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First published online July 20, 2007
Journal of Experimental Biology 210, 2657-2666 (2007)
Published by The Company of Biologists 2007
doi: 10.1242/jeb.004382
Disruptive coloration elicited on controlled natural substrates in cuttlefish, Sepia officinalis
1 Marine Biological Laboratory, Woods Hole, MA 02543, USA
2 Department of Life Science, National Tsing Hua University, Hsinchu,
Taiwan
* Author for correspondence (e-mail: lmathger{at}mbl.edu)
Accepted 2 May 2007
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Summary |
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Key words: edge, body pattern, color change, contrast, camouflage, reflectance, vision, cuttlefish, Sepia officinalis
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Introduction |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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),
cephalopods produce multiple camouflage patterns, and they can change them
almost instantaneously, using their neurally controlled chromatophore system
(Hanlon and Messenger, 1988;
Hanlon and Messenger, 1996;
Messenger, 2001). The
expression of camouflage body patterns is a visually driven behavior, and in
laboratory experiments it has been shown that certain background variables,
such as contrast, brightness, edge and size of objects, etc., are crucial for
the expression of these patterns (Holmes,
1940; Hanlon and Messenger,
1988; Hanlon and Messenger,
1996; Marshall and Messenger,
1996; Chiao and Hanlon,
2001a; Chiao and Hanlon,
2001b; Chiao et al.,
2005; Mäthger et al.,
2006; Shohet et al.,
2006; Barbosa et al.,
2007; Kelman et al.,
2007).
Although there is a good deal of variation in the details of the body
patterns shown by cuttlefish for camouflage, the variations fall under three
pattern types: (1) uniform (or uniformly stippled), (2) mottle and (3)
disruptive (Hanlon and Messenger,
1988; Hanlon,
2007). The overall framework that guides our experimental testing
of this visual sensorimotor system is that cuttlefish (and cephalopods in
general) might be using a simple visual cue (or a `sampling rule') for each of
the three primary camouflage pattern types. Uniform backgrounds elicit uniform
body patterns in cuttlefish (Hanlon and
Messenger, 1988; Chiao and
Hanlon, 2001a; Mäthger et
al., 2006) (Fig.
1A,B). Yet perfectly uniform backgrounds in nature are rare. On
non-uniform backgrounds, disruptive camouflage can be evoked on a checkerboard
in which the light squares are roughly equal in area to the White square (WS)
skin component shown on the mantle of cuttlefish
(Chiao and Hanlon, 2001a;
Chiao and Hanlon, 2001b)
(Fig. 1C). Furthermore, large
numbers of small black and white checkers with areas of roughly 4 and 12% of
the animal's WS component (see Fig.
1E for location of WS) elicit mottled body patterns
(Barbosa et al., 2004). We have
subsequently been able to elicit mottle patterns on natural substrates in the
laboratory (A.B., L.M.M. and R.T.H., unpublished results).
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;
Chiao and Hanlon, 2001b;
Chiao et al., 2005;
Mäthger et al., 2006;
Barbosa et al., 2007).
On a background of natural materials, cuttlefish show disruptive coloration
when settled on a mixture of light and dark objects
(Fig. 1D). This was first shown
by Holmes (Holmes, 1940). The
patterns that make up disruptive coloration are thought to break up the
recognizable outline of the animal by creating `false' lines and edges
(Cott, 1940), and it has been
shown experimentally that disruptive coloration is a successful visual
mechanism to achieve camouflage, at least against bird predators
(Cuthill et al., 2005;
Stevens et al., 2006).
Although it is well known that cuttlefish are masters of disguise, no
controlled studies on natural substrates have yet been performed. Cuttlefish
are known to show disruptive coloration on natural gravel as well as a picture
of natural gravel (Chiao et al.,
2005). However, the visual features of the substrate were
manipulated by changing the contrast and edge features of the two-dimensional
picture of the substrate, not the natural substrate itself. In the present
study we expanded this idea. We took information (e.g. object size, contrast,
edge, etc.) from many recent experiments on artificial substrates and created
a substrate using natural rocks glued permanently to a Plexiglass sheet. This
substrate's features (e.g. the spectral reflectance of rocks, contrast, edges)
were those thought to elicit disruptive coloration in cuttlefish. We then
added graded light sand in successively greater quantities to this glued rock
substrate, thus altering the substrate's visual features.
The aim of this study was to examine the visual cues that are required to elicit disruptive coloration in a controlled and measured natural scene. The results confirm previous laboratory studies using artificial substrates but, more importantly, they extend our understanding of the visual sampling rules that govern cuttlefish camouflage behavior in nature.
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Materials and methods |
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Substrates
Various rocks were collected at a local beach in Woods Hole and glued onto
a Plexiglas sheet using aquarium sealant. The resulting substrate was a
circular area covered by rocks (34 cm in diameter; S1 in
Fig. 2). It contained several
visual cues known to evoke disruptive patterning in cuttlefish: (1) light
objects roughly equal in area to that of the cuttlefish WS component, (2) high
contrast and (3) distinct edges (Chiao and
Hanlon, 2001a; Chiao and
Hanlon, 2001b; Chiao et al.,
2005). Substrates are referred to as S1–S6
(Fig. 2). Substrate S1 was used
in all consecutive trials, in which sand was added to S1 to gradually remove
these disruptive cues (S2–S6 in Fig.
2). Sand was collected locally and dried before being sieved at
the Sedimentation Laboratory of the United States Geological Survey (Coastal
and Marine Geology Program, Woods Hole Science Center, MA, USA). We chose mean
grain size 
0 (1 mm diameter) for this experiment
[=–log2 (diameter in mm)]. The experiment consisted of
six experimental treatments: (1) the rock substrate without any sand, S1; (2)
rock substrate with half a cup of 0 sand spread evenly over the
substrate, S2 (one cup=0.2366 l); (3) one cup of sand, S3; (4) two cups of
sand, S4; (5) four cups of sand, S5; (6) sand only, S6. Substrate 6 consisted
of 0 sand glued onto a Plexiglas sheet, and covered with three cups of
loose sand. There were no rocks in substrate 6. The effects of increasing the
amount of sand on the rock substrate were to reduce the number of light rocks
in the visual environment, as well as to decrease overall contrast and alter
edge characteristics. Cuttlefish were exposed to trials in random order.
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Experimental set-up
Trials were performed in an experimental tent covered with black plastic
sheeting, preventing visual distraction to the animals. The substrate was
placed in a small tank (55 cmx39 cmx13 cm) supplied with running
sea water. A circular plastic arena (24 cm diameter, 10 cm height), lined with
black felt, was placed over the substrate. A Sony DCR-VX1000 digital video
camera was mounted above the tank, and controlled remotely. A monitor outside
the chamber allowed observation during the trial without disturbing the
animal. Lighting was provided by a circular fluorescent light (Sylvania
circline daylight, 40 W; Osram Sylvania, Munich, Germany; light intensity at
the surface of the substrate was approximately 1000 Lux).
Procedure
Every animal was allowed a minimum of 5 min acclimation time. An animal was
considered acclimated when excessive hovering movements had ceased and a
stable body pattern was shown. The camera was set to record for 1 s every 30
s. Trials were 30 min long, yielding 60 s of total footage. Sand (S2–S5)
was removed from the substrate using a siphon.
Disruptive grading
From the video recordings, we took 10 images per animal per substrate,
yielding a total of 540 images that were graded for disruptiveness using the
grading scheme described in Mäthger et al.
(Mäthger et al., 2006).
To prevent experimenter bias, images were randomly renamed before grading, and
the background was removed using Adobe Photoshop. The origin of images was
only re-established after grading was completed. Disruptive patterning in
Sepia officinalis most commonly consists of 11 individual dark and
light components, which are independent physiological units that can be shown
singly or in combination with each other
(Hanlon and Messenger, 1988).
The components are produced by selective expansion (for dark components) and
retraction (for light components) of chromatophores, which either expose or
cover underlying white reflectors (see Fig.
1E for a description of which chromatic components were used for
grading). After studying many thousands of images, we have written a detailed
handbook for laboratory use, describing each component's level of expression
and outlining how to grade each component. Each component can be shown with
varying degrees of expression, from 0 (no expression) to 1 (weak expression),
2 (moderate expression) and 3 (strong expression). Using this grading scheme,
an animal can be given a total grade ranging from 0 (no expression of any
disruptive components) to 33 (maximum expression of all 11 disruptive
components, resulting in a strongly disruptive body pattern).
Characterization of substrate features
Image analysis
Still images of the six substrates (S1–S6 in
Fig. 2) were taken using a Sony
DCR-VX1000 digital video camera. These images were used to characterize
several substrate features, including rock size distribution, sand coverage,
overall contrast and number of light objects in the scene. Because the
settings of the camera were automatically adjusted when these images were
taken, it was impossible to derive overall intensities of all six substrates
directly from these still images. Instead, spectral reflectance measurements
of rocks and sand were used to calculate relative mean intensities (see
below). To determine the rock size distribution of each substrate, rock areas
were individually measured using an image processing and analysis program (US
National Institute of Health;
http://rsb.info.nih.gov/nih-image/).
The WS areas of the nine cuttlefish were also measured to assess the
relationship between average rock size and average WS size. The sand coverage
for each substrate was computed directly by subtracting visible rock areas
from the total area. For characterizing global contrasts of natural scenes, we
computed Root-Mean-Square (RMS) contrast
(Crms=
{[
I2–(I)2/N]/N},
where I is individual pixel values and N is the total number
of pixels). The Crms is commonly used to gauge the overall
contrast of natural images (e.g. Bex and
Makous, 2002). Pixel values in each image were used to calculate
RMS contrast directly. In addition, the number of light objects in each
substrate was determined by computing Weber contrast
[WC=(Iobject–Ibackground)/Ibackground,
where Iobject is the intensity of each rock and
Ibackground is the averaged intensity of the entire image]
and then counting the numbers of rocks whose Weber contrasts were greater than
1. Typically, any object with a Weber contrast greater than 1 means that its
intensity is at least twice that of the background. Similar to vertebrate
vision (e.g. Shapley and Enroth-Cugell,
1984), the cuttlefish visual system may use an early
transformation of the visual input akin to the Weber contrast transformation.
The divisive normalization by substrate mean intensity embodied by this
transformation amplifies the salience of light objects on a background.
Although counting the numbers of rocks whose Weber contrasts were greater than
1 is arbitrary in nature, it provides a meaningful measure of object salience
on a given substrate.
To examine the edge characteristics of S1–S6, and evaluate whether
cuttlefish can be discerned on a given substrate by the edge-detection
mechanisms of potential predators, a standard Laplacian of Gaussian (LoG)
operator was applied to images of substrates with and without an animal. The
sensitivity threshold for the LoG operator was set the same across all images,
and the standard deviation (the 
value) of the LoG operator was 2. In a
supplementary figure (supplementary material Fig. S1) we present additional
results with values of 1 and 3.
The LoG operator was first proposed to implement an edge-detection
algorithm that is similar to the receptive field property of retinal ganglion
cells in the vertebrate retina (Marr and
Hildreth, 1980). In brief, edges (defined as abrupt changes in
intensity within an image) can be located by taking the first derivative
(
I/x and I/y)
of an image I(x,y) in both x and y
dimensions. However, at a gradient peak in the first derivative, by taking the
second derivative (2I/x2
and 2I/y2), the presence
of `zero-crossings' can be depicted, which represent the positions of edges in
an image. Performing a derivative twice in succession is equivalent to the
Laplacian operator
[
2=2I/x2+2I/y2].
Because of noises in the processed image, a Gaussian operator
{G(x,y)=exp[–(x2+y2)/(2
2)],
where is the standard deviation of the Gaussian function} is often
required to smooth the image before applying the Laplacian operator. These two
operations can be combined effectively to create the LoG operator
[2G(x,y)=2G/x2+2G/y2].
The LoG operator has been suggested as a biologically plausible detector
algorithm, although other efficient algorithms are available
(Marr and Hildreth, 1980;
Stevens and Cuthill, 2006).
Edge detection plays an important role in differentiating between an object
and its background, and the vertebrate visual system has been shown to have
edge-detection mechanisms that may aid in object recognition
(Burr et al., 1989;
Gordon, 1997).
Spectral measurements
Using a spectrometer (USB2000; Ocean Optics, FL, USA), reflectance spectra
were taken of 83 rocks of the substrate. We used the same circular light
source as that used during experiments. This provided an even and diffuse
field of illumination. The fiber (positioned vertically) was set to measure an
area with a diameter of 2 mm. Each rock was measured two to three times and
measurements were averaged. A diffuse reflection standard (WS-1, Ocean Optics)
was used to standardize measurements. Eighteen reflectance spectra were taken
of the sand, and these were averaged to yield a single reflectance value.
After measuring the reflectance spectra of the rocks and sand used in this
study, the relative photon catch (PC; amount of light absorbed by a
photoreceptor and available for vision) was determined. This is given by:
PC=
[(1–exp(–kS(
)l)xR()]d,
after Warrant and Locket (Warrant and
Locket, 2004), where S() is the spectral
sensitivity of the visual pigment, R() is the spectral
composition of the light reflected from the rock/sand, l is the
length of the rhabdom [400 µm (Hanlon
and Messenger, 1996)] and k is the quantum efficiency of
transduction [0.0067 µm–1
(Warrant and Nilsson, 1998);
for further details, see Mäthger et al.
(Mäthger et al.,
2006)].
To compute the relative mean intensity, we calculated the weighted sums of PCrock and PCsand for each substrate using sand coverage as a weighting factor, and normalized the PC relative to a white surface. Weber contrasts of rocks derived directly from spectral measurements were also calculated [WC=(PCobject–PCaverage)/PCaverage, where PCobject is the PC of the rock under consideration and PCaverage is the averaged PC of all reflectance spectra]. This allowed us to compare this method with the previous image analysis method.
Consideration of light versus dark objects in the background
Previous experiments performed on artificial substrates showed that an
important background feature eliciting disruptive coloration is the presence
of light objects on a dark background [not dark objects on a light background
(Chiao and Hanlon, 2001a;
Chiao and Hanlon, 2001b)]. To
test this on natural substrates, we looked in detail at the cuttlefish images
obtained on substrate 4 (two cups of sand). We divided all images (10 images
per animal, total of 90) into `disruptive' and `non-disruptive' and counted
the number of light and dark rocks in the vicinity of the animal (`vicinity'
was defined as a circular region of interest with a radius of 1 ML around the
center of the animal's head). Only rocks of sizes ranging from 40–120%
of the animal's WS area were counted. On artificial substrates, we learnt that
the sizes of light objects need to be approximately 40–120% of the WS
area in order to elicit disruptive coloration (e.g.
Chiao and Hanlon, 2001a;
Chiao and Hanlon, 2001b;
Barbosa et al., 2007). Using
the spectral reflectance data from the substrate rocks and sand, we computed
Michelson contrast
[MC=(PCrock–PCsand)/(PCrock+PCsand),
where PCrock is the PC of each rock and PCsand is the PC
of the sand; only absolute values are used] between the sand and each rock.
Because Weber contrast is used in a more global context, using Michelson
contrast was preferable because it is generally used for side-by-side regular
repeating patterns. The rocks included in this count ranged in contrast from
29–77% (dark rocks) and 39–78% (light rocks).
Because of the small sample size of this part of the analysis, we did not
perform any statistical tests. Instead, in a separate experiment, we presented
cuttlefish with four additional substrates: (A) pure sand (grain size
0), (B) sand with 15 black rocks spread evenly, (C) sand with 15 white rocks
spread evenly throughout the arena, and (D) sand with eight white and eight
black rocks distributed evenly. Substrates are referred to as SA to SD. The
rocks ranged in size from 0.2 cm2 to 0.7 cm2; which
equaled approximately 35–100% of the animals' WS component. The
Michelson contrast (see above for method) between the rocks and sand was high:
75% (sand versus white rocks) and 73% (sand versus black
rocks). Twelve cuttlefish, ranging in size from 3.0 to 3.5 cm ML, were
used.
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; Mäthger et al.,
2006; Barbosa et al.,
2007). Thus, we felt it was interesting to compare the average
rock size in S1–S6 with the average WS size of nine cuttlefish placed on
these natural substrates. Overall, the average rock size was 54% of WS size on
S1, and it gradually decreased to 41%, 28%, 18% and 5% of WS size on
S2–S5, respectively (Fig.
3A).
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The rate of sand coverage (expressed as percent of substrate covered) can be seen in Fig. 3B. Although no sand was used on S1 (rocks only), sand was added on S2 to S6, reaching 100% coverage for S6. Note that four cups of sand covered most of the rocks, and thus the sand coverage was close to 100% in S5. Relative mean intensities derived from reflectance measurements indicate that all six substrates are low in brightness (compared with a white surface), and averaged intensities slightly decreased from S1 to S6 (Fig. 3C). In Fig. 3D, we show that the global contrast (expressed as RMS contrast) is highest in S1 and drops to low contrast in S5 and S6.
The number of lighter rocks visible in each substrate, shown in Fig. 3E, illustrates specifically that, as sand is added to the rock substrate, fewer and fewer white rocks are visible. As expected, this correlates with a reduction in the animals' disruptive coloration (see below).
Fig. 4 shows the Weber contrast for three white rocks in substrate S1 (Fig. 4A, circled in color; reflectance spectra shown in Fig. 4B). In comparison with the average reflectance spectra of the substrate (dark line in Fig. 4B), the Weber contrast of the three white rocks was 2.8, 1.87 and 1.81. Note that the intensities of the three white rocks are saturated in image S1 of Fig. 4A (pixel values are 255 for all three rocks); however, spectral reflectance measurements reveal that the rock outlined in blue is more reflective than the other two rocks. In Fig. 4C, we show the Weber contrast distribution of all rocks of S1. The plot indicates that most of the rocks are either darker (negative values) or lighter (positive values) than the averaged background. In total, 13 rocks had a Weber contrast greater than 1.
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Body pattern changes
The body patterning shown by cuttlefish changed in response to increasing
the amount of sand to the rock substrate
(Fig. 5A). Although animals
showed strong disruptive patterning when on S1 (no sand), the disruptive grade
decreased as sand was added. Animals did not show any disruptive coloration on
S5 (four cups of sand) and S6 (full sand).
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In a follow-up experiment, we presented cuttlefish with four substrates (Fig. 5C): (A) pure sand, (B) sand with black rocks spread evenly, (C) sand with white rocks spread evenly, and (D) sand with white and black rocks spread evenly. Cuttlefish showed non-disruptive body patterning when on SA (sand) and SB (sand with black rocks); the body patterns shown on these substrates did not differ statistically (t=1.39, P=0.19, N=12). Disruptive patterning was observed only on SC (sand with white rocks) and SD (sand with white and black rocks; Fig. 5C). The body patterns shown on SC and SD differed significantly from those shown on sand (t=8.7, P<0.001, N=12 for SC; t=6.33, P<0.001, N=12 for SD).
Edge characteristics
Adding sand to the rock substrate S1 modified edge characteristics of the
rocks. Fig. 6 shows that the
edges become less conspicuous and increasingly fragmented as more sand is
added to the substrate. This correlates with the apparent reduction in the
number and size of the rocks (Fig.
2). The different body patterns shown on S1–S6 may thus also
correlate with edge information of each substrate. By applying a standard
edge-detection algorithm (LoG), we show that creating false edges in
disruptive patterns makes cuttlefish less apparent in these LoG-filtered
images (Fig. 6, compare the
second and third columns). By contrast, covering the substrate with sand
reduces edge information in the scene, and cuttlefish with uniform patterns
are just as difficult to detect using a LoG operator
(Fig. 6E,F) because uniform
body patterns do not have false edges, as those seen in disruptive coloration.
Although these LoG-filtered images were generated using only one value
(representing one receptive field size), other values (corresponding
to different receptive field sizes) were also implemented (see supplementary
material Fig. S1), and the general trend holds true across all different
values.
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;
Hanlon and Messenger, 1988;
Hanlon and Messenger, 1996;
Chiao and Hanlon, 2001a;
Chiao and Hanlon, 2001b;
Chiao et al., 2005;
Shohet et al., 2006;
Kelman et al., 2007). Even
though it is known that cuttlefish are masters of disguise both in the
laboratory and in nature, there are no quantitative accounts of cuttlefish
camouflage on natural substrates.
Most of our knowledge on cuttlefish camouflage comes from laboratory
studies using artificial substrates, such as checkerboards and similar
two-dimensional patterns. The advantage of using checkerboard substrates is
that specific variables can be changed one at a time, allowing for
well-controlled experiments (e.g.
Ramachandran et al., 1996;
Chiao and Hanlon, 2001a;
Mäthger et al., 2006). By
contrast, natural substrates are highly variable in size, contrast,
brightness, color and texture, and this makes experimentation with them
challenging.
Object size, contrast and edge determine expression of disruptive coloration in cuttlefish
In our study, we kept the baseline rock substrate steady by gluing rocks in
place. The visual features were then altered by adding sand in known
quantities. Even though every substrate differed slightly from trial to trial
because of the animals' movements, causing sand to be slightly shifted, the
characteristics of each substrate (e.g. numbers of light and dark rocks
visible, contrast, edges, etc.) changed in the same manner.
Adding sand to the rock substrate had several visual effects: (1) it
visually decreased the size, number and spatial distribution of rocks, (2) it
filled in the spaces and eliminated sharp edges that were created by shadows
cast between rocks, and (3) it fragmented and reduced the number of long
conspicuous edges. As fewer light objects were visible and the visual
environment became increasingly uniform, cuttlefish responded correspondingly
by reducing their disruptiveness and becoming more uniform. Furthermore, fewer
and fewer rock edges were visible and, correspondingly, the disruptive pattern
lost more of its false edges, particularly in the skin components Transverse
mantle lines, Anterior head bar and Median mantle stripes
(Fig. 6; see
Fig. 1E for disruptive
components). These disruptive components are effective in breaking body
outlines (Cott, 1940;
Hanlon and Messenger, 1988;
Cuthill et al., 2005;
Stevens and Cuthill, 2006;
Stevens et al., 2006), and
help render both false and true edges of animals detectable but not
recognizable on a rock substrate characterized by distinct edges. When no
edges were visible in the background, the animals became uniform and minimized
detection of their true body outline by burying in the sand. That is, in the
parlance of Cott (Cott, 1940),
they switched from disruptive coloration to general resemblance of the
background. In our edge analysis, we limited ourselves to only one
edge-detection algorithm (LoG, values 1, 2, 3; Figs
6,
7, supplementary material Fig.
S1). There are numerous edge-detection algorithms available and many of those
would without doubt detect edges of even the most camouflaged cuttlefish.
However, this was not the purpose of this study, and for illustrative
purposes, the LoG, which has been suggested as a useful biological
edge-detection algorithm (Stevens and
Cuthill, 2006), appeared as an effective operator. It is
interesting to compare the camouflaged patterns analyzed in
Fig. 6 with those of
Fig. 7, in which we show an
image of a cuttlefish that chose disruptive coloration on natural sand. The
animal is easily visible, but in addition, the LoG operator tells us that the
animal's false lines (around the WS and Head bar) stand out conspicuously,
making it vulnerable to potential predators that use edge information to
locate prey.
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;
Chiao and Hanlon, 2001a;
Chiao and Hanlon, 2001b;
Chiao et al., 2005;
Mäthger et al., 2006;
Barbosa et al., 2007;
Kelman et al., 2007), we can
conclude that disruptive coloration is the preferred camouflage pattern of
cuttlefish under the following known circumstances: first, when the
environment contains a few light objects with sizes comparable to the area of
the WS component within a visual field of approximately 1 ML around the
animal. Note that dark objects alone on a background, even with contrasts
similar to light objects, definitely do not evoke disruptive coloration in
S. officinalis. Second, when the contrast levels of light objects
versus background are high (Michelson contrast of between 40 and
80%). Third, when the visual environment is characterized by distinct edges
around large objects. However, Chiao et al. showed that merely having object
edges without contrast between the objects is not enough to evoke disruptive
coloration (Chiao et al.,
2005).
Interestingly, very little mottling was evoked by any of the six substrates
(S1–S6). Previous experiments have shown that mottled patterns are
evoked on non-uniform substrates with a large number of high-contrast small
black and white checks with areas of roughly 4 and 12% of the animal's WS
components (Barbosa et al.,
2004; Barbosa et al.,
2007). This indicates that background features including object
size, contrast and edge information were such that either disruptive or
uniform patterns were the most appropriate body pattern to show, and that we
need to vary some or all of these features if we wish to evoke mottled body
patterns.
Chromatic skin components limit the type of background objects used as visual cues
In disruptive coloration, an animal's appearance is broken into several
large and high-contrast dark and light areas that function partly by taking an
observer's attention away from the true body outline of the animal
(Cott, 1940). Depending on the
environment of the animal, the light areas of the animal's pattern can be a
variety of shades; however, in many animals, they are bright white (e.g.
birds, fish, snakes, sharks, insects, crustaceans), which is an indication
that white plays an important role in animal camouflage
(Cott, 1940;
Graul, 1973;
Edmunds, 1974;
Merilaita, 1998;
Myrberg, 1990;
Peterson and Peterson,
2002).
Disruptive coloration in S. officinalis is typically made up of 11
distinct chromatic skin components, five light components (when fully
expressed, these are bright white in white light) and six dark components
(when fully expressed, these are dark brown)
(Hanlon and Messenger, 1988)
(see Fig. 1). These components
are distinct neurophysiological units made up of thousands of chromatophores
that are innervated directly from the brain
(Hanlon and Messenger, 1996;
Messenger, 2001;
Tublitz et al., 2006).
Although cuttlefish can change patterns quickly, this means that they are
limited in diversity by the fixed number of light and dark chromatic
components they can show, and consequently, this will affect which visual cues
turn on a particular camouflage pattern.
This and previous studies have shown that cuttlefish cue on well-defined
large light objects in their environment to turn on disruptive coloration and
that the object size must be comparable to the animal's WS component
(Chiao and Hanlon, 2001a;
Chiao and Hanlon, 2001b;
Chiao et al., 2005;
Barbosa et al., 2007). The WS
component is one example that illustrates how disruptive coloration may work
by creating an illusion so that an observer perceives the conspicuous WS as a
random sample of the background (e.g. light rock), rather than the back of a
cuttlefish. It is interesting to note that, with some exceptions, the light
components (e.g. WS, White head bar, White anterior triangle) generally
comprise a larger area of the skin compared with the dark components (e.g.
Median mantle stripes, Anterior and Posterior transverse mantle lines), which
may partially explain why cuttlefish cue on large light but not dark
objects.
In summary, cuttlefish prove to be ideal organisms for the study of
camouflage because within a fraction of a second they analyze complex visual
fields and translate the newly acquired information into the most appropriate
body pattern (Hanlon and Messenger,
1996; Marshall and Messenger,
1996; Chiao and Hanlon,
2001a; Chiao and Hanlon,
2001b). In this study we examined the visual cues required to
produce disruptive camouflage in cuttlefish, S. officinalis, placed
on one particular type of natural substrate. By continually testing our ideas
of how cuttlefish perceive complex backgrounds, we hope eventually to
understand the specific visual stimuli that evoke specific types of camouflage
patterns. Before we can fully understand the rules that govern disruptive
coloration, specific laboratory experimentation with quantitatively controlled
visual stimuli combined with tests of increasingly natural materials (which
have three-dimensionality that computer-generated substrates lack) must
continue in parallel with field studies, which have only rarely been
attempted.
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Acknowledgments |
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Footnotes |
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