← Prev: Pigments & Color Next: Pollination Ecology →

Module 3: Floral Scent & Volatile Biochemistry

Flowers advertise through chemistry. A single rose emits ~400 distinct volatile organic compounds (VOCs) into the air. This module derives the three biosynthetic origins of floral volatiles - terpenoids (MEP/MVA pathways), phenylpropanoids (shikimate), and fatty- acid-derived green-leaf volatiles - catalogues the icons of floral scent (linalool, methyl benzoate, benzyl acetate), builds a physical dispersal model for scent plumes, and closes with the rotten-meat mimicry of Rafflesia and Amorphophallus.

1. Three Biosynthetic Origins

Virtually every floral volatile belongs to one of three chemical families defined by biosynthetic origin:

Terpenoids (isoprenoid)

Built from C5 isoprene units (IPP + DMAPP)
Plastidial MEP pathway (pyruvate + GAP)
Cytosolic MVA pathway (acetyl-CoA)
Monoterpenes (C10): linalool, geraniol, limonene
Sesquiterpenes (C15): beta-caryophyllene, germacrene

Phenylpropanoids / benzenoids

Derived from shikimate -> phenylalanine
PAL diverts Phe to cinnamate -> volatiles
Chain-shortening (beta-oxidation) yields benzenoids
Methyl benzoate, benzyl acetate, eugenol, vanillin
Nocturnal moth flowers heavily invest here

Fatty-acid derivatives

13-Lipoxygenase oxidises C18 FAs
HPL cleaves to C6/C9 aldehydes
Green-leaf volatiles: (Z)-3-hexenal, (E)-2-hexenol
Jasmonate-family signals (C12 jasmonic acid)
Rapid release on tissue damage

1.1 Terpene biosynthesis

Plants run two parallel isoprenoid pathways. The cytosolic mevalonate (MVA) pathway starts from acetyl-CoA and supplies sesquiterpenes (C15), sterols and ubiquinone; the plastidial methylerythritol phosphate (MEP) pathway starts from pyruvate + glyceraldehyde-3-phosphate and supplies monoterpenes (C10), diterpenes, carotenoids and chlorophyll. Both pathways converge on the universal isoprenyl diphosphates:

\[ \text{IPP} \;\rightleftharpoons\; \text{DMAPP} \]

\[ \text{DMAPP} + \text{IPP} \xrightarrow{\text{GPPS}} \text{GPP (C10)} \xrightarrow{\text{TPS-b}} \text{linalool} \]

The committed step for each volatile is a specific terpene synthase (TPS). TPS proteins have a highly-conserved DDxxD motif that chelates Mg²⁺ / Mn²⁺ to ionise the allylic diphosphate, generating a carbocation that cyclises into the product. Linalool synthase produces 3S-linalool from GPP by water addition; myrcene synthase produces the acyclic triene.

1.2 Phenylpropanoid biosynthesis

Starting from phenylalanine, PAL yields cinnamate; CCR and CAD reduce cinnamate to cinnamaldehyde (spice of cinnamon). Methyl benzoate is made by chain-shortening cinnamate to benzoate via beta-oxidation, then O-methylation by BAMT:

\[ \text{Cinnamate} \xrightarrow{\beta\text{-oxidation}} \text{Benzoate} \xrightarrow{\text{BAMT} + \text{SAM}} \text{methyl benzoate} + \text{SAH} \]

BAMT (benzoic acid/salicylic acid carboxyl methyltransferase) uses S-adenosyl methionine as the methyl donor; it is highly expressed in petunia and snapdragon corolla lobes. Benzyl acetate (jasmine) is synthesised by BEAT, which acetylates benzyl alcohol using acetyl-CoA.

2. Biosynthetic Pathway Overview

Below is a compact route map showing the three biosynthetic origins leading to key floral volatiles.

3. Circadian Emission & Clock Control

Scent is not emitted constantly. It is gated in time by the circadian clock so that emission coincides with pollinator activity. In petunia (moth-pollinated) methyl benzoate emission peaks 2-3 h after sunset, falls to near zero by midnight, and is undetectable during the day. Four key regulators:

CCA1 / LHY - morning clock genes; in scent emitters they set the phase anchor.
TOC1 - evening loop partner; direct transcriptional activator of terpene synthases in Oenothera.
ODORANT1 (ODO1) - MYB-family TF in petunia that activates BAMT, CCMT (caffeic acid methyltransferase) and 30+ scent genes.
SPL9 / DOF - fine-tuning integrators that couple circadian time to development; mutants show phase shifts of 4-6 hours.

The net result is a pollinator-matched timing: roses emit in the afternoon to catch bees; jasmine and petunias emit at dusk for crepuscular moths; Cestrum nocturnum (night jasmine) peaks at 2 a.m. for owlet moths; some cacti peak near midnight for long-tongued bats.

Python

script.py81 lines

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

# ======================================================================
# Circadian emission of floral volatiles
# ----------------------------------------------------------------------
# Many flowers emit scent in a daily rhythm, gated by SPL9 and DOF
# transcription factors. Scent peaks coincide with pollinator activity:
#   - bee-pollinated: noon-afternoon
#   - moth-pollinated: dusk-evening
#   - hawk moths: late night
#   - bats: midnight
# ======================================================================

t = np.linspace(0, 48, 1000)    # hours over 2 days

def gauss_day(t, peak, sigma=3):
    # Repeating gaussian every 24h
    x = (t - peak) % 24
    x[x > 12] -= 24
    return np.exp(-(x/sigma)**2)

sources = [
    ('Rosa damascena (rose, bee)',        14,  3.0, '#f472b6'),
    ('Jasminum (moth)',                   20,  2.5, '#fde68a'),
    ('Petunia axillaris (hawk moth)',     22,  2.0, '#c084fc'),
    ('Nicotiana attenuata (hawk moth)',   23,  1.8, '#60a5fa'),
    ('Cactus (bat)',                       1,  2.2, '#86efac'),
    ('Rafflesia (carrion fly)',           12,  4.0, '#f97316'),
]

fig, axes = plt.subplots(2, 1, figsize=(12, 8))
fig.patch.set_facecolor('#0a0308')

ax = axes[0]; ax.set_facecolor('#1a0612')
for name, peak, sig, col in sources:
    ax.plot(t, gauss_day(t, peak, sig), color=col, linewidth=2.2, label=name)
for d in [0, 24, 48]:
    ax.axvspan(d, d+12, color='#fef08a', alpha=0.05)   # day
    ax.axvspan(d+12, d+24, color='#1e1b4b', alpha=0.15) # night
ax.set_xlabel('Hour of day (repeating 24 h)', color='white')
ax.set_ylabel('Relative scent emission', color='white')
ax.set_title('Circadian emission profiles vs pollinator guild',
             color='#fbcfe8', fontsize=11)
ax.legend(fontsize=9, facecolor='#1a0612', edgecolor='#f472b6', labelcolor='white', ncol=2)
ax.set_xlim(0, 48); ax.set_ylim(0, 1.05)
ax.grid(True, alpha=0.2, color='#f472b6')
ax.tick_params(colors='white')

# Composition: Fourier analysis of Nicotiana scent blend
ax = axes[1]; ax.set_facecolor('#1a0612')
compounds = {
    'Benzyl acetone' : (23, 2.2, 1.0,  '#c084fc'),
    'Linalool'       : (22, 2.5, 0.6,  '#f472b6'),
    'Methyl benzoate': (23, 2.0, 0.4,  '#fbbf24'),
    'Benzaldehyde'   : (22, 2.8, 0.2,  '#93c5fd'),
    'Nicotine (repel)':(3,  4.0, 0.3,  '#a3e635'),
}
for name, (peak, sig, amp, col) in compounds.items():
    ax.plot(t, amp*gauss_day(t, peak, sig), color=col, linewidth=2.2, label=name)
for d in [0, 24, 48]:
    ax.axvspan(d, d+12, color='#fef08a', alpha=0.05)
    ax.axvspan(d+12, d+24, color='#1e1b4b', alpha=0.15)
ax.set_xlabel('Hour of day', color='white')
ax.set_ylabel('Compound emission', color='white')
ax.set_title('Nicotiana attenuata scent blend: attractants (night) + defensive nicotine (morning)',
             color='#fbcfe8', fontsize=11)
ax.legend(fontsize=9, facecolor='#1a0612', edgecolor='#f472b6', labelcolor='white', ncol=2)
ax.set_xlim(0, 48); ax.grid(True, alpha=0.2, color='#f472b6')
ax.tick_params(colors='white')

plt.tight_layout()
plt.savefig('output.png', dpi=150, bbox_inches='tight', facecolor='#0a0308')

print("Scent timing is under clock control:")
print("  CCA1/LHY, TOC1 circadian oscillator → SPL9/DOF → terpene synthases")
print("  Diurnal: bee-pollinated; nocturnal: moth-pollinated")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

4. Gas-Phase Dispersal: Scent Plumes

A scent molecule leaves a petal through a trichome or osmophore (scent-producing gland) and diffuses into the surrounding air. Two regimes:

Near-field (r < 1 cm): molecular diffusion dominates; concentration follows Fick's second law with \(D \approx 6\times10^{-6}\,\text{m}^2/\text{s}\) for monoterpenes.
Far-field (r > 10 cm): advection by wind dominates; concentration follows a Gaussian plume.

4.1 Diffusion from a petal pore

Scent production starts inside osmophore cells where volatiles accumulate to saturation (the vapour pressure at 25 °C for linalool is about 21 Pa, giving a gas-phase density of ~1.3 mg/m³). Emission through stomatal or cuticular pores is governed by Hagen-Poiseuille flow for the gas phase combined with cuticular partition coefficient \(K_{cw}\):

\[ J = \frac{D_{\text{cut}} K_{cw}}{\ell_{\text{cut}}} (C_{\text{in}} - C_{\text{out}}) + \rho v_{\text{stom}} \]

cuticular permeation + stomatal convection

4.2 Far-field Gaussian plume

Outside the flower the concentration obeys the advection-diffusion equation:

\[ \frac{\partial C}{\partial t} + U \frac{\partial C}{\partial x} = K_y \frac{\partial^2 C}{\partial y^2} + K_z \frac{\partial^2 C}{\partial z^2} \]

For a continuous point source emitting at rate \(Q\), the steady-state Gaussian solution along the wind direction is:

\[ C(x,y,z) = \frac{Q}{2\pi U \sigma_y \sigma_z} \exp\!\left(-\frac{y^2}{2\sigma_y^2}\right) \exp\!\left(-\frac{(z-h)^2}{2\sigma_z^2}\right) \]

A honeybee's olfactory detection threshold for floral monoterpenes is about\(5\times10^{-15}\,\text{kg/m}^3\) (roughly 10⁷ molecules/cm³). The simulation below computes the downwind distance at which this threshold is reached for a typical rose (50 ng/s emission rate).

Python

script.py85 lines

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

# ======================================================================
# Gaussian plume model: scent dispersal from a flower
# ----------------------------------------------------------------------
# Passive tracer from a continuous point source at origin in wind U (x-dir).
# Classical Gaussian plume (Pasquill-Gifford) for neutral atmosphere:
#
#   C(x,y,z) = (Q / (2*pi*U*sigma_y*sigma_z)) *
#              exp(-y^2/(2*sigma_y^2)) *
#              (exp(-(z-h)^2/(2*sigma_z^2)) + exp(-(z+h)^2/(2*sigma_z^2)))
#
# We use power-law dispersion coefficients sigma = a*x^b for open ground
# and compute the ground-level concentration along the downwind axis.
# ======================================================================

# Emission rate of floral VOC (linalool, ng/s for rose)
Q = 50.0e-9      # kg/s  (50 ng/s)
U = 1.5          # m/s   gentle breeze
h = 0.5          # flower height above ground

# Pasquill-Gifford for class D (neutral) rural
sigma_y = lambda x: 0.08*x / np.sqrt(1 + 0.0001*x)
sigma_z = lambda x: 0.06*x / np.sqrt(1 + 0.0015*x)

def C(x, y, z):
    sy = sigma_y(x); sz = sigma_z(x)
    pre = Q / (2*np.pi*U*sy*sz + 1e-30)
    yt  = np.exp(-y**2/(2*sy**2 + 1e-30))
    zt  = np.exp(-(z-h)**2/(2*sz**2 + 1e-30)) + np.exp(-(z+h)**2/(2*sz**2 + 1e-30))
    return pre * yt * zt

# 2D ground-level map (z=0, 1 m bee flight altitude)
xs = np.linspace(0.5, 50, 250)
ys = np.linspace(-10, 10, 200)
X, Y = np.meshgrid(xs, ys)
Z = np.vectorize(C)(X, Y, 1.0)

# Detection threshold for a honeybee antenna (approx 100 molecules / cm^3
# for highly-tuned ORs e.g. queen-mandibular): ~5e-15 g/m^3 for linalool
thresh = 5e-15

fig = plt.figure(figsize=(14, 6))
fig.patch.set_facecolor('#0a0308')

ax1 = plt.subplot(1, 2, 1); ax1.set_facecolor('#1a0612')
im = ax1.pcolormesh(X, Y, np.log10(Z + 1e-30), cmap='spring', shading='auto',
                    vmin=-14, vmax=-8)
cs = ax1.contour(X, Y, Z, levels=[thresh], colors='#93c5fd', linewidths=2)
ax1.clabel(cs, inline=True, fmt='bee detection limit', colors='#93c5fd', fontsize=9)
ax1.set_xlabel('Downwind distance x (m)', color='white')
ax1.set_ylabel('Crosswind y (m)', color='white')
ax1.set_title('Gaussian plume: floral scent concentration\n(log10 kg/m^3 at z=1m)',
              color='#fbcfe8', fontsize=11)
cb = plt.colorbar(im, ax=ax1); cb.set_label('log10 C', color='white')
ax1.tick_params(colors='white')
for sp in ax1.spines.values(): sp.set_color('#f472b6')

# Centre-line concentration vs distance
Cline = np.vectorize(C)(xs, 0, 1.0)
ax2 = plt.subplot(1, 2, 2); ax2.set_facecolor('#1a0612')
ax2.semilogy(xs, Cline, color='#f472b6', linewidth=2.5, label='Centreline C(x,0,1m)')
ax2.axhline(thresh, color='#93c5fd', linestyle='--', label='Bee threshold')
ax2.set_xlabel('Distance downwind (m)', color='white')
ax2.set_ylabel('Concentration (kg/m^3)', color='white')
ax2.set_title('Centre-line concentration:\nbees lose scent trail at ~20 m',
              color='#fbcfe8', fontsize=11)
ax2.legend(facecolor='#1a0612', edgecolor='#f472b6', labelcolor='white')
ax2.grid(True, alpha=0.2, color='#f472b6', which='both')
ax2.tick_params(colors='white')
for sp in ax2.spines.values(): sp.set_color('#f472b6')

plt.tight_layout()
plt.savefig('output.png', dpi=150, bbox_inches='tight', facecolor='#0a0308')

# Estimate maximum detection distance
idx = np.argmin(np.abs(Cline - thresh))
print(f"Maximum downwind detection distance : ~{xs[idx]:.1f} m")
print(f"Centre-line C at  1 m : {Cline[0]:.2e} kg/m^3")
print(f"Centre-line C at 50 m : {Cline[-1]:.2e} kg/m^3  (attenuation {Cline[0]/Cline[-1]:.1e}x)")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

Typical results: a rose emitting 50 ng/s at 1.5 m/s wind can be detected by bees to about 20-30 m downwind. This sets the scale of single-flower "scent zones" and justifies the use of inflorescences - combining flowers concentrates VOCs locally, extending effective advertising range.

5. Pollinator-Specific Scent Blends

Scent chemistry correlates with pollinator guild through convergent evolution (Knudsen & Gershenzon 2006):

Bee-pollinated

Rich in terpenes (linalool, geraniol, ocimene)
Damascones and damascenones (rose)
Diurnal emission (peak around noon-14:00)
2-methylbutyl isobutyrate - honey-attractant (Apis preference)

Moth-pollinated (Sphingidae)

Phenylpropanoids (methyl benzoate, benzyl acetate)
Benzaldehyde - "almond-sweet"
Nocturnal emission (dusk to midnight)
Pale/white petals; deep tubular corolla

Fly-pollinated (carrion)

Dimethyl disulfide + dimethyl trisulfide (meat-rotten sulfur)
Indole, putrescine, cadaverine (amines)
Thermogenic: volatilises odours, mimics body heat
Examples: Rafflesia, Amorphophallus, Hydnora

Bat-pollinated

Sulfur-containing aliphatics (but pleasant)
Dihydrosulfide compounds (garlic-fermented)
Midnight emission; huge nectar volumes
Examples: Agave, Ceiba, durian

Python

script.py80 lines

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

# ======================================================================
# Volatile-compound 'fingerprint' spectra
# ----------------------------------------------------------------------
# Pseudo-GC chromatograms (retention time vs signal) for three pollinator
# guilds.  Bee-pollinated roses are dominated by terpenes; moth flowers
# favour aromatic nitrogenous compounds; carrion flowers emit sulfides.
# ======================================================================

rt = np.linspace(0, 30, 2000)          # retention time (min)
def peak(rt, mu, sig, A):
    return A * np.exp(-((rt-mu)/sig)**2)

# Bee-pollinated blend (rose)
bee = (peak(rt, 5.5, 0.15, 0.8)        # linalool
     + peak(rt, 7.2, 0.20, 0.5)        # geraniol
     + peak(rt, 9.1, 0.12, 0.4)        # nerol
     + peak(rt, 11.3,0.15, 0.6)        # damascenone
     + peak(rt, 15.0,0.18, 0.3))       # phenethyl alcohol

# Moth (Nicotiana)
moth = (peak(rt, 6.0, 0.18, 0.6)       # linalool
      + peak(rt, 8.5, 0.15, 0.8)       # benzyl acetone
      + peak(rt, 10.2,0.18, 1.0)       # methyl benzoate
      + peak(rt, 12.4,0.20, 0.4)       # benzaldehyde
      + peak(rt, 14.8,0.15, 0.3))      # methyl salicylate

# Carrion fly (Rafflesia / Amorphophallus)
carr = (peak(rt, 2.1, 0.10, 1.0)       # dimethyl disulfide
      + peak(rt, 3.0, 0.10, 0.7)       # dimethyl trisulfide
      + peak(rt, 4.5, 0.15, 0.5)       # indole
      + peak(rt, 5.5, 0.15, 0.4)       # putrescine
      + peak(rt, 7.0, 0.12, 0.3))      # cadaverine

fig = plt.figure(figsize=(13, 8))
fig.patch.set_facecolor('#0a0308')

ax1 = fig.add_subplot(3,1,1); ax1.set_facecolor('#1a0612')
ax1.plot(rt, bee,  color='#f472b6', linewidth=1.5)
ax1.fill_between(rt, 0, bee, color='#f472b6', alpha=0.4)
ax1.set_title('Bee-pollinated (Rosa): terpenes dominate',
              color='#fbcfe8', fontsize=11)
ax1.annotate('linalool', (5.5, 0.85), color='#fde68a', fontsize=9, ha='center')
ax1.annotate('geraniol', (7.2, 0.55), color='#fde68a', fontsize=9, ha='center')
ax1.annotate('damascenone', (11.3, 0.65), color='#fde68a', fontsize=9, ha='center')
ax1.set_ylabel('GC signal', color='white'); ax1.tick_params(colors='white')
ax1.grid(True, alpha=0.2, color='#f472b6')

ax2 = fig.add_subplot(3,1,2); ax2.set_facecolor('#1a0612')
ax2.plot(rt, moth, color='#c084fc', linewidth=1.5)
ax2.fill_between(rt, 0, moth, color='#c084fc', alpha=0.4)
ax2.set_title('Moth-pollinated (Nicotiana): aromatic phenylpropanoids',
              color='#fbcfe8', fontsize=11)
ax2.annotate('methyl benzoate', (10.2, 1.02), color='#fde68a', fontsize=9, ha='center')
ax2.annotate('benzyl acetone', (8.5, 0.82), color='#fde68a', fontsize=9, ha='center')
ax2.set_ylabel('GC signal', color='white'); ax2.tick_params(colors='white')
ax2.grid(True, alpha=0.2, color='#f472b6')

ax3 = fig.add_subplot(3,1,3); ax3.set_facecolor('#1a0612')
ax3.plot(rt, carr, color='#a3e635', linewidth=1.5)
ax3.fill_between(rt, 0, carr, color='#a3e635', alpha=0.4)
ax3.set_title('Fly-pollinated (Rafflesia, Amorphophallus): sulfides & amines',
              color='#fbcfe8', fontsize=11)
ax3.annotate('dimethyl disulfide', (2.1, 1.05), color='#fde68a', fontsize=9, ha='center')
ax3.annotate('indole', (4.5, 0.55), color='#fde68a', fontsize=9, ha='center')
ax3.set_xlabel('Retention time (min)', color='white')
ax3.set_ylabel('GC signal', color='white'); ax3.tick_params(colors='white')
ax3.grid(True, alpha=0.2, color='#f472b6')

plt.tight_layout()
plt.savefig('output.png', dpi=150, bbox_inches='tight', facecolor='#0a0308')

for name, spec in [('bee',bee),('moth',moth),('carrion',carr)]:
    print(f"{name:<8} total emission (a.u.): {np.trapezoid(spec, rt):.3f}")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

6. Corpse Flowers: Rotten-Meat Mimicry

Some flowers advertise not reward but deception, mimicking the odour of animal carcasses to attract blow flies (Calliphoridae) and flesh flies (Sarcophagidae) which normally lay eggs on carrion. The three showcase species are:

Amorphophallus titanum (titan arum)- up to 3 m inflorescence; dominant VOCs are dimethyl trisulfide, dimethyl disulfide, methyl thioacetate, isovaleric acid. Thermogenic spadix reaches 36 °C, volatilising compounds and broadcasting "body heat" cue.
Rafflesia arnoldii - world's largest single flower (1 m). Main VOCs: dimethyl disulfide, indole, putrescine. Not thermogenic but emits during day when flies are active.
Hydnora africana - subterranean parasite; flower breaks through soil and traps dung beetles for hours.

The trap flowers are typically brood-site deceivers: flies are lured, sometimes imprisoned briefly, then released covered with pollen. No eggs successfully develop - pure parasitism of the fly's reproductive behaviour. See also Module 4 for further deception strategies (sexual mimicry by Ophrys orchids).

Thermogenesis-enhanced dispersal: heat increases vapor pressure exponentially via Clausius-Clapeyron, \(P_{\text{sat}}(T) = P_0 \exp(-\Delta H_{\text{vap}}/RT)\). For dimethyl disulfide \(\Delta H_{\text{vap}} \approx 35\,\text{kJ/mol}\), a temperature rise from 20 °C to 36 °C increases vapor concentration at the source by a factor of\(\exp((1/293 - 1/309)\cdot 35000/8.314) \approx 3\).

7. Summary Table

Three classes

Terpenoids (IPP/DMAPP -> TPS); phenylpropanoids (Phe -> PAL); fatty-acid derivatives (LOX/HPL)

Terpene biosynthesis

Parallel MEP (plastid, C10) and MVA (cytosol, C15) pathways; TPS with DDxxD Mg2+ motif

Phenylpropanoids

Phe -> cinnamate -> benzoate -> methyl benzoate by BAMT + SAM

Key compounds

Linalool (rose), benzyl acetate (jasmine), methyl benzoate (snapdragon), methyl salicylate

Circadian gating

CCA1/LHY, TOC1, ODORANT1, SPL9/DOF align emission with pollinator activity

Dispersal

Near-field diffusion + far-field Gaussian plume; rose detection ~20-30 m downwind for bees

Pollinator syndromes

Terpenes -> bees; phenylpropanoids -> moths; sulfides -> carrion flies; sulfur aliphatics -> bats

Corpse flowers

Rafflesia, Amorphophallus; DMDS + indole + thermogenesis; fly brood-site deceit

Vapor-pressure scaling

Clausius-Clapeyron: 20->36 degC increases source vapor ~3x for DMDS

References

Knudsen, J. T., Eriksson, R., Gershenzon, J. & Stahl, B. (2006). Diversity and distribution of floral scent. The Botanical Review, 72, 1-120.
Dudareva, N., Negre, F., Nagegowda, D. A. & Orlova, I. (2006). Plant volatiles: recent advances and future perspectives. Critical Reviews in Plant Sciences, 25, 417-440.
Pichersky, E. & Gershenzon, J. (2002). The formation and function of plant volatiles: perfumes for pollinator attraction and defense. Current Opinion in Plant Biology, 5, 237-243.
Verdonk, J. C., Haring, M. A., van Tunen, A. J. & Schuurink, R. C. (2005). ODORANT1 regulates fragrance biosynthesis in petunia flowers. The Plant Cell, 17, 1612-1624.
Kessler, D., Gase, K. & Baldwin, I. T. (2008). Field experiments with transformed plants reveal the sense of floral scents. Science, 321, 1200-1202.
Muhlemann, J. K., Klempien, A. & Dudareva, N. (2014). Floral volatiles: from biosynthesis to function. Plant, Cell & Environment, 37, 1936-1949.
Bouwmeester, H., Schuurink, R. C., Bleeker, P. M. & Schiestl, F. (2019). The role of volatiles in plant communication. The Plant Journal, 100, 892-907.
Meeuse, B. J. D. (1978). The physiology of some sapromyophilous flowers. In The Pollination of Flowers by Insects (A. J. Richards ed.), 97-104.
Raguso, R. A. (2008). Wake up and smell the roses: the ecology and evolution of floral scent. Annual Review of Ecology, Evolution, and Systematics, 39, 549-569.
Kolosova, N., Gorenstein, N., Kish, C. M. & Dudareva, N. (2001). Regulation of circadian methyl benzoate emission in diurnally and nocturnally emitting plants. The Plant Cell, 13, 2333-2347.
Pasquill, F. & Smith, F. B. (1983). Atmospheric Diffusion, 3rd ed. Halsted Press.

← Prev: Pigments & Color Next: Module 4 - Pollination Ecology →