Flowering plants have evolved a number of different approaches to reproduction. Some use their own pollen and self-fertilize, while others use pollen from other nearby plants. This fertilization by other plants is called ‘outcrossing’ and introduces new genetic variation into each generation, which is extremely important for the evolutionary process.

Some flowering plants rely on animals to help with pollination, attracting visitors with floral scents and rewarding the visitors with sugar-rich nectar. But scent and nectar also attract herbivores that damage the plants. This causes a dilemma for flowering plants, which has led some to evolve to not produce scent or to offer no nectar while masquerading as a plant that does. Previous studies into the costs and benefits of such strategies have looked at the effects of either floral scent or nectar, but no-one has uncoupled the effects of these two traits on both pollination and herbivore attack.

Kessler et al. have addressed this issue in wild tobacco plants, which can both self-fertilize and outcross, and which produce varying amounts of scent and nectar. The experiments were conducted under mesh tents and in field trials in the plant's natural habitat: the Great Basin Desert in Utah. Kessler et al. used a gene-silencing technique called ‘RNA interference’ to inhibit the production of scent or nectar, either separately or together. When grown in field trials, under conditions that prevent self-fertilization, these tobacco plants are frequently visited by a hummingbird and three species of hawkmoth. All four of these animals pollinate the tobacco plants, but one of the moths also lays eggs that hatch into caterpillars, which damage the plant. Kessler et al. monitored the effects that the loss of scent, nectar or both had on visits by each pollinator and on outcrossing.

These experiments revealed that scent is essential to attract one hawkmoth species but not for another (called Hyles lineata). Furthermore, while, the hummingbird needs nectar, the H. lineata moth does not; but this moth won't visit flowers that lack both scent and nectar. The experiments also show that, for the moth that lays its eggs on the tobacco plants, both scent and nectar increase pollination and egg laying, but nectar has a stronger effect. Thus reducing nectar, as this tobacco plant does in the wild, is one strategy that can be used to reduce herbivore attack by caterpillars. Together, these findings highlight that it is important to study both herbivores and pollinators when attempting to understand the evolution of floral traits.

Plants attract pollinators to move pollen from one flower to another to ensure outcrossing and frequently pay for these pollination services by rewarding floral visitors with nectar (Kevan and Baker, 1983; Schemske and Bradshaw, 1999; Raguso and Willis, 2005). Even though it is clear that both floral scent and nectar provide fitness benefits for plants, rewardless flowers have evolved in all major groups of angiosperms (Renner, 2006). Orchids, in particular, have frequently evolved deceptive pollination systems, in which flowers attract pollinators by mimicking mating partners or oviposition sites without offering rewards (Schiestl, 2005). But rewardless, nectar-free, flowers are commonly found within species that normally provide nectar, and this is surprising, as the occasional nectar-free flowers would have a disadvantage if visitors have the sensory abilities to avoid rewardless flowers (Karban, 2015). Early on, theorists (Bell, 1986) recognized that most flowers hide their rewards, for example, deep in the corolla tube, which thwarts the easy visual evaluation of a flower's standing nectar volume and developed an ESS model for the proportion of cheating flowers and discriminating visitors that would be evolutionarily stable. Researchers have since uncovered evidence consistent with the predictions of the model. Gilbert et al. (1991) found that nectar secretion was highly variable within plants of a population and suggested that floral visitors could distinguish between low and high nectar secreting plants. Recent research suggests that hawkmoth pollinators can use humidity as a proxy for the presence of nectar (von Arx et al., 2012).

To examine the importance of nectar for pollination services and to study the fitness advantages of nectar-cheating plants, researchers have used native varieties with reduced nectar accumulation, introgression lines (Brandenburg et al., 2012a, 2012b), artificial flowers (e.g., Ishii et al., 2008), or conducted direct manipulations of nectar quantities by adding artificial nectar to flowers (e.g., Mitchell and Waser, 1992; Jersáková et al., 2008). Ishii et al. (2008) found that pollinators avoided inflorescences with greater numbers of empty flowers. Smithson (2002) added nectar to rewardless orchids, and while the addition changed bee behavior, it did not influence plant fitness. Brandenburg et al. (2012a) found that nectar-deficient Petunia lines produced fewer seeds than did nectar-replete control plants, because Manduca sexta moths reduced their probing times in low-nectar plants, which in turn, reduced pollen transfer and thus seed set.

While rewards keep pollinators moving pollen from one plant to another, other cues, such as floral scent, provide honest signals that advertise the occurrence of the rewards (Wright and Schiestl, 2009). Floral scent is known to play a central role in attracting insect pollinators to flowers (Galen and Newport, 1988; Jürgens et al., 2002; Klahre et al., 2011; Byers et al., 2014; Riffell et al., 2014). The effect of floral scent on the pollination success of single pollinator species has been studied mainly with scent augmentations and additions to existing scent bouquets (e.g., Majetic et al., 2009; Shuttleworth and Johnson, 2010). Shuttleworth and Johnson (2010), for example, showed that single sulphur compounds are responsible for the shift between wasp and fly pollination in Eucomis (Hyacinthaceae). Byers et al. (2014) found altered bumblebee visitation rates in response to single volatile compounds which were added to the scent bouquet of Mimulus species. In most studies, only one pollinator species was investigated at a time, frequently in very specialized model systems, often the sexually deceptive pollination systems of orchids (Schiestl, 2005; Schiestl and Schlüter, 2009). Several studies investigated fitness outcomes of these manipulations. Majetic et al. (2009) for example, found a positive effect on both pollinator visitation and seed production in Hesperis matronalis by augmenting inflorescences with scent extracts. Kessler et al. (2008) genetically manipulated the biosynthesis of the most abundant floral volatile, benzylacetone (BA), in the flowers of the wild tobacco (Nicotiana attenuata) to demonstrate that BA emission is required to maximize both maternal and paternal fitness in the field when the entire native community of floral visitors had access to the flowers. Few studies have investigated the influence of scent and nectar on the entire pollinating community simultaneously.

Floral traits are shaped not only by interactions with mutualists, but also with antagonist, such as herbivores, florivores, seed feeders, or nectar robbers (Strauss and Whittall, 2006; Andrews et al., 2007; Kessler et al., 2013). Both floral scent and nectar are known to influence antagonists and again manipulative experiments have been essential in illuminating these interactions. Floral scents allow florivores to locate their host plants (Theis, 2006; Theis and Adler, 2012). Several studies have shown that individual compounds in floral scent bouquets specifically deter florivores and nectar robbers (Galen et al., 2011; Junker et al., 2011; Kessler et al., 2013) and thus complex floral scents can both attract pollinators and deter antagonists. Nectar quantity has also been shown to influence herbivory, as inferred by adding nectar or sugar solutions to flowers to increase the standing volume of nectar. In both Datura wrightii (Adler and Bronstein, 2004), as well as N. attenuata (Kessler, 2012) nectar addition increased oviposition by M. sexta, a pollinator as an adult as well as a devastating herbivore as a caterpillar for both plant species. The inference from both studies is that plants could reduce their herbivore load by producing only small amounts or no nectar at all.

As suggested by the early ESS modeling efforts (Bell, 1986), a few nectar-free plants dispersed in a large population of nectar-producing plants could realize a fitness benefit from reduced herbivore loads, if at low frequencies pollinators cannot learn their locations (Gumbert and Kunze, 2001) or otherwise identify their lack of rewards (Smithson and MacNair, 1997; Smithson, 2009). A few studies investigated the influence of floral traits for both pollinators and herbivores at the same time. Theis et al. (2014), for example, found that floral sesquiterpenoids from Cucurbitaceae were the best predictors of flower preference for both the specialist pollinator squash bees and the specialist herbivore cucumber beetle. Schiestl et al. (2014) showed that the herbivory-induced reduction of floral volatiles reduced the attractiveness of flowers to pollinators. These studies however were focusing on floral signals and did not consider the function of floral rewards, and so while the ESS explanation for rewardless flowers being maintained by frequency dependent selection is well established (Bell, 1986), we still lack studies that evaluate the relative selective pressures of mutualistic and antagonistic floral visitors on floral reward provisioning strategies.

In this study, we rigorously investigate the consequences of reducing floral scent and nectar independently and simultaneously by RNAi not only for mutualistic interactions but also for oviposition rate, which is an excellent predictor of future herbivory. We measured outcrossing rates afforded by the entire natural pollinator community in N. attenuata's native habitat, by three specific pollinators (hummingbirds and two hawkmoth species), and oviposition by M. sexta. Different approaches were used in the field and laboratory to study pollination and herbivory. At our field site at the Lytle Ranch Preserve in SW Utah, we conducted common garden experiments using plants silenced by RNAi in the production of floral scent (CHAL; Kessler et al., 2008), floral nectar (SWEET9; Lin et al., 2014), or both (CHALxSWEET9), in comparison to empty vector-transformed control plants (EV). The RNAi constructs silenced only the targeted pathways and were otherwise isogenic, and the transformed plants were morphologically indistinguishable from EV transformed and wild-type (WT) plants. This approach allowed for the study of the entire panoply of interactions simultaneously, including different floral visitors, and herbivores to gain an unbiased picture of the interactions of individual players in the complex web of interactions that occur when flowers advertise for pollinator services.

To examine the natural genetic variability in floral advertisement and rewards for pollinator services in native populations of N. attenuata plants, seeds from plants from 13 native populations, collected between 1993 and 2009 within a 200 km radius of our field station in the SW USA (Utah, Arizona), were used. The variance in standing nectar volume was assessed by selecting 52 plants in the glasshouse. The average standing nectar volume was 3.3 μL and varied between single genotypes between 1.3 μL and 5.7 μL per flower (Figure 1). In addition to this fourfold difference in nectar volume, we also assessed the variability of BA emission from individual flowers from the same sample of plant populations. The average emission was 5.0 ng BA per flower per night, varying between a minimum emission of 0.5 ng and a maximum of 33.9 ng, equivalent to a 70-fold difference in emission rates (Figure 1). Nectar sugar concentrations were in contrast, rather constant. Sugar concentration in newly opened flowers varied between 14.6 and 22.8%, with an average of 18.2%.

Figure 1

Download asset Open asset

In a second screening, 424 individual plants were screened for the presence or absence of nectar and scent. Seeds for this experiment were collected from 75 natural populations in Utah, Arizona, and California between 1990 and 2009. Of these, two plants produced no nectar at all. These lines (83 and 84) were inbred for two generations and plants of the T₂ generations of both lines also did not produce any nectar in any flowers at all stages of development (Figure 1), demonstrating that the lack of nectar production was heritable. All flowers from all 424 plants in this analysis produced BA in some measureable quantity. We found no evidence for a linear correlation between BA emission and nectar volume (y = 0.1875x + 1.02, R² = 0.01), BA emission and nectar sugar concentration (y = 0.1233x + 3.89, R² = 0.01), or nectar volume and nectar sugar (y = −1.4471x + 22.99, R² = 0.41). While we cannot exclude that genotypes completely lacking floral scent occur in native populations, our results demonstrate that native populations harbor nectar-free ‘cheating’ plants at low frequencies. BA emission however does not occur over the entire night, but is commonly released in a sharp peak in the first part of the night (Kessler et al., 2010), depending on the genotype and the environment. Hence, there are times in the night were one plant is emitting BA, while a neighboring plant is not in genetically diverse populations. Since nectar and scent production were uncorrelated in natural populations, we used RNAi to completely uncouple nectar and scent production in all combinations in an isogenetic background to rigorously examine their costs and benefits.

To investigate the consequences of a lack in nectar, floral scent, and both on pollination and herbivory, EV, SWEET9, CHAL, and CHALxSWEET9 plants were randomly planted 4 m apart in a field plot in the SW USA (Utah) in the plant's native habitat (Figure 2A). Since N. attenuata is a self-compatible plant, anthers were removed from five flowers per plant, and all additional flowers were removed. Hence, seeds of treated flowers were produced entirely from pollinator-mediated outcrossing with neighboring WT plants, which were planted between the rows of transformed plants and were allowed to flower normally. To evaluate the overall pollinator attractiveness of the lines, we allowed floral visitors free access to treatment plants for the flowers' lifetime (3 days). All three lines lacking either floral scent, nectar or both, produced significantly fewer seeds in comparison to similarly antherectomized EV plants (Figure 2B; Friedman signed rank test χ² = 10.22, df = 3, n = 16–20, p = 0.017). CHAL plants produced only 22.9% (p < 0.001), Sweet9 9.7% (p < 0.001), and CHALxSWEET9 11.6% (p < 0.001) of the seeds produced by EV from outcrossed pollen. The most frequent floral visitors of N. attenuata at night in the field at the time of the experiment were M. sexta, Manduca quinquemaculata, and Hyles lineata hawkmoths (Sphingidae), and Archilochus alexandri hummingbirds (Trochilidae) during the day time. In order to evaluate each species individually, we covered experimental plants at night with mesh cones (Kessler et al., 2010) to only allow hummingbirds to access the flowers. While CHAL (75.6%; Friedman signed rank test χ² = 7.66, df = 2, n = 24, p = 0.02; p = 0.835) plants produced almost as many seeds as EV plant, SWEET9 plants produced considerably fewer seeds than did EV plants (13.1%; p = 0.074) and significantly fewer seeds than did CHAL plants (17.4%; p = 0.005; Figure 2C).

Figure 2 with 3 supplements see all

Download asset Open asset

Of the three hawkmoth species occurring in the field, we examined two (M. sexta and H. lineata) in a tent set-up. To simulate field conditions for the moths, we used a mesh tent large enough for natural moth pollination behavior (24 m × 8 m; Figure 2D) and examined the consequences of pollination services from single hawkmoths of a given species at a time, something that was not possible to do in the field. To avoid inbreeding or artificial larval diet effects, only moths that had been reared from field-collected eggs and maintained on a plant diet were used.

We used a different moth on each experimental day. M. sexta strongly distinguished between EV and all other lines (Figure 2E; Friedman signed rank test χ² = 29.90, df = 3, n = 30, p < 0.001). Although SWEET9 lines produced only 44.6% of the seeds produced by EV plants (p = 0.006), the avoidance of plants which produce no floral scent had an even stronger effect on seed set. CHAL plants produced only 17.4% of the seeds of EV (p < 0.001), just as CHALxSWEET9 plants which produced only 5.2% of the seeds produced by EV plants (p < 0.001). Interestingly, H. lineata, a second naturally occurring hawkmoth species, showed a different response to that of M. sexta (Figure 2F; Friedman signed rank test χ² = 12.35, df = 3, n = 35, p = 0.006). Both CHAL (96.6%; p = 0.853) and SWEET9 (111.69%; p = 0.578) plants produced a number of seeds equivalent to those of EV plants. Only when the flowers lacked both floral traits (CHALxSWEET9) was seed set mediated by H. lineata pollinator activity significantly reduced (21.3%; p = 0.003).

Our results highlight the value of using both floral scent attraction as well as a reward in order to cope with an unpredictable community of pollinators. While floral scent is an essential floral cue for M. sexta, it is clearly not for H. lineata. While nectar is essential for pollination by A. alexandri, the lack of nectar does not reduce the pollination services provided by H. lineata visitations. Though more generalist pollinators such as H. lineata seem to pollinate even when scent or nectar is missing, they strongly reject plants which are unable to produce both floral traits. However, for the more specialized pollinator, M. sexta, both floral scent attraction and nectar reward are important to maximize outcrossing. Although all lines lacking a certain floral trait showed reduced seed set, plants unable to produce nectar still produced more than twice as many seeds than plants unable to produce floral scent. Again, we found a stronger negative effect in plants unable to produce both traits. In contrast, A. alexandri, the only daytime visitor, rejected plants which did not produce nectar, but surprisingly was also less attracted to plants unable to produce floral scent, although visiting flowers at times when BA is not emitted. One explanation for this could be the accumulation of floral scent in nectar (Raguso, 2004), which could influence the taste of the nectar for these pollinators (Kessler and Baldwin, 2007). Another hypothesis could be that hummingbirds also use olfaction and are able to use nectar scent to locate a proper nectar source. Birds, long suspected to use scent in homing and navigation (Keeton, 1974) have recently been suggested to use scent for prey localization (Mantyla et al., 2008).

Hummingbirds, in contrast to hawkmoths, have smaller territories and pollinate flowers only in a small area close to their nests (Norton et al., 1982). This allows them to learn cues and plant positions which are correlated with the presence of food sources (Ackerman et al., 1994; Campbell et al., 1997; Hurly and Healy, 2002) or even with the quality of the food (Pérez et al., 2011). In contrast to hummingbirds, large hawkmoths like M. sexta cover much larger distances each night and rely more on direct perception rather than memory in visiting their food sources (Raguso and Willis, 2003, 2005), as they most likely never visit the same plant twice in nature, although moths are also known to learn when exploring new food sources (Riffell et al., 2013). The inferences we can draw about learning in moths from our results are limited. The pollination assays were conducted with naive moths that lacked experience with rewardless and scentless N. attenuata plants. Experienced H. lineata might have behaved differently, as has been shown in orchid pollinating bumblebees which were able to learn to avoid deceptive orchids (Internicola and Harder, 2012). Our experiments did not directly measure the behavior of the floral visitors and data on the variance in seed set resulting from pollinator visitations, limits the inferences we can draw about pollinator behavior. While there was no evidence that pollinators can sense the presence of nectar, we cannot reject this hypothesis (von Arx et al., 2012).

For future research, it would be interesting to investigate how geitonogamy (Fisogni et al., 2011) would be influenced in rewardless plants. We measured only the pollen flow from one plant to another and not within a given inflorescence. The behaviors of the three tested pollinator species appear to be quite different, and these differences have the potential to influence pollen transfer within an inflorescence and thereby decrease the chance of receiving outcrossed pollen. H. lineata, M. sexta, and M. quinquemaculata, for example, show different patterns of movement in both the field and the tent. While H. lineata moves sequentially from one flower to the next—visiting all flowers within one inflorescence (even visiting the same flower multiple times)—and from one plant to the next plant, both Manduca species visit only a few flowers within an inflorescence before moving on and skipping as many as 10 intervening plants (personal observations).

N. attenuata is capable of producing the full set of seeds of comparable mass and number from self-pollination as from outcrossing (Baldwin et al., 1997). Hence, the plant can compensate for the lack of pollinators by selfing, and the function of producing floral nectar and scent is to ensure outcrossing to maintain genetic variability. Given that N. attenuata seeds can spend hundreds of years in the seed bank before germinating (Preston and Baldwin, 1999), outcrossing will likely be associated with longevity in the seedbank. In other systems outcrossing is thought to increase herbivore resistance in future generations (Bello-Bedoy and Núnez-Farfán, 2011).

How does the production of floral scent or nectar influence M. sexta herbivory? To answer this question, the number of flowers was reduced to the same number on all plants on a given day and oviposited eggs were counted the following morning. In the field, all lines lacking a certain floral trait had reduced loads of eggs (Figure 2G; Friedman signed rank test χ² = 11.15, df = 3, n = 4, p = 0.0109). On CHAL plants, only 43.1% (p = 0.046) of the eggs found on EV plants were oviposited. The effect was even stronger in plants unable to produce nectar (SWEET9, 10.8%; CHALxSWEET9, 6.5%, p = 0.046). Comparable results were obtained in a similar experiment, conducted in the tent with single moths, (Figure 2H; χ² = 13.73, df = 3, n = 11, p = 0.0033). CHAL plants received only 81.9% (p = 0.096) of the eggs of EV plants, while SWEET9 received 52.5% (p = 0.058) and CHALxSWEET9 received 48.4% (p = 0.007) of eggs compared with EV. To control for any other leaf-based influences on oviposition, we conducted control experiments with plants without flowers, by using non-flowering plants or plants from which flowers had been removed before the experiment. In the non-flowering stage, no differences in oviposition were found between the different lines (Friedman signed rank test χ² = 2.81, df = 3, n = 6, p = 0.4214). In the field, oviposition rates (over eight experimental days) were too low for statistical analyses, when plants did not flower. Although, plants unable to produce floral scent had a significantly lower chance of receiving an egg from M. sexta, nectar seems to play a larger role in host choice. In contrast to the pollination results, we found no elevated effect in the CHALxSWEET9 cross compared to the single silenced line, which is consistent with the strong effect of nectar alone. The fact that plants lacking floral scent have reduced ovipositions as well as reduced seed set is probably due to the difficulties hawkmoths have in locating scentless N. attenuata flowers (Kessler et al., 2008).

It is intriguing that it is the reward more than the attractant which influences the female hawkmoth's oviposition decisions, while the attractant plays a similar role as the reward when the same species is acting as a pollinator. Of course our conclusions are context dependent, depending on the frequency of low-nectar or low-scented plants in a native population. Clearly, there needs to be an attractive component (e.g., floral scent) coupled with the nectar reward to allow moths to associate food sources with certain floral signals (Daly and Smith, 2000) or oviposition sites. Similarly, moths would learn to avoid floral advertisements if they are not associated with a reward (Haber, 1984; Smithson, 2001). In an evolutionary context, our findings suggest that for an interaction with M. sexta, N. attenuata plants should minimize their nectar accumulation while maximizing their floral scent emission in order to maximize fitness. Indeed N. attenuata produces tiny amounts of nectar in comparison to other sympatric plant species that compete for M. sexta's pollination services (Raguso et al., 2003; Riffell et al., 2008), and thus seems to have optimized the attractant–reward proportions to maximize its fitness when dealing with this pollinator which is also an herbivore, albeit at a different life stage. D. wrightii, for example, a plant which shares M. sexta as both pollinator and herbivore, is a perennial plant that accumulates 20 times more nectar, produces a large amount of foliage that allows it to better compensate for losses to herbivores, and recruits fewer pollinating species than does N. attenuata (Kessler, 2012).

Pollination success of N. attenuata is ensured by recruiting several pollinator species, which may influence the evolution of floral scent and nectar reward. In the absence of M. sexta, species such as hummingbirds which only pollinate plants capable of producing nectar, and probably will also only continue to pollinate if a sufficient quantity of nectar is available, are important pollen vectors. H. lineata also provides adequate pollination services, but without apparent evolutionary pressure on these N. attenuata floral traits, as this species seems to require neither an attractive signal nor a reward, at least if these plants are surrounded by other nectar and scent producing plants. Moreover H. lineata is also not an herbivore on N. attenuata during its larval stage. These two sphingid species show dramatic differences in their responses to both floral traits. Only M. sexta seems to rely on the cues provided by the flower and distinguishes between honest and cheating plants in a mixed N. attenuata population. For H. lineata, either floral scent or nectar is sufficient for the moth to provide full pollination services. In bee pollinated Brassica rapa plants, the honesty of a floral signal—that a certain scent would consistently lead to a reward—plays a key role in the plants attractiveness to pollinators (Knauer and Schiestl, 2015), something which seems not to be the case for N. attenuata and its pollinators, as we found no association between BA emission and nectar volume, or nectar sugar concentration. Our data are consistent with a trade-off between rewarding pollinators and avoiding herbivores in the evolution of floral nectar in N. attenuata, but why is BA emission so variable? Since antagonists also use floral scent to locate host plants, increasing floral scent emissions would not only increased M. sexta pollination but could also incur costs by possibly increasing florivory or nectar robbing. Carpenter bees, for example, tend to rob more N. attenuata flowers on plants that emit BA than from plants that do not (Kessler et al., 2008) and experimentally enhancing BA emissions increased rates of browsing (Baldwin et al., 1997).

In this study, all pollinators visited N. attenuata at different times between dusk and dawn and it is an interesting question how this temporal order of nectar removal influences the pollination services they provide for the plants. A. alexandri visits N. attenuata approximately 2 h before dusk until dusk and again in the morning after dawn for 2 h. H. lineata visits from 1 h before dusk until 1 h after dawn, and finally both Manduca species pollinate and oviposit after dusk until dawn, with periods of enhanced activity depending on moonlight, temperature, and other conditions. We had expected that these different visiting times would blur the differences in the seed set amongst the lines differing in scent and nectar production when plants were exposed to the entire community of floral visitors at the field station, but the results were just the opposite. The differences between EV plants and SWEET9 as well as CHAL plants were maximized when plants were exposed to the entire community of pollinators at once, suggesting that the behavioral responses of each different pollinator to a given floral trait reinforce each other in ways that can only be understood through more detailed behavioral observations. N. attenuata flowers secrete nectar slowly over the night until approximately 4 am (Kessler, 2012), theoretically keeping a low but constant amount of nectar over the entire night, even if floral visitors removed the majority of nectar in the dusk or in the first half of the night. Unfortunately, we do not know if nectar secretion patterns change after flowers have been visited and nectar has been removed, but from nectar measurements, we know that flowers visited by M. sexta (which invariably removes the standing volume of nectar) again contain a residual amount of nectar in the morning. Similar questions remain about the costs associated with the production of nectar. One would expect that nectaring hummingbirds or white-lined sphinx moths in the dusk could influence M. sexta oviposition by removing all the nectar in the 2 h before M. sexta becomes active. Clearly, there remains a large gap between our understanding of how particular flower traits influence the behavior of the flower visitors and the pollination services that they provide.

Our model system appears particular in that pollinators are at the same time herbivores, but this ecological dilemma is not uncommon for many plant species (Irwin, 2010), and the lessons we draw from our study are likely applicable for other plant systems with differently structured interactions with insects. For example, in the Silene latifolia–Hadena bicruris nursery pollination system, male moths provide a fitness benefit for the plant, while interactions with females come with the substantial costs of seed-feeding caterpillars (Labouche and Bernasconi, 2009). The highly specialized Yucca-Tegiticula pollination/seed predation system would be another example. Plants rarely interact with just one partner and usually face multiple selection pressures from mutualists and antagonists at the same time, which may shape the different floral traits of a plant. In most cases, there will be less and more effective pollinators (Conner et al., 1995; Barthelmess et al., 2006; Matsuki et al., 2008; Miller et al., 2014), as well as herbivores which are attracted by floral signals such as scent or nectar (Adler and Bronstein, 2004; Theis, 2006). Even purely mutualistic interactions like those known from bee pollination systems, probably evolved in the context of avoiding herbivory and excluding less efficient pollinators at the same time. Pollinator networks are known to be highly flexible and change frequently over time within a season (Olesen et al., 2008), as well as between years (Olesen et al., 2011) and this flexibility likely explains why plants recruit additional pollinator species rather than specialize if they rely on outcrossing. Most pollinator networks are nested (Pawar, 2014) and this nestedness is thought to stabilize mutualistic networks (Rohr et al., 2014), reducing interspecific competition and enhancing the number of coexisting species (Bastolla et al., 2009), with the end result of increasing the coexistence of several pollinating species on a plant. The high temporal plasticity in species composition coupled with the low variation in network structure properties in pollination networks makes such a scenario likely (Petanidou et al., 2008).

Flowers face a multidimensional challenge. They have to ensure outcrossing by an unpredictable number of pollinating species and individuals, all of which have different preferences and behaviors, and at the same time need to keep herbivores at bay. Here, we demonstrate that nectar-free, pollinator cheating, varieties of N. attenuata occur in nature, and that producing no nectar can substantially reduce ovipositions by M. sexta, but that this comes with a reduction in pollination services. While cheating on the nectar rewards works for one pollinator species, it does not for others. In contrast, cheating with regards to the attractant floral scent has little influence on M. sexta oviposition, but dramatically reduces outcrossing by M. sexta. All three examined pollinators show different responses to nectar and scent and thus the interactions of multiple species are likely responsible for fine-tuning the evolution of both nectar and floral scent, as both H. lineata, as well as A. alexandri are important pollen vectors in the absence of M. sexta. Both traits in combination are required to maximize maternal fitness in nature particularly in ensuring M. sexta visitations, which we hypothesize delivers the greatest diversity of pollen genotypes compared to H. lineata and A. alexandri. Hence, the close association between N. attenuata and M. sexta may result from M. sexta's ability to move pollen over longer distances than the other pollinators, and thus provide superior outcrossing services among N. attenuata populations which are frequently isolated by the large distances that frequently occur between fires, and hence N. attenuata populations. The long time between fires at any particular location may place a premium on outcrossing rates, as heterozygosity may increase herbivore resistance in future generations (Mescher et al., 2009; Bello-Bedoy and Nunez-Farfan, 2011), as well as longevity in the seedbank. We conclude that herbivores, as well as pollinators shape the evolution of floral traits and that it makes little sense to study floral traits as if they only mediate pollination services.

1. 1. Ackerman JD
   2. Rodriguez-Robles JA
   3. Melendez EJ

   (1994) A meager nectar offering by an epiphytic orchid is better than nothing

   Biotropica 26:44–49.

   https://doi.org/10.2307/2389109

   • Google Scholar
2. 1. Adler LS
   2. Bronstein JL

   (2004) Attracting antagonists: does floral nectar increase leaf herbivory?

   Ecology 85:1519–1526.

   https://doi.org/10.1890/03-0409

   • Google Scholar
3. 1. Andrews E
   2. Theis N
   3. Adler L

   (2007) Pollinator and herbivore attraction to Cucurbita floral volatiles

   Journal of Chemical Ecology 33:1682–1691.

   https://doi.org/10.1007/s10886-007-9337-7

   • Google Scholar
4. 1. Baldwin IT
   2. Preston C
   3. Euler M
   4. Gorham D

   (1997) Patterns and consequences of benzyl acetone floral emissions from Nicotiana attenuata plants

   Journal of Chemical Ecology 23:2327–2343.

   https://doi.org/10.1023/B:JOEC.0000006677.56380.cd

   • Google Scholar
5. 1. Barthelmess EL
   2. Richards CM
   3. Mccauley DE

   (2006) Relative effects of nocturnal vs diurnal pollinators and distance on gene flow in small Silene alba populations

   New Phytologist 169:689–698.

   https://doi.org/10.1111/j.1469-8137.2005.01580.x

   • Google Scholar
6. 1. Bastolla U
   2. Fortuna MA
   3. Pascual-Garcia A
   4. Ferrera A
   5. Luque B
   6. Bascompte J

   (2009) The architecture of mutualistic networks minimizes competition and increases biodiversity

   Nature 458:1018–1020.

   https://doi.org/10.1038/nature07950

   • Google Scholar
7. 1. Bell G

   (1986) The evolution of empty flowers

   Journal of Theoretical Biology 118:253–258.

   https://doi.org/10.1016/S0022-5193(86)80057-1

   • Google Scholar
8. 1. Bello-Bedoy R
   2. Nunez-Farfan J

   (2011) The effect of inbreeding on defence against multiple enemies in Datura stramonium

   Journal of Evolutionary Biology 24:518–530.

   https://doi.org/10.1111/j.1420-9101.2010.02185.x

   • Google Scholar
9. 1. Brandenburg A
   2. Kuhlemeier C
   3. Bshary R

   (2012a) Hawkmoth pollinators decrease seed set of a low-nectar Petunia axillaris line through reduced probing time

   Current Biology 22:1635–1639.

   https://doi.org/10.1016/j.cub.2012.06.058

   • Google Scholar
10. 1. Brandenburg A
    2. Kuhlemeier C
    3. Bshary R

    (2012b) Innate adjustment of visitation behavior to rewarding and reward-minimized Petunia axillaris (Solanacea) plants by hawkmoth Manduca sexta (Sphingidae)

    Ethology 118:654–661.

    https://doi.org/10.1111/j.1439-0310.2012.02055.x

    • Google Scholar
11. 1. Bubner B
    2. Gase K
    3. Berger B
    4. Link D
    5. Baldwin I

    (2006) Occurrence of tetraploidy in Nicotiana attenuata; plants after Agrobacterium -mediated transformation is genotype specific but independent of polysomaty of explant tissue

    Plant Cell Reports 25:668–675.

    https://doi.org/10.1007/s00299-005-0111-4

    • Google Scholar
12. 1. Byers KJ
    2. Bradshaw HD Jr
    3. Riffell JA

    (2014) Three floral volatiles contribute to differential pollinator attraction in monkeyflowers (Mimulus)

    The Journal of Experimental Biology 217:614–623.

    https://doi.org/10.1242/jeb.092213

    • Google Scholar
13. 1. Campbell DR
    2. Waser NM
    3. Melendez-Ackerman EJ

    (1997) Analyzing pollinator-mediated selection in a plant hybrid zone: hummingbird visitation patterns on three spatial scales

    The American Naturalist 149:295–315.

    https://doi.org/10.1086/285991

    • Google Scholar
14. 1. Conner J
    2. Davis R
    3. Rush S

    (1995) The effect of wild radish floral morphology on pollination efficiency by four taxa of pollinators

    Oecologia 104:234–245.

    https://doi.org/10.1007/BF00328588

    • Google Scholar
15. Book

    1. Crawley MJ

    (2007)

    The R book

    West Sussex: John Wiley & Sons Ltd.

    • Google Scholar
16. 1. Daly KC
    2. Smith BH

    (2000)

    Associative olfactory learning in the moth Manduca sexta

    The Journal of Experimental Biology 203:2025–2038.

    • Google Scholar
17. 1. Fisogni A
    2. Cristofolini G
    3. Rossi M
    4. Galloni M

    (2011) Pollinator directionality as a response to nectar gradient: promoting outcrossing while avoiding geitonogamy

    Plant Biology 13:848–856.

    https://doi.org/10.1111/j.1438-8677.2011.00453.x

    • Google Scholar
18. 1. Galen C
    2. Kaczorowski R
    3. Todd SL
    4. Geib J
    5. Raguso RA

    (2011) Dosage-dependent impacts of a floral volatile compound on pollinators, larcenists, and the potential for floral evolution in the alpine skypilot Polemonium viscosum

    The American Naturalist 177:258–272.

    https://doi.org/10.1086/657993

    • Google Scholar
19. 1. Galen C
    2. Newport ME

    (1988) Pollination quality, seed set, and flower traits in Polemonium viscosum: complementary effects of variation in flower scent and size

    American Journal of Botany 75:900–905.

    https://doi.org/10.2307/2444010

    • Google Scholar
20. 1. Gaquerel E
    2. Heiling S
    3. Schoettner M
    4. Zurek G
    5. Baldwin IT

    (2010) Development and validation of a liquid chromatography-electrospray ionization-time-of-flight mass spectrometry method for induced changes in Nicotiana attenuata leaves during simulated herbivory

    Journal of Agricultural and Food Chemistry 58:9418–9427.

    https://doi.org/10.1021/jf1017737

    • Google Scholar
21. 1. Gilbert FS
    2. Haines N
    3. Dickinson K

    (1991) Empty flowers

    Functional Ecology 5:29–39.

    https://doi.org/10.2307/2389553

    • Google Scholar
22. 1. Gumbert A
    2. Kunze J

    (2001) Colour similarity to rewarding model plants affects pollination in a food deceptive orchid, Orchis boryi

    Biological Journal of the Linnean Society 72:419–433.

    https://doi.org/10.1111/j.1095-8312.2001.tb01328.x

    • Google Scholar
23. 1. Haber WA

    (1984) Pollination by Deceit in a mass-flowering tropical tree Plumeria rubra L. (Apocynaceae)

    Biotropica 16:269–275.

    https://doi.org/10.2307/2387935

    • Google Scholar
24. 1. Hurly TA
    2. Healy SD

    (2002) Cue learning by rufous hummingbirds (Selasphorus rufus)

    Journal of Experimental Psychology 28:209–223.

    https://doi.org/10.1037/0097-7403.28.2.209

    • Google Scholar
25. 1. Internicola AI
    2. Harder LD

    (2012) Bumble-bee learning selects for both early and long flowering in food-deceptive plants

    Proceedings. Biological Sciences/the Royal Society 279:1538–1543.

    https://doi.org/10.1098/rspb.2011.1849

    • Google Scholar
26. 1. Irwin RE

    (2010) Evolutionary ecology: when pollinators are also herbivores

    Current Biology 20:R100–R101.

    https://doi.org/10.1016/j.cub.2009.12.005

    • Google Scholar
27. 1. Ishii HS
    2. Hirabayashi Y
    3. Kudo G

    (2008) Combined effects of inflorescence architecture, display size, plant density and empty flowers on bumble bee behaviour: experimental study with artificial inflorescences

    Oecologia 156:341–350.

    https://doi.org/10.1007/s00442-008-0991-4

    • Google Scholar
28. 1. Jersáková J
    2. Johnson SD
    3. Kindlmann P
    4. Pupin AC

    (2008) Effect of nectar supplementation on male and female components of pollination success in the deceptive orchid Dactylorhiza sambucina

    Acta Oecologica 33:300–306.

    https://doi.org/10.1016/j.actao.2008.01.001

    • Google Scholar
29. 1. Junker RR
    2. Gershenzon J
    3. Unsicker S

    (2011) Floral odor bouquet loses its ant repellent properties after inhibition of terpene biosynthesis

    Journal of Chemical Ecology 37:1323–1331.

    https://doi.org/10.1007/s10886-011-0043-0

    • Google Scholar
30. 1. Jürgens A
    2. Witt T
    3. Gottsberger G

    (2002) Flower scent composition in night-flowering Silene species (Caryophyllaceae)

    Biochemical Systematics and Ecology 30:383–397.

    https://doi.org/10.1016/S0305-1978(01)00106-5

    • Google Scholar
31. 1. Kallenbach M
    2. Oh Y
    3. Eilers EJ
    4. Veit D
    5. Baldwin IT
    6. Schuman MC

    (2014) A robust, simple, high-throughput technique for time-resolved plant volatile analysis in field experiments

    The Plant Journal 78:1060–1072.

    https://doi.org/10.1111/tpj.12523

    • Google Scholar
32. Book

    1. Karban R

    (2015)

    Plant sensing and communication

    Chicago: The University of Chicaco Press.

    • Google Scholar
33. 1. Keeton WT

    (1974)

    Advances in the study of behavior

    Advances in the study of behavior, London, NY, Academic Press.

    • Google Scholar
34. 1. Kessler D

    (2012) Context dependency of nectar reward-guided oviposition

    Entomologia Experimentalis et Applicata 144:112–122.

    https://doi.org/10.1111/j.1570-7458.2012.01270.x

    • Google Scholar
35. 1. Kessler D
    2. Baldwin IT

    (2007) Making sense of nectar scents: the effects of nectar secondary metabolites on floral visitors of Nicotiana attenuata

    The Plant Journal 49:840–854.

    https://doi.org/10.1111/j.1365-313X.2006.02995.x

    • Google Scholar
36. 1. Kessler D
    2. Diezel C
    3. Baldwin IT

    (2010) Changing pollinators as a means of escaping herbivores

    Current Biology 20:237–242.

    https://doi.org/10.1016/j.cub.2009.11.071

    • Google Scholar
37. 1. Kessler D
    2. Diezel C
    3. Clark DG
    4. Colquhoun TA
    5. Baldwin IT

    (2013) Petunia flowers solve the defence/apparency dilemma of pollinator attraction by deploying complex floral blends

    Ecology Letters 16:299–306.

    https://doi.org/10.1111/ele.12038

    • Google Scholar
38. 1. Kessler D
    2. Gase K
    3. Baldwin IT

    (2008) Field experiments with transformed plants reveal the sense of floral scents

    Science 321:1200–1202.

    https://doi.org/10.1126/science.1160072

    • Google Scholar
39. 1. Kevan PG
    2. Baker HG

    (1983) Insects as flower visitors and pollinators

    Annual Review of Entomology 28:407–453.

    https://doi.org/10.1146/annurev.en.28.010183.002203

    • Google Scholar
40. 1. Klahre U
    2. Gurba A
    3. Hermann K
    4. Saxenhofer M
    5. Bossolini E
    6. Guerin PM
    7. Kuhlemeier C

    (2011) Pollinator choice in Petunia depends on two major genetic loci for floral scent production

    Current Biology 21:730–739.

    https://doi.org/10.1016/j.cub.2011.03.059

    • Google Scholar
41. 1. Knauer AC
    2. Schiestl FP

    (2015) Bees use honest floral signals as indicators of reward when visiting flowers

    Ecology Letters 18:135–143.

    https://doi.org/10.1111/ele.12386

    • Google Scholar
42. 1. Krügel T
    2. Lim M
    3. Gase K
    4. Halitschke R
    5. Baldwin IT

    (2002) Agrobacterium-mediated transformation of Nicotiana attenuata, a model ecological expression system

    Chemoecology 12:177–183.

    https://doi.org/10.1007/PL00012666

    • Google Scholar
43. 1. Labouche A
    2. Bernasconi G

    (2009) Male moths provide pollination benefits in the Silene latifolia—Hadena bicruris nursery pollination system

    Functional Ecology 24:534–544.

    https://doi.org/10.1111/j.1365-2435.2009.01658.x

    • Google Scholar
44. 1. Lin IW
    2. Sosso D
    3. Chen LQ
    4. Gase K
    5. Kim SG
    6. Kessler D
    7. Klinkenberg PM
    8. Gorder MK
    9. Hou BH
    10. Qu XQ
    11. Carter CJ
    12. Baldwin IT
    13. Frommer WB

    (2014) Nectar secretion requires sucrose phosphate synthases and the sugar transporter SWEET9

    Nature 508:546–549.

    https://doi.org/10.1038/nature13082

    • Google Scholar
45. 1. Majetic CJ
    2. Raguso RA
    3. Ashman TL

    (2009) The sweet smell of success: floral scent affects pollinator attraction and seed fitness in Hesperis matronalis

    Functional Ecology 23:480–487.

    https://doi.org/10.1111/j.1365-2435.2008.01517.x

    • Google Scholar
46. 1. Mantyla E
    2. Alessio GA
    3. Blande JD
    4. Heijari J
    5. Holopainen JK
    6. Laaksonen T
    7. Piirtola P
    8. Klemola T

    (2008) From plants to birds: higher avian predation rates in trees responding to insect herbivory

    PLOS ONE 3:0002832.

    https://doi.org/10.1371/journal.pone.0002832

    • Google Scholar
47. 1. Matsuki Y
    2. Tateno R
    3. Shibata M
    4. Isagi Y

    (2008) Pollination efficiencies of flower-visiting insects as determined by direct genetic analysis of pollen origin

    American Journal of Botany 95:925–930.

    https://doi.org/10.3732/ajb.0800036

    • Google Scholar
48. 1. Mescher MC
    2. Delphia CM
    3. de Moraes CM
    4. Stephenson AG

    (2009) Inbreeding in horsenettle influences herbivore resistance

    Ecological Entomology 34:513–519.

    https://doi.org/10.1111/j.1365-2311.2009.01097.x

    • Google Scholar
49. 1. Miller TJ
    2. Raguso RA
    3. Kay KM

    (2014) Novel adaptation to hawkmoth pollinators in Clarkia reduces efficiency, not attraction of diurnal visitors

    Annals of Botany 113:317–329.

    https://doi.org/10.1093/aob/mct237

    • Google Scholar
50. 1. Mitchell RJ
    2. Waser NM

    (1992) Adaptive significance of Ipomopsis aggregata nectar production: pollination success of single flowers

    Ecology 73:633–638.

    https://doi.org/10.2307/1940769

    • Google Scholar
51. 1. Norton ME
    2. Arcese P
    3. Ewald PW

    (1982)

    Effect of intrusion pressure on territory size in Black-chinned hummingbirds (Archilochus alexandri)

    The Auk 99:761–764.

    • Google Scholar
52. 1. Olesen JM
    2. Bascompte J
    3. Elberling H
    4. Jordano P

    (2008) Temporal dynamics in a pollination network

    Ecology 89:1573–1582.

    https://doi.org/10.1890/07-0451.1

    • Google Scholar
53. 1. Olesen JM
    2. Stefanescu C
    3. Traveset A

    (2011) Strong, long-term temporal dynamics of an ecological network

    PLOS ONE 6:e26455.

    https://doi.org/10.1371/journal.pone.0026455

    • Google Scholar
54. 1. Pawar S

    (2014) Why are plant-pollinator networks nested?

    Science 345:383.

    https://doi.org/10.1126/science.1256466

    • Google Scholar
55. 1. Petanidou T
    2. Kallimanis AS
    3. Tzanopoulos J
    4. Sgardelis SP
    5. Pantis JD

    (2008) Long-term observation of a pollination network: fluctuation in species and interactions, relative invariance of network structure and implications for estimates of specialization

    Ecology Letters 11:564–575.

    https://doi.org/10.1111/j.1461-0248.2008.01170.x

    • Google Scholar
56. 1. Pérez G
    2. Lara C
    3. Viccon-Pale J
    4. Signoret-Poillon M

    (2011)

    Memory for location and visual cues in white-eared hummingbirds Hylocharis leucotis

    Current Zoology 57:468–476.

    • Google Scholar
57. 1. Preston CA
    2. Baldwin IT

    (1999) Positive and negative signals regulate germination in the post- fire annual

    Nicotiana Attenuata. Ecology 80:481–494.

    https://doi.org/10.1890/0012-9658(1999)080[0481:PANSRG]2.0.CO;2

    • Google Scholar
58. Book

    1. R Development Core Team

    (2007)

    R: a language and environment for statistical computing

    Vienna: R Foundation for Statistical Computing.

    • Google Scholar
59. 1. Raguso RA

    (2004) Why are some floral nectars scented?

    Ecology 85:1486–1494.

    https://doi.org/10.1890/03-0410

    • Google Scholar
60. 1. Raguso RA
    2. Henzel C
    3. Buchmann SL
    4. Nabhan GP

    (2003) Trumpet flowers of the Sonoran Desert: floral biology of Peniocereus cacti and sacred Datura

    International Journal of Plant Sciences 164:877–892.

    https://doi.org/10.1086/378539

    • Google Scholar
61. 1. Raguso RA
    2. Willis MA

    (2003)

    Evolution and ecology taking flight: butterflies as model systems. Rocky mountain biological lab symposium series

    Evolution and ecology taking flight: butterflies as model systems. Rocky mountain biological lab symposium series, Chicago, University of Chicago Press.

    • Google Scholar
62. 1. Raguso RA
    2. Willis MA

    (2005) Synergy between visual and olfactory cues in nectar feeding by wild hawkmoths, Manduca sexta

    Animal Behaviour 69:407–418.

    https://doi.org/10.1016/j.anbehav.2004.04.015

    • Google Scholar
63. 1. Renner SS

    (2006)

    Plant-pollinator interactions: from specialization to generalization

    Plant-pollinator interactions: from specialization to generalization, Chicago, University of Chicago Press.

    • Google Scholar
64. 1. Riffell JA
    2. Alarcón R
    3. Abrell L
    4. Davidowitz G
    5. Bronstein JL
    6. Hildebrand JG

    (2008) Behavioral consequences of innate preferences and olfactory learning in hawkmoth–flower interactions

    Proceedings of the National Academy of Sciences of USA 105:3404–3409.

    https://doi.org/10.1073/pnas.0709811105

    • Google Scholar
65. 1. Riffell JA
    2. Lei H
    3. Abrell A
    4. Hildebrand JG

    (2013) Neural basis of a pollinator's buffet: olfactory specialization and learning in Manduca sexta

    Science 339:200–204.

    https://doi.org/10.1126/science.1225483

    • Google Scholar
66. 1. Riffell JA
    2. Shlizerman E
    3. Sanders E
    4. Abrell L
    5. Medina B
    6. Hinterwirth AJ
    7. Kutz JN

    (2014) Flower discrimination by pollinators in a dynamic chemical environment

    Science 344:1515–1518.

    https://doi.org/10.1126/science.1251041

    • Google Scholar
67. 1. Rohr RP
    2. Saavedra S
    3. Bascompte J

    (2014) On the structural stability of mutualistic systems

    Science 345:1253497.

    https://doi.org/10.1126/science.1253497

    • Google Scholar
68. 1. Rothe E
    2. Schöttner M
    3. Kessler D
    4. Baldwin IT

    (2013)

    Determination of nectar nicotine concentration in N. attenuata

    Bio-Protocol 3:.

    • Google Scholar
69. 1. Schemske DW
    2. Bradshaw HD

    (1999) Pollinator preference and the evolution of floral traits in monkeyflowers (Mimulus)

    Proceedings of the National Academy of Sciences of USA 96:11910–11915.

    https://doi.org/10.1073/pnas.96.21.11910

    • Google Scholar
70. 1. Schiestl FP

    (2005) On the success of a swindle: pollination by deception in orchids

    Die Naturwissenschaften 92:255–264.

    https://doi.org/10.1007/s00114-005-0636-y

    • Google Scholar
71. 1. Schiestl FP
    2. Kirk H
    3. Bigler L
    4. Cozzolino S
    5. Desurmont GA

    (2014) Herbivory and floral signaling: phenotypic plasticity and tradeoffs between reproduction and indirect defense

    The New Phytologist 203:257–266.

    https://doi.org/10.1111/nph.12783

    • Google Scholar
72. 1. Schiestl FP
    2. Schlüter PM

    (2009) Floral isolation, specialized pollination, and pollinator behavior in orchids

    Annual Review of Entomology 54:425–446.

    https://doi.org/10.1146/annurev.ento.54.110807.090603

    • Google Scholar
73. 1. Schwachtje J
    2. Kutschbach S
    3. Baldwin IT

    (2008) Reverse genetics in ecological research

    PLOS ONE 3:e1543.

    https://doi.org/10.1371/journal.pone.0001543

    • Google Scholar
74. 1. Shuttleworth A
    2. Johnson SD

    (2010) The missing stink: sulphur compounds can mediate a shift between fly and wasp pollination systems

    Proceedings of the Royal Society B-Biological Sciences 277:2811–2819.

    https://doi.org/10.1098/rspb.2010.0491

    • Google Scholar
75. 1. Smithson A

    (2001)

    Cognitive ecology of pollination: animal behaviour and floral evolution

    Cognitive ecology of pollination: animal behaviour and floral evolution, Cambridge, Cambridge University Press.

    • Google Scholar
76. 1. Smithson A

    (2002) The consequencess of rewardless ness in in orchids: reward-supplementation experiments with Anacamptis morio (Orchideaceae)

    American Journal of Botany 89:1579–1587.

    https://doi.org/10.3732/ajb.89.10.1579

    • Google Scholar
77. 1. Smithson A

    (2009) A plant's view of cheating in plant–pollinator mutualisms

    Israel Journal of Plant Sciences 57:151–163.

    https://doi.org/10.1560/IJPS.57.3.151

    • Google Scholar
78. 1. Smithson A
    2. MacNair MR

    (1997) Negative frequency-dependent selection by pollinators on artificial flowers without rewards

    Evolution 51:715–723.

    https://doi.org/10.2307/2411148

    • Google Scholar
79. 1. Strauss SY
    2. Whittall JB

    (2006)

    Ecology and evolution of flowers

    Ecology and evolution of flowers, Oxford, Oxford University Press.

    • Google Scholar
80. 1. Tautenhahn R
    2. Böttcher C
    3. Neumann S

    (2008) Highly sensitive feature detection for high resolution LC/MS

    BMC Bioinformatics 9:504.

    https://doi.org/10.1186/1471–2105-9–504

    • Google Scholar
81. 1. Theis N

    (2006) Fragrance of Canada thistle Cirsium arvense attracts both floral herbivores and pollinators

    Journal of Chemical Ecology 32:917–927.

    https://doi.org/10.1007/s10886-006-9051-x

    • Google Scholar
82. 1. Theis N
    2. Adler LS

    (2012) Advertising to the enemy: enhanced floral fragrance increases beetle attraction and reduces plant reproduction

    Ecology 93:430–435.

    https://doi.org/10.1890/11-0825.1

    • Google Scholar
83. 1. Theis N
    2. Barber NA
    3. Gillespie SD
    4. Hazzard RV
    5. Adler LS

    (2014) Attracting mutualists and antagonists: plant trait variation explains the distribution of specialist floral herbivores and pollinators on crops and wild gourds

    American Journal of Botany 101:1314–1322.

    https://doi.org/10.3732/ajb.1400171

    • Google Scholar
84. 1. von Arx M
    2. Goyret J
    3. Davidowitz G
    4. Raguso RA

    (2012) Floral humidity as a reliable sensory cue for profitability assessment by nectar-foraging hawkmoths

    Proceedings of the National Academy of Sciences of USA 109:9471–9476.

    https://doi.org/10.1073/pnas.1121624109

    • Google Scholar
85. 1. Weinhold A
    2. Kallenbach M
    3. Baldwin IT

    (2013) Progressive 35S promoter methylation increases rapidly during vegetative development in transgenic Nicotiana attenuata plants

    BMC Plant Biology 13:99.

    https://doi.org/10.1186/1471-2229-13-99

    • Google Scholar
86. 1. Wright GA
    2. Schiestl FP

    (2009) The evolution of floral scent: the influence of olfactory learning by insect pollinators on the honest signalling of floral rewards

    Functional Ecology 23:841–851.

    https://doi.org/10.1111/j.1365-2435.2009.01627.x

    • Google Scholar
87. 1. Wu J
    2. Hettenhausen C
    3. Schuman MC
    4. Baldwin IT

    (2008) A comparison of two Nicotiana attenuata accessions reveals large differences in signaling induced by oral secretions of the specialist herbivore Manduca sexta

    Plant Physiology 146:927–939.

    https://doi.org/10.1104/pp.107.114785

    • Google Scholar

Author details

1. Danny Kessler

   Department of Molecular Ecology, Max-Planck Institute for Chemical Ecology, Jena, Germany

   Contribution

   DK, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   dkessler@ice.mpg.de

   Competing interests

   No competing interests declared.

2. Mario Kallenbach

   Department of Molecular Ecology, Max-Planck Institute for Chemical Ecology, Jena, Germany

   Contribution

   MK, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

3. Celia Diezel

   Department of Molecular Ecology, Max-Planck Institute for Chemical Ecology, Jena, Germany

   Contribution

   CD, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

4. Eva Rothe

   Department of Molecular Ecology, Max-Planck Institute for Chemical Ecology, Jena, Germany

   Contribution

   ER, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

5. Mark Murdock

   Department of Molecular Ecology, Max-Planck Institute for Chemical Ecology, Jena, Germany

   Present address

   University of Pittsburgh, Pennsylvania, United States

   Contribution

   MM, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

6. Ian T Baldwin

   Department of Molecular Ecology, Max-Planck Institute for Chemical Ecology, Jena, Germany

   Contribution

   ITB, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   baldwin@ice.mpg.de

   Competing interests

   ITB: Senior editor, eLife.

   "This ORCID iD identifies the author of this article:" 0000-0001-5371-2974

• 5,037

  views

• 983

  downloads

• 58

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.