Supporting Information Notes S1
Supplemental volatile analyses for ‘Natural selection to increase floral scent emission,
but not flower size or colour in bee-pollinated Penstemon digitalis’
by Amy L. Parachnowitsch, Robert A. Raguso and André Kessler.
Volatile collections: Following Kessler and Baldwin (2001), inflorescences and
leaves were separately enclosed in 500ml polyethylene cup “trapping chambers”.
Ambient air was pulled through the chambers and activated charcoal absorbent vials
(ORBO-32, SIGMA-Aldrich) at about 450-500ml min-1 using 12-V battery operated
vacuum pumps (GAST®, Inc.). Collections were made over four days during peak
flowering (June 21-24) for eight hours per day during peak pollinator activity (start time
between 10:30 – 11:00). Each day two ambient and three vegetative controls (one per
population) were collected in addition to 27 floral bouquets. In addition to scent
collections, the number of open flowers was counted for each plant.
VOC quantification: ORBO-32 absorbent vials were each eluted with 350mL of
dimethylchloride (SIGMA®) with 430 ng tetralin added for an internal standard and
samples were stored in 1.5ml GC-vials with glass inserts. Samples were analyzed using a
Varian 2200 GC/MS equipped with an EC WAX-column (30 m, 0.25 mm internal
diameter, 0.25 µm film thickness; Alltech Associates, USA). Helium was used as a
carrier gas at a constant flow of 1 ml/min under the following GC oven conditions: 45 °C
for 6 min, increased to 130 °C at 10 °C/min, increased to 180 °C at 5 °C/min, increased to
230 °C at 20 °C/min with a 5 min hold at 230 °C, increased to 250 °C followed by a final
hold at 250 °C for 5 min. Peak areas were integrated using Varian software for VOC
quantification and were expressed as tetralin (internal standard) equivalents. We then
subtracted the mean of the two air controls for a given day; negative values were
converted to zero. To control for differences in the number of open flowers, for floral
specific compounds we also calculated emission per flower by dividing the GC peak
areas by the number of open flowers.
VOC identification: We identified compounds by comparing the mass spectra
with those in the NIST compound library (National Institute of Standards and
Technology, Gaithersburg, MD) and by comparing retention times and mass spectra with
those of authentic standards. This did not always allow for determination of which
isomer was present and four compounds could not be identified. In addition, SPME
analyses of dissected floral organs were used to verify the tissue specificity of the
individual VOCs. Methods generally followed those described by Goodrich et al. (2006),
except that in this study we used 100 μm polydimethylsiloxane (PDMS) fibers to collect
odors in headspace chambers constructed from oven-baked 10ml glass scintillation vials
with nylon resin oven bag (Reynolds, Inc.) material used as a gasket.
Mass spectra of unknown compounds. Only ions with a relative abundance (% of base
peak) above 20 % are shown
Unknown
m/z (relative abundance)
1
57 (100), 70 (44.5), 83 (42.7), 69 (24.5)
2
69 (100), 79 (38.3), 135 (28.6), 67 (26.5), 107 (25.3), 81 (20.1)
3
69 (100), 83 (76.4), 97 (72.0), 73 (62.2), 70 (55.8), 67 (52.0), 57 (48.9),
113 (47.0), 127 (45.1), 111 (43.6), 82 (33.2), 81 (29.1), 96 (25.4), 84
(24.5), 68 (21.7)
4
91 (100), 57 (94.2), 69 (69.3), 71 (58.0), 83 (36.0), 97 (35.8), 85 (34.9), 93
(32.4), 70 (25.9), 105 (21.7)
VOC variation: VOCs were variable among individuals (Table 2), however the
mean variation in all volatiles was higher across sampling days (CV = 1.271) than within
plants (CV = 0.949), for those plants with two sampling days (N = 6 plants), suggesting
that the measured scent is reflective of each plant’s production during peak flowering.
Floral-specific volatile emission: Floral volatiles were defined as peaks with
three times the emission in flower samples compared with the same compound in
vegetative controls, based on means from all samples. In addition, we ran a RF algorithm
to determine the minimum number of volatiles that distinguished floral emission. For
these we used data from individual plants (NR = 4, TH = 3, WF = 2) with both vegetative
and floral emissions. Given the sample sizes, we did not have the power to use
MANOVA, and individual t-tests of the compounds (assuming unequal variances) were
not significant (not shown). However, linalool and cis--ocimene were marginally higher
in floral samples (Ps = 0.08) and methyl cinnamate was marginally higher in vegetative
emissions (P = 0.07).
List of compounds found to distinguish floral from leaf scents with Random Forest
analysis, including the model frequency and the mean decrease in accuracy (MDA).
Mean peak area  SE is for leaf specific and floral samples across all sampling days from
the same individuals (N = 9)
Compound
Leaf emission Floral emission Model Frequency MDA
linalool
0.063  0.055 0.401  0.162
0.71 0.0482
trans-2-octenal
0.334  0.310 0.450  1.117
0.71 0.0321
bergamotene
0.141  0.138 0.146  0.063
0.24 0.0123
methyl cinnamate 0.347  0.124 0.069  0.031
0.19 0.0043
-farnasene
0.15 0.0076
0.087  0.087 0.091  0.052
References
Goodrich KR, Zjhra ML, Ley CA, Raguso RA. 2006. When flowers smell fermented:
The chemistry and ontogeny of yeasty floral scent in pawpaw (Asimina triloba :
Annonaceae). International Journal of Plant Sciences 167(1): 33-46.
Kessler A, Baldwin IT. 2001. Defensive function of herbivore-induced plant volatile
emissions in nature. Science 291(5511): 2141-2144.