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.