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First published online December 14, 2006
Journal of Experimental Biology 210, 37-45 (2007)
Published by The Company of Biologists 2007
doi: 10.1242/jeb.02616
Flower tracking in hawkmoths: behavior and energetics
1 Arizona Research Laboratories, Division of Neurobiology, University of
Arizona, Gould-Simpson Building Room 611, 1040 E. 4th Street, Tucson, AZ
85721, USA
2 Arizona Research Laboratories, Division of Neurobiology, University of
Washington, 98195, USA
* Author for correspondence (e-mail: jspray{at}neurobio.arizona.edu)
Accepted 24 October 2006
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Key words: hawkmoth, flower tracking, feeding rate
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) and
capable of inducing flower motions
(http://faculty.washington.edu/danielt/sprayberry06.mov).
Hawkmoths compensate for such flower motions by tracking them through space
(Farina et al., 1994;
Kern and Varju, 1998). The
diurnal hawkmoth Macroglossum stellatarum successfully tracks an
expanding and contracting disc at frequencies from 0.15-5 Hz
(Farina et al., 1994), and
similar behavior has been demonstrated in the crepuscular hawkmoth Manduca
sexta (Moreno et al.,
2000). However, the effect of frequency and direction of flower
motion on tracking performance is poorly understood, and the impact of flower
motions on feeding performance is completely unknown.
Although it is recognized that pollinator feeding performance can impact
the fitness of both Lepidoptera and plants
(Hill, 1989;
Mothershead and Marquis,
2000), the majority of feeding studies have focused on visitation
dynamics (Goulson et al., 1998;
Miyake and Yahara, 1998) and
effects of nectar concentration (Josens and
Farina, 2001). Consequently, the effects of flower motion dynamics
on plant-pollinator systems are poorly understood. Perching insects might not
be challenged by flower motion, but hovering feeders could experience
significant impacts. Feeding from flowers that are difficult to track could
increase handling time and/or lower nectar intake. While flower tracking
putatively improves feeding performance, hovering flight is metabolically
expensive (Willmott and Ellington,
1997), and the maneuvering associated with flower tracking may add
considerable costs. This study combines experimental data on tracking motions
and feeding rates with a theoretical model to examine the energetic
consequences of floral tracking during nectar feeding bouts.
The energetics of flower tracking could have implications for hawkmoth
ecology and evolution. Given this, there may be concerns when using results
from captive-reared animals to speculate about impacts on the ecology of
natural populations. Although previous studies on sensory control of feeding
behavior in M. sexta have found comparable results for both
captive-reared and wild moths (Raguso and
Willis, 2002; Raguso and
Willis, 2005), feeding rates could differ between the populations.
Therefore, we also measured feeding rates of wild Manduca to
ascertain how well our colony of moths performed.
This study uses both experimental and theoretical techniques to answer four questions. (1) What frequencies and directions of moving flower are best tracked by Manduca sexta? (2) How well does M. sexta feed from flowers moving at various frequencies and in different directions? (3) Based upon measured tracking and feeding performance and the associated costs of maneuvering, what are the energetic consequences of flower tracking across tested frequencies and directions? (4) Are feeding rates of colony-reared M. sexta comparable to wild populations?
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).
Robotic flower experiments: feeding while tracking
This experimental setup was designed to collect 3D motion trajectories of
hawkmoths feeding from a moving robotic flower, allowing simultaneous
measurement of tracking performance and feeding rates. Moths were flown in a
1.50 mx0.61 mx1.83 m flight chamber, which was dimly lit with
incandescent under-counter lights strung around the top perimeter. The overall
luminance was less than 1 cd m-2, the lowest resolution of our
Gossen Mavolux 5032C light meter (B&H Photo Video). The walls of the
chamber were white poster board decorated with green paper leaves to provide
visual texture. The floor and the ceiling of the chamber were both black. The
only food source in the chamber was a robotic artificial flower. The flower
was a lynx-motion 5-axis robotic arm programmed (in MatLab, by N. Jacobs) to
move unidimensionally in all three dimensions: horizontal (H),
vertical (V) and looming (L). All axes of motion are in the
frame of reference of the feeding moth throughout this manuscript
(Fig. 1A). A Sony digital
high-8 video camera (30 frames s-1) was positioned to record a
lateral view of the robotic flower (V-L plane). One mirror
was positioned above the robot and reflected the dorsal view
(H-L plane) onto a mirror placed behind the robot in the
lateral plane, so that the single video camera could capture both views.
Mirror positions were adjusted so that these two views (the
V-L and H-L planes) were orthogonal in the
spatial region occupied by a feeding moth. Videos of feeding bouts were used
to extract 3D information of the flowers' and moths' paths by digitizing
orthogonal views using a MatLab program (provided by M. S. Tu). Pixel to
centimeter conversions, using an 8 cm rod and a 10 cm cube, were performed on
both planes (V-L and H-L). The two
orthogonal planes were then normalized relative to the L-axis
coordinate, giving the 3D coordinates of both the moth and the flower for each
digitized frame.
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Likewise, the nectar volume used to measure feeding rates was in the range of
volumes found in hawkmoth-pollinated flowers [3-80 µl
(Raguso et al., 2003)].
Hawkmoths were subjected to two different experimental treatments that varied frequency (0-3 Hz) and direction (H, V and L axes). We collected video and nectar consumption data of moths feeding from a flower moving at 1, 2 and 3 Hz in the H (left-right) and V (up-down) axes. Data were collected only for 1 and 2 Hz in the L (approach-recede) axis because moths appeared unable to feed from a flower moving in the L axis at 3 Hz. Five samples were collected for the H axis at 1 and 2 Hz, the V axis at 1, 2 and 3 Hz, and the L axis at 1 Hz. Three samples were collected for the H axis at 3 Hz treatment, and four samples were collected for the L axis at 2 Hz treatment. The amplitude of flower motion was held constant at 1 cm. For horizontally and vertically moving flowers, this was an 8.7° visual displacement. For looming flowers, 1 cm amplitude is equivalent to 7.4° of expansion. Five control events (moths feeding from a stationary flower) were also collected.
Measurement of feeding rates in the field
To assess feeding rates of wild Manduca, we monitored feeding
activity on artificial flowers with a known nectar volume at the Sonoran
Desert Museum. The artificial flowers were identical to those used in
laboratory studies and had a mean nectar volume of 42.1±0.99 µl.
Artificial flowers were arranged around a single Datura wrightii
bloom to attract moths. The artificial flowers were visited by one wild moth
on each of three nights. A single moth visitor would completely drain the
nectar reservoir of a `flower', and typically empty all available artificial
flowers. This gave us multiple feeding measurements from three different
Manduca.
Feeding activity on artificial flowers was filmed (Sony miniDV camcorder,
30 frames s-1) under infrared lighting conditions to avoid
disrupting natural light levels (White et
al., 2003). Feeding rates were calculated as (nectar volume -
evaporative volume)/visit time. As all feeding events resulted in a completely
emptied nectar tube, the nectar volume consumed was 42.1 µl. The
evaporative volume was measured from control flowers. The controls had
identical nectar volumes and flower morphology, but with black petals to hide
them from visitors. They were corked at the end of the feeding session and
later weighed to determine evaporative loss. The mean evaporation of control
nectar volumes during filming for three nights spent in the field was used as
the evaporative volume in feeding rate calculations (1.9 µl). Visit time
was calculated (using digitized video) by taking the number of video frames a
moth's proboscis was seen to be in the flower divided by the frame rate (30
frames s-1).
Measurement of feeding rates in the lab
Nectar consumption was measured by weighing nectar tubes before and after
feeding bouts, then calculating nectar consumed as the mass difference between
the two. Assuming a density of 1, the nectar mass was converted into volume
(µl). The evaporative loss of nectar from tubes over the course of an
experiment was insignificant (1-3.6 µl, Student's t-test,
P=0.948). Feeding rate was defined as (volume of nectar
consumed)/(visit time). Visit time was determined from digitized
video data. Start time was set as the first time that the moth's distance to
the nectary was less than average proboscis length of the colony (8.7 cm).
Stop time was set as the last time in a digitized feeding bout the moth's
distance to the flower was less than the average proboscis length. Visit time
was simply calculated as stop minus start
(Fig. 1B).
Analysis of tracking performance
Using digitized data, we measured the mean distance between the hawkmoth's
head and the flower for all feeding bouts. These calculations excluded the
moth's approach and departure from the flower. The digitized data were also
used to compute the cross-correlation of the moth's and flower's spatial
trajectories, which provides two metrics of tracking performance: (1) the
cross correlation coefficient (r), which accounts for how well a
hawkmoth mimics the flower's path, and (2) the lag of behavioral response,
which can be used to calculate phase. The cross-correlation coefficient
r measures how well the flower's path predicts the moth's path, i.e.
how well the moth is tracking the flower. The latency of response is the first
time step where r peaks, giving the delay at which correlation
between the two signals is maximal (see Results). The phase of moth response
is taken as (latency)/(period of the flower motion). Prior to
cross-correlation analysis, spatial trajectories of both the moth and flower
are put through a firstorder, low-pass, Butterworth filter using the
`filtfilt' function in Matlab (providing phase free filtering) to remove high
frequency digitizing error. The corner frequency for this filter was 28.4 Hz.
Additionally, any slow linear trends in the moths' trajectories are removed by
detrending the data using the `detrend' function in MatLab.
Modeling energy gain
To track moving flowers, moths must necessarily accelerate and decelerate
their body mass. As frequency and amplitude of flower motion increase, the
rates of acceleration and deceleration will also increase, thereby changing
the energetic cost of flight (
out). At the
same time, any changes in nectar uptake that occur as a result of changing
flower dynamics will alter the influx of energy
(in). To understand the energetic consequences of
tracking we modeled the rate of net energy gain
(gain), which is simply the difference between the
rates of energy intake and energy output:
 | (1) |
The rate of energy intake is a linear function of the sucrose
concentration, the energy content of sucrose, and the mass rate of nectar
uptake:
 | (2) |
where [S] is the sucrose concentration (0.27), 
is the
energy content of sucrose (1.54x104 J g-1)
(Kingsolver and Daniel, 1979),
and f is the mass rate of nectar uptake in g s-1,
calculated from the measured feeding rate from each trial.
We modeled the flight costs (rate of energy expenditure) as a linear sum of
the rate of energy expenditure required to hover
(hover) plus the energy required to track a moving
flower (track). Resting metabolic rate was ignored
because it is small compared to overall energy expenditures
(Kingsolver and Daniel, 1979;
Willmott and Ellington, 1997).
Because of metabolic inefficiencies, the animal needs to ingest more energy
than the mechanical power required to move. Ellington
(Ellington, 1984) calculated
that muscle efficiency for sphingid moths ranges from a minimum of 6%
(assuming perfect elastic storage of inertial energy) to a maximum of 17%
(assuming muscles accelerate the wings without aid of elastic storage). For
this model we assigned muscle efficiency (
) to be the mean of these two
estimates, 11.5%:
 | (3) |
Mechanical hovering costs (hover) were presumed
constant for a moth of given mass and estimated
(Willmott and Ellington, 1997)
to be 30 W kg-1. The instantaneous cost of tracking
(track) was taken to be the power required to move
the body mass as it tracks flower position:
 | (4) |
where m is mass, a(t) is acceleration and
v(t) is velocity of the moth. Because moths were tracking
flowers moving as periodic functions, we modeled moth position as a simple
periodic function:
 | (5) |
Thus:
 | (6) |
 | (7) |
Substituting Eqn 6 and Eqn 7 into Eqn 4 gives:
 | (8) |
Since energy is expended in both directions within an axis of motion
(represented in a periodic position function as positive and negative), we
calculate total energy as the absolute value of Eqn 8. Because that rectified
function is periodic at four times the driving frequency, the average of the
function is taken over a quarter cycle:
 | (9) |
We obtained the frequency and amplitude values to calculate power by
performing a fast fourier transform (FFT) analysis on the moth's path through
space in all dimensions. To remove high frequency digitizing error, the
digitized data were filtered as described in above `Analysis of tracking
performance'. The mean was also removed from the signal prior to running the
FFT analysis. Removing the mean created an artificial peak by forcing the
origin value to zero. Discarding this artifact, we used the top three
remaining peaks from the FFT analysis (see Results). The total power output
for an individual axis was taken as the summed power output calculated from
the individual peaks. Since tracking a flower necessarily involves motion in
all three axes, even if the flower only moves in one, we sum the power output
components of each axis to predict total tracking cost.
 | (10) |
where j=1, 2, 3 corresponds to the horizontal, vertical and looming motion axes, and i=1, 2, 3 corresponds to the frequency and amplitude peaks in each motion axis.
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Feeding rates
Feeding rates were relatively independent of frequency, but do show
direction effects (Fig. 2). The
empty data point caused by moths being unable to feed from L axis 3
Hz flowers makes it difficult to run a standard ANOVA on the entire dataset.
When analyzing individual axes, only the L motion axis showed a weak
effect of frequency (P=0.09, ANOVA). Additionally, we ran an ANOVA
on: (1) control, H and V axes through 3 Hz; (2) control,
H, V and L axes through 2 Hz; (3) H and V
axes at 1, 2 and 3 Hz; (4) H, V and L axes at 1 and 2 Hz.
All these analyses returned the same basic findings. There is no effect of
frequency on feeding rate. However, there are several direction effects. The
V axis returns as statistically distinct from control flowers in
analyses 1 (P<0.05) and 2 (P<0.01). The L
axis returns as statistically distinct from the H axis in analyses 2
(P<0.05) and 4 (P<0.05), and distinct from the control
in analysis 1 (P<0.05).
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Feeding rates of wild Manduca were measured to determine if colony moth feeding performance was comparable to hawkmoths in the field. Data were collected from three wild Manduca feeding on stationary artificial flowers. The mean feeding rates for these animals were 3.5, 6.6 and 9.6 µl s-1, as compared to control data collected in the lab (4.62, 6.75, 8.08, 8.42 and 9.71 µl s-1). Feeding rates of wild Manduca and colony M. sexta are statistically indistinguishable from each other (Student's t-test, P=0.662).
Tracking performance
If the hawkmoths were actively tracking moving flowers, they should
maintain a constant distance to a flower while feeding. The mean distance was
calculated from digitized data for all feeding bouts
(Table 1). Mean distance
typically remains constant across frequency; both the H and
V axes showed no effect of frequency. However, the L axis
did show a significant increase in mean distance with increasing frequency
(ANOVA, P<0.05). Additionally, an ANOVA on all data showed no
frequency effects, but a significant direction effect (H versus L,
P<0.05). If moths were successfully tracking flowers, the
measurements of moth to flower distance during a feeding bout should have low
variance. Indeed we see very low variance for all treatments, with the
exception of moths feeding from looming flowers moving at 2 Hz
(Fig. 3). Here, the increase in
distance seen in Table 1 is
accompanied by significant increase in variance (P<0.05,
ANOVA).
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Cross correlation of the digitized flower and moth paths provided a measurement of how well a moth followed a flower, giving both a maximum correlation coefficient that indicates how well a moth mimicked the flower's path (rm) and the latency of the moth's response. The response latency is determined from the time lag associated with the maximum cross-correlation value (rm) (Fig. 4). The closer rm is to one, the better the moth is tracking the flower. When comparing the mean rm values across treatments, there was a significant decrease with increasing frequency in all motion axes (P<0.01, ANOVA; Fig. 5). This decrease was not linear for the H and V axes; rather mean rm values for both 1 Hz and 2 Hz flowers were statistically indistinguishable, then exhibited a sharp decline at 3 Hz. Likewise, the rm values for the L axis showed a sharp drop at 2 Hz. When looking at both latency and phase of hawkmoth response to moving flowers, there were strong frequency and direction effects. In all motion axes, both latency and phase of response increased with frequency (P<0.001, ANOVA; Fig. 6). Additionally, the latency of response to V axis flowers is significantly larger (P<0.001, ANOVA). Accounting for all data, M. sexta seem to track low frequency H axis flowers the best, and are poorest at tracking high frequency L axis flowers.
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Energetics of flower tracking
To ascertain whether or not the energy expended to track flowers has a
significant impact on energy gained, we calculated the net rate of energy gain
(gain) for each feeding bout. Rate of energy gain
was simply modeled as the rate of energy in minus the rate of energy out
(in-out; Eqn 1).
Calculations of energy input were made using measured feeding rates.
Calculations of energy output utilized coefficients from an FFT analysis of
moth position vectors (see Materials and methods). We used the top three peaks
from the FFT analysis (Fig. 7),
the results of which are summarized in
Table 2. Flower motion did
indeed increase the amount of energy consumed during a feeding bout. However,
this had minimal impact on the net rate of energy gain
(gain), because the magnitude of energy out
(out) is minute (2-3%) relative to the energy in
(in) (Fig.
8).
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)
(http://faculty.washington.edu/danielt/sprayberry06.mov).
Hawkmoths compensate for these motions by exhibiting a skilled tracking
behavior, maintaining a constant distance to a moving nectary
(Table 1). Although M.
sexta are indeed talented at tracking moving flowers, their ability to do
so varies with both frequency and direction (Figs
5,
6). We found that M.
sexta were best able to track flowers moving with a frequency of 1 Hz.
While moths accurately mimicked the flower path for the 2 Hz treatment in the
H and V axes, they were farther out of phase with flower
motion than moths feeding from flowers in the 1 Hz treatment. M.
sexta were easily able to track flowers moving in the horizontal and
vertical motion axes. However, they had a markedly lower ability to track
flowers moving in the L axis. In fact, M. sexta's tracking
ability in this direction declined sharply as frequency increased, so that
they were unable to feed from a flower moving at 3 Hz.
Previous work on flower tracking in the hawkmoth Macroglossum
stellatarum stated they were able to track 0.5-5 Hz stimuli in the
looming axis (no other directions were studied)
(Farina et al., 1994). These
results are different than those presented in this paper, but to some extent
this may be a result of methodological differences. First, this study used a
mechanical stimulus in ramp experiments, moving the flower at a constant
velocity of 3 cm s-1 (a speed that would roughly correlate with a
1.5 Hz stimulus in the paradigm of our experiments). However, when
investigating the range of frequencies M. stellatarum was able to
track, Farina et al. used the purely visual stimulus of a projected expanding
and contracting disc (Farina et al.,
1994). As frequency increased above 3 Hz, the phase of moth
response increased dramatically, from trailing 20° out of phase at 3 Hz to
approximately 115° at 5 Hz. The highest phase delay at which M.
sexta were capable of tracking a mechanical looming flower was 109°.
It is possible that M. stellatarum would not have been able to feed
from a mechanical flower oscillating at 5 Hz. However, the phase differential
exhibited by M. sexta while tracking horizontal and vertical 3 Hz
flowers (85-123°) is much larger than M. stellatarum tracking 3
and 4 Hz stimuli (15-50°), indicating that M. stellatarum can
indeed track faster moving flowers.
There are several potential reasons for this difference. Hawkmoth visual
systems display changes in tuning of motionprocessing that are matched to
their ecology (O'Carroll et al.,
1997), and M. stellatarum are diurnal and active in much
brighter light regimes than M. sexta. Given the higher light levels,
M. stellatarum's visual system is likely better at resolving faster
moving flowers. M. stellatarum have higher spatial resolution
(1.8°) (Warrant et al.,
1999) than M. sexta (3°; E. Warrant, personal
communication) in their frontal field of view. Additionally, M.
stellatarum has a much shorter proboscis (2.4 cm)
(Krenn, 1990) than M.
sexta (8.7 cm). At this shorter distance their higher angular resolution
results in a much finer cartesian resolution (0.07 cm for M. stellatarum
versus 0.46 cm for M. sexta). However, recent experiments on
object-sensitive descending neurons in M. sexta show that their
visual system responds to an object oscillating at frequencies as high as 6 Hz
(J.D.H.S., manuscript in preparation). If vision is not responsible for the
roll off in M. sexta's tracking ability, it is less likely that the
difference in tracking ability between the two species is attributable to the
difference in tuning of their visual systems. In fact, in Farina et al.'s
study (Farina et al., 1994)
M. stellatarum were clearly trying to track frequencies they were
incapable of physically following, indicating that their visual inputs are
trying to drive motions that the musculoskeletal system is ultimately unable
to produce. It is possible that the aerodynamic maneuverability of hawkmoths
is limiting their tracking behavior, and M. stellatarum's smaller
size contributes to greater maneuverability, allowing them to track flowers at
higher frequencies.
Feeding performance
Flower tracking performance is likely to be vital in allowing a moth to
maintain contact with a nectar source. For example, the hawkmoth-pollinated
flowers Nicotinia alata and Nicotinia forgetiana have
corolla widths of 0.6-0.7 cm (Ippolito et
al., 2004) and nectar volumes of 5.82-9.43 µl
(Kaczorowski et al., 2005), and
thus the depth of the nectar reservoir will range between 0.015 and 0.034 cm.
Even a slight shift of the proboscis during feeding will result in loss of
contact with the nectar. This will either increase the time the moth has to
stay at the flower to drain all of the nectar, or cause them to acquire less
than a full nectar load from that visit. Flower tracking should mitigate these
effects by minimizing loss of contact with the nectar reservoir.
Interestingly, feeding rates do not seem to be tightly correlated with
tracking ability. In fact, hawkmoths feed equally well from poorly tracked
flowers moving at 3 Hz in the H and V axis as they do from
easily tracked flowers moving at 2 Hz, indicating that suboptimal tracking is
still sufficient for maintaining nectar contact. However, the fact that M.
sexta are completely unable to feed from flowers moving at 3 Hz in the
looming axis indicate that some base level of flower tracking is necessary.
Flower tracking, then, seems to have a threshold effect on feeding rates;
either the moth tracks sufficiently to feed well, or it cannot feed from the
flower at all.
Even though feeding rates remain constant, the energetic cost associated
with tracking moving flowers should increase as the cube of frequency and as
the square of amplitude. We explored the consequences of this by constructing
a model of net energy gain using measured inputs of nectar uptake and physical
work performed while feeding. We do indeed find that tracking costs rise
(Fig. 8), but these cost
increases are low relative to the rate of energy intake and do not impact net
energy gain on the scale of an individual feeding bout. The hawkmoth
Macroglossum stellatarum can consume 211 µl of nectar in a day
(Kelber, 2003), but even on
this scale the 0.4% increase in tracking cost seen between control and 2 Hz
flowers results in a loss of only 0.86 µl of nectar. It is unlikely that
the energetic cost of tracking flowers is of much significance. However, the
effects of direction of flower motion have a stronger impact than frequency on
net energy gain. Moths display significantly lower feeding rates when tracking
flowers moving in the looming axis, but this tracking costs as much, or more,
than tracking flowers moving horizontally or vertically.
Potential evolutionary implications
Considering the tight coevolution between plants and pollinators,
surprisingly little attention has been paid to the vibrational properties of
flowers. Although Etnier and Vogel (Etnier
and Vogel, 2000) suggest torsional mechanics of flowers play an
important role in their ability to withstand wind forces, there was relatively
little discussion of the dynamics of that motion. However, torsional motions
could play a central role in plant-pollinator interactions. Torsional
properties of stems could convert what would be poorly tracked L axis
motions into more easily tracked lateral, or H axis, motions.
Unfortunately, current studies on torsional mechanics of flowers have been
phylogenetically limited, and do not include hawkmoth pollinated flowers. The
resonant motion dynamics of flowers are impacted not only by environmental
stimuli (such as wind), but also by their biomechanical properties. In fact,
it is reasonable to assume that the frequency of flower motion scales with the
square root of the stem's flexural stiffness
(Timoshenko et al., 1974).
Additionally, flowers are capable of modifying stem stiffness during
development by changing their allocation of biomass
(Niklas, 1998), indicating
there is some control over biomechanical properties of stems. As such, there
is potential for an evolutionary response of hawkmoth-pollinated flowers to
hawkmoth tracking performance.
The extent to which there is coevolution between the visualmotor systems of
pollinating insects and the mechanical properties of the plants that they
pollinate remains unexplored. Recent work
(Goyret and Raguso, 2006) shows
that mechanosensory information from flowers has significant impacts on flower
handling in M. sexta. Additionally, floral genes that have a strong
effect on pollinator preference are known to drive adaptation in some
plant-pollinator systems (Schemske and
Bradshaw, 1999). It is reasonable to hypothesize that any floral
trait impacting pollinator fitness could be subjected to selection. Although
some essential amino acids found in Lepidopteran eggs can only be derived from
larval diet (O'Brien et al.,
2002), adult nectar meals contribute significantly to egg
production (Hill, 1989).
Because flower motion dynamics affect net energy gain, they could logically
impact hawkmoth fitness. Given that, the biomechanical properties of
hawkmoth-pollinated flowers could potentially be under selection.
However, this would require that hawkmoths actively prefer to feed from flowers with the motion dynamics that have the highest fitness benefit (i.e. those flowers which they gain the most energy from). In addition, the specificity of plant-pollinator interactions may be important. The data presented here suggest there is a clear need for further studies of flower-tracking in hawkmoths, a unique and exciting system in which to examine intricate plant-pollinator relationships.
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Acknowledgments |
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References |
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