Behavioral
Ecology
The official journal of the
ISBE
International Society for Behavioral Ecology
Behavioral Ecology (2014), 25(4), 933–944. doi:10.1093/beheco/aru071
Original Article
Effect of flower visual angle on flower
constancy: a test of the search image
hypothesis
Hiroshi S. Ishii and Hikaru Masuda
Department of Environmental Biology and Chemistry, Graduate School of Science and Engineering,
Toyama University, 3190 Gofuku, Toyama 930-8555, Japan
Received 6 August 2013; revised 25 March 2014; accepted 30 March 2014; Advance Access publication 6 May 2014.
Pollinators often sequentially visit 1 flower type while bypassing other equally rewarding flower types, behavior known as flower constancy. One explanation for flower constancy is that pollinators use the search image of a specific flower type to efficiently find flowers because they are often cryptic. The so-called search image hypothesis predicts that temporal specialization to 1 flower type by
an individual pollinator declines as flower conspicuousness increases because if flowers are conspicuous, pollinators can divide their
attention among different flower types. To test this prediction, we investigated visitation sequences of individual bumble bees (Bombus
ignitus) to a patch of blue and yellow artificial flowers. We used different flower sizes and interflower distances as independent determinants of flower visual size and noisiness of the background and, thus, of flower crypsis. Flower color selectivity and flower search
time decreased and subsequently increased with visual size of the nearest flowers, with a minimum of approximately 15° of the visual
angle. Within-bout constancy, measured as the tendency of flying to the same flower types in succession compared with the expectation of a given flower selectivity within a bout, increased with visual size of the nearest flowers. Flower selectivity and within-bout
constancy did not differ among arrays that shared the same floral visual angle, indicating that visual size, rather than flight distance
or flower size itself, had a substantial effect on flower choices. Our results suggest that flower visual size and background noisiness
affect flower crypsis and constancy, providing support for the search image hypothesis.
Key words: Bombus, choice behavior, flower color, plant–animal interaction, short-term memory, visual search.
Introduction
Pollinators often fly among plants of the same species while
bypassing other available flower species even if they are equally
or considerably more rewarding, a behavior known as flower
constancy (Heinrich 1976, 1979; Waser 1986). Flower constancy
provides obvious reproductive benefits to animal-pollinated
plants because it often promotes conspecific pollination such that
a higher percentage of pollen will be successfully transferred to
flowers of the same species, resulting in higher rates of successful fertilization (Waser 1978; Campbell 1985). Thus, any factor
that influences flower constancy can positively affect floral evolution (Chittka et al. 1999; Jones 2001; Ishii 2006). In contrast, the
benefits of flower constancy to pollinators are less obvious because
specializing on any one flower type and bypassing other valuable
ones may increase travel cost (Gegear and Laverty 2004, 2005;
Grüter and Ratnieks 2011).
Address correspondence to H.S. Ishii. E-mail: [email protected].
© The Author 2014. Published by Oxford University Press on behalf of
the International Society for Behavioral Ecology. All rights reserved. For
permissions, please e-mail: [email protected]
Till date, several nonexclusive hypotheses have been presented to
explain flower constancy (reviewed by Chittka et al. 1999; Grüter
and Ratnieks 2011). For example, flower constancy may arise as
a learned preference and, therefore, may differ among individuals with different visitation history (Hill et al. 1997; Gegear and
Laverty 2001). Individual pollinators may be resistant to switching
from preferred plant species to an unfamiliar one because assessing reward value of the novel species may require substantial time
(the costly information hypothesis: Waser 1986; Chittka et al. 1999)
or because learning handle skill of novel flower species involves
a phase of poor efficiency (the learning investment hypothesis:
Chittka et al. 1999; Grüter and Ratnieks 2011). Additionally, flower
constancy may be related to the limited capacity of short-term or
working memory (Chittka et al. 1999; Gegear and Laverty 2005;
Ishii 2005; Raine and Chittka 2007a). It has been proposed that
the contents of short-term memory, which has extremely limited
capacity, are more rapidly retrievable than those of long-term
memory (Menzel 2001). Thus, pollinators may continue to choose
the plant type they last visited because they can react to a flower
of the same species (similar to the one just visited) more rapidly
Behavioral Ecology
934
than to another flower whose sensory traits must be uploaded from
long-term memory (Chittka et al. 1999; Gegear and Laverty 2005;
Raine and Chittka 2007a).
The notion that constancy is related to short-term memory is
based on 2 distinct hypotheses. The first hypothesis is the interference hypothesis, which states that temporal specialization to
1 flower type, that is, flower constancy, leads to efficiency if a
pollinator can hold the information of how to manipulate only
one or a few flower types at any given time in its short-term
memory (Lewis 1986; Waser 1986; Dukas 1995) and if retrieval
of motor learning task from longer-term memories requires
time (Chittka et al. 1999; Menzel 2001). However, the interference hypothesis does not appear to sufficiently explain flower
constancy (Goulson 2000; Raine and Chittka 2007a) because
several empirical studies have found no or extremely small differences between handling times when the last visit was to the
same or a different flower species (Laverty 1994; Dukas 1995;
Gegear and Laverty 1995).
The second hypothesis is the search image hypothesis, which
states that flower constancy occurs because pollinators use a “search
image” that is attributable to attentional focus (Levin 1978; Chittka
et al. 1999; Goulson 2000; Raine and Chittka 2007a). Animals are
considered to use an attentional mechanism to focus selectivity on
different aspects of information (Chittka and Raine 2006) because
the amount of information usually exceeds the capacity that can
be simultaneously processed in the short-term (or working) memory (Dukas 2004). Although studies on attention have mainly been
focused on human and vertebrates (e.g., Dukas and Kamil 2001;
Dukas 2004), the existence and characteristics of visual attention
have recently been demonstrated in insects such as Drosophila (van
Swinderen and Greenspan 2003), Apis (Giurfa and Menzel 1997),
and Bombus (Morawetz and Spaethe 2012; Nityananda and Pattrick
2013; Wang et al. 2013). For example, bumble bees are capable of
learning visual cues associated with cryptic predators and avoiding
them while discriminating between high rewarding targets and less
rewarding or punishing (with quinine) distractors, thus showing a
capacity for divided attention (Wang et al. 2013). The principle of
the search image hypothesis is that when flowers are cryptic, it is
preferred that pollinators pay all (or most) of their attention to a
single flower type at one time (Wilson and Stine 1996; Goulson
2000). This idea was first outlined by Tinbergen (1960) to explain
prey selection patterns by predators and was subsequently applied
to pollinator flower choice by Levin (1978). However, little evidence
exists for the formation of search images in pollinators (Gegear and
Laverty 2001; Grüter and Ratnieks 2011).
The search image hypothesis assumes that animals are searching for cryptic food (Dukas and Real 1993; Wilson and Stine 1996;
Goulson 2000). One prediction of this hypothesis is that temporal
specialization by individual pollinators declines as flower conspicuousness increases because if flowers are conspicuous, pollinators
can divide their attention among different flower types at one time
(Goulson 2000). A similar idea was presented by Dukas and Ellner
(1993) to predict predator diet choice behavior. Their idea was supported by experiments with pigeons (Columba livia) and blue jays
(Cyanocitta cristata), in which search image effects were evident only
when their prey were cryptic (Bond 1983; Bond and Riley 1991;
Reid and Shettleworth 1992; Dukas and Kamil 2001). However, no
such experiments have examined pollinator flower choices. Goulson
(2000) proposed that when viewed against a backdrop of other floral displays of either the same or different plant species, any particular flower may effectively be cryptic. However, he only assessed
flight time to the next flower, and no data have been presented on
the relationship between flower constancy and flower crypsis.
Several studies have shown that flower constancy declines as
flower density (and thus crypsis in a noisy backdrop of other floral
displays) declines (Chittka et al. 1997; Gegear and Thomson 2004;
Ishii 2005). Goulson (2000) interpreted these results as a consequence of decline of the search image effects associated with the
decreased flower crypsis, similar to the prediction by Dukas and
Ellner (1993). However, flower density affects not only visual crypsis of flowers but also flight distance between flowers. Therefore,
the authors of these previous experiments attributed their results
to increasing travel costs (Gegear and Thomson 2004) or shortterm memory fading during longer flights (Chittka et al. 1997;
Raine and Chittka 2007a). Furthermore, in case of the effect of
flower density, the opposite prediction may also be possible if the
visual angle of the target flower is near the threshold of detectable size for pollinators, that is, flower selectivity for one flower type
may increase with interflower distance because flower visual size
declines and flower crypsis may subsequently increase with interflower distance. This prediction arises because the eyes of insects
have limited resolving power. For example, several studies have
shown that the minimum visual angle of a single object that the
honey bees can detect is approximately 5° for stimuli containing
green-receptor contrast (achromatic vision) and approximately 15°
for stimuli containing color contrast, but not with green-receptor
contrast (chromatic vision) (Giurfa et al. 1996; Hempel de Ibarra
et al. 2001). In contrast, the smallest detectable angle does not substantially change between these stimuli for bumble bees (Dyer et al.
2008) and is 3°–8°, depending on the individual’s eye size (Spaethe
and Chittka 2003).
In this study, we examined the relationship between flower crypsis and constancy to test the search image hypothesis. We observed
bumble bees, Bombus ignitus (Smith), foraging in mixed arrays of
blue and yellow artificial flowers and assessed their responses to
variation in flower size and interflower distance (flower density).
Flower size and interflower distance were considered to be independent determinants of flower visual image size and noisiness of
the background and, thus, of flower crypsis. Flight time between
flowers was recorded as an indicator of flower search time. We
addressed the following question: does the visual angle of the adjacent flowers or the noisiness of the other floral displays affect search
time and flower constancy independent of flower density? Based
on our results, we discuss how visual constraints may affect flower
choice behavior and foraging economics in pollinators.
Materials and Methods
Bees and flight cage
Experiments were conducted in a 2 m (width) × 4 m (depth)
× 2 m (height) ultraviolet-transmitting screen cage. The cage
was situated near a large, open window in a laboratory room in
Toyama University, Japan, that was well illuminated by daylight.
Illumination intensity was measured with a luxmeter (TM-205,
Tenmars Electronics Co., Taiwan) and was 900–1600 lux, sufficiently above the threshold illumination that bumble bees require
for foraging efficiently (<700 lux: Chittka and Spaethe 2007). In
the cage, we constructed low walls (height = 30 cm) on all sides
with plywood. The inner sides of the walls and floor of the screen
cage were painted green (color code: gemgreen381844, Kampe
Hapio Co. Ltd, Osaka, Japan). The temperature ranged from 25 to
Ishii and Masuda • Flower visual angle and constancy
30 °C so that the bees would be active, and relative humidity was
maintained above 70% to prevent the artificial flowers from drying. Responses to artificial flowers were studied in 21 individually
marked worker bumble bees, B. ignitus, from 3 different colonies (6,
7, and 8 individuals from each colony) provided by the ItabashiWard Firefly Breeding Institute (Tokyo, Japan). Six individuals
from a colony were used for the preliminary experiment, and 15
individuals from 2 colonies were used for the main experiment (see
below). Eye length of the test bees, measured with a digital caliper (CD-15PS, Mitsutoyo Co., Japan) as the distance of the longest
surface perimeter of the left eye through the center, ranged from
2.6 to 2.8 mm. Following Spaethe and Chittka (2003), who investigated the relationship between minimum visual angle and eye size
of individual Bombus terrestris, we assumed that the minimum visual
angle that they can detect is 5°–6° if objects’ color distances are
reliably discriminated against the background. Colonies were connected to the cage via a tunnel constructed from transparent acrylic
resin. The tunnel was gated to control the entry of bees into the
enclosure.
Artificial flowers
Figure 1a illustrates the artificial flowers used in the experiments.
Each flower was made of a plastic microtube embedded in a styrene foam sphere (ø = 2, 4, or 6 cm), which was identical in shape
from all directions. The nectar providers within the artificial flowers
(Figure 1b) were constructed following Makino and Sakai (2007).
Before the experiments, 120 µL of 30% (w/w) sucrose solution
(hereafter called nectar) was supplied to each nectar provider. The
nectar inside the provider was conveyed to the top loops through
a silk thread and subsequently accumulated in the loops. After
bees consumed the accumulated nectar, it was slowly replenished
(Figure 1c). Thus, it was unprofitable for bees to make an immediate return to flowers that had just been probed because nectar
was not immediately replenished. The styrene spheres were painted
blue or yellow (color code: blue381851 and yellow381646, Kampe
Hapio Co. Ltd). The spectral reflectance of the flower colors and
green floor of the screen cage are shown in Figure 1d. These
values were obtained using a charge-coupled-device spectrometer (BRC112, BWTEK Inc.), deuterium–tungsten light source
(BDS130, BWTEK Inc.), and white standard (SRR, BWTEK
Inc.) for every 1 nm between 300 and 800 nm wavelengths. For the
measurements, Y-shaped fiber optic cable (FRP-400-0.22-1.5-UV,
BWTEK Inc.) was used. The lower tip of the Y is the probe and
was pointed to the object to be measured, and the upper 2 ends of
the Y were connected to the light source and spectrometer, respectively. Because we are interested in diffuse reflection, the probe was
placed at a constant angle of 45° relative to the object surface in
a small box painted black on the inside, which excludes influence
from external light source (Chittka and Kevan 2005). The colors
of these stimuli were specified in a hexagonal color model (Chittka
et al. 1992: Figure 1e), color-opponent coding model (COC)
(Backhaus 1991), and receptor noise-limited model (RN) (Vorobyev
and Osorio 1998), taking into account receptor data for bumble
bees (Peitsch et al. 1992). The color distance between the yellow
and blue stimuli was 0.36 hexagon units, 9.02 COC units, and
15.88 RN units. Chromatic distance of the blue and yellow stimuli
from the background in each model and receptor-specific contrasts
(i.e., receptor quantum catches relative to background: Hempel
de Ibarra et al. 2001; Vorobyev et al. 2001) are shown in Table 1.
These color distances are reliably discriminated by bumblebees in
935
both simple and complex foraging environments (Dyer and Chittka
2004; Dyer 2006).
Experimental design
We conducted the preliminary experiment from July to September
2010 and the main experiment from June to August 2011. Before
the experiments, each test bee was trained on an array of 24 artificial flowers (6 × 4 Cartesian grid) consisting of equal numbers of
all types of artificial flowers used in our experiments (4 flowers per
flower type × 2 colors × 3 sizes). The interflower distances during training were 10 cm. Training was conducted with other forager
bees from the same hive. Because we used relatively small colonies
(15–40 workers), only a few to several individual workers simultaneously foraged in the training arena. Accordingly, we could easily
distinguish the bees in the arena that have learned to work on each
flower type. After a bee had learned to work on each flower type,
the bee was anesthetized by chilling at approximately 4 °C and subsequently numbered by gluing a tag to the thorax.
The experiments were conducted between 8:00 and 16:00 h
when the weather was clear and the cage was well illuminated with
scattered daylight. During the experiment, we spaced 20 (preliminary experiment) or 16 (main experiment) blue and yellow flowers
in alternating pairs of rows, so that bees usually experienced an
equal distance from both colors (both nearest and second-nearest
neighbors) on leaving a flower (Figure 2a). The unit interflower
distance (distance between the centers of the nearest flowers: d in
Figure 2a) was 5, 10, 15, or 20 cm, and the diameter of artificial
flowers (ø in Figure 1a) was 2, 4, or 6 cm. Table 2 lists the diameters of artificial flowers and unit interflower distances used in our
experiments and the visual angles of the nearest flowers (v) in those
11 types of arrays. We tested 11 of the 12 potential combinations
because one combination (d = 5 cm and ø = 6) was simply impossible to realize (i.e., 6-cm flowers were extremely large to locate
with a 5-cm interflower distance). The visual angles of the nearest
flowers were determined using flower size relative to interflower distance and calculated as
v = 2 × arctan( ø / 2d ). (1)
Figure 2b shows examples of visual images in several experimental arrays from the viewpoint of a foraging bee (from flower α in
Figure 2a). When flowers are small (2 cm) and interflower distance
is long (20 cm), the visual angle of the target flower ranges around
the threshold of detectability of the test bees (5°–6°; see above).
Detectability of flowers probably increases as flowers become
larger and/or interflower distance becomes shorter. However,
when flower size is intermediate (4 cm) and interflower distance is
extremely short (5 cm), from the viewpoint of a foraging bee, the
visual images of the flowers overlap each other (Figure 2b). Thus,
our experiment incorporated situations in which bees can marginally find close flowers on leaving a flower with their limited resolving power and in which backgrounds were cluttered with a noisy
backdrop of other floral displays.
We used 6 individual bees from a colony for the preliminary
experiment from July to September 2010 and 15 individual bees
from 2 colonies (7 and 8 from the each) for the main experiment
from June to August 2011. In both experiments, we tested each bee
in each of the 11 arrays (i.e., 66 and 165 trials were conducted in
the preliminary and main experiments, respectively). During the
trials, we removed all forager bees except 1 test bee to exclude
interactions between bees. The tested order of the 11 arrays was
Behavioral Ecology
936
(b)
(a)
Two silk loops
7 ｍｍ
The diameter of a loop is 0.9 mm,
and the diameter of the thread is 0.49 mm
Microtube (0.2 ml)
Silk thread
20 ｍｍ
ø
tied to the upper loops.
The diameter of the thread is 0.33 mm
Liquid content
(c)
Replenished volume of
sucrose solution (µl)
styrene foam
sphere painted
yellow or blue
Lead sinker
0.3
0.2
0.1
0.0
30
60
120
180
Time after touching
with filter paper (sec)
(d)
Reflected radiation (%)
30% sucrose solution
(e)
100
blue
yellow
80
60
blue
blue
yellow
Background
green
40
20
bluegreen
UV-blue
Background
green
green
UV
UV-green
0
300
400
500
600
700
Wavelength (nm)
800
Figure 1
Properties of artificial flowers used in this study. (a) An artificial flower with a nectar provider inside it. The diameter of a flower (ø) could be 2, 4, or 6 cm.
(b) A nectar provider. (c) Changes in the volume of the nectar (30% sucrose solution) in the silk loops at the top of a nectar provider after touching the loops
with a piece of filter paper (means ± standard deviations [SDs]; N > 18 for each data point). The amount of nectar was measured using 1.0-µL calibrated
capillary tubes (Drummond Scientific, Broomall, PA). The curve is based on the least-squares regression fit of y = ae−cx, where y and x are the replenished
volume of sucrose solution and time after touching with filter paper, respectively (a = 0.213***, c = 0.025***; ***P < 0.001). (d) Spectral reflectance of blue
and yellow flowers and green background used in the experiments. (e) Color loci of the stimuli in the bee color hexagon (Chittka et al. 1992).
randomly assigned for each bee, and all flowers were exchanged
before each new trial. The behavior of bees was recorded using a
voice recorder (preliminary experiment: IC Recorder ICD-UX71,
Sony, Tokyo, Japan) or digital video camera (main experiment:
Handycam HDR-CX500, Sony) during a single bout (a roundtrip between the hive and foraging array) per trial. For the main
experiment, however, if a single bout consisted of fewer than 200
visits to flowers (49 cases out of 165 trials), we recorded the visitation sequences over a few successive bouts (mean ± standard error
[SE] = 2.41 ± 0.09 bouts per trials, N = 49) so that we could obtain
a reliable data set with more than 200 visits per trial. The video
camera was set on a 1.8-m tripod, which was placed diagonal to the
foraging arena (50 cm from the nearest flower), and a wide-angle
converter lens (VCL-HGA07, Sony) was attached so that no flowers would be hidden in the dead angle of the camera (Figure 2a).
Based on these recordings, we documented the coordinates and
color of artificial flowers and approach type (visit or inspection)
along the tested order. A visit was defined when a bee rested on
the flower with closed wings and inserted its proboscis in the nectar
provider. In turn, an inspection was defined when a bee hovered in
front of a flower or when it touched the flower while continuing to
beat its wings and did not insert its proboscis in the nectar provider.
Ishii and Masuda • Flower visual angle and constancy
937
Table 1
Color properties of artificial flowers
Chromatic distance between target and
background in 3 different models
Receptor-specific contrasts (receptor quantum
catches relative to background)
Human color
Hexagon
COC
RN
UV receptor
Blue receptor
Green receptor
Blue
Yellow
0.23
0.13
5.77
3.25
11.20
4.68
2.64
2.07
7.10
1.39
1.54
2.62
Chromatic distance between target and background (i.e., color contrast) was calculated according to the color hexagon model (Hexagon) (Chittka et al. 1992),
color-opponent coding model (COC) (Backhaus 1991), and receptor noise-limited model (RN) (Vorobyev and Osorio 1998), respectively. Receptor-specific
contrast is the receptor quantum catches for the stimulus relative to that for the background (Hempel de Ibarra et al. 2001) and is greater than 1 if the stimulus
has positive contrast to the green background for the focal receptor and smaller than 1 if the stimulus has negative contrast to the green background. In our
study, even the blue stimulus had positive contrast to the green background for green receptor. This could occur because hymenopteran green receptor has
relatively wide sensitive range compared with UV and blue receptor (Peitsch et al. 1992) and even the blue stimulus substantially excites the green receptor.
Larger values are shown in bold face.
Figure 2
Design of the color dimorphic floral array used in experiments. (a) We spaced 20 (preliminary experiment) or 16 (main experiment) blue and yellow flowers in
alternating pairs of rows, so that bees usually had an equal choice of both colors (both nearest and second-nearest neighbors) on leaving a flower. The distance
between the centers of the nearest flowers was defined as the unit interflower distance, d; thus, the distance to the second-nearest flowers was √2d. The thick arrow
at the lower left indicates the direction in which the video camera was pointed. (b) Images from the point of view of foraging bees in several example arrays
(Table 2). Greek letters in panels a and b correspond to each other. In this study, it is assumed that the bee is just leaving flower α and facing toward flower δ. In
(b), visual angles of the flowers were calculated from 2 × arctan(ø/2D), where ø is flower diameter and D is the distance from the center of flower α to that of focal
flowers. Flowers β–µ were then depicted in the proportional size to the directional angles between flowers (e.g., directional angle between flowers δ and β is 45° and
between γ and β is 90°). Limitations in the resolution power of the eyes of the bees are ignored. Array identities (A2, A1, A4, etc.) are identical to those in Table 2.
Behavioral Ecology
938
Table 2
Combinations of flower diameters and unit interflower
distances used in the experiments
Array
identity
Diameter of
flower (ϕ; cm)
Unit interflower
distance (d; cm)
Visual angle of
nearest flowers (v)
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
A11
2
4
2
4
6
2
4
6
2
4
6
5
5
10
10
10
15
15
15
20
20
20
22.62°
43.60°
11.42°
22.62°
33.40°
7.63°
15.18°
22.62°
5.72°
11.42°
17.06°
As for the main experiment, we replayed the video image at onefourth the normal speed and measured the handling time on flowers visited and interflower flight time between the 100th and 150th
visits in each trial using a digital stopwatch (HS-80TW-1JH, Casio,
Tokyo, Japan) to the nearest 0.001 s. Handling time was defined
as the time between when a bee landed on and left a flower, and
interflower flight time was defined as the time between the moment
a bee left one flower and landed on the next flower. Interflower
flight time was recorded only if the flight did not involve flower
inspection.
Data analysis
Flower constancy is defined as the tendency to visit 1 flower type
sequentially while bypassing other equally or more rewarding
flower types (Waser 1986), thereby encompassing both reward-independent flower selectivity by individuals (e.g., Waser 1986; Kearns
and Inouye 1993) and an assortative visitation sequence according
to flower type within a single trip by an individual (Stanton 1987;
Jones 1997; Ishii 2005). An assortative visitation sequence is defined
as a visitation sequence of a single trip by an individual, during
which constant flight occurs more often than would be expected if
the sequence of visits was randomly allocated at a given frequency
of visits to each flower type by the individual. In this article, we
use the terms “color selectivity” and “within-bout constancy” (after
Jones and Reithel 2001) to indicate reward-independent flower
selectivity and assortative visitation sequence, respectively. Color
selectivity was calculated for each trial for each bee as the ratio
between the number of visits to blue flowers and the number of
total visits in a trial because all test bees preferred blue to yellow
flowers in our experiments. Within-bout constancy was calculated
for each trial for each bee using Bateman’s index (Bateman 1951):
Within-bout constancy =
( AB )1/2 − (CD )1/2
(2)
( AB )1/2 + (CD )1/2
where A is the number of flights from a blue flower to a blue flower,
B the number from a yellow to a yellow flower, C the number from
a blue to a yellow flower, and D is the number from a yellow to a
blue flower (flights involving inspection were removed). Index values
ranged from −1 (nonassortative sequence) to 0 (random sequence)
to +1 (completely assortative sequence). Bateman’s index cannot be
calculated if an individual bee shows perfect preference for 1 flower
type in each trial because the denominator in the formula becomes
0. However, perfect preference in a trial was never observed in
our experiments. The influence of interflower distance, flower
size, their interaction, and colony identity on color selectivity and
within-bout constancy was analyzed using a linear mixed model
(LMM) that considered bee identity as a random effect.
To assess whether the size of artificial flowers can affect bee
behavior through factors other than appearance, handling time
on artificial flowers was compared among flowers of different sizes
using an LMM. In this model, bee identity was treated as a random
effect, and interflower distance, flower size, their interaction, and
colony identity were considered as fixed effects. Flight time to the
nearest flowers was also analyzed using an LMM to determine the
interactive effect of flower size and interflower distance on search
time. In this model, bee identity was treated as a random effect,
and interflower distance, flower size, their interaction, colony identity, arrival flower color (blue vs. yellow), and type of flight (constant
vs. switching) were fixed effects. The proportion of flights to nearest
flowers to the total number of flower transitions was analyzed using
a generalized linear mixed model (GLMM) with binomial errors
and a logit link function, in which individual flights were considered dichotomous response variables, for which values of 1 were
assigned to flights to the nearest flower and values of 0 to other
flights (flights involving inspection were removed). The proportion
of inspections to the total number of approaches was also analyzed
using a GLMM, in which individual approaches were considered
dichotomous response variables, for which 1 was assigned to an
inspection and 0 to a visit. In these models, bee identity was treated
as a random effect, and interflower distance, flower size, their interaction, and colony identity as fixed effects. To test whether inspections more likely occurred at the particular flower type (blue vs.
yellow) or after the particular flight type (constant vs. switching
flight), we further compared the proportion of inspections between
flower types and flight types using the Wilcoxon signed-rank tests
for 165 pairs of the trials in the main experiment.
LMMs, GLMMs, and Wilcoxon signed-rank tests were performed using the “nlme,” “glmmML,” and “stats” packages in
the free software R version 2.15.2 (R Development Core Team
2012). Responsible factors fitted in LMMs satisfied normality.
Data for the 1st to 50th visits in each trial were removed from
the analyses to reduce any effects of learning during previous trials. However, excluding these data did not substantially affect the
results because behavioral trends in bees changed little over time
although strength of color selectivity slightly decreased as bees
gained experience with the array (Supplementary Figure S1 and
Supplementary Table S1).
Results
In the following topics, we present the results of the main experiment. Results of the preliminary experiments are shown in
Supplementary Figure S2 and Supplementary Table S2. We did
not pool the data of the preliminary and main experiments because
their experimental conditions differed in relation to the number
of artificial flowers arranged, leading to different responses of the
bees. In general, within-bout constancy, proportion of visit to the
nearest flowers, and proportion of inspection were higher in the
preliminary experiment than in the main experiment. Whether
this dissimilarity reflects differences among bees, colonies, flower
arrangements, or consequences of other unnoticed experimental
conditions cannot be distinguished. However, overall behavioral
responses to the flower size and interflower distances were similar
between these experiments.
Ishii and Masuda • Flower visual angle and constancy
939
Color selectivity
Handling time of flowers
We observed a significant interactive effect of flower size and interflower distance on color selectivity (Table 3a and Figure 3a). When
interflower distance was large (d = 15 or 20 cm), color selectivity
decreased with flower size, whereas when interflower distance was
short (d = 5 or 10 cm), it increased with flower size. Consequently,
bees showed minimum color selectivity when the visual angle of
nearest flowers was intermediate. Color selectivity did not significantly change between colonies (t13 = 0.963, P = 0.353), and its
effect had been removed from the statistical model. Among the
arrays that shared the same visual angle (Table 2), color selectivity did not significantly differ [(A3, A10: v = 11.42°) F1,14 = 0.834,
P = 0.377; (A1, A4, A8: v = 22.62°) F1,29 = 0.723, P = 0.402].
Handling time of artificial flowers did not significantly differ among
flower sizes or among interflight distances (Table 3c). In addition,
their interactive effect on handling time was not significant (F1,4963=
0.0238, P = 0.878). This result implies that the larger surface of
larger flowers would not have improved (nor receded) the facility
for landing because handling time did not differ among different
flower sizes. Handling time did not significantly change between
colonies (t13 = 0.786, P = 0.446), and its effect was removed from
the statistical model.
Within-bout constancy
In contrast to color selectivity, bees showed strong within-bout constancy when interflower distance was short and flower size was large
(Table 3b and Figure 3b). The interaction between flower size and
interflower distance did not have a significant effect on within-bout
constancy (F1,147 = 3.5058, P = 0.063). Consequently, within-bout
constancy increased with the visual angle of the nearest flowers.
Within-bout constancy did not significantly change between colonies (t13 = −0.426, P = 0.677), and its effect was removed from the
statistical model. Among arrays that shared the same visual angle
(Table 2), within-bout constancy did not differ significantly [(A3,
A10: v = 11.42°) F1,14 = 1.890, P = 0.191; (A1, A4, A8: v = 22.62°)
F1,29 = 0.063, P = 0.804].
Flight time to the nearest flower
In contrast to handling time, the interaction between flower size
and interflower distance had a significant effect on flight time to the
nearest flower (Table 3d and Figure 3c). Flight time to the nearest
flower increased with increased unit interflower distance. However,
among arrays that shared the same unit interflower distance, flight
time to the nearest flower decreased with increased visual angle
of the nearest flowers when the visual angle was small, whereas
the opposite pattern occurred when the visual angle of the nearest flowers was large. Flight time to the nearest flowers was always
shorter when bees flew to blue flowers than to yellow flowers, and
this pattern was the same when bees flew to flowers of the same
color (constant flight) compared with that when they flew to flowers of a different color (switching flight) (Table 3d). Flight time did
not significantly change between colonies (t13 = −1.733, P = 0.107),
and its effect was removed from the statistical model.
Table 3
Effects of flower size and interflower distances on bumble bee behavior
Fixed factors
(a) Color selectivity
Intercept
Flower size (ϕ)
Unit distance (d)
ϕ×d
(b) Within-bout constancy
Intercept
Flower size (ϕ)
Unit distance (d)
(c) Handling time on a flower
Intercept
Flower size (ϕ)
Unit distance (d)
(d) Flight time to nearest flowers
Intercept
Flower size (ϕ)
Unit distance (d)
ϕ×d
Arrival flower color (blue vs. yellow)
Type of flight (constant vs. switching)
(e) Proportion of visits to nearest flowers
Intercept
Flower size (ϕ)
Unit distance (d)
(f) Proportion of inspections
Intercept
Flower size (ϕ)
Unit distance (d)
Statistical values
P value
0.0356
0.0094
0.0024
0.0006
t147 = 13.199
t147 = 3.844
t147 = 3.800
t147 = −4.793
<0.0001
0.0002
0.0002
<0.0001
0.2349
0.0080
−0.0052
0.0181
0.0033
0.0010
t148 = 13.000
t148 = 2.430
t148 = −5.364
<0.0001
0.0163
<0.0001
1.7611
0.0126
0.0026
0.1216
0.0132
0.0040
t4965 = 14.478
t4965 = −0.961
t4965 = −0.657
<0.0001
0.3367
0.5111
0.1237
0.0384
0.0295
−0.0030
0.0904
0.0117
0.0267
0.0055
0.0014
0.0004
0.0057
0.0058
t4782 = 4.632
t4782 = 6.917
t4782 = 21.548
t4782 = −8.142
t4782 = 15.980
t4782 = 2.015
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.0440
−0.0159
−0.0824
0.0613
0.0483
0.0068
0.0020
z = −0.329
z = −12.159
z = 30.174
0.7422
<0.0001
<0.0001
−3.1546
−0.0365
−0.0063
0.1597
0.0187
0.0056
z = −19.757
z = 1.947
z = −1.137
<0.0001
0.0516
0.2556
Coefficients
SEs
0.4695
0.0360
0.0092
−0.0030
Results of LMMs (a–d) and GLMMs (e and f; with binomial errors and logit link function) are shown. All models considered individual bees as a random effect.
The effect of colony identity was removed from the models because it was never significant (P > 0.10 for all models), and the interactive effect between flower
size and interflower distance was removed if it was not significant. For the analysis of flight time to the nearest flowers (d), arrival flower color (blue vs. yellow)
and type of flight (constant vs. switching) were also considered as explanatory variables.
Behavioral Ecology
940
Figure 3
Interacting effects of interflower distance, d, and flower size, ϕ, on (a) color selectivity, (b) within-bout constancy, (c) flight time to nearest flowers, (d) proportion
of visits to the nearest flowers, and (e) proportion of inspection. In (a) and (b), each point represents the mean (±SE) of 15 individual bees. In (c–e), each point
represents the expected value (±SE) calculated from LMM (c) and GLMMs (d and e), in which individual bees were considered a random effect. In (d) and
(e), the analyses considered appropriate transformations (logit link function) of the means of the dependent variables to linearize their relationships to the
independent variables, and back transformation of results from these analyses resulted in asymmetrical SEs.
Proportion of visits to the nearest flower
Proportion of inspections
The proportion of visits to the nearest flower increased with
interflower distance and decreased with flower size (Table 3e and
Figure 3d). Consequently, it decreased with visual angle of the
nearest flowers. Among arrays that shared the same flower visual
angle (Table 2), the proportion of visits to the nearest flowers was
significantly higher when unit interflower distance was longer
[(A3, A10: v = 11.42°) Wald’s z = 7.762, P < 0.0001; (A1, A4, A8:
v = 22.62°) Wald’s z = 5.655, P < 0.0001]. The proportion of visits
to the nearest flower did not significantly change between colonies
(z = −0.261, P = 0.794), and its effect was removed from the statistical model.
In this study, most aspects of bees’ behaviors were analyzed using
the data from which inspections and flights involving inspection
had been excluded. However, this exclusion did not substantially
affect the results because the proportion of inspections was consistently low irrespective of the experimental conditions (Table 3f and
Figure 3e). Inspections rarely occurred unless bees returned to the
same flower in a short interval (Figure 4) although the proportion of
inspections was significantly larger on blue flowers than on yellow
flowers (W = 10004, P < 0.0001; Wilcoxon signed-rank test for 165
pairs of each trial). Flight type (constant or switching) before the
focal visit did not significantly affect the proportion of inspections
Ishii and Masuda • Flower visual angle and constancy
941
Figure 4
Relationship between return interval and proportion of inspections for blue and yellow flowers. Return interval was defined as the number of flowers visited
before returning to the same flower. Each point represents the mean (±SD) of 15 individual bees. Arcsine square root-transformed proportions were used to
calculate the means (+SDs), and back transformation was performed for the figure.
(W = 6578, P = 0.708). The proportion of inspection did not significantly change between colonies (z = 0.609, P = 0.5420), and its
effect was removed from the statistical model.
Discussion
This study is the first to demonstrate the effect of flower visual size
on flower constancy. Flower choice behavior changed with variation
in flower size and interflower distance and, thus, with flower visual
angle of the nearest flower but did not differ among arrays that
shared the same flower visual angle, indicating that visual image
size had a substantial effect on choice behaviors. We now consider
key features of such responses and their implications based on our
observations.
Color selectivity by individuals
In our experiments, color selectivity was lowest in arrays for which
the visual angle of the nearest flower was intermediate (approximately 15°: Figure 3a and Supplementary Figure S2a). Among
arrays that shared the same interflower distances, the flight times
were the shortest when the visual angles of the nearest flowers
were approximately 15° (Figure 3c). Several studies have shown a
positive correlation between decision time and accuracy of bees’
choices (Chittka et al. 2003; Ings and Chittka 2008). These studies suggested that improved accuracy in solving discrimination tasks
comes at a cost in decision time and that if bees are forced to make
rapid decisions, accuracy will suffer (speed–accuracy trade-off). In
appearance, our results could be interpreted as the consequences of
a speed–accuracy trade-off, if preferred flower type was the target
(rewarding) and the less-preferred one was the distractor (unrewarding or less rewarding). However, both flower types were equally
rewarding in our experiments, and thus, there were no benefits to
the bees for spending time to avoid the less-preferred flower type.
Blue flowers may have been regarded by the bees as targets
and yellow ones as distractors. In our experiments, flight time was
shorter when bees flew to blue flowers than when they flew to yellow flowers (Table 3d), and this may be because blue flowers had
higher contrast with the background than yellow flowers (Table 1).
Accordingly, preference to blue flowers may be attributed to their
higher contrast with the background than yellow ones (Table 1)
because reduced search time will increase foraging efficiency.
Additionally, both bumble bees (Chittka et al. 2001; Raine et al.
2006) and honey bees (Giurfa et al. 1995) are known to show innate
preference to purple and blue flowers, which were also the colors
most associated with high nectar rewards in nature (Giurfa et al.
1995; Raine and Chittka 2007b). Positive relationships between
flight time and color selectivity may, thus, have been observed
because bees “accurately” tried to choose blue flowers at a cost in
decision time. However, Dyer and Chittka (2004) reported that a
speed–accuracy trade-off in bumble bees occurs only when flower
colors are similar and not for clearly distinguishable colors like blue
and yellow, such as those used in our experiments. Furthermore, visits to yellow flowers were probably not mistakes because inspection
of both colored flowers rarely occurred unless the bees returned to
the same flower in a short interval (Figure 4). Even after accounting for return intervals, inspection occurred rather less frequently
on yellow flowers than on blue flowers (Figure 4), probably because
accumulated scent mark (cf. Goulson et al. 1998) on blue flowers
prevents bees to visit, given that blue flowers were, on an average,
more frequently visited. Moreover, we observed a slight but significant decline in color selectivity as bees gained experience with the
array (Supplementary Figure S1 and Supplementary Table S1),
also supporting the notion that visits to yellow flowers were not mistakes by the foragers. An explanation other than speed–accuracy
trade-off is, therefore, required for the simultaneous decrease and
increase of color selectivity and flower search time with increasing
visual angle, with a minimum at approximately a 15° visual angle.
First, let us consider situations when the visual angles of the nearest flowers were smaller than 15° (Figure 2b). Following Spaethe
and Chittka (2003), taking into account eye length of the test
bees (2.6–2.8 mm), we can assume that a visual angle of 5°–6° is
the marginally detectable visual size for the test bees. Thus, when
visual angles of the nearest flowers ranged from 5° to 15°, larger
flowers would be more easily detectable because signals from more
photoreceptors will indicate the presence of the target with greater
reliability (Spaethe et al. 2001). This reasoning may explain why
bees took longer to find smaller flowers when the visual angles of
the nearest flowers were less than 15°, similar to the findings of
Spaethe et al. (2001). In contrast, when the visual angles of the
nearest flowers were larger than 15°, bees may have faced a signalto-noise problem (Chittka et al. 1994). Visual angles with more than
15° clearly exceed the minimum visual angle that bumble bees can
detect (Spaethe and Chittka 2003; Dyer et al. 2008). However, the
outline of each flower may have become obscured as images of
these flowers overlapped with each other with increasing visual size
(Figure 2b). The detectability of an individual flower depends on the
Behavioral Ecology
942
degree to which the flower generates receptor signals that exceed
the noisy fluctuations of the background (Chittka and Spaethe
2007). Accordingly, flower crypsis may have increased with the noisy
backdrop of other floral displays, as suggested by Goulson (2000),
although use of motion parallax may more or less overcome this
problem (Zhang et al. 1995; Kapustjanskij et al. 2010). In addition,
bees had to choose among more flowers as the number of easily
detectable flowers increased. The accuracy of choosing the nearest
flower decreased with increased visual angle of the nearest flowers
(Figure 3d and Supplementary Figure S2c), indicating that bees had
some trouble selecting the nearest flower when their visual images
were large. These results explain why bees took longer to travel to
the nearest flowers when their visual angles were extremely large.
The pattern of color selectivity shown in our experiment appears
to be consistent with the prediction of the search image hypothesis
that flower constancy declines as flower conspicuousness increases
(Goulson 2000). Namely, the benefit in using the search image of a
specific flower type may have exceeded the cost of bypassing other
flower types when the visual angles of the nearest flowers were
small because it would be worthwhile to efficiently locate a small
flower. In addition, this possibility may have been true when the
visual angles of the nearest flowers were extremely large because it
would also be worthwhile to find cryptic flowers in the noisy background. This interpretation is consistent with the finding by Forrest
and Thomson (2009) that bumble bees showed stronger color preference among equally rewarding blue and red flowers when these
flowers were presented against a complex background compared
with when presented against a simple background.
Within-bout constancy
Several studies of flower constancy have not analyzed assortative visitation sequences of single individuals (e.g., Gegear and
Thomson 2004; Otterstatter et al. 2005). However, analyzing
assortative visitation sequences (i.e., within-bout constancy) is crucial for flower constancy research. Within-bout constancy probably occurs if information on the most recently visited flower type
stored in short-term memory dominates other contents in working memory (Stanton 1987; Jones 1997; Ishii 2005). Accordingly,
within-bout constancy can provide insight into how short-term
memory dynamics influence flower choice behaviors by pollinators. In our experiments, within-bout constancy was conspicuous
when the visual angles of the nearest flowers were large (Figure 3b
and Supplementary Figure S2b). Flower visual image sizes, rather
than flight distances, were probably the major determinant for this
pattern because within-bout constancy did not significantly differ
among arrays that shared the same visual angle, whereas it did differ among arrays that shared the same interflower distance.
One possible interpretation for this pattern is that it reflects temporal changes in the relative weighting of short-term and reference
memories (Raine and Chittka 2007a). When the visual angles of
the nearest flowers are large, bees can easily detect the colors of
neighbor flowers on leaving a flower, when the relative weighting
of short-term memory for the last-visited flower is largest. In such
a situation, color choices would be made under the strong influence of short-term memory, which holds the information regarding the flower type that bees have last visited and would, thus,
lead to within-bout constancy. In contrast, if the visual angles of
the nearest flowers are small, bees would take additional time to
detect neighbor flowers (or their color). Such additional time may
increase the relative weighting of reference memory and would
thus reduce within-bout constancy. Different responses to flower
visual sizes between “color selectivity” and “within-bout constancy” may reflect such short-term (or working) memory dynamics. Considering that flight time to the nearest flowers was less than
0.8 s throughout our experiments, one may ponder that such a
timescale may be extremely short to fade short-term memory and
retrieve reference memory because previous studies have indicated
that the influence of short-term memory on subsequent flower
choices lasts 1–2 s (Raine and Chittka 2007a). Nevertheless, our
results imply that weighting of short-term and reference memories
can change over a shorter timescale. According to Srinivasan and
Lehrer (1985), honey bees take only 10 ms to identify the color of
an object. Thus, if they cannot acquire new stimuli within 10 ms,
the likelihood of retrieving reference memory content into working
memory may increase, even if the influence of short-term memory
potentially lasts for 1–2 s.
In this study, we used a free-flying arena to investigate successive
visitation sequences in the context of flower visual angles. However,
the limitation of this experiment is that it could not control the bees’
distance from the flowers at the point where they make a choice.
The bees probably made decisions with regard to the next flower to
visit before leaving the flower or at the early stage of flight because
flower inspections rarely occurred unless bees returned to the same
flower in a short interval (Figure 4). Additional experiments in
which the distance between the decision point and flowers can be
controlled (e.g., a Y-maze or multiarm-maze protocol; Giurfa et al.
1996; Hempel de Ibarra et al. 2001) would be useful to determine
the decision point and the visual angle used by bees at this point.
Moreover, in natural habitats, detectability (or crypsis) of a flower
would change depending on flower size, plant distribution, and background objects. For example, in a meadow near Berlin, Germany,
median inter- and intraspecies distances among inflorescences of
5 bumblebee-pollinated plants were 2–40 and 12–70 cm, respectively, corresponding to approximately 56°–4.2° and 14°–2.5° visual
angles from the next inflorescences, based on the assumption that
all inflorescences are 3 cm in diameter (Chittka et al. 1997). Forrest
and Thomson (2009) showed that color selectivity was strengthened
when flowers were presented against a foliage photograph compared with that against a monochrome background, probably indicating that background objects other than flowers would also affect
flower crypsis and flower constancy. Accumulating knowledge of the
conditions that affect flower crypsis based on the pollinators’ vision
is expected to be useful for understanding the mechanisms underlying flower constancy and its effects on plant evolution.
Supplementary Material
Supplementary material can be found at http://www.beheco.
oxfordjournals.org/
Funding
This work was supported by a grant-in-aid for young scientists
from the Japan Society for the Promotion of Science to H.S.I.
(20770013).
We thank A. Ushimaru and K. Ohashi for insightful discussions about this
study, T.T. Makino for advice in using the artificial flowers, and S. Dono and
A. Yanagisawa for their help in collecting data. We greatly appreciate ItabashiWard Firefly Breeding Institute for providing bumble bee colonies. This study
complies with the laws of Japan, the country in which it was performed.
Handling editor: Glauco Machado
Ishii and Masuda • Flower visual angle and constancy
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