, and lilac alcohol (Table 1).

Odour classes

In S. latifolia, floral odour was dominated by monoterpenoids (> 44%) followed by benzenoids (> 23%). There were large differences in the composition of odour profiles between day and night. In the night, the monoterpenoids were emitted in significantly higher relative amounts, whereas the unknown compounds were produced in significantly larger relative amounts during the day (Table 1). In S. dioica, the same two chemical classes as in S. latifolia were prominent in the floral odour, but were found to occur in reciprocal proportions, with > 46% being benzenoids and > 33% monoterpenoids. The monoterpenoids were found in significantly higher relative amounts during the night, whereas the relative amounts of the irregular terpenes were found in significantly higher amounts during the day (Table 1).

Total amount of odour

Silene latifolia emitted significantly more odour during the night (mean total absolute amount ± SE: 184.03 ± 60.30 ng h−1 per flower) than during the day (66.78 ± 26.03 ng h−1 per flower; t = -2.615, d.f. = 19, P = 0.017), whereas the amount of odour emitted by S. dioica did not show a significant difference between day and night (day: 109.13 ± 27.22 ng h−1 per flower; night: 106.89 ± 32.10 ng h−1 per flower; t = 0.829, d.f. = 15, P = 0.420).

Species differences: floral odour bouquet

We found that the floral odour bouquets differed significantly between S. latifolia and S. dioica, both during the day and at night (Fig. 2). The reduction of the 28 odour compounds with a PCA produced 10 PCA factors explaining 71.6% of the total variance. From the factor-loading data of the first two components (not shown), we concluded that the lilac aldehydes A and B, lilac alcohol as well as benzaldehyde, phenylacetaldehyde and the unknown compound 1 were important for the differentiation of S. latifolia from S. dioica. A canonical discrimination analysis with the first two discriminant functions explaining 100% of the variance (Discriminant function 1: Eigenvalue = 1.08, χ2 = 103.53, P < 0.001; discriminant function 2: eigenvalue = 0.36, χ2 = 30.84, P < 0.001) revealed that the floral odour bouquets differed significantly between the two species (Fig. 2). Species differences were more pronounced during the day than at night (Pairwise comparison: day: F1,7 = 21.364, P < 0.001, night: F1,7 = 16.379, P < 0.001). The differences in odour bouquets between day and night within species were greater in S. latifolia than in S. dioica, in which no significant difference between day and night could be detected (Pairwise comparison: S. latifolia: F1,7 = 11.013, P < 0.001, S. dioica: F1,7 = 1.375, P = 0.255).


Figure 2.  Plot of the first two functions of the discriminant analysis of odour compounds classifying the two species S. latifolia and S. dioica.

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Manipulation of floral odour

Interspecific dye transfer per flower within plots was significantly different between treatment and control plots (t-test: t = -3.563, d.f. = 15, P < 0.01), with a higher dye transfer rate in treatment plots (Fig. 3a). Dye transfers among plots were not significantly different between treatment and control plots, both for interspecific and intraspecific transfers (intraspecific: t = -0.778, d.f. = 15, P = 0.45; interspecific: t = 0.582, d.f. = 15, P = 0.57). Thus, the treatment did not attract more pollinators specifically to the treatment plots. Transfer occurred more frequently from S. dioica to S. latifolia than vice versa (Wilcoxon signed-rank test: Z = -3.67, P < 0.001, Fig. 3b). Furthermore, significantly more transfers took place within plots than between plots (Wilcoxon signed-rank test: Z = -3.67, P < 0.001; Fig. 3b).


Figure 3.  (a) Proportion of interspecific transfer per flower within plots (**t-test: t = -3.563, d.f. = 15, P < 0.01). Interspecific transfer was significantly enhanced in plots in which S. latifolia and S. dioica were manipulated to emit equal amounts of phenylacetaldehyde. (b) Direction and distance of transfer: Transfer from S. dioica (SD) to S. latifolia (SL) and conversely (Wilcoxon signed-rank test: ***Z = -3.67, P < 0.001). Transfers took place significantly more often from S. dioica to S. latifolia than vice versa. (c) Transfer within and between plots (Wilcoxon signed-rank test: ***Z = -3.67, P < 0.001). Most transfers occurred within plots.

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  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References

Floral isolation is a form of ecological isolation, where gene flow between plant species is prevented by the specific interaction with pollinators (Grant, 1992; Coyne & Orr, 2004). Although floral isolation is thought to be common (Grant, 1994), we know little about the mechanisms involved. In this study, we demonstrated that floral odour differences between two closely related Silene species are important for floral isolation, and thus for reproductive isolation.

Species differences in floral odour

In our study, the odour of the two species, S. latifolia and S. dioica, was found to be dominated by monoterpenoids and benzenoid compounds that are known as typical floral odour constituents (Knudsen et al., 1993; Knudsen & Gershenzon, 2006) and that have been identified previously in these species (Jürgens et al., 2002; Jürgens, 2004; Dötterl et al., 2005b). Interestingly, although most compounds were emitted by both species, the floral odour bouquets of the two species were distinct from each other, because the relative amounts of individual compounds emitted differed between the species. These results are in agreement with those reported previously for the two study species (Jürgens et al., 2002; Jürgens, 2004) and for other related plant species that differ in floral odour (Knudsen & Tollsten, 1993; Tollstein et al., 1994; Jürgens et al., 2003; Huber et al., 2005; Raguso et al., 2006; Salzmann et al., 2006). Remarkably, some of the compounds contributing most to the species differences have been identified to be electrophysiologically and/or behaviourally active in several insect species. The lilac aldehydes are known to be attractive to noctuid moths or elicit electrogrammic responses (Raguso et al., 1996; Raguso & Light, 1998; Meagher, 2002; Plepys et al., 2002a,b; Dötterl et al., 2005a, 2006). Benzaldehyde is a common compound occurring in floral odours of many plant families (Knudsen et al., 2006) and is, like the lilac aldehydes, electrophysiologically and/or behaviourally active in butterflies (Omura et al., 1999a,b; Andersson, 2003; Andersson & Dobson, 2003), moths (Haynes et al., 1991; Heath et al., 1992; Bruce & Cork, 2001; Meagher, 2002) and beetles (Pierce et al., 1990; Huber et al., 2005), but is not attractive to H. bicruris (Dötterl et al., 2005a), a specialist pollinator of these two Silene species. Phenylacetaldehyde is a widespread floral volatile (Knudsen et al., 2006) and is found to be highly attractive to butterflies and various moth species including H. bicruris (Cantelo & Jacobson, 1979; Haynes et al., 1991; Heath et al., 1992; Honda et al., 1998; Omura et al., 1999a,b; Landolt et al., 2001; Meagher, 2001, 2002; Cunningham et al., 2004, 2006; Huber et al., 2005).

Earlier studies investigating S. latifolia and S. dioica have not compared the two species directly, because authors have grouped them into different floral syndromes based on their floral attributes. In contrast to these studies, however, we avoided an a priori classification into pollination syndromes and instead compared, in both species, odour emissions during both day and night. This direct comparison is important, because different pollinators are active during day and night, and a breakdown of odour differences, for example, during the night could open a window during which floral isolation is reduced.

Rhythmicity, day–night emission

Relative amounts of single odour compounds emitted differed not only between species but also within species between day and night. Silene latifolia showed a strong periodical odour production by emitting more scent during the night than during the day. Most notably, lilac aldehydes A and B, and lilac alcohol were emitted more strongly during the night. These compounds are known to elicit behavioural responses in the nocturnal pollinator H. bicruris and Autographa gamma (Plepys et al., 2002a; Dötterl et al., 2005a). Stronger emission of these compounds during the night may therefore increase attraction of flowers for pollinators and therefore be adaptive. Similar results have been reported for other moth-pollinated plants that emit strong floral odours at night (Knudsen & Tollsten, 1993) and exhibit periodical odour production (Loughrin et al., 1990; Nilsson et al., 1990; Heath et al., 1992; Miyake & Yahara, 1998; Raguso et al., 2003; Huber et al., 2005).

In contrast to S. latifolia, S. dioica produced similar amounts of odour during the day and at night. Surprisingly, however, lilac aldehydes A and B, and lilac alcohol were also emitted in higher relative amounts in S. dioica at night. This result was unexpected, because S. dioica is thought to be visited primarily by diurnal pollinators. This periodicity may potentially be adaptive for attracting additional nocturnal pollinators to S. dioica, or represent a phylogenetic inertia, inherited from a moth-pollinated ancestor.

Overall, our study found clear differences in floral odour between the two species both during the day and at night, but the differences at night were less pronounced than initially expected, because S. dioica was unexpectedly found to display periodicity in the emission of some odour components similar to S. latifolia.

Manipulation of floral odour

Our field experiment revealed that transfer of fluorescent dye, a pollen analogue, between species was increased in plots in which the similarity of floral odour was experimentally increased, compared with control plots in which the species-characteristic floral odour differences remained unchanged. These results provide strong evidence for an important role of odour in floral isolation, and thus in reproductive isolation between the species. Importantly, we used only a single compound in our experiments and thus demonstrated that assortative flower visitation by pollinators can be strongly influenced by a single compound whose synthesis is likely dependent on the expression of a single gene encoding phenylacetaldehyde synthase (Kaminaga et al., 2006). A similar result of relative simple genetic control of floral isolation has been reported for flower colour in Mimulus, in which allelic differences at a single locus affecting flower colour significantly influenced visitation rates by different pollinators (Bradshaw et al., 1995, 1998; Schemske & Bradshaw, 1999, 2003).

Although experimental studies in which floral odour is augmented with synthetic blends provide a powerful tool to assess the importance of floral odour differences for pollinator attraction and floral isolation, studies using this technique remain scarce (Baldwin et al., 1997; Dobson et al., 1999; Cunningham et al., 2004). So far, most odour-manipulation studies applied extracts from different flower parts to test behavioural effects on pollinators (Hossaert-McKey et al., 1994; Nogueira et al., 2001; Ashman et al., 2005), but importance of single compounds has not yet been assessed. Given that many closely related and potentially interfertile species differ in floral odour, this approach could be used more widely to assess the importance of floral odour differences for the maintenance of species boundaries. We suggest that at least in moth-pollinated plants, where pollinator attraction is primarily odour driven, and few or even single compounds have been shown to be sufficient to attract specific pollinators (Plepys et al., 2002a; Dötterl et al., 2005a; Huber et al., 2005), few loci might be involved in the production of floral scent compounds. These may be crucial for premating reproductive isolation, and odour differences between species could evolve rapidly as a consequence of selection for the maintenance of reproductive isolation.

Transfer of the pollen analogue between species was found to be significantly higher from S. dioica to S. latifolia than vice versa, confirming similar results of fluorescent dye movements in mixed experimental S. latifoliaS. dioica arrays investigated by Van Putten (2002). Additionally, the findings of a genetic study in two S. latifoliaS. dioica contact zones showed evidence for pollen flow from S. dioica onto S. latifolia, because most hybrid individuals carried the chloroplast haplotype of S. latifolia (Minder et al., 2007). Transfers of pollen analogues in our study were more often found within plots than between plots, as also observed in the study by Van Putten (2002), who further found that the dye frequency on S. latifolia and S. dioica flowers decreased with increasing distance from the dye source, for both interspecific and intraspecific flower visitations. This distance pattern possibly reflects the optimal foraging strategy of pollinators (Charnov, 1976).


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References

Although the floral odour of S. latifolia and S. dioica was composed of similar odour constituents, differences in relative amounts and in the periodical odour production resulted in a distinct chemical identity contributing to floral isolation. The result of our field experiment revealed that a single compound could influence pollen transfer between species, and thus species integrity. We therefore propose that floral odour is an important mechanism contributing to the reproductive isolation between closely related plant species. Studies investigating the molecular basis of odour differences could be very promising in the future to learn more about how floral isolation works on a molecular basis and how it evolved between recently diverged species.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References

The authors thank S. Dötterl (University of Bayreuth) and R. Kaiser (Givaudan) for kindly providing reference compounds used in this study; Robin Clery (Givaudan) for the help with identifying scent compounds; J. A. Shykoff (Université Paris-Sud) for constructive comments; F. Steinebrunner (ETH Zurich) and G. A. Schwarzenbach (University of Zurich) for helpful remarks on an earlier version of the manuscript; P. Page (ETH Zurich) for language assistance. Funding was provided by an ETH Zurich TH-grant to AW and FPS (Grant-N°TH - 32/03-2).


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. References
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