Chemical Profiles of Floral Structures in Nicotiana alata and N. forgetiana: Pollen and Corolla Volatiles in Parental Species and Natural Hybrid CorollasAbstract
Nicotiana alata (hawkmoth-pollinated) and N. forgetiana (hummingbird-pollinated) are two closely related species that were considered reproductively isolated due to their divergent pollination systems. However, the discovery of a natural hybrid zone challenges this view. Floral volatile profiles of these species and their natural hybrids were characterized to identify possible mechanisms and consequences of unexpected gene flow. Organ-specific (corolla, pollen) and diel (day, night) volatile emissions were analyzed using HS-SPME-GC-MS. Corolla scents were dominated by oxygenated monoterpenoids, while pollen blends were rich in microbial-associated compounds, such as acetoin. Hybrid scent profiles more closely match their floral phenotype than their genomic ancestry, with intermediate hybrids emitting blends distinct from those of both parental species. The discovery of a natural hybrid zone, combined with phenotype-specific scent emissions, challenges the view of strict pollinator isolation. Floral scent presents a complex, modular trait that may facilitate hybridization in this system.
Keywords:
Chemical Ecology; Floral VOCs; HS-SPME-GC-MS; Nicotiana; Pollen Volatiles
Introduction
A wide variety of Volatile Organic Compounds (VOCs) produced by flowers are critical mediators of pollinator attraction and behavior, influencing the reproductive success of flowering plants and evolutionary processes such as speciation and hybridization (Knudsen et al., 1993; Schiestl & Johnson, 2013). VOCs are emitted from multiple flower structures, including petals, nectaries, and pollen, and are composed mainly of phenylpropanoids, terpenoids, fatty acid derivatives, and nitrogenous compounds, among other classes (Loreto & Schnitzler, 2010; Maffei, 2010; Bertoli et al., 2011; Muhlemann et al., 2014). In pollination biology, the integrated study of visual traits, such as flower color and morphology, and olfactory cues provides a more comprehensive understanding of plant-pollinator attraction strategies (Faegri & van der Pijl, 1979; Fenster et al., 2004; Dobson, 2006).
Advances in headspace sampling and gas chromatography have enabled detailed characterization of floral VOCs under field conditions, revealing not only the blend compositions but also their critical role in pollinator attraction and reproductive isolation (Raguso, 2008; Schiestl, 2015). Floral VOCs may vary widely spatially across populations and geographic ranges, and even among different floral tissues and structures within the same flower and at different times of the day, influencing pollinator visitation and plant reproductive success (Majetic et al., 2007; 2008; 2009). These temporal patterns fine-tune attraction and may reduce interspecific interference when co-flowering species share pollinators. These variations in floral scent are one of the many ways flowers communicate their availability to pollinators.
Multiple floral olfactory cues can serve as advertisements of resources, playing a key role in fine-tuning pollinator accuracy across different scales of interaction (Fenster et al., 2004; Armbruster, 2014). Petal-emitted VOCs generally attract pollinators from a wider range of distances, while VOCs emitted from nectaries and pollen indicate food availability at a closer range or reflect the metabolic activity of pollen-associated microbiota (Dobson & Bergström, 2000; Muhlemann et al., 2014; Schaefer et al., 2004; Schiestl, 2015). If long-range cues mediate initial attraction and short-range cues provide information on reward availability, then fine-scale spatial variation in floral scent can differentially influence pollinator behavior (Raguso & Willis, 2002, 2005; García et al., 2021), supporting the need to analyze volatile emissions separately from different floral structures, such as corolla and pollen.
Olfactory cues represent a critical component of the broader suite of floral signals that mediate plant-pollinator interactions, with their relative importance differing among pollinator groups (Fenster et al., 2004). Hummingbirds, for example, rely mainly on visual cues, such as flower color, morphology, and spatial display, while exhibiting relatively limited dependence on floral scent compared to other pollinators, including hawkmoths (Fenster at al., 2004; Cronk & Ojeda, 2008; Raguso, 2008). In contrast, many nocturnal pollinators, particularly hawkmoths, depend strongly on specific floral scent blends for host location and discrimination, often responding to precise combinations of volatile organic compounds (Raguso & Willis, 2002; 2005; Policha et al., 2016; 2019). Hawkmoths, the primary pollinators of Nicotiana alata Link & Otto, are innately attracted by specific volatile compounds (Haverkamp et al., 2016) and use scent as a long-distance cue, feeding on any visually matching flowers once they enter a scent cloud, regardless of whether those flowers emitted the scent (Raguso & Willis, 2002). Nonetheless, pollinators like hawkmoths and bees can learn to associate odors with rewards, even when visual cues differ or are absent (Daly & Smith, 2000; Wright & Schiestl, 2009; Lawson et al., 2018; Cook et al., 2020). This behavioral flexibility can enhance gene flow and promote the formation of hybrid zones at species contact zones (Rezende et al., 2020; Turchetto et al., 2022).
The genus Nicotiana is an excellent model for exploring the interplay among floral scent emissions, pollinator behavior, and hybridization. With over 100 species, Nicotiana includes numerous hybrid species, polyploids, and homoploids (Chase et al., 2003; 2021; Clarkson et al., 2004; Knapp, 2020), and exhibits remarkable floral diversity in shape, pollinator rewards, color, and scent emission, attracting a wide range of pollinators (Raguso et al., 2006; Knapp, 2010; Kaczorowski et al., 2012; García & Benítez-Vieyra, 2020; Popova et al., 2020; Couto et al., 2024). Hybridization is well documented in the genus Nicotiana due to its role in speciation (Chase et al., 2003; 2021; Clarkson et al., 2004). Because hybridization is a well-known evolutionary driver of plant diversity in many species (Soltis & Soltis, 2009; Schley et al., 2022; Turchetto et al., 2022) and most ancient hybridization events in Nicotiana are inferred to have occurred in the Neotropics (Clarkson et al., 2017), we focused on two Neotropical diploid Nicotiana species that naturally form a hybrid zone.
This study examines Nicotiana alata, N. forgetiana Hemsl., and a natural hybrid population found in a contact zone between the two species in southern Brazil (Figure 1A). The hybrid status of this natural atypical population was initially identified based on its floral morphology and coloration (Teixeira et al., 2022; Couto et al., 2023) and was recently confirmed by genomic data (Couto et al., 2026). The two parental species belong to the Alatae section, representing the most recent diploid clade in Nicotiana (Clarkson et al., 2017; Knapp, 2020). Both species share n = 9 chromosomes and have distinct distributions in South American grasslands. Nicotiana alata is widespread across southern Brazil, northeastern Argentina, and southern Paraguay, while N. forgetiana is restricted to open areas in the southern Brazilian Atlantic Forest. Their ranges overlap in open areas of this region (Teixeira et al., 2022).
The geographical location of Nicotiana species populations, hybrid populations, and plant phenotypes. A: Geographical map of populations; B: N. forgetiana-like hybrid; C: N. alata-like hybrids; D: canonical allopatric N. alata and N. forgetiana morphology; E and F: intermediate phenotype hybrids.
Nicotiana alata has strongly scented, night-opening flowers with large, narrow-tubed white corollas, while N. forgetiana has smaller magenta flowers with a faint odor that open during the day (Figure 1D) (Kaczorowski et al., 2005; Raguso et al., 2006). Those two species can produce hybrids under controlled laboratory conditions but were considered isolated due to their different pollination systems, hawkmoths and hummingbirds, respectively (Ippolito et al., 2004; Bissell & Diggle, 2008). However, bees have been observed collecting nectar and pollen from N. forgetiana and accessing N. alata pollen from closed flowers in contact zones (Turchetto et al., 2022). The roles of bees and other animals as secondary pollinators and potential facilitators of hybridization of these species remain to be investigated. The recent finding of sympatric populations of these species with individuals exhibiting atypical phenotypes, ranging from flowers with parental canonical morphology but a mosaic of parental genotype (Figure 1B- C, Couto et al., 2026), to individuals with classic hybrid intermediate features (Figure 1E- F), challenges the previous understanding of complete isolation. In this context, given the unexpected observation of bee visitation in the hybrid zone, we conducted a structure-specific analysis to disentangle the contributions of different floral organs and identify which may be driving pollinator responses.
Previous studies of flower scent across the Alatae section focused on corolla emissions and established similarities and differences in the volatile profiles of N. alata and N. forgetiana (Raguso et al., 2003; 2006). However, the structure-specific emission profiles, corolla, and pollen contributions to the overall bouquet of these species remain uncharacterized (Couto et al., 2024). Because different floral structures can produce distinct volatile blends associated with different ecological functions (e.g., pollinator attraction and reproductive processes), a structure-specific approach is essential to disentangle their respective contributions. Furthermore, the discovery of a natural hybrid population, along with the unexpected observation of bee visitation (Teixeira et al., 2022; Turchetto et al., 2022), highlights the need for deeper investigation into the volatile emission profiles of these species. This study aims to (1) identify structure-specific scent blends at different times of emission in relation to the parental species' pollination systems; and (2) analyze the emission profiles of hybrid individuals, comparing how their phenotypically defined classes relate to the parental species.
Materials and methods
Study system and sampling design
The plants used for scent collection were grown in a greenhouse from seeds obtained through open pollination in the field. Details on germination, cultivation, and greenhouse maintenance are described in Couto et al. (2023). Seed accessions were obtained from two natural populations of N. alata, two populations of N. forgetiana, and one natural population of hybrids in a contact zone (Table 1). Populations Forg05 and Ala16 are located in the contact zone, physically close to the hybrid population Hyb01. Populations Forg04 and Ala15 are distant from the contact zone and from each other. However, it is worth noting that previous molecular and morphological studies have shed light on the complex nature of this species system. The taxonomic classification of the magenta-flowered N. forgetiana has been questioned based on morphological evidence (Teixeira et al., 2022) and molecular studies (Augsten et al., 2025), suggesting that it should be recognized as two distinct species. One group occurs in canyon regions along the Serra Geral border in Santa Catarina, Brazil, matching the ‘Rastroensis’ morphotype 1 (Kaczorowski et al., 2005; Teixeira et al., 2022). The other is found in Rio Grande do Sul’s interior, in rocky outcrops or forest clearings, exhibiting the morphology previously attributed to N. forgetiana (morphotype 2), where it overlaps with N. alata (Teixeira et al., 2022). In this research, seeds from N. forgetiana morphotype 2 were collected and germinated; herein, they are referred to exclusively as N. forgetiana. The hybrid flowers were classified according to their phenotypes: Nicotiana alata-like, the flowers that followed canonical N. alata appearance; N. forgetiana-like, the flowers that followed canonical N. forgetiana appearance; and intermediate, the flowers that exhibit classic hybrid intermediate characteristics. The collection of plant material was conducted in accordance with all relevant guidelines and legislation. The necessary permits were obtained in accordance with Brazilian laws for the collection of genetic material for taxonomic and evolutionary research (SISGEN permit number AB0DE7E and SISBIO permit number 68511). One voucher specimen from each population was collected in the greenhouse and deposited in the herbaria ICN (Universidade Federal do Rio Grande do Sul) and BHCB (Universidade Federal de Minas Gerais) (Table 1).
Sampled data of Nicotiana species used in this study. The seeds were collected in the allopatric population of Nicotiana alata (Ala) and N. forgetiana (Forg), and one hybrid (Hyb) population (Couto et al., 2023); Sampling size (n) = one flower/corolla and five flowers/pollen, all from the same single individual.
Volatile collection and consecutive analysis were conducted in January 2023, during the summer in Brazil. Petals and anthers from five N. alata, four N. forgetiana, and ten hybrid individuals were collected. To ensure corolla tissue samples were pollen-free upon collection, the flowers were marked and emasculated one day before complete anthesis. Corollas were collected whole and placed in a 20-mL headspace vial, which was sealed, yielding one corolla per sample. For pollen samples, open anthers from five flowers from the same individual were collected and immediately placed in a headspace vial, which was then sealed. Collections were conducted at 11:00 h (day) and 02:00 h (night) to coincide with peak pollinator activity (bees and hawkmoths; Rodrigues et al., 2018; Fenske et al., 2018). Ambient controls (empty vials) and bud controls (unopened flower buds) were not included, as the scent profiles of the parental species have already been well characterized (Raguso et al., 2003; 2006). Given our focus on comparative profiling across tissues and hybrid phenotypes, we prioritized sampling of fully opened corollas and anthers.
Identification of VOC profile compounds and descriptive analysis
The sealed vials were kept at room temperature until analysis that same day. Volatile compounds were analyzed using headspace solid-phase microextraction (HS-SPME; Supelco Inc., Sigma-Aldrich) coupled with gas chromatography-mass spectrometry (GC-qMS).
Samples were conditioned for 15 minutes at 30 °C before exposure to a 100-µm polydimethylsiloxane/divinylbenzene (PDMS/DVB) SPME fiber for an additional 30 minutes. The 30 °C conditioning temperature was selected to approximate ambient conditions in the species' natural temperate habitat during the flowering period, minimizing thermal stress and avoiding the induction of artifactual emissions that might occur at higher temperatures. The fiber was then injected into the GC-qMS port at 250 °C for 10 minutes, followed by fiber cleaning for 10 minutes. Analyses were performed using an autosampler (CTC CombiPAL, Zwingen, Switzerland) coupled to a GC-qMS (QP2010, Shimadzu, Kyoto, Japan). All analyses used splitless injections on a polar DB-WAX column (30 m length × 0.25 mm I.D. × 0.25 µm film thickness). The oven temperature program and carrier gas conditions followed Raguso et al. (2003): initial temperature 60 °C held for 3 min, increased at 10 °C/min to 260 °C, and held for 7 min; helium carrier gas flow rate was 1.0 mL/min. Unlike the solvent extraction method used in that study, we introduced samples via SPME fiber thermal desorption rather than liquid injection, with adsorption parameters optimized for whole corolla and pollen tissues (15 min headspace equilibration followed by 30 min fiber exposure, both at 30 °C). Peaks from total ion chromatograms (TICs) were detected and integrated using Shimadzu’s GCMSsolutions software (v. 2.6). Compound putative identification was performed by comparing spectral similarity and retention index (RI) values with the NIST library. Identifications were accepted when both the mass spectral similarity score was ≥75%, and the experimental RI matched the reference within ±10 units. The retention indices were calculated using a C7-C30 alkane series (Supelco, Bellefonte, USA), analyzed under the same conditions as the samples.
Following identification, compound abundances were expressed as relative percentages, calculated from the normalized peak area (Peak Areacompound/Areatotal × 100). This method does not allow absolute quantification of each compound but provides the mean abundance of each compound in a complex blend, which is internally comparable (Valença et al., 2022). The raw compound data are available in Supplementary Material S2.
To visualize blend compositions and their variations across color phases, emission times, and structural origins, class boxplots were generated for each iteration using the ggplot2 package (Wickham, 2016). To quantify the relative mean abundance on time-related variation, diurnal/nocturnal ratios were calculated, and the results were normalized by applying log2-transformation (Log2(R), R = day abundance/night abundance; Positive log2 values indicate higher day emissions: +1 = 2-fold, +2 = 4-fold, +3 = 8-fold; negative values indicate higher night emissions: -1 = 2-fold, -2 = 4-fold, -3 = 8-fold).
Multivariate Analysis
To compare volatile profiles among species, hybrid phenotypes, structure emissions (corolla vs. pollen), and diel time emissions, we performed multivariate analysis of relative abundance data using the vegan v. 2.7-3 package (Oksanen et al., 2026) in the R environment (R Core Team, 2021). The relative abundance data were transformed using Hellinger transformation (Legendre & Legendre, 2012) to reduce the influence of rare compounds (Kirichenko-Babko et al., 2021). To visualize the overall variation, we performed non-metric multidimensional scaling (NMDS), an unconstrained method suited for exploratory analysis (Eisen et al., 2022). As a safeguard against data overexploitation, no constrained analysis was performed due to the imbalance in the sample sizes. The NMDS was performed using Bray-Curtis dissimilarity matrices and was run with 999 random starts to ensure a stable solution. To identify the volatile compounds most strongly associated with the observed ordination patterns, we fitted environmental vectors onto the NMDS ordination using the envfit function, which projects compounds as vectors whose direction and length represent the strength and significance of their correlation with the sample distribution. All ordinations were plotted with the ggplot2 v. 4.0.2 (Wickham et al., 2016) package.
To test for significant differences in volatile composition among groups, we performed permutational multivariate analysis of variance (PERMANOVA) using the adonis2 function in the vegan package (Oksanen et al., 2026) with 999 permutations. Due to unbalanced sample sizes across groups, we conducted separate PERMANOVA models for specific biologically relevant comparisons rather than a single global model. The groups tested were pollen and corolla of Nicotiana alata vs. N. forgetiana. The groups tested for corolla were: N. alata vs. N. alata-like hybrids; N. forgetiana vs. N. forgetiana-like hybrids; intermediate hybrids vs. N. forgetiana; and intermediate hybrids vs. N. alata. Bray-Curtis dissimilarity matrices were used as the distance metric for all tests.
Results
Thirty-one compounds were putatively identified in corollas and pollen samples of Nicotiana alata, N. forgetiana, and their natural hybrids (Supplementary Material Table S2). Terpenes account for 13 compounds, including two sesquiterpenes, four oxygenated monoterpenoids, and seven monoterpenes. The remaining 18 compounds include one aromatic ester, three aromatic aldehydes, eight fatty acid derivatives, and six compounds classified as others. Among the six compounds classified as others, three were exclusively found in pollen blends (acetoin, acetic acid, and γ-valerolactone). Oxygenated monoterpenoids were the predominant class in corolla scent (1,8-cineole, α-terpineol, linalool, and terpinen-4-ol) (Figure 2), with 1,8-cineole being ubiquitous and most abundant. In the pollen scent, the dominant compounds were 2,3-butanediol, 4-methyl-1-pentanol, γ-valerolactone, acetic acid, acetoin, and octyl acetate, with acetic acid being the most abundant compound. The full table of compounds for each individual is available in Supplementary Material S1.
Diversity of floral VOCs in Nicotiana species and different hybrid phenotypes at various times of emissions and structures.
In corolla blends, the blend complexity varied from 28 in Nicotiana alata, 18 in N. forgetiana, 20 in Nicotiana alata-like hybrids, 19 in N. forgetiana-like, and 16 in intermediate phenotype hybrids. Compounds exclusive to N. alata’s corollas were (E)-B-farnesene, benzaldehyde, linalool, and hexanal; and 4-methyl-1-pentanol was exclusively found in intermediate hybrid samples. There were no exclusive compounds in N. forgetiana’s corollas. On the other hand, day emissions yielded 25 compounds, whereas night emissions yielded 26.
For pollen blends, richness ranged from 13 in N. alata blends to 8 in N. forgetiana. Compounds exclusive to N. alata pollen blends were 1,8-cineole, 3-octanol, benzyl alcohol, methyl salicylate, α-terpineol, and terpinen-4-ol; 2-methylbutanal was exclusive to N. forgetiana pollen.
Time-of-emission ratios show that Nicotiana alata corollas nearly doubled scent emission (Log2 mean increase = -0.955) (Table 2), with β-pinene showing the highest mean increase, 4 times the emission (Log2 mean increase = -2.31). Nicotiana forgetiana corolla emissions were two times higher than day emissions (Log2 mean increase = 1.04). Pollen emissions in both parental species followed the corolla trend of higher time of emission. In night emissions of N. alata pollen, acetoin had the highest increase of all compounds, reaching nearly eight times higher abundance emission (Log2 mean increase = -3.65). Among hybrids, there was no expressive total increase. Still, it all followed the expected trend for their flower phenotypes: N. alata-like flowers were nocturnal (Log2 mean increase = -0.485), N. forgetiana-like flowers were diurnal (Log2 mean increase = +0.669), and intermediate flowers were nocturnal (Log2 mean increase = -0.037).
Diurnal and nocturnal VOC emissions in Nicotiana species and hybrids in different structures.
NMDS ordinations of overall corolla volatile emission profiles and corolla vs. pollen profiles are presented in Figure 3A and 3B, respectively. The NMDS of volatile corolla compounds (Fig. 3A) revealed no clear grouping among samples, either by identity or by time of emission. The NMDS analysis yielded a stress value of 0.171, indicating a reliable representation of the structure. Environmental vector fitting identified four compounds that were significantly correlated with the ordination (p < 0.001): α-pinene and β-pinene, which dominated the positive NMDS1 axis, and 1-hexanol and benzyl alcohol, dominating the negative NMDS1 axis. Consistent with the ordination, PERMANOVA analysis of overall corolla volatile profiles revealed no significant effect of groups (species and hybrid phenotypes) or time of emission (df = 5, R² = 0.303, F = 1.30, p = 0.179), indicating that these factors collectively explained 30.3% of the variation in corolla scent composition. However, this effect was not statistically significant (Fig. 3C). The PERMANOVA comparisons revealed no significant differences between corolla emissions of N. alata and N. forgetiana (R² = 0.307, F = 0.89, p = 0.667; Fig. 3C), though this analysis was limited by small sample size (n = 4). Similarly, no significant differences were detected between N. alata and N. alata-like hybrids (R² = 0.198, F = 0.87, p = 0.605; Fig. 3C), nor between N. forgetiana and N. forgetiana-like hybrids (R² = 0.482, F = 0.93, p = 0.600; Fig. 3C). However, intermediate hybrids differed significantly from both parental species: from N. alata (R² = 0.332, F = 2.99, p = 0.031) and from N. forgetiana (R² = 0.411, F = 4.19, p = 0.039) (Fig. 3C).
Ordination analysis and PERMANOVA results of VOC of Nicotiana species and different hybrid phenotypes at various times of emissions and structures. A: NMDS of corolla volatile profiles, circles: day emissions; triangles: night emissions B: NMDS of corolla and pollen volatiles. C: Diagram of total scent variation PERMANOVA results. Legend: N. forgetiana (dark red/brown), N. forgetiana-like (light red), Intermediate (magenta), N. alata-like (light blue), N. alata (dark blue). , day emissions (circles); night emissions (triangles); pollen (light blue); corolla (orange).
In contrast, NMDS ordination of both corolla and pollen samples (Figure 3B) showed clear separation between tissue types, with corolla and pollen samples forming distinct clusters. The NMDS stress value was 0.095, indicating an excellent fit to the dissimilarity structure. Environmental vector fitting identified 12 compounds significantly correlated with the ordination (p < 0.001), including: sabinene, β-pinene, 1,8-cineole, limonene, and β-myrcene dominating the NMDS1 positive axis and the corolla clustering; acetic acid and γ-valerolactone dominating the NMDS1 negative axis and the pollen clustering. PERMANOVA testing the effects of structure (corolla vs. pollen), time of emission, and their interaction revealed a highly significant difference in volatile composition (R² = 0.578, F = 9.58, p < 0.001), with the model explaining 57.8% of the total variation (Fig. 3C). For pollen samples, PERMANOVA revealed no significant differences between N. alata and N. forgetiana or between day and night emissions (R² = 0.642, F = 0.90, p = 0.667). However, this analysis was constrained by a limited sample size (n = 4) (Fig. 3C).
Discussion
This study aimed to expand knowledge of floral volatile emissions in the Nicotiana alata-N. forgetiana species system, focusing on corolla and pollen volatiles separately and including their natural hybrids. The characterization of volatile emissions from whole corollas and anthers using HS-SPME-GC-MS provided organ-resolved, temporally distinct scent profiles for this species system. Despite sharing similar corolla chemical profiles, the parental species’ pollen and corolla emissions are fundamentally distinct. The corolla compound profile of canonical N. alata and N. forgetiana and the parental-like hybrids was similar at both times of emission, but the intermediate phenotypes showed distinctions from both canonical emissions.
Pollen and corolla emissions of N. alata and N. forgetiana were found to be dominated by monoterpenes and oxygenated monoterpenoids, both known to be highly emitted by Angiosperms (Knudsen et al., 1993; Kesselmeier & Staudt, 1999). The VOC profiles of pollen and corolla of the parental species differed in composition and relative abundance, consistent with previous studies on these structural emissions (Dobson & Bergström, 2000; Piskorski et al., 2011; Caser & Scariot, 2022). Although the canonical species’ blends were not significantly different from one another, this result should be interpreted with caution given our current sample size. Compounds belonging to the “cineole cassette” (Raguso et al., 2006) are linked to the conserved terpineol synthase in Alatae species (Fähnrich et al., 2011). The enzyme was identified in the petal epidermis and pistils of N. alata (Fähnrich et al., 2011); its presence in pollen-producing tissues (e.g., anthers) has not been explored. In pollen blends, compounds such as 1,8-cineole and α-terpineol may originate from the adsorption of corolla volatiles onto the pollen kit rather than from biosynthesis within anther tissue (Dobson & Bergström, 2000).
Compounds commonly associated with microbial metabolism, such as acetoin and 2,3-butanediol, were detected in Nicotiana pollen; however, these volatiles are also reported in plant-microbe interaction contexts, and their occurrence in plant tissues cannot be ruled out based on current evidence (Ryu et al., 2003; Sharifi & Ryu, 2018; Farré-Armengol et al., 2017). Similar microbial-associated volatiles, particularly 1-octen-3-ol and other eight-carbon compounds, have been documented in the floral scents of Yucca brevifolia Engelm. (Joshua tree) and Dracula Luer orchids, where they are thought to play roles in attracting specialized pollinators (Svensson et al., 2016; Policha et al., 2016). In Dracula orchids, these compounds are part of the fungal mimicry to attract mushroom flies (Policha et al., 2019), while in Yucca they occur in a plant with specialized pollination (a mutualistic relationship with yucca moths; Smith et al., 2008), but their actual functional role remains to be tested. In contrast, Nicotiana alata and N. forgetiana are pollinated by hawkmoths and hummingbirds, respectively (Ippolito et al., 2004), with sporadic visits by honeybees also visiting flowers in contact zones (Turchetto et al., 2022). The presence of microbial-associated volatiles in pollen of these Nicotiana species is, therefore, unlikely to reflect a highly specialized pollinator strategy. Instead, it might suggest the presence of an active microbiota.
Pollen grains’ morphological structure and nutrient-rich composition qualify them as suitable colonization surfaces (Nicolson & Human, 2013; Blackmore et al., 2007), with different plant species composed of distinctive bacterial and fungal microbiomes, and congeneric species having more similar microbiota (Manirajan et al., 2018). Acetoin (2-hydroxybutanone) and 2,3-butanediol are adjacent bacterial and fungal metabolites in the fermentation metabolic pathway (Xiao & Lu, 2014) and are known VOCs produced by rhizobacterium Bacillus sp., which can prime plant innate systemic immune response against pathogens in N. benthamiana Domin. and other species (Wu et al., 2018; Cofer et al., 2018). Cardinale & Schnell (2024) highlighted the growing body of literature on the seed microbiome, focusing on mutualistic bacteria and fungi, and reiterated the importance of this microbiota and its yet-unknown origin. Rodríguez et al. (2018) suggest that pollen grains may serve as a potential source of the seed microbiome, forming a pollen-microbe-seed system for rhizosphere and phyllosphere colonization in new habitats. The identification of fermentation pathway substrates and products in pollen blends on N. alata and N. forgetiana is an important addition to pollen-microbe-seed system research.
The volatile profiles of the natural hybrids reveal a complex pattern of inheritance that reflects their underlying genomic mosaicism. Importantly, the present hybrid classifications (N. alata-like, N. forgetiana-like, and intermediate) were based solely on floral phenotype. Genomic data from the maternal plants of these same individuals (Couto et al., 2026) reveal that phenotype is a poor predictor of overall genotype in this system, which is expected for hybrid zones (e.g., Giudicelli et al., 2024). For instance, individuals classified as N. alata-like in this study (17.1 and 17.3) were offspring of a maternal plant that was phenotypically N. forgetiana-like (Hyb17) but had a higher genomic contribution from N. alata. This decoupling of morphology from genome highlights the complex genetic architecture of these traits, and the hybrid’s corolla profile mirrors this pattern. The hybrids with parental morphologies’ emissions are indistinguishable from the canonical species’ emissions, while intermediate hybrids’ emissions are distinct from both parents’ blends. These results help reconcile the apparent dichotomy between phylogenetic constraints and pollinator-mediated selection on flower emissions (Delle-Vedove et al., 2017). Phylogenetic relationships primarily constrain blend composition, as evidenced by the shared cineole cassette in section Alatae (Raguso et al., 2003; 2006). In contrast, pollinator selection strongly shapes emission timing, as shown by distinct diel rhythms that are conserved even in hybrids and depend on their floral phenotype. The hybrid profiles are particularly revealing, demonstrating that the genetic modules controlling blend composition can be inherited independently from those regulating emission timing, leading to mosaic phenotypes. This modularity may be a key genetic mechanism facilitating the evolution of novel scent profiles within the bounds of a conserved phylogenetic background.
The scent profiles of hybrids have important implications for pollinator behavior and, consequently, for gene flow between species. Previous studies suggested that, even though N. alata and N. forgetiana do not have strong post-zygotic barriers, they are effectively isolated due to their divergent pollination syndromes. However, in sympatry with artificial hybrids, their pollinators begin to visit both parental species (Ippolito et al., 2004). Our findings provide a chemical basis for this behavioral shift, with intermediate hybrids possibly serving as chemical bridges through their distinct scent profiles. Compounding this effect, all species and hybrid classes in this system possess UV-absorbing floral pigments, rendering them visually similar to pollinators with UV-sensitive vision (Couto et al., 2023). In the absence of strong visual differentiation, scent becomes the primary cue for pollinator decision-making. Once attracted by the "scent cloud" of one flower type, whether from a parent or a hybrid, hawkmoths may subsequently visit other flowers in the area that share similar UV-absorbent patterns, even if those flowers differ in color or scent intensity (Raguso & Willis, 2002). This breakdown of pollinator constancy, facilitated by hybrid scent profiles and visual uniformity, likely contributes to the extensive introgression observed in contact zones (Couto et al., 2026). This study provides the first comprehensive, organ-resolved, and temporally explicit volatile profiles for Nicotiana alata, N. forgetiana, and their natural hybrids, revealing the multifaceted and dynamic nature of their floral emissions. The results confirmed that scent composition is largely phylogenetically conserved yet also demonstrate that emission timing is finely tuned by pollinator selection, a duality clearly illustrated by the mosaic scent profiles of the hybrids. Unlike other systems in which hybrids exhibit strictly parental or intermediate emissions (e.g., García et al., 2023; Bischoff et al., 2014), the complex inheritance patterns observed highlight the value of this study system. Future research should focus on the chemical ecology of pollination in these southern Brazilian grasslands to understand the impact of secondary contact on species boundaries and the evolution of pollinator-mediated signals.
Supplementary Material
The following online material is available for this article:
Supplementary Material S1:
Supplementary Material S2:
Acknowledgments
This study was supported by Universidade Federal do Rio Grande do Sul (UFRGS), Programa de Pós-Graduação em Botânica da Universidade Federal do Rio Grande do Sul (PPGBOT-UFRGS).
References
-
Armbruster WS. 2014. Floral specialization and angiosperm diversity: phenotypic divergence, fitness trade-offs and realized pollination accuracy. AoB PLANTS 6: plu003. doi: 10.1093/aobpla/plu003.
» https://doi.org/10.1093/aobpla/plu003 -
Augsten M, Freitas LB, Backes A, Turchetto C, Stehmann JR. 2025. Molecular and ecological evidence reveal a speciation process in Nicotiana sect. Alatae (Solanaceae) in the southern Brazilian plateau. Botanical Journal of the Linnean Society 204: 1-16. doi: 10.1093/botlinnean/boaf006.
» https://doi.org/10.1093/botlinnean/boaf006. - Bertoli A, Fambrini M, Doveri S, Leonardi M, Pugliesi C, Pistelli L. 2011. Pollen aroma fingerprint of two sunflower (Helianthus annuusL.) genotypes characterized by different pollen colors. Chemistry & Biodiversity 8: 1766-1775.
-
Bischoff M, Jürgens A, Campbell DR. 2014. Floral scent in natural hybrids of Ipomopsis (Polemoniaceae) and their parental species. Annals of Botany 113: 533-544. doi: 10.1093/aob/mct279
» https://doi.org/10.1093/aob/mct279 - Bissell EK, Diggle PK. 2008. Floral morphology in Nicotiana: architectural and temporal effects on phenotypic integration. International Journal of Plant Sciences 169: 225-240.
-
Blackmore S, Wortley AH, Skvarla JJ, Rowley JR. 2007. Pollen wall development in flowering plants. New Phytologist 174: 483-498. doi: 10.1111/j.1469-8137.2007.02060.x
» https://doi.org/10.1111/j.1469-8137.2007.02060.x -
Cardinale M, Schnell S. 2024. Is the plant microbiome transmitted from pollen to seeds? Frontiers in Microbiology 15: 1343795. doi: 10.3389/fmicb.2024.1343795.
» https://doi.org/10.3389/fmicb.2024.1343795. -
Caser M, Scariot V. 2022. The contribution of volatile organic compounds (VOCs) emitted by petals and pollen to the scent of garden roses. Horticulturae 8: 1049. doi: 10.3390/horticulturae8111049
» https://doi.org/10.3390/horticulturae8111049 -
Chase MW, Knapp S, Cox AV, Clarkson JJ, Butsko Y, Joseph J, et al 2003. Molecular systematics, GISH and the origin of hybrid taxa in Nicotiana (Solanaceae). Annals of Botany 92: 107-127. doi: 10.1093/aob/mcg087
» https://doi.org/10.1093/aob/mcg087 -
Chase MW, Christenhusz MJM, Palsson RL, Fay MF, Dodsworth S, Conran JG, et al 2021. Species delimitation in Nicotiana sect. Suaveolentes (Solanaceae): reciprocal illumination leads to recognition of many new species. Curtis’s Botanical Magazine 38: 266-286. doi: 10.1111/curt.12410
» https://doi.org/10.1111/curt.12410 -
Clarkson JJ, Dodsworth S, Chase MW. 2017. Time-calibrated phylogenetic trees establish a lag between polyploidization and diversification in Nicotiana (Solanaceae). Plant Systematics and Evolution 303: 1001-1012. doi: 10.1007/s00606-017-1416-9
» https://doi.org/10.1007/s00606-017-1416-9 -
Clarkson JJ, Knapp S, Garcia VF, Olmstead RG, Leitch AR, Chase MW. 2004. Phylogenetic relationships in Nicotiana (Solanaceae) inferred from multiple plastid DNA regions. Molecular Phylogenetics and Evolution 33: 75-90. doi: 10.1016/j.ympev.2004.05.002
» https://doi.org/10.1016/j.ympev.2004.05.002 -
Cofer TM, Seidl-Adams I, Tumlinson JH. 2018. From Acetoin to (Z)-3-Hexen-1-ol: The diversity of volatile organic compounds that induce plant responses. Journal of Agricultural and Food Chemistry 66: 11197-11208. doi: 10.1021/acs.jafc.8b03730
» https://doi.org/10.1021/acs.jafc.8b03730 -
Cook B, Haverkamp A, Hansson BS, Roulston T, Lerdau M, Knaden M. 2020. Pollination in the Anthropocene: a moth can learn ozone-altered floral blends. Journal of Chemical Ecology 46: 987-996. doi: 10.1007/s10886-020-01211-4
» https://doi.org/10.1007/s10886-020-01211-4 -
Couto MAMS, Teixeira MC, Gope A, Backes A, Rodrigues DM, Soares GLG, et al 2023. Floral trait variation in a putative hybrid zone between specialist pollination systems: How could it impact pollinator attraction? Botanical Journal of the Linnean Society 203: 289-302. doi: 10.1093/botlinnean/boad021
» https://doi.org/10.1093/botlinnean/boad021 -
Couto MAMS, Soares GLG, Turchetto C. 2024. Exploring scent in wild tobacco: comparison of volatile compounds across pollinator functional groups and Nicotiana sections. Evolutionary Ecology 38: 409-432. doi: 10.1007/s10682-024-10301-8
» https://doi.org/10.1007/s10682-024-10301-8 -
Couto MAMS, Soares LS, Rodrigues DM, Burgos VGC, Bombarely A, Freitas LB, et al 2026. Genomic evidence of hybridization between two species in Nicotiana sect. Alatae Annals of Botany 138: 621-632. doi: 10.1093/aob/mcag083
» https://doi.org/10.1093/aob/mcag083 -
Cronk Q, Ojeda I. 2008. Bird-pollinated flowers in an evolutionary and molecular context. Journal of Experimental Botany 59: 715-727. doi: 10.1093/jxb/ern009
» https://doi.org/10.1093/jxb/ern009 -
Daly KC, Smith BH. 2000. Associative olfactory learning in the moth Manduca sexta Journal of Experimental Biology 203: 2025-2038. doi: 10.1242/jeb.203.13.2025
» https://doi.org/10.1242/jeb.203.13.2025 -
Delle-Vedove R, Schatz B, Dufay M. 2017. Understanding intraspecific variation of floral scent in light of evolutionary ecology. Annals of Botany 120: 1-20. doi: 10.1093/aob/mcx055
» https://doi.org/10.1093/aob/mcx055 -
Dobson HEM, Bergström G. 2000. The ecology and evolution of pollen odors. Plant Systematics and Evolution 222: 63-87. doi: 10.1007/BF00984096
» https://doi.org/10.1007/BF00984096 - Dobson, HEM. 2006. Relationship between floral fragrance composition and type of pollinator. In: Dudareva N, Pichersky E. (eds.) Biology of Floral Scent. Boca Raton, CRC Press. pp. 147-198
-
Eisen KE, Powers JM, Raguso, RA, Campbell DR. 2022. An analytical pipeline to support robust research on the ecology, evolution, and function of floral volatiles. Frontiers in Ecology and Evolution 10: 1006416. doi: 10.3389/fevo.2022.1006416
» https://doi.org/10.3389/fevo.2022.1006416 -
Fähnrich A, Krause K, Piechulla B. 2011. Product variability of the ‘cineole cassette’ monoterpene synthases of related Nicotiana species. Molecular Plant 4: 965-984. doi: 10.1093/mp/ssr021
» https://doi.org/10.1093/mp/ssr021 - Faegri K, van der Pijl L. 1979. The principles of pollination ecology. Oxford, United Kingdom, Pergamon Press
-
Farré-Armengol G, Filella I, Llusià J, Peñuelas J. 2017. β-Ocimene, a Key Floral and Foliar Volatile Involved in Multiple Interactions between Plants and Other Organisms. Molecules 22: 1148. doi: 10.3390/molecules22071148
» https://doi.org/10.3390/molecules22071148 -
Fenske MP, Nguyen LP, Horn EK, Riffell JA, Imaizumi T. 2018. Circadian clocks of both plants and pollinators influence flower seeking behavior of the pollinator hawkmoth Manduca sexta Scientific Reports 8: 2842. doi: 10.1038/s41598-018-21251-x
» https://doi.org/10.1038/s41598-018-21251-x -
Fenster CB, Armbruster WS, Wilson P, Dudash MR, Thomson JD. 2004. Pollination syndromes and floral specialization. Annual Review of Ecology, Evolution, and Systematics 35: 375-403. doi: 10.1146/annurev.ecolsys.34.011802.132347
» https://doi.org/10.1146/annurev.ecolsys.34.011802.132347 -
García Y, Friberg M, Parachnowitsch AL. 2021. Spatial variation in scent emission within flowers. Nordic Journal of Botany 39: e3014. doi: 10.1111/njb.03014
» https://doi.org/10.1111/njb.03014 -
García MS, Benítez-Vieyra SM. 2020. Is variation in flower length among native and non-native populations of Nicotiana glauca a product of pollinator-mediated selection? Evolutionary Ecology 34: 893-913. doi: 10.1007/s10682-020-10082-w
» https://doi.org/10.1007/s10682-020-10082-w -
García Y, Ostevik KL, Anderson J, Rieseberg LH. 2023. Floral scent divergence across an elevational hybrid zone with varying pollinators. Oecologia 201: 45-57. doi: 10.1007/s00442-022-05289-3
» https://doi.org/10.1007/s00442-022-05289-3 -
Giudicelli GC, Pezzi PH, Guzmán‑Rodriguez S, Turchetto C, Bombarely A, Freitas LB. 2024. Historical and ongoing hybridisation in Southern South American grassland species. Scientific Reports 14: 27989. doi: 10.1038/s41598-024-79584-9
» https://doi.org/10.1038/s41598-024-79584-9 -
Ippolito A, Fernandes GW, Holtsford TP. 2004. Pollinator preference for Nicotiana alata, N. forgetiana, and their F1 hybrids. Evolution 58: 2634-2644. doi: 10.1111/j.0014-3820.2004.tb01618.x
» https://doi.org/10.1111/j.0014-3820.2004.tb01618.x -
Haverkamp A, Yon F, Keesey IW, Mißbach C, Koenig C, Hansson BS, et al 2016. Hawkmoths evaluate scenting flowers with the tip of their proboscis. eLife 5: e15039. doi: 10.7554/eLife.15039
» https://doi.org/10.7554/eLife.15039 -
Kaczorowski RL, Gardener MC, Holtsford TP. 2005. Nectar traits in Nicotiana section Alatae (Solanaceae) in relation to floral traits, pollinators, and mating systems. American Journal of Botany 92: 1270-1283. doi: 10.3732/ajb.92.8.1270.
» https://doi.org/10.3732/ajb.92.8.1270. -
Kaczorowski RL, Seliger AR, Gaskett AC, Wigsten SK, Raguso RA. 2012. Corolla shape vs. size in flower choice by a nocturnal hawkmoth pollinator. Functional Ecology 26: 577-587. doi: 10.1111/j.1365-2435.2012.01982.x
» https://doi.org/10.1111/j.1365-2435.2012.01982.x -
Kesselmeier J, Staudt M. 1999. Biogenic volatile organic compounds (VOC): An overview on emission, physiology and ecology. Journal of Atmospheric Chemistry 33: 23-88. doi: 10.1023/A:1006127516791
» https://doi.org/10.1023/A:1006127516791 -
Kirichenko-Babko MB, Danko YM, Danylkiv JM, Majerek D. 2021. Comparison of the use of species abundance and presence-absence data for diversity assessment. Journal of Physics: Conference Series 1736: 012044. doi: 10.1088/1742-6596/1736/1/012044
» https://doi.org/10.1088/1742-6596/1736/1/012044 -
Knapp S. 2010. On ‘various contrivances’: pollination, phylogeny and flower form in the Solanaceae. Philosophical Transactions of the Royal Society B 365: 449-460. doi: 10.1098/rstb.2009.0236
» https://doi.org/10.1098/rstb.2009.0236 -
Knapp S. 2020. Biodiversity of Nicotiana (Solanaceae). In: Ivanov NV, Sierro N, Peitsch MC. (eds.) The tobacco plant genome. Compendium of plant genomes. Cham, Switzerland, Springer. pp. 21-41. doi: 10.1007/978-3-030-29493-9_2
» https://doi.org/10.1007/978-3-030-29493-9_2 -
Knudsen JT, Tollsten L, Bergström LG. 1993. Floral scents: a checklist of volatile compounds isolated by head-space techniques. Phytochemistry 33: 253-280. doi: 10.1016/0031-9422(93)85502-I
» https://doi.org/10.1016/0031-9422(93)85502-I -
Lawson DA, Chittka L, Whitney HM, Rands SA. 2018. Bumblebees distinguish floral scent patterns and can transfer these to corresponding visual patterns. Proceedings of the Royal Society B: Biological Sciences 285: 20180661. doi: 10.1098/rspb.2018.0661
» https://doi.org/10.1098/rspb.2018.0661 - Legendre P, Legendre L. 2012. Numerical Ecology. 3 ed. Amsterdan, Elsevier. Vol. 24.
- Loreto F, Schnitzler JP. 2010. Abiotic stresses and induced BVOCs. Trends in Plant Science 15: 154-166.
-
Maffei ME. 2010. Sites of synthesis, biochemistry and functional role of plant volatiles. South African Journal of Botany 76: 612-631. doi: 10.1016/j.sajb.2010.03.003
» https://doi.org/10.1016/j.sajb.2010.03.003 - Majetic CJ, Raguso RA, Tonsor SJ, Ashman TL. 2007. Flower color-flower scent associations in polymorphic Hesperis matronalis (Brassicaceae). Phytochemistry 68: 865-874
- Majetic CJ, Raguso RA, Tonsor SJ, Ashman TL. 2008. The impact of biochemistry vs. population membership on floral scent profiles in colour polymorphic Hesperis matronalis Annals of Botany 102: 911-922
- Majetic CJ, Raguso RA, Tonsor SJ, Ashman TL. 2009. Sources of floral scent variation: Can environment define floral scent phenotype? Plant Signaling & Behavior 4: 129-131
-
Manirajan BA, Maisinger C, Ratering S, Rusch V, Schwiertz A, Cardinale M, et al 2018. Diversity, specificity, co-occurrence and hub taxa of the bacterial-fungal pollen microbiome. FEMS Microbiology Ecology 94: fiy112. doi: 10.1093/femsec/fiy112
» https://doi.org/10.1093/femsec/fiy112 -
Muhlemann JK, Klempien A, Dudareva N. 2014. Floral volatiles: from biosynthesis to function. Plant, Cell & Environment 37: 1936-1949. doi: 10.1111/pce.12314
» https://doi.org/10.1111/pce.12314 -
Nicolson SW, Human H. 2013. Chemical composition of the ‘low quality’ pollen of sunflower (Helianthus annuus, Asteraceae). Apidologie 44: 144-152. doi: 10.1007/s13592-012-0169-2
» https://doi.org/10.1007/s13592-012-0169-2 - Oksanen J, Simpson G, Blanchet F, Kindt R, Legendre P, Minchin PR, et al 2026. vegan: Community Ecology Package. R package version 2.7-3.
-
Piskorski R, Kroder S, Dorn S. 2011. Can pollen headspace volatiles and pollenkitt lipids serve as reliable chemical cues for bee pollinators? Chemistry & Biodiversity 8: 1201-1214. doi: 10.1002/cbdv.201100040
» https://doi.org/10.1002/cbdv.201100040 -
Policha T, Davis A, Barnadas MM, Dentinger BT, Raguso RA, Roy BA. 2016. Disentangling visual and olfactory signals in mushroom-mimicking Dracula orchids using realistic three-dimensional printed flowers. New Phytologist 210: 1058-1071. doi: 10.1111/nph.13855
» https://doi.org/10.1111/nph.13855 -
Policha T, Grimaldi DA, Manobanda R, Troya A, Ludden AM, Dentinger BT, et al 2019. Dracula orchids exploit guilds of fungus visiting flies: New perspectives on a mushroom mimic. Ecological Entomology 44: 457-470. doi: 10.1111/een.12720
» https://doi.org/10.1111/een.12720 -
Popova V, Ivanova T, Stoyanova A, Nikolova V, Hristeva T, Zheljazkov VD. 2020. GC-MS composition and olfactory profile of concretes from the flowers of four Nicotiana species. Molecules 25: 2617. doi: 10.3390/molecules25112617
» https://doi.org/10.3390/molecules25112617 -
R Core Team. 2021. R: A language and environment for statistical. R. Foundation for Statistical Computing, Vienna, Austria. Available at: <Available at: https://R-project.org/ >. Accessed on 10 Nov 2025.
» https://R-project.org/ -
Raguso RA, Willis MA. 2002. Synergy between visual and olfactory cues in nectar feeding by naïve hawkmoths, Manduca sexta Animal Behaviour 64: 685-695. doi: 10.1006/anbe.2002.4010
» https://doi.org/10.1006/anbe.2002.4010 -
Raguso RA, Levin RA, Foose SE, Holmberg MW, McDade LA. 2003. Fragrance chemistry, nocturnal rhythms and pollination ‘syndromes’ in Nicotiana Phytochemistry 63: 265-284. doi: 10.1016/S0031-9422(03)00113-4
» https://doi.org/10.1016/S0031-9422(03)00113-4 -
Raguso RA, Willis MA. 2005. Synergy between visual and olfactory cues in nectar feeding by wild hawkmoths, Manduca sexta Animal Behaviour 69: 407-418. doi: 10.1006/anbe.2002.4010
» https://doi.org/10.1006/anbe.2002.4010 -
Raguso RA, Schlumpberger BO, Kaczorowski RL, Holtsford TP. 2006. Phylogenetic fragrance patterns in Nicotiana sections Alatae and Suaveolentes Phytochemistry 67: 1931-1942. doi: 10.1016/j.phytochem.2006.05.038
» https://doi.org/10.1016/j.phytochem.2006.05.038 -
Raguso RA. 2008. Start making scents: the challenge of integrating chemistry into pollination ecology. Entomologia Experimentalis et Applicata 128: 196-207. doi: 10.1111/j.1570-7458.2008.00683.x
» https://doi.org/10.1111/j.1570-7458.2008.00683.x -
Rezende L, Suzigan J, Amorim FW, Stehmann JR, Brito VLG. 2020. Can plant hybridization and polyploidy lead to pollinator shift? Acta Botanica Brasilica 34: 229-242. doi: 10.1590/0102-33062019abb0411
» https://doi.org/10.1590/0102-33062019abb0411 -
Rodrigues DM, Caballero-Villalobos L, Turchetto C, Souza-Chies TT, Kuhlemeier C. 2018. Do we truly understand pollination syndromes in Petunia as much as we suppose? AoB PLANTS 10: ply057. doi: 10.1093/aobpla/ply057
» https://doi.org/10.1093/aobpla/ply057 -
Rodríguez CE, Mitter B, Barret M, Sessitsch A, Compant S. 2018. Commentary: seed bacterial inhabitants and their routes of colonization. Plant and Soil 422: 129-134. doi: 10.1007/s11104-017-3368-9
» https://doi.org/10.1007/s11104-017-3368-9 -
Ryu C, Farag MA, Hu C, Reddy MS, Wei H, Paré PW, et al 2003. Bacterial volatiles promote growth in Arabidopsis Proceedings of the National Academy of Sciences of the United States of America 100: 4927-4932. doi: 10.1073/pnas.0730845100
» https://doi.org/10.1073/pnas.0730845100 -
Sharifi R, Ryu C. 2018. Revisiting bacterial volatile-mediated plant growth promotion: lessons from the past and objectives for the future. Annals of Botany 122: 349-358. doi: 10.1093/aob/mcy108
» https://doi.org/10.1093/aob/mcy108 -
Schaefer HM, Schaefer V, Levey DJ. 2004. How plant-animal interactions signal new insights in communication. Trends in Ecology & Evolution 19: 577-584. doi: 10.1016/j.tree.2004.08.003
» https://doi.org/10.1016/j.tree.2004.08.003 -
Schiestl FP. 2015. Ecology and evolution of floral volatile-mediated information transfer in plants. New Phytologist 206: 571-577. doi: 10.1111/nph.13243
» https://doi.org/10.1111/nph.13243 -
Schiestl FP, Johnson SD. 2013. Pollinator-mediated evolution of floral signals. Trends in Ecology & Evolution 28: 307-315. doi: 10.1016/j.tree.2013.01.019
» https://doi.org/10.1016/j.tree.2013.01.019 -
Schley RJ, Twyford AD, Pennington RT. 2022. Hybridisation: a ‘double-edged sword’ for Neotropical plant diversity. Botanical Journal of the Linnean Society 199: 331-356. doi: 10.1093/botlinnean/boab070
» https://doi.org/10.1093/botlinnean/boab070 -
Smith CI, Godsoe WK, Tank S, Yoder JB, Pellmyr O. 2008. Distinguishing coevolution from covicariance in an obligate pollination mutualism: asynchronous divergence in Joshua tree and its pollinators. Evolution 62: 2676-2687. doi: 10.1111/j.1558-5646.2008.00500.x
» https://doi.org/10.1111/j.1558-5646.2008.00500.x -
Soltis PS, Soltis DE. 2009. The role of hybridisation in plant speciation. Annual Review of Plant Biology 60: 561-588. doi: 10.1146/annurev.arplant.043008.092039.
» https://doi.org/10.1146/annurev.arplant.043008.092039. -
Svensson GP, Raguso RA, Flatz R, Smith CI. 2016. Floral scent of Joshua trees (Yucca brevifolia sensu lato): Divergence in scent profiles between species but breakdown of signal integrity in a narrow hybrid zone. American Journal of Botany 103: 1793-1802. doi: 10.3732/ajb.1600033
» https://doi.org/10.3732/ajb.1600033 -
Teixeira MC, Quintana IVQ, Segatto ALA, Turchetto C, Maestri R, Freitas LB, et al 2022. Changes in floral shape: insights into the evolution of wild Nicotiana (Solanaceae). Botanical Journal of the Linnean Society 199: 267-285. doi: 10.1093/botlinnean/boab085
» https://doi.org/10.1093/botlinnean/boab085 -
Turchetto C, Segatto ALA, Turchetto-Zolet AC. 2022. Biotic and abiotic factors in promoting the starting point of hybridization in the Neotropical flora: implications for conservation in a changing world. Botanical Journal of the Linnean Society 200: 288-302. doi: 10.1093/botlinnean/boac037
» https://doi.org/10.1093/botlinnean/boac037 -
Valença CAS, Hollanda LM, Jain S, Caramão EB. 2022. Chromatographic Profiles of Ethyl Acetate Extracts Produced by Bacillus sp. collected from the Mangroves in the Brazilian Northeast. Journal of the Brazilian Chemical Society 33: 1375-1385. doi: 10.21577/0103-5053.20220081
» https://doi.org/10.21577/0103-5053.20220081 -
Wright GA, Schiestl FP. 2009. The evolution of floral scent: the influence of olfactory learning by insect pollinators on the honest signaling of floral rewards. Functional Ecology 23: 841-851. doi: 10.1111/j.1365-2435.2009.01627.x
» https://doi.org/10.1111/j.1365-2435.2009.01627.x -
Wickham H, Chang W, Henry L, Pedersen TL, Takahashi K, Wilke C, et al 2016. ggplot2: Create Elegant Data Visualizations Using the Grammar of Graphics. R package version 4.0.2, CRAN. Available at: <Available at: https://CRAN.R-project.org/package=ggplot2 >. Accessed on 10 Nov 2025
» https://CRAN.R-project.org/package=ggplot2 -
Wu L, Li X, Ma L, Borriss R, Wu Z, Gao X. 2018. Acetoin and 2,3-butanediol from Bacillus amyloliquefaciens induce stomatal closure in Arabidopsis thaliana and Nicotiana benthamiana Journal of Experimental Botany 69: 5625-5635. doi: 10.1093/jxb/ery326
» https://doi.org/10.1093/jxb/ery326 -
Xiao Z, Lu JR. 2014. Strategies for enhancing fermentative production of acetoin: A review. Biotechnology Advances 32: 492-503. doi: 10.1016/j.biotechadv.2014.01.002
» https://doi.org/10.1016/j.biotechadv.2014.01.002
