Chapter 5 Web Topics

5.1 Constructing Color Models

It has only recently become feasible to compute the detectability of a target object from an animal receiver’s perspective using the ARTS model, due to the large amount of data and information required to estimate each of the four spectra. In some cases, the computation can be simplified a little by making a few assumptions or focusing the question. For example, if one is interested in comparing hue contrast for different color patches in the same environment or for the same color patch against different backgrounds, then the environmental transmission step can be omitted. If one is interested in comparing the minimum detection distance for different signal patches, then some of the complex hue components can be reduced. In this unit we will take a closer look at the methods and equations used to estimate each of the four ARTS steps, and then describe two studies that successfully used this approach to answer meaningful questions about object detectability.

Ambient irradiance

The first step is to determine the available light spectrum. This must be an irradiance measurement using a spectroradiometer and a cosine-correcting hemispherical sensor or integrating sphere, as described in Web Topic 4.2. The key point to add here is that this measurement must be made in the precise habitat in nature in which the target object or signal is made. Many such spectra are presented in the main text in Figures 5.3–5.6. There are also several published standard spectra available for daylight (Vorobyev et al. 1998; Chiao et al. 2000). For reflected light signals in both terrestrial and aquatic environments, available light is always assumed to be downwelling light. We will refer to this spectrum as A(λ) and it is measured over the entire range of wavelengths relevant to the receiving animal in question (usually 320–700 nm to include most organisms).

Target and background radiance

The second step is to measure the reflectance spectra for both the target and the background against which the target is viewed. This must be a radiance measurement taken through a tubular, angle-restricted light probe connected to a spectroradiometer. As described in Web Topic 4.2, the measurement must be taken relative to a white standard and corrected for dark noise. We will refer to these as Rt (λ) and Rb (λ), for target and background spectra, respectively. Some target and background spectra are shown in main text Figures 5.3, 5.9, and 5.28.

Receiver sensitivity

To adequately evaluate the receiver’s sensitivity, we need to know how many types of photoreceptor cells the receiver possesses, which ones are integrated for use in color vision and which for brightness contrast, and the absorbance spectrum for each one. It is of course important to know the absorbance curve for the photopigment associated with each receptor type. In addition, we need to know the effects of any filtering materials, such as oil droplets or corneal pigments that may be associated with each photocell type. There are several ways to obtain this information, either by directly measuring physiological responses by each cell type to presentations of a series of monochromatic light stimuli directed into the eye, or by measuring the components and multiplying their effects. For example, the spectral sensitivity for the ith cell type, Si (λ) can be computed as:

Here, Pi (λ) is the photopigment absorbance, Di (λ) is the transmission spectrum of colored oil droplets, and M(λ) is the transmission spectrum of the ocular media (which would be the same for all cell types in a single-chambered eye but could be cell-type specific in a compound eye). This measurement or calculation is made for each photocell type, resulting in a series of spectra. Examples are given in the main text in Figures 5.26, 5.27B, and 5.28A.

Photon catch

Computation of the photon catch for each photocell type is a bit more complex because we need to account for the fact that the eye is viewing a complex world and may undergo some adaptation to the amount and color of the ambient light and to the background hue. This physiological phenomenon is called color constancy, and requires us to correct the photocell sensitivity estimate above for this color adaptation. We must also normalize the spectral sensitivity of each photocell type at each wavelength. This is done by computing ki (λ), which incorporates the background reflection Rb (λ) and ambient light A (λ) spectra as follows:

Now we can compute photon catch for each photocell:

The next step is to normalize the maximum excitation of each photoreceptor cell type to unity using the following formula:

Using the relative excitation of each photocell type, we compute the three-dimensional coordinates of each color (target and background) in the color space. For a trichromat, this color space is a two-dimensional triangle, with photocell types designated as S (short), M (medium), and L (long). The (x, y) location of a color within the triangle is computed as follows:

For a tetrachromat, we add the VS (very short) photocell type and a third dimension:

Hue contrast

Now we can compute hue contrast, D (also called chromatic contrast, Cc), by taking the difference between the target and background values for each coordinate, i.e., Δx = xbxt:

The z term would, of course, be omitted for the trichromat. The variable D is the Euclidian distance between the target and background points within the color space. The longer the distance, the greater the hue contrast perceived by the particular receiver. Achromatic contrast can be computed in the same way if the photoreceptor types that the receiver uses for brightness contrast are known. The calculations would be made using only these photocell types. For example, bees use their green receptors for achromatic contrast, while the trichromatic primates use combined output from the medium- and long-wavelength cones (Spaethe et al. 2001; Mullen and Kingdom 2002). Birds possess specialized double cones for achromatic vision using their medium- and long-wavelength cones with the oil droplets removed (Hart et al. 2000; Osorio and Vorobyev 2005).

Vorobyev and Osorio (1998) suggest that a better estimate of color discrimination abilities is obtained if receptor noise is also incorporated into the calculation of D. There are three sources of receptor noise. Dark noise is caused by a constant low level of spontaneous firing by the photoreceptor cells, even in absolute darkness, which is sometimes called “false photons.” Dark noise is only likely to pose a problem for object detection under extremely low levels of ambient light. Photon noise is caused by statistical fluctuations in the capture of photons by the photoreceptors. It increases approximately as the square root of ambient light intensity, and is most likely to be a constraining factor only under intermediate levels of light illumination. Neural noise is caused by the effects of neural transduction in the analysis of visual input. At very high light levels, a small difference in contrast may not be detectable. Given the context in which D is being estimated, the most constraining of these noise factors will set the threshold limit on the receiver’s ability to discriminate two hues. In practice, the combined effects of error can be measured physiologically and specified with an error term, which can then be incorporated into the computation of hue and contrast discrimination. For more details, refer to Vorobyev and Osorio 1998, Kelber et al. 2003, Goldsmith and Butler 2003, Montgomerie 2006, and Lind and Kelber 2009.

An excellent example  of the use of this method for calculating hue contrast (Figure 1) can be found in Théry et al. (2005), who asked whether female crab spiders waiting on flowers to ambush pollinator prey (bees) were cryptic to both their prey and their predators (birds). To humans, the yellow spiders match the yellow centers of marguerite daisies very well, and white spiders match the white peripheral petals, but bees and birds have different color sensitivities so the spiders may not be as well camouflaged to these receivers. The spiders can change their colors to match at least some flower backgrounds (Chittka 2001; Insausti, 2008). Measurements of chromatic and achromatic contrast were made for yellow females against both the yellow center and white peripheral petals of marguerite daisies. The yellow color matched the yellow flower center very well, i.e., D was not greater than the hue discrimination threshold for either bee or bird vision. But, the yellow spider did contrast significantly with the white petals, and achromatic contrast was also significant for both visual systems.

A sampling of additional studies using color models to address perception of color signals include Osorio et al. 2004, Uy and Endler 2004, Cheroske and Cronin 2005, Sumner et al. 2005, Moyen et al. 2006, Schaefer et al. 2007, Doucet et al. 2007, Théry et al. 2007, 2008, Schultz et al. 2008, Cummings et al. 2008, Osorio and Vorobyev 2008, Bridges et al. 2008, Casey et al. 2008, Herrera et al. 2008, Avilés and Soler 2009, Cheney et al. 2009, Langmore et al. 2009, Defrize et al. 2010, and Sztatecsny et al. 2010.

Figure 1: Contrast between crab spiders (Thomisus onustus) and flower backgrounds. Spiders can change their body color depending on the background on which they are residing (see Figure 14.1D in the main text). Bar graphs show chromatic and achromatic contrast between a yellow female spider and the yellow center and white periphery of marguerite daisy flowers computed from photoreceptor sensitivities of a Hymenopteran eye and bird eye. The dashed lines show discrimination thresholds for Hymenopteran and bird visual systems. Spiders match the hue of the yellow center for both types of receivers, but do not match the petals, and are darker than both center and periphery from the perspective of achromatic brightness contrast. (After Théry and Casas 2002; Théry et al. 2005.)


Studies that specifically model the transmission of visual signals or target objects are usually interested in evaluating the distance at which an object is just barely detectable. This distance is called the sighting distance or the maximum detection distance. One might ask questions that compare the maximum signal transmission range for alternative color signals, or that compare crypticity values of different coloration strategies. The visibility of an object generally depends more on its contrast than on its size, although one might need to set the size boundary above the threshold at which the visual system’s resolution can just distinguish an object, or develop different models for large and small objects.

Transmission models begin with the inherent contrast of the object against its background, C0 (λ) measured at a very short distance. Contrast can be measured as Euclidian distance (provided earlier in this unit), or more simply as the difference between the reflectance of the target minus the background reflectance, divided by either background reflectance or background plus target reflectance:

We then examine how the perceived contrast falls off with increasing distance d using the following type of equation:

Here, α (λ) is the beam attenuation coefficient of the medium. The reciprocal of this value is attenuation length, La, the maximum distance at which a large and contrasting object can just be detected. It is both medium- and wavelength-specific. Typical values for attenuation lengths are shown in Table 1. The beam attenuation coefficient is empirically measured in a specified ambient light context and environment. Radiance measurements of a contrasting object would be taken using a narrow probe at varying distances away from the object. The value α  is then the slope of the regression line for the log-log plot of radiance versus distance. We can use α to calculate the falloff in radiance over any distance d.

Table 1. Beam attenuation lengths in various environments.

Wavelength (nm)
Clear air 3.8 km 5.0 km 6.0 km 6.7 km 7.4 km 7.9 km
Moderate fog 50 m 50 m 50 m 50 m 50 m 50 m
Pure water ? 23 m 28 m 5.4 m 2.0 m 0.49 m
Oceans ? 1–10 m 1–15 m 1–5 m 1–2 m ?

Source: Dusenbery 1992.

If we let Cmin(λ) be the minimum contrast for object detection for a given visual system, then we can substitute Cmin(λ) for C(λ) in the equation above and solve the equation for d:

This distance d(λ) is the maximum distance at which the object is detectable, and is referred to hereafter as the sighting distance, or dsighting(λ) (Johnsen 2002). This distance depends on the product of two factors: (1) the relationship of the inherent contrast of the object to the minimum contrast threshold of the viewer, called the contrast factor, given by

and (2) the attenuation length or the penetration factor, given by

An interesting application of this sighting distance model can be found in Johnsen and Sosik (2003), where the effectiveness of two potentially cryptic color strategies of pelagic fish, diffuse colored reflectance and specular mirrored reflectance, were examined under different viewing situations. Johnsen used the general model described above, but the penetration factor required two components to deal with the effects of viewing a target while looking into the sun versus viewing a target with the sun behind the viewer. Their sighting distance model was therefore:

Here, α (λ) is the beam attenuation coefficient of the object in water and KL(λ) is the attenuation coefficient of the background radiance (KL is greatest for downward viewing and zero for horizontal viewing). They used the cod (Gadus morhua) as the model viewer, which has a measured Cmin of 0.02, which is well within the range of other marine fish, and known cone sensitivity curves. The viewing conditions they considered are illustrated in Figure 2A and include looking down on the fish from above, looking up from below, looking horizontally into the sun so the fish is backlit, and looking horizontally away from the sun so the fish is sidelit. Figure 2B shows a schematic illustration of the way the two types of reflectance—diffuse and specular—were modeled. Downwelling and upwelling radiances were measured directly at different depths (equivalent curves in Figure 5.6 in the main text showed some examples of these measurements). The target object luminance (Lt) was computed for each case by multiplying ambient irradiance by the reflectance strategy.


Mismatch condition
Viewing angle
Sighting distance (cm)
Diffuse reflection
Specular reflectance
Cryptic in open ocean, viewed near coast From above
Into sun
Away from sun
Cryptic at noon, viewed at sunset From above
Into sun
Away from sun
Cryptic in one azimuth, viewed in another (noon) Into sun
Away from sun
Cryptic at 50 m depth, viewed shallower (noon) From above
Into sun
Away from sun

Figure 2: Sighting distances of mirrored and colored fish under different viewing conditions. (A) The four different positions for predators viewing a prey fish (black oval in center) showing the directions of object luminance (Lt) and background luminance (Lb) in each case. The clusters of inward pointing arrows around the prey fish depict the diffuse ambient irradiance illuminating the fish on all sides. The position of the sun is shown at the top. (B) Schematic of the way object and background luminances were modeled for horizontal viewing. (Top) Diffuse reflectance from a colored fish surface computed as RIs/π, where R is the diffuse reflectance from the fish’s surface (vertical line) and Is is the diffuse ambient irradiance at the fish’s surface. (Bottom) Specular reflectance from a mirrored fish surface, computed as RL´b, where b is the irradiance falling on the fish when viewer is looking into the sun (single small arrow). (C) Sighting distances for a large (1 m) colored fish with diffuse reflectance, cryptic at 50 m, viewed near noon from different positions and at shallower water depths. (D) Sighting distances for a large (1 m) fish with specular reflectance viewed near noon at different depths, with the symbols same as in (C). (E) Table showing ranges of sighting distances (in cm) for a small (6 mm) target object cryptic in one optimal condition when being viewed in another set of conditions. Bold values indicate significantly shorter ranges for specular or diffuse reflection strategy under the given set of conditions. (After Johnsen and Sosik 2003.)

In contrast to benthic (deep water) marine fish, for which light is generally dimmer and less directional, pelagic (surface water) fish must contend with changing conditions that render them cryptic under some circumstances and highly visible under others. Crypsis was generally better with mirrored reflection than diffuse reflection, especially when looking horizontally away from the sun at the fish, because the background radiance is high and the mirrored sides are bright and highly reflective. Colored surfaces were more cryptic when looking horizontally into the sun and looking down from above. Both strategies were very conspicuous when viewed from below because they cast a strong silhouette against the bright downwelling irradiance. Both strategies were also quite visible at very shallow depths. An important insight from these studies is that a prey fish cannot control the viewing angle and lighting conditions in which it is viewed. Predators, therefore, can overcome many of these crypsis strategies of the prey. For example, predators can move in circular patterns to view prey from a series of angles and sun angles. They can also drive prey into shallower water where they are more visible. It appears that the best strategy for maximizing crypsis in pelagic organisms is transparency (Johnsen 2003).

Further reading

Hart, N. S. and M. Vorobyev. 2005. Modelling oil droplet absorption spectra and spectral sensitivities of bird cone photoreceptors. Journal of Comparative Physiology A-Neuroethology Sensory Neural and Behavioral Physiology 191: 381–392.

Hastad, O. and A. Odeen. 2008. Different ranking of avian colors predicted by modeling of retinal function in humans and birds. American Naturalist 171: 831–838.

Kelber, A., M. Vorobyev, and D. Osorio. 2003. Animal colour vision—behavioural tests and physiological concepts. Biological Reviews 78: 81–118.

Lovell, P. G., D. J. Tolhurst, C. A. Parraga, R. Baddeley, U. Leonards, and J. Troscianko. 2005. Stability of the color-opponent signals under changes of illuminant in natural scenes. Journal of the Optical Society of America A-Optics Image Science and Vision 22: 2060–2071.

Montgomerie, R. 2006. Analyzing colors. In Bird Coloration (Hill, G. E. and K. J. McGraw, eds.), pp. 90–147. Cambridge, MA: Harvard University Press.

Kelber, A. and D. Osorio. 2010. From spectral information to animal colour vision: experiments and concepts. Proceedings of the Royal Society of London, Series B-Biological Sciences 277: 1617–1625.

Wachtler, T., U. Dohrmann, and R. Hertel. 2004. Modeling color percepts of dichromats. Vision Research 44: 2843–2855.

Literature cited

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Cassey, P., M. Honza, T. Grim, and M. E. Hauber. 2008. The modelling of avian visual perception predicts behavioural rejection responses to foreign egg colours. Biology Letters 4: 515–517.

Cheney, K. L., C. Skogh, N. S. Hart, and N. J. Marshall. 2009. Mimicry, colour forms and spectral sensitivity of the bluestriped fangblenny, Plagiotremus rhinorhynchos. Proceedings of the Royal Society of London, Series B-Biological Sciences 276: 1565–1573.

Cheroske, A. G. and T. W. Cronin. 2005. Variation in stomatopod (Gonodactylus smithii) color signal design associated with organismal condition and depth. Brain Behavior and Evolution 66: 99–113.

Chiao, C. C., D. Osorio, M. Vorobyev, and T. W. Cronin. 2000. Characterization of natural illuminants in forests and the use of digital video data to reconstruct illuminant spectra. Journal of the Optical Society of America A-Optics Image Science and Vision 17: 1713–1721.

Chittka, L. 2001. Camouflage of predatory crab spiders on flowers and the colour perception of bees (Aranida: Thomisidae/Hymenoptera : Apidae). Entomologia Generalis 25: 181–187.

Cummings, M. E., J. M. Jordao, T. W. Cronin, and R. F. Oliveira. 2008. Visual ecology of the fiddler crab, Uca tangeri: effects of sex, viewer and background on conspicuousness. Animal Behaviour 75: 175–188.

Defrize, J., M. Théry, and J. Casas. 2010. Background colour matching by a crab spider in the field: a community sensory ecology perspective. Journal of Experimental Biology 213: 1425–1435.

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Johnsen, S. 2002. Cryptic and conspicuous coloration in the pelagic environment. Proceedings of the Royal Society of London Series B-Biological Sciences 269: 243–256.

Johnsen, S. and H. M. Sosik. 2003. Cryptic coloration and mirrored sides as camouflage strategies in near-surface pelagic habitats: Implications for foraging and predator avoidance. Limnology and Oceanography 48: 1277–1288.

Johnsen, S. 2003. Lifting the cloak of invisibility: The effects of changing optical conditions on pelagic crypsis. Integrative and Comparative Biology 43: 580–590.

Kelber, A., M. Vorobyev, and D. Osorio. 2003. Animal colour vision—behavioural tests and physiological concepts. Biological Reviews 78: 81–118.

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5.2 Perception of Polarized Light

A beam of pure polarized light consists of a population of electromagnetic waves with parallel electric vectors. The natural world contains complex patterns of polarized light that humans cannot perceive but which are very useful for animals that can distinguish different electric vectors of light. In Web Topic 4.1, we described some of the basic sources of natural polarized light from scattering and reflection. In this section we shall first clarify how polarized light is quantified and measured and then describe the patterns of polarized light in nature. Next, we show how photoreceptors can be made sensitive to the planes of polarized light and outline some of the uses animals make of polarized light.

Quantifying light polarization

Natural polarized light is rarely pure (i.e., comprised of 100% parallel e-vectors). Instead, it is partially polarized to varying degrees because it contains some fraction of e-vectors oriented in other directions. Four components are needed to completely describe polarized light are:

I = the overall intensity of the light beam
p = the degree of linear polarization, or the fraction of the overall light intensity that is linearly polarized parallel to a reference plane, which can range from 0–100%
α = the angle of strongest linear polarization, expressed in angles from 0–180º
ε = the fraction of circularly polarized light

Circularly polarized light is relatively rare in nature and often ignored, but later in this unit we describe some examples of animals that do in fact reflect circularly polarized light. The other three parameters are essential components for describing a beam of polarized light. The photoelectric device for measuring and computing these components is called a polarimeter. It consists of a radiometer that measures light intensity (in watts or photon flux per unit time, see Web Topic 4.2) with a rotating linear polarizer in front of the light-receiving lens. Three simultaneous or rapidly sequential measurements of light intensity must be made at three independent angles of rotation, typically 0, 60, and 120º. Using these values, one can compute the three essential components as follows:

I = 2(I0 + I60 + I120)/3
p = (Q2 + U2)1/2/I
α = 0.5 arctan(U/Q)

Here, Q = 2(2I0 + I60 + I120)/3 and U = –2(I120I60) 3–1/2. These three components—intensity, degree of polarization, and angle of polarization—are analogous to the color components of brightness, saturation, and hue, respectively. Also analogous to color measurements, polarized light can be measured for a very small point in space using a narrow light acceptor, called point-source polarimetry, or globally over a very large area, called imaging polarimetry. In presenting the results of imaging polarimetry, it is best to provide separate images of the three components (Figure 1). Overall intensity is typically displayed with a normal color image of a scene, degree of polarization is displayed with a grayscale image of the same scene where white may represent either 0% or 100% polarization depending on the situation, and angles of polarization are depicted with different hues (Horváth and Varjú 2004).

Figure 1: Imaging polarimetry. (A) Normal color image of a shiny black plastic sheet laid on an asphalt road. (B) Same subject showing degree of polarization, where darker areas are more strongly polarized. (C) Same subject showing angle of polarization. These patterns were measured by Gabor Horváth with imaging polarimetry.

Environmental patterns of polarized light

There are two main sources of polarized light in nature— reflection off of smooth surfaces and scattering in the sky by atmospheric particles. In both cases, the degree of polarization is very high at certain viewing angles, but the fraction of light that is polarized decreases gradually as the viewer or the light source moves away from this angle, and may become essentially nonpolarized at other angles. Figure 2 illustrates this concept for scattering in the sky.

Figure 2: Polarization of scattered light. Light from the sun, depicted by yellow arrows, is scattered by small particles (black point in center) in the atmosphere (note: The sun is not visible, but is located beyond the upper left corner of this illustration). This scattered light is vertically polarized when viewed from an angle of 90º relative to the sun. The plane of polarization of the scattered light is perpendicular to the plane defined by the incident and the scattered ray. Light that is scattered at an angle of 0º is nonpolarized and at intermediate angles it is partially polarized. (After Wehner 2001.)

Scattering creates a band of strongly polarized light across the sky that changes position as the sun moves (Figure 3). At sunrise and sunset the polarized band arcs across the middle of the sky with respect to the observer and can reach a maximum of 90% polarization on a clear day, but is usually less than this value. At noon the band is close to the horizon. Wavelengths in the middle of the visual range (blue and green) exhibit the strongest polarization intensity. The polarization intensity of red wavelengths is reduced by dust in the atmosphere, and UV intensity is reduced by multi-path scattering of the short wavelengths. Hazy and overcast skies increase this secondary scattering and may completely obliterate the polarization cues. However, conditions of patchy clouds with at least some open areas of sky will still retain their polarization pattern with respect to the sun and enable animals that rely on sky polarization to continue their activities (Wehner 1976, 2001; Cronin and Marshall 2011).

Figure 3: Pattern of polarized light in the sky. (A) Noon. (B) Sunrise. The blue dashed lines show the orientation of the e-vectors within the strongly polarized band. Less strong bands of polarized light form concentric circles around the sun. (After Wellington 1974; Wehner 1976.)

A similar pattern persists when the sky is viewed from underwater, but the degree of polarization is lower (maximum p of about 70%) because of multi-path scattering of light by the water. Figure 4 illustrates the pattern of polarized light entering through Snell’s window (see Web Topic 4.1) for a viewer within about 15 m of the surface. The position of the band is tilted at lower sun angles as a result of refraction of light at the air–water boundary. Outside of Snell’s window and at increasing depths, there is still a considerable amount of polarized light from extensive scattering in the water, but the sky pattern is attenuated and the e-vector orientation is primarily horizontal. At depths between 100–200 m below the surface, light is still linearly polarized at about 30% at noon, and long (red) and short (blue) wavelengths are more strongly polarized than middle wavelengths (Cronin and Shashar 2001; Sabbah et al. 2006; Cronin and Marshall 2011.)

Figure 4: Pattern of polarized light underwater. (A) Noon. (B) Sunrise. The band of maximum polarization forms a ring around the observer. The e-vector within the band is perpendicular to the scattering plane and parallel to the band. Snell’s window is defined by a viewing angle of 48.7º to either side of the vertical normal, beyond which light is internally reflected. (After Hawryshyn 1992.)

Light that is reflected off of the smooth surface of a dielectric material is completely horizontally polarized at Brewster’s angle of incidence and becomes increasingly less polarized at incident angles above and below Brewster’s angle. In nature, this source of polarized light arises from the reflection of light off of a smooth water surface such as a pond or lake (Figure 5A), and from specular reflection off of silvery scales (Figure 5B, C).

Figure 5: Polarization of reflected light. (A) Light incident on a smooth dielectric surface such as water is horizontally polarized (Wehner 2001). (B) Cross-section of a fish’s body. Downwelling light reflecting off of silvery fish scales containing guanine crystals is polarized at certain viewing angles where Brewster’s angle conditions are met. (C) Underwater photo of a fish with normal lens (top) and with an imaging polarizer (bottom), where white regions indicate stronger degree of polarization. Dorsal side of fish reflects 70–95% horizontally polarized light (upper arrow), whereas side flanks reflect 30–50% polarized light (lower arrow) (Denton and Rowe 1994; Shashar et al. 2000.)

Receptor sensitivity to polarized light

Sensitivity to polarized light has evolved many times independently and is found in a wide range of animals, including many orders of insects, spiders, crustaceans, cephalopods, fish, amphibians, reptiles, and birds (see Roberts et al. 2011 for a phylogenetic tree of taxa showing polarization sensitivity). Humans can perceive the linear polarization of light but this ability is believed to be a byproduct of the ocular media or foveal region and has no biological function. Species with well-developed polarization vision possess specialized sets of receptors sensitive to different e-vectors of polarized light. The neural output from several differently-angled cells or units are combined in additive and subtractive ways in higher-level neurons that comprise a polarization-opponent system, analogous to the color-opponent system. Thus different e-vectors of polarized light may be analyzed and perceived as if they were different colors. Behavioral experiments and neurophysiological studies support the view that the degree and orientation of polarized light is perceived as a graded stimulus (Horváth and Varjú 2004).

The visual pigment rhodopsin is inherently sensitive to the plane of polarized light. The crucial double bond of the retinal chromophore must be aligned in the same direction as the plane of polarization (e-vector) of the light for the molecule to absorb a photon. Dichroism is the general term for molecules or photoreceptor cells with selective sensitivity to the angle of polarization. Rhabdomeric photoreceptors are inherently more sensitive to polarized light than ciliary receptors, but both types of photoreceptor cells possess mechanisms to make them more dichroic (Roberts et al. 2011).

In a normal rod or cone cell, the long axes of the retinal molecules are maintained in a fixed horizontal position parallel to the plane of the disc membrane and perpendicular to the direction of incoming (axial) light shining on the photoreceptor (Figure 6A). The opsin molecules are relatively free to move along the disc membrane and can rotate around at random compass angles when viewed from above. No matter what the orientation of the e-vector of polarized light is, a similar number of retinal chromophores will be oriented parallel to it, and the response of the cell will be the same. However, if one were to shine light transversely from the side of the rod or cone, the response of the cell would become dependent on the polarization angle of the light—horizontally polarized light would be absorbed by retinal molecules oriented perpendicular to the ray’s direction, but vertically polarized light would not be absorbed by any retinal molecules. The cell is now selectively sensitive to just one plane of polarized light, i.e., it is dichroic.

Rhodopsin molecules are anchored on the tubular membranes of microvilli in rhabdomeric photoreceptors. Even if the rhodopsin molecules were randomly oriented on this rolled surface, as on a ciliary cell disc (Figure 6B), the cell would respond more strongly to axial light polarized in a direction parallel to the microtubules. Rhodopsin molecules on the sides of the tubules are more likely to be properly oriented to absorb this e-vector of light, compared to rhodopsin molecules on the tops and bottoms of the tubules, which are equally likely to respond to all e-vector orientations. The theoretical dichroic ratio of the cell’s response to parallel versus perpendicular polarized light is around 2. In fact, many rhabdomeric cells have dichroic ratios much higher than this value, up to 15. The orientation of rhodopsin molecules in specialized polarization-sensitive receptors is often strongly aligned parallel to the tubules, as shown in Figure 6C. This alignment is caused by connections between rhodopsin molecules and the cytoskeleton within the tubules, and by linkages between rhodopsin molecules on adjacent tubules (Roberts et al. 2011).

Figure 6: Sensitivity to polarized light in rhabdomeric and ciliary photoreceptors. (A) Ciliary receptors do not respond differently to light of different polarization orientations when shone from above (axial rays), but they will selectively absorb horizontally polarized light shone from the side (transverse rays). (B) Rhabdomeric receptors with rhodopsin chromophore molecules oriented randomly on the plasma surface, as shown here, would respond more strongly to axial rays polarized parallel to the microtubules than to rays polarized perpendicular to the microtubules. (C) Rhabdomeric cells specialized for polarized light detection contain retinal chromophores oriented parallel to the tubules, resulting in strong absorption of parallel polarized light.

The fine structure of polarization-sensitive rhabdomeric receptors has been studied most extensively in terrestrial insects (field crickets, honey bees, desert ants, and house flies) that detect the oscillation plane of polarized skylight with a group of specialized ommatidia situated at the dorsal rim area (DRA) of the compound eye. The dorsal rim ommatidia have properties that make them especially suitable for polarization vision. All of the ommatidia in the DRA, as well as the individual rhabdomere cells within each ommatidium, are sensitive to the same wavelength of light so that true color analysis is not compromised. Various adaptations are employed to make the visual field (light acceptance angle) of each ommatidium (or group of ommatidia) very broad for viewing a large portion of the sky. Each ommatidium contains two sets of strongly polarization-sensitive photoreceptors with orthogonally-arranged orientations. Neural outputs from the two sets are compared antagonistically so that each cell reports the intensity of incident polarized light of a given angle. Within the DRA, the main microvillar directions of adjacent ommatidia are rotated from front to back in a fan-shaped pattern. It is believed that output from at least three ommatidia types sensitive to different angles are combined and compared in polarization-opponent neurons to provide the insect’s brain with information on the orientation of celestial polarization, much like the measurement procedure of a polarimeter (Labhart and Meyer 1999; Horváth and Varjú 2004). Figure 7 shows cross sections through the specialized ommatidia compared to the regular ommatidia for these major groups of polarization-sensitive insects.

Figure. 7: Optical, spectral, and structural characteristics of the specialized ommatidia in the dorsal rim area of six insect species. Top row: these optical specializations generally increase the visual field of the ommatidium. Second row: polarization-sensitive ommatidia are often associated with a single hue type. Third row: the number of rhabdomeres in each ommatidium that contribute to polarization sensitivity. Fourth row: the ratio of these that are oriented in the two orthogonal positions. Fifth row: cross-sections through specialized dorsal rim ommatidia, colors approximate the hue sensitivity of the receptor (violet represents UV). Sixth row: regular dorsal ommatidia, which are used for color and spatial analysis; they lack the strict orthogonal orientation, and have other adaptations such as long and twisted rhabdoms, misaligned microvilli, and/or randomly oriented retinal chromophores so they are not differentially sensitive to the e-vector of light. (Labhart and Meyer 1999.)

Sensitivity to polarized light has been documented in vertebrates, primarily teleost fish, but also in some amphibian and reptile species (Horváth and Varjú 2004). How can ciliary photoreceptor cells become differentially sensitive to polarized light? Four mechanisms have been proposed. The most evident mechanism is to tilt the outer segment of the cone cell receptor on its side, so that the flat surfaces of the discs are parallel to the direction of incoming light cells (Fineran and Nicol 1978; Novales Flamarique and Hawryshyn 1998; Novales Flamarique 2011). This adaptation has been well-documented in anchovy fish (Figure 8). The same strategy has been employed in the extraocular polarized light detectors of amphibians and reptiles; the intracranial pineal body and frontal organ of amphibians contains cone-like receptors with longitudinal disc orientation, and the parietal eye of reptiles contains a ring of cone-like receptors lying on their sides (Hamasaki and Eder 1977; Adler 1976). Coho salmon (Oncorhynchus kisutch) have achieved a degree of polarization sensitivity by partially tilting the discs within the cones (Roberts et al. 2004).

Figure 8: Anchovy fish cones specialized for polarization sensitivity. (A) Side-on view of bay anchovy (Anchoa mitchilli) cones. Cones are arranged in rows of alternating long cones (LC, pink) and short cones (SC, blue) with bilobed outer segments. The disc stacks in the outer segments of these cones are tilted longitudinally so they are parallel to the incoming light direction (upward from the bottom). The discs in the two cone types are oriented orthogonal to each other, as shown in the top-down view in (B). This view also shows their flat-sided shape, which facilitates orthogonal packing. The cross-hatched areas are zones in which the discs of the two cone types overlap. Several mechanisms ensure that the long cones respond selectively to perpendicular e-vectors (Eperp, perpendicular to the plane of the page and indicated by the dot-filled circle; the orthogonal e-vector Epar is parallel to the plane of the page and indicated by double-headed arrows). Unpolarized incident light undergoes dichroic absorption when it passes through the bilobed outer segments of the short cells, and is further perpendicularly polarized as it reflects off the multilayered guanine platelets (yellow) and on to the lone cone outer segments. Likewise, light passing through the dichroic-absorbing short cones is further polarized as it reflects off the multilayered tapetum and back to the long cones. (After Novales Flamarique and Hárosi 2002.)

A second mechanism for making vertebrate cone cells dichroic is to guide light transversally onto adjacent cone cells by scattering or reflection. This idea arose from the observation that the polarization sensitivity in some species is found in double cones. In fish such as trout, the cones for color vision are arranged in highly regular mosaics with a square arrangement of red and green double cones, UV cones between them, and a blue cone in the middle, as shown in Figure 9A, B. The partitioning membrane between the red and green cones has a bulge that directs light transversally to the red cone and on to the UV cone, which is the primary polarized light sensor (Novales Flamarique, Hawryshyn and Hárosi 1998). Birds also possess double cones, which are arranged in orderly mosaic patterns of four or six double cones surrounding one or two single cones. The principle cone contains clear oil droplets and there is no screening pigment between the two cones, leading to the possibility that sideways scattering from the droplets could direct polarized light transversely to the secondary cone (Young and Martin 1984; Waldvogel 1990). However, this proposal is by no means proven (Muheim 2011).

Figure 9: Reflection and scattering in double cones. (A) Side view and (B) top view of the cone mosaics of the rainbow trout (Oncorhynchus mykiss), showing UV-, blue-, green- and red-sensitive cones. Axial incident light is reflected from the tilted partitioning membrane surface in two directions (single-headed arrows). The UV receptor receives transverse rays. The quadrilateral arrangement of four double cone units in a square mosaic leads to orthogonal e-vector reception; double-headed arrows in (B) indicate the dominant plane of polarization of the reflected light (Eh = horizontal e-vector, Ev = vertical e-vector). (A, B after Novales Flamarique, Hawryshyn and Hárosi 1998.)

A third possible mechanism that could facilitate selective e-vector absorption in vertebrate, as well as invertebrate, photoreceptors is to provide each receptor with a polarizing filter. Invertebrates with a cuticular carapace can produce surface structures that selectively reflect or transmit certain e-vectors of light (dichroism or birefringence, respectively). The lenses of ommatidia may contain polarizing filters that either enhance the selectivity or the polarization sensors, or act like polariod glasses to filter out glare in insects that hunt on the water’s surface (Horváth and Varjú 2004). Birds, reptiles, and amphibians can detect the Earth’s magnetic field by optical means with specialized photoreceptors (Philips et al. 2001; Wiltschko and Wiltschko 2006). The design of these receptors could make them simultaneously sensitive to the plane of polarized light if they cause the alignment of rhodopsin molecules within discs, but the true mechanism for polizarized light detection remains unknown (Muheim 2011). Birds appear to primarily use their sensitivity to polarized light to recalibrate their magnetic detectors before they fly at night, taking advantage of the strong overhead arch of polarized light at sunset (Moore and Phillips 1988; Phillips and Moore 1992; Muheim et al. 2006).

Finally, some recent research has suggested that cone cells might be able to align adjacent rhodopsin molecules in a parallel fashion using underlying protein–protein interactions, similar to the protein–cytoskeleton interactions demonstrated in rhabdomeric photoreceptors (Nair et al. 2002; Roberts and Needham 2007; Elliott et al. 2008). These interactions allow rhodopsin to oligomerize and form rafts of parallel chromophores, which would make the photoreceptor cell potentially sensitive to axially oriented polarized light (Roberts et al. 2011).

Functions of polarized light vision

The functions of polarized light vision include celestial cues for compass orientation, detection of aquatic habitat, reduction of flare from the water surface, increased contrast and prey detection underwater, and social communication. We take these up in turn.

Bees, ants, crickets, burrowing beetles, spiders, and probably many other arthropods that routinely return to the points of departure of their foraging journeys (e.g., nests, burrows, webs) use the patterns of polarized light in the sky for navigation. The natural sky patterns provide the animal with a compass if it knows the time of day, and with the time of day if it knows the direction. In fact, receivers can infer any particular compass direction from any particular sector of the sky at any time of day (Rossel 1993; Wehner 2001). Experimental evidence has recently been obtained demonstrating that honeybees truly use polarized light information to navigate to a food source (Kraft et al. 2011). The point to emphasize is that the polarization sensors do not provide the animal with individual e-vector directions, but with the compass direction of head orientation derived from global processing of e-vector gradients in the sky (Heinze and Homberg 2007). Behavioral and neurophysiological studies on the desert ant Cataglyphis suggest how this compass might work. Output from the polarization-sensitive photoreceptors in the dorsal rim area of the eye converge onto sets of at least three large-field polarization-sensitive interneurons, called POL neurons, located within a restricted area of the second visual ganglion. Each point of the compass is characterized by a particular response ratio of three POL neurons. There is some kind of neural network translating the broadband compass responses of the POL neurons into narrowly tuned responses of particular ‘compass neurons.’ A particular compass neuron should be activated whenever the animal is heading in a particular compass direction. Figure 10 illustrates the proposed compass model based on neurophysiological data. For further details on the neurophysiological basis of polarization analysis, see the review by Homberg et al. 2011.

Figure 10: Proposed mechanism for the e-vector compass in insects. (A) The e-vector pattern in the sky with sun (yellow point) at 60º elevation; orientation and size of the blue bars represent the angle and degree of polarization. (B) Array of polarization detectors in the dorsal rim area (DRA), showing left (L) and right (R) visual fields. The position of the pink bars shows the fan-like orientation of tuned e-vector sensors; only a few of the 55–75 polarization (POL) detectors per eye are shown here. Each detector consists of a pair of orthogonally arranged photoreceptors (see Figure 7). The dashed line in the center indicates the animal’s longitudinal body axis. (C) Response ratios of three large-field POL neurons, represented here by false colors. If the animal rotates relative to the skylight pattern (see (B), black arrow), different false colors show up (see (C), white arrow). (D) Hypothetical compass neurons arranged in a circular array. Each compass neuron encodes a particular response ratio based on input from the broadly tuned POL neurons. The filled pink circle indicates the compass neuron that is maximally excited when the animal faces the solar azimuth. (From Wehner 2001. Reproduced with permission from the Journal of Experimental Biology.)

Insects such as water beetles, bugs, dragonflies, and butterflies that seek water for breeding can make use of the polarized light reflected off the water’s surface during dispersal flights to detect aquatic habitat. Because light reflected off of water is horizontally polarized, these insects are especially attracted to horizontally polarized light sources on the ground and may possess photoreceptors that are selectively sensitive to this plane of polarization. Backswimmers Notonecta glauca are a prime example, as well as dragonflies, mayflies, and waterstriders. Similarly, butterflies may take advantage of reflection off of smooth, shiny leaves to detect optimal oviposition sites. Insects that hunt on the water’s surface, such a Dolichopodid flies and waterstriders, have vertical polarizers to reduce glare (Horváth and Varjú 2004).

Another major function of polarization sensitivity is improved underwater vision and foraging. The marine environment imposes a greater challenge for visual predators and prey than the terrestrial environment because of the strong scattering and absorption of light by the water. Maximum beam attenuation length in water is approximately 15 meters, compared to 6 kilometers in air. Scattering degrades the contrast between objects and the background by interposing veiling light between the observer and the object, much like a dense fog (Lythgoe 1979). As we saw above, the ocean is also characterized by partially polarized light. The plane of polarization is mostly horizontal, but the pattern of polarized light varies greatly as a function of time of day and viewing angle. A vertically polarized visual analyzer reduces the amount of scattered light perceived and greatly increases visibility and contrast (Figure 11). This effect is analogous to the benefit we obtain by wearing Polaroid glasses to reduce street glare (horizontally polarized reflections).

Figure 11: Contrast enhancement with polarization-sensitive vision. Photos of a school of small silvery fish (Caesio suevica), taken with no polarizing filter (left), a horizontal filter (middle) and a vertical filter (right). Notice how much more visible the fish are with the vertical filter, which removes the horizontally polarized veiling and background light. (Courtesy of N. Shashar.)

Underwater visual animals can gain much more than haze reduction if they possess sensitivity to the full range of e-vectors with a polarization-opponent system. Biological tissues reflect and scatter light with different polarization characteristics. Just as with color, animals that are able to perceive polarization differences have additional contrast cues they can use to detect objects (Wehner 2001; Cronin et al. 2003; Sabbah and Shashar 2006). Two particular cryptic strategies by marine prey organisms—transparency and mirrored reflection—can be broken by predators with polarization sensitivity. Transparent animals are never completely transparent, although they can be very difficult to see from a distance. However, transparent tissues may modify the light transmitted through the body. Some tissues are birefringent, in which the refractive index varies with the plane of polarized light and light waves are split into unequally reflected or transmitted waves. Other materials, called quarter-wave retarders, are able to depolarize incident waves that are initially polarized. Thus some objects in the ocean medium may be less polarized along some vector than the background, while other objects may be more polarized. In addition, transparent animals may reflect and scatter some of the unpolarized downwelling light so that the prey animal stands out against the horizontally polarized background light (Figure 12) (Johnsen et al. 2011). The squid Loglio pealei is a polarization-sensitive visual predator that can detect zooplankton prey at 70% greater distance under partially polarized lighting than under nonpolarized lighting (Shashar et al. 1998). Moreover, under normal lighting, the squid were far more likely to attack transparent glass beads that were made polarization-active by heat stressing compared to transparent beads that were not polarization-active. Similar studies have also discovered polarization sensitivity in predatory crayfish (Tuthill and Johnsen 2006).

Figure 12: Polarization breaks transparency. Two views of a transparent prawn (Lucifer spp.). The photo on the left shows the prawn under normal lighting with no polarizing filters on the lens. The photo on the right shows the same animal viewed through cross-polarizers. Two orthogagonal filters are positioned in front of the lens so that the background illumination is minimized. The animal is bright because it is scattering unpolarized light at other angles. The transparent tissues (cuticle and muscles) are also birefringent and modify the horizontally polarized transmitted light to various oblique angles. (Photo courtesy of N. Shashar.)

The logic behind the crypticity strategy of mirrored reflection is to match the background illumination. Many pelagic fish use silvery specular reflectance from guanine crystals in their scales as camouflage to reduce detection. The schooling fish in Figure 11 are attempting to use mirrored reflection to match their brightness to the background, but because they scatter more unpolarized light relative to the background, they become more visible when viewed through a vertical polarizing filter. In addition, silvery fish produce strong horizontally polarized specular reflectance at certain viewing angles (see Figure 2B,C), and a polarization-sensitive predator can perceive this reflection along the dorsal side of the prey (Shashar et al. 2000). Schooling fish may also be able to detect each other’s complex polarization reflections and use this information along with other senses to maintain school integrity (Rowe and Denton 1997).

The final function of polarization-sensitive vision is social signaling to conspecifics. A dramatic example occurs in the cuttlefish (Sepia officinalis) which uses controlled reflection of polarized light to produce species-specific signals on the arms, eyes, and forehead (Figure 13) (Shashar et al. 1996; Boal et al. 2004; Mäthger et al. 2009a). In contrast to the nonpolarized achromatic visual signals which are used in aggressive and courtship contexts, the polarized signals appear to be general indicators of sex and species identity. Females in particular respond differentially to images of conspecifics with and without the polarized signals. The patterns are turned on during normal alert activities, but disappear when the animal lies camouflaged in the sand and when it is is engaged in aggressive interactions, attacking prey, copulation, and egg-laying. The signals arise from reflecting iridophores in a chromatophore organ. Iridophores contain flat guanine platelets that produce partially polarized reflections. Groups of iridophores are oriented at different angles so the patterns are visible under a range of horizontal viewing angles (Chiou et al. 2007). As in other cephalopods, these iridophores are dynamic cells, capable of undergoing ultrastructural changes on neural command (Cooper et al. 1990; Shashar et al. 2001; Cronin et al. 2003; Mäthger et al. 2009b). Such changes shift the iridophores between organized and disorganized alignments, which enables the animals to change their polarization reflectances on time scales of a second or less. Cuttlefish eyes, like those of many other cephalopods, have a horizontal band of specialized orthogonal polarization-sensitive photoreceptors (Talbot and Marshall 2011). As mentioned previously, cuttlefish also use their polarization sensitivity while foraging to break the prey camouflage strategies of transparency and specular reflectance.

Figure 13: Frontal display of a cuttlefish, Sepia officinalis. The left panel shows an alert animal under normal white light illumination with no camera filter. The right panel shows the same photo with a horizontal polarizing light filter, which reveals a striking polarized light pattern on the forehead, eyes, and arms. (From Cronin et al. 2003. Reproduced with permission from the Journal of Experimental Biology.)

As we saw in the main text, mantis shrimp possess eyes with phenomenal color discrimination abilities. One possible reason for their system of many narrow but overlapping photoreceptor absorbance curves is their need for color constancy given the large changes in light quality at different water depths (Cheroske et al. 2009). They seem to have one more backup system for visual communication under conditions of variable light quality: the use of polarization signals. Polarization is much more predictable and stable with increasing depth then spectral quality. A large fraction of their compound eyes—the dorsal and ventral hemispheres—are devoted to spatial resolution and achromatic brightness contrast, and these ommatidia are polarization-sensitive. The color receptors are located in rows 1–4 of the midband, while midband rows 5–6 all have the same photopigment and are specialized for polarization detection. Their location adjacent to the wavelength detectors, paired arrangement with receptors having perpendicular e-vectors, and neural wiring similar to the color detectors suggests that they are specialized for a polarization-opponent system. Many stomatopod species have body parts that are obviously specialized for the reflection of strongly polarized light and are used in behavioral contexts that seem clearly linked to intraspecific communication (Figure 14). Species-specific patterns based on differential reflection of partially linearly polarized light could be unusually direct and easy to interpret since no other objects in the scene are likely to have a similar appearance. They would also be private and invisible to animals lacking polarization sensitivity. The polarization of specific body parts must be produced structurally in the carapace, as they do not change over time and are even present in the molt casts (Marshall et al. 1999; Kleinlogel et al. 2003; Cronin and Marshall 2004).

Figure 14: Potential polarized-light signal in the mantis shrimp Haptosquilla trispinosa. Successive frames captured on digital video through a polarization-switching, liquid crystal filter that rotates the plane of a polarization analyzer 90º between frames; the e-vector plane transmitted by the filter in each frame is shown by the white line (H, horizontal; V, vertical). The shrimp is sitting at its burrow entrance displaying its antennae, claws, and maxillipeds. Only the maxillipeds differentially reflect horizontally polarized light (arrows). These structures are powder blue under white light illumination (Cronin et al. 2003).

Polarized-light signaling has been found in terrestrial forest environments, where the intensity and spectral composition of light varies greatly (see Figure 5.4 in the main text). Although natural polarization is limited because of strong filtering by the canopy, contrasting polarized light reflectance can provide a more consistent pattern for species recognition than colored signals. The butterfly Heliconius cydno reflects iridescent colors from the wings. The reflected light is both chromatically saturated and 90% polarized (Sweeney et al. 2003). Males of this species appear to recognize females based on this polarization. When the reflected light from females is artificially depolarized, males approach them much less frequently. A survey of the presence of polarized light reflectance from 144 species of nymphalid butterflies found 75 species with polarized patterns. These species were significantly more likely to inhabit forest habitats than open habitats (Douglas et al. 2007).

Finally, a few animal taxa have been found to reflect circularly polarized light, in which the electric vector rotates either clockwise (right-handed) or counterclockwise (left-handed) while the wave travels. Although very rare in the natural environment (starlight becomes partially circularly polarized, as well as underwater backscattered light outside of Snell’s window), it is not difficult to produce with manmade materials, and circularly polarized reflection (CPR) is widespread among scarab beetles. In a survey of 16,650 species from 1320 genera, 89% showed some level of CPR ranging from very low degrees of polarization up to a maximum of 97% (Pye 2010). Most cases had left-handed rotation. These beetles are generally green in color, but they can change in appearance from brilliant, metallic green to black when viewed with left- and right-handed polarizing filters (Hegedüs et al. 2006; Goldstein 2006; Pye 2010). The fine structure of the exoskeleton consists of tightly packed hexagon cells with a cone structure (Jewell et al. 2007; Sharma et al. 2009). Under the light microscope, the cones appear yellow in the center with a green surround. Concentric nested arcs encircle the cones to form a helical structure (Figure 15). Behavioral tests demonstrate that the beetles respond selectively to objects of different degrees and rotations of CPR (Brady and Cummings 2010).

Visual reception mechanisms for CPR have not been studied in the beetles yet, but in another group that also shows CPR, the mantis shrimps (stomatopods), a potential visual mechanism has been described (Chiou et al. 2008). In a linearly polarized light wave, the x and y vibrational e-vector components are in phase; when these vectors are out of phase, an elliptical wave results, and when the vectors are 90º out of phase, a circular wave results. If a circularly polarized light wave travels through a birefringent material with a thickness and refractive index that slows the wave in one e-vector orientation by 1/4 of a wavelength (called a quarter-wave retarder), the two vectors are brought back into phase, and the wave becomes linearly polarized. A few species of stomatopods (Odontodactylus) with the usual midband linear-polarized light detectors have placed such quarter-wave retarders in a layer over these photoreceptor cells to filter the incoming light. These species have been shown to distinguish behaviorally between left-CPR and right-CPR objects. Moreover, three species were found to have sex-specific circular reflectance patterns on body parts used for behavioral displays in males (Chiou et al. 2008). Circularly polarized light signals thus create a private communication channel in both scarab beetles and stomatopods.

Figure 15: Reflection of circularly polarized light from the scarab beetle Crysina gloriosa. (A, B) Photo of a beetle under unpolarized light illumination using a left-hand and right-hand circular polarizing filter. (C) Light microscope photo of the same beetle’s exoskeleton showing the packed, conical cells, approximately 10 mm in diameter, with yellow centers and green surrounds. (D) An x-y section of a confocal microscope image showing concentric rings that form the helical reflective surface (Sharma et al. 2009).

Further reading

Horváth, G. and D. Varjú. 2004. Polarized Light in Animal Vision. Berlin: Springer-Verlag.

The entire July 2001 issue of the Journal of Experimental Biology contains excellent review articles from the “Second Workshop on Ultraviolet and Polarization Vision.”

The March 2011 issue of Philosophical Transactions of the Royal Society of London – B contains a series of articles on “New directions in the detection of polarized light” stimulated by a small international meeting in 2008 on Heron Island, Australia.

Useful websites

Literature cited

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5.3 Evolution of Primate Color Vision


A set of at least two photoreceptor types with pigments having different but overlapping absorption curves is essential for color vision, and larger sets with three or four photoreceptor types are even better. We now have a good understanding of how small changes in the amino acid sequence of the opsin protein component of different rhodopsin visual pigments can shift the wavelength of peak absorption to produce these photoreceptor types. Scientists have also identified the DNA regions that house the genetic code for the different opsins. Comparative studies of the opsin genes in a range of species provide us with invaluable information about the evolution of photopigments and color vision across the ancient and more recent animal taxa. In this Web Topic, we will briefly review the evolution of photopigments in the early vertebrates, outline the subsequent evolutionary history of mammals, and then examine the interesting examples of primate trichromacy and the source of our own color vision ability.

Early vertebrates

Vertebrates arose shortly after the Cambrian explosion, approximately 505 million years ago (mya). The first ancient ancestral vertebrates were the lampreys, an order within the paraphyletic jawless cartilaginous fishes (Agnatha). Instead of jaws, lampreys possess a suction mouth with rasping teeth and feed by attaching themselves to larger prey. They do swim around, live in shallow, clear water, and they have excellent eyes. Recent sequencing of their opsin genes indicates that they have four cone pigment types and good color vision (Collin et al. 2003). It is believed that these pigment genes all evolved from a single ancestral opsin gene which was duplicated and then subjected to different mutations and selection that favored a broad range of overlapping pigments. Rod opsins and vision adapted to low light conditions did not evolve until the jawed fish (Gnathostomata) arose around 485 mya. This rod gene, along with the four cone genes, persisted in subsequent vertebrate groups, including teleost fish, reptiles, amphibians, and birds. Table 1 below lists these basic vertebrate opsin classes.

Table 1: Basic vertebrate photoreceptor pigment classes and the range of peak wavelengths for each class.

Pigment name
Wavelength range
Color range
LWS Cone 495–570 Green–red
SWS1 Cone 355–450 UV–violet
SWS2 Cone 415–480 Violet–blue
RH1 Rod 460–530 Green
RH2 Cone 470–530 Green

Source: After Bowmaker 2008.

Species can modify these opsin genes via several mechanisms. First, mutations within an opsin gene can lead to a spectral shift in the sensitivity of the pigment. A single nucleotide substitution may lead to the replacement of an amino acid that alters the interaction between the chromophore and opsin, leading to a spectral shift. There are a relatively small number of amino acid substitution sites that lead to meaningful spectral shifts, and the effects of different substitutions are usually additive (Yokoyama and Radlwimmer 2001; Yokoyama 2002). Selection will favor substitutions that fine-tune a species’ color vision system to its environment and specific visual tasks. Second, a species may modify the ancestral vertebrate pattern of four spectrally distinct cone classes by the loss of one or more of the cone classes, followed by shifts in the wavelength peak. Third, new pigments in the same class can be generated by gene duplication. Mutations in the duplicated genes can then lead to the divergence in their absorption peaks, creating two or more spectrally distinct pigments within a single opsin class (Bowmaker and Hunt 2006). This is the classic “duplicate and diverge” strategy. Finally, the sequential order of these last two steps can be reversed in a “diverge and duplicate” strategy, where several alternative alleles with different absorption peaks arise at a gene locus, followed by a duplication event that fixes one allele at a new locus (Surridge et al. 2003).

Mammals first arose in the early Triassic period about 250 mya from a Therapsid reptile ancestor. Most early mammals were small and nocturnal, being dominated ecologically by the larger dinosaurs. This situation persisted until the end of the Cenozoic era 65 mya, when the dinosaurs went extinct. Although birds and modern reptiles clearly retained tetrachromatic color vision from their fish and reptilian ancestors, the early nocturnal mammals lost two of their cone pigments, specifically RH2 and SWS2. They retained the two spectrally extreme classes, but shifted their absorbance peaks inward toward the middle of the visible wavelength range. For several mammalian species whose genome has been sufficiently well-sequenced, the retained short-wave SWS1 gene is located on an autosomal chromosome while the long-wave LWS gene is located on the X chromosome (Ahnelt and Kolb 2000). This chromosomal pattern could be widespread and ancient in mammals, and, as we shall see below, has led to some bizarre sexual differences in color vision abilities in a few species. A rod-rich retina with dichromatic vision was probably the norm for the ancient nocturnal species, although marine mammals and a few terrestrial mammals lost another cone pigment (SWS1) and became monochromats (Ahnelt and Kolb 2000). Interestingly, the echidna and platypus (monotremes) share the LWS and SWS2 genes with reptiles, birds, and fishes, suggesting that the mammalian loss of SWS2 and RH2 occurred in the common ancestor of marsupial and placental mammals (Wakefield et al. 2008). When mammals finally radiated in the Pleistocene, becoming larger, diurnal, and carnivorous, most did not improve their color vision capabilities by evolving new long-wave pigments. Trichromatic color vision has evolved only a few times in select groups, but these independent evolutionary events enable us to examine the potential factors favoring better color vision (Bowmaker 1998; Yokoyama 2000; Bowmaker and Hunt 2006).


Recent studies of the spectral characteristics of photoreceptors in four Australian marsupial species—the fat-tailed dunnart (Sminthopsis crassicaudata), the bandicoot (Isoodon obesulus), the honey possum (Tarsipes rostratus), and the quokka (Setonix brachyurus)—suggest that trichromacy may be present in these species (Arrese et al. 2002; Arrese et al. 2006). The first two species belong to the polyprotodont marsupial taxonomic division; the second two belong to the diprotodont division. Microspectrophotometry has indicated that these species possess three classes of cone photoreceptors maximally sensitive in the UV, green, and red ranges. Molecular analysis has shown that the UV pigment belongs to the ancestral SWS1 gene class and that the red pigment belongs to the LWS class; the middle wavelength pigment may be derived from the rod RH1 pigment (Cowing et al. 2008). The intriguing possibility is, therefore, that in some marsupials—in marked contrast to placental mammals—the RH2 opsin gene has been retained and is expressed (Arrese et al. 2002). Middle wavelength cone types seem to be present in some species but absent in others (e.g., wallabies Macropus eugenii, which are clearly dichromats in behavioral tests; Deeb et al. 2003).

A color vision model was developed for the honey possum to test three alternative hypotheses for the selective advantage of its trichromatic receptor tuning. This mouse-like mammal is a crepuscular nectarivore that feeds primarily on yellow and red flowers. The absorption peak for the longer wavelength cone type is 557 nm, more red-shifted than the LWS cones of other marsupials. The results of the model suggested that the visual task selecting for improved color vision was not detecting red and yellow against a green leaf background, for which an even further red-shifted peak would be optimal. Instead, the tuning may be designed for discriminating the stages of maturity (green to yellow) of the animal’s major nectar food source, the flowers of Banksia attenuata (Sumner et al. 2005).


The Old World Catarrhine primates, including macaques, baboons, guenons, great apes, and humans, possess three cone photopigment types (Figure 1) and true trichromatic vision, often called routine trichromatic vision to distinguish it from the New World primate system described below. The short wavelength pigment belongs to the SWS1 class and the two longer wavelength pigments both belong to the LWS class. The most likely scenario for the evolution of trichromacy in this taxon is a duplication of the ancestral LWS gene followed by divergence of the spectral peaks (Surridge et al. 2003; Jacobs 2007, 2008). This duplication event must have occurred at the base of the catarrhine lineage around 30–40 million years ago. The green photopigment gene appears to be the duplicated one, inserted immediately downstream of the red gene and its locus control region (LCR) on the X chromosome. The red gene also shows greater homology with the ancestral mammalian LWS gene (Dulai et al. 1999).

Figure 1: Photopigment absorbance curves for Catarrhine primates, including humans. The dashed line shows the rod opsin absorbance curve (R), and peak wavelengths for the blue (S, or short), green (M, or medium), and red (L, or long) cone types are indicated at the top of each curve.

Color vision is much more variable in New World platyrrhine primates, a monophyletic group that includes owl monkeys, capuchins, marmosets, squirrel and spider monkeys, howlers, and others. Most of these species exhibit allelic trichromacy, which is based on only two opsin gene loci, an autosomal SWS1 gene as in Old World primates, and a polymorphic LWS gene located on the X chromosome. The LWS locus has several allelic forms that encode pigments with different wavelength peaks between about 535 and 565 nm. The occurrence of this polymorphic X-linked photopigment gene leads to individual and sex-based differences in color vision capabilities. Males possess only one allelic form of the LWS gene located on their single X chromosome. Together with the autosomal SWS gene, males have two cone pigment types and are always dichromats. Females have two X chromosomes. Heterozygous females that inherit different LWS alleles from each parent possess two long-wave cone pigments in addition to the short-wave pigment and are trichromats; homozygotic females at this locus are dichromats like the males. Even more variation is introduced by the presence of three LWS alleles in most species, so there are three different types of heterozygous female trichromats plus three different dichromat phenotypes (Mollon 1989; Boissinot et al. 1998; Talebi et al. 2006; Jacobs 2007, 2008). Some members of the lemur family (strepsirrhine primates) also exhibit allelic trichromancy (Tan and Li 1999).

There are two interesting exceptions to this New World primate color vision pattern. The howler monkeys, Alouatta, have evolved routine trichromacy with virtually the same photopigment absorption curves as the Catarrhine primates. Even the critical amino acid substitutions are the same. The howler monkey opsin genes are otherwise very similar to other platyrrhine opsin alleles, indicating that routine trichromacy in howlers is a recent and independent evolutionary event. It is believed that they diverged from a platyrrhine ancestor with the allelic trichromacy system by duplicating one of the X chromosome alleles and then fixing it at an adjacent new locus—the diverge and duplicate strategy (Surridge et al. 2003). As in the catarrhines, male and female howlers possess the same visual capabilities (Kainz et al. 1998; Dulai et al. 1999). The second exception is found in the nocturnal owl monkeys (Aotidae). Their rod-rich retinas are specialized for nocturnal vision and their SWS cone pigment gene has mutated to the point of losing functionality. These New World monkeys are monochromats with a single X-linked LWS gene (Jacobs et al. 1996; Silveira et al. 2001; Jacobs 2007, 2008).

Evolution of two color-opponent systems

The evolution of a third cone pigment is only one of the critical steps that must occur to achieve true trichromatic color vision from a dichromat ancestor. In addition, a second color-opponent system must be established. Two additional steps or conditions must be met: (1) the new visual pigment must be expressed in a distinct class of photoreceptors, and (2) patterns of neural wiring must develop that can extract chromatic information by comparing the degree of excitation of the new and preexisting classes of photoreceptors. How did this happen in trichromatic primates, especially in the context of the X-linked location of two of the opsin genes?

All trichromatic primates appear to possess two color-opponent systems. The ancestral dichromatic mammals had a single color-opponent system based on two cone pigments, a short-wave pigment (S) absorbing maximally in the blue region and a long-wave pigment (L) absorbing in the yellow-green region. These opsin genes were independent, located on separate chromosomes with their own locus control regions (LCR). Prior selection for a functional photoreceptor mosaic would have favored genetic control over opsin gene expression in specific cone types arranged in an adaptive configuration in the retina (Ahnelt and Kolb 2000). Antagonistic (inhibitory) interactions between the neural outputs of these two cone types led to the ancestral color-opponent system called the blue–yellow chromatic channel. With the duplication and divergence of the long-wave pigment gene into middle- (M) and long-wave pigments, the second color-opponent system had to involve antagonistic interactions between cones separately containing these two pigments. Even though the absorption peaks for these pigments are not very far apart (green and yellow-green), this opponent system permits greater sensitivity in the green to red region of the light spectrum, and is called the red–green chromatic channel. Figure 2 shows the classic evidence for two color-opponent systems in the macaque, a catarrhine primate. Similar neural evidence for two color-opponent systems has also been described for individual platyrrhine monkeys with allelic trichromacy (i.e., heterozygotic females) (Yeh et al. 1995).

Figure 2: Color-opponent systems in a primate trichromat. Wavelength-specific neural responses of cell types in the lateral geniculate nucleus of the macaque monkey. The top two plots show the red–green (R–G) system with antagonistic interactions between M and L cones, the middle two plots show the blue–yellow (B–Y) system with antagonistic interactions between S and M cones, and the bottom plots show the achromatic white-black system with additive input from the M and L cones (the S cones are not involved in brightness discrimination). (After Jacobs 1981.)

How did the M and L cone types develop the ability to express just one opsin pigment gene per cell? The mechanism appears to be different for each of the three independent origins of primate trichromacy. In the case of allelic trichromacy, the process of random X-chromosome inactivation was already established to prevent overdosing of X-linked enzymes in females. In each cell of a female mammal, one of the X chromosomes is de-activated on a random basis. A given photoreceptor cell therefore expresses only one of the two opsin alleles in a heterozygous female. In the case of the catarrhine primates, the gene duplication event involved only the duplication and head-to-tail insertion of the opsin coding region of the green (M) gene, and it shares the locus control region with the red (L) gene. The two alleles apparently compete for access to the joint control region, so that only one allele is expressed in a given cell on a random basis (Smallwood et al. 2002). Finally, in the howler monkey case, the duplication and insertion event involved the entire opsin gene plus control region, so the M and L genes each possess their own LCR. Selection must have then operated to regulate the expression of one or the other gene in each cone cell (Smallwood et al. 2002; Bowmaker and Hunt 2006).

How did the neural connectivity become established for a second color-opponent system? In particular, how can the wiring mechanism work in a species in which some individuals only have two pigment genes while others have three? The answer lies in the pre-adapted primate fovea. Nonprimate mammals sum the outputs of several cone receptors onto ganglion cells before transmission to the brain. This pooling not only entails loss of spatial resolution, but it also means that it would not be possible to derive an additional chromatic signal from a third (new) cone type without parallel modification of neural connections. However, early primates evolved a specialized fovea for fine-scale resolution of spatial details. The fovea has an additional class of retinal ganglion cells, the midget ganglions that receive primary input from a single cone cell. It is these midget ganglion cells that encode the red–green signal in trichromatic species. Thus prior selection for finer achromatic resolution may have established the neural pathways that pre-adapted the primates for separate use of M and L cone opsins and trichromatic vision (Ahnelt and Kolb 2000; Surridge et al. 2003).

Advantages of trichromacy

Trichromatic color vision was initially thought to have evolved in diurnal primates for finding food, especially for detecting red and yellow fruit against a green background (Allen 1879; Mollon 1989; Osorio and Vorobyev 1996). However, primate diets vary widely, and the exact visual perception tasks on which selection is acting are controversial. In addition to frugivory, leaves are important foods for Old World anthropoids and howler monkeys and it might be that the need to locate edible leaves at particular stages of maturation, rather than fruit, has been crucial in giving a selective advantage to trichromats (Surridge et al. 2003). Recent efforts to understand the specific advantages and consequences of trichromatic color vision have taken three approaches: color models of the hue contrast for different types of food from the perspective of dichromat versus trichromat receivers; experimental tests comparing color discrimination abilities of dichromatic and trichromatic individuals; and field observations of the food selection, foraging success, and fitness of receivers with different color vision capabilities.

Most modeling studies find that trichromats are better able to distinguish typical food objects compared to dichromats, and in allelic trichromats the phenotypes with the greatest spread in the L/M (red–green) opsin types fare best, although the differences are often small (Osorio and Vorobyev 1996; Sumner and Mollon 2000a, b; Dominy and Lucas 2001; Regan et al. 2001; Lucas et al. 2003; Osorio et al. 2004; Riba-Hernandez et al. 2004, 2005; Stoner et al. 2005; De Aráujo et al. 2006; Leonhardt et al. 2009). For example, Osorio et al. (2004) used squirrel monkeys and tamarins as model platyrrhine allelic trichromats and compared the ability of the polymorphic visual system types to distinguish a large variety of foods. About 100 known food plants were collected and measured spectrally, and hue contrasts with the background were computed for the visual phenotypes. With three pigment gene alleles, there are six different visual phenotypes, and the researchers were particularly interested in evaluating the color discrimination ability of the anomalous trichromats, with their M and L pigment peaks very close together compared to the normal trichromatic phenotype with more widely separated M and L peaks. They also examined the effects of bright versus dim ambient light on the discrimination abilities of the different visual phenotypes. Figure 3 shows some of their results for squirrel monkeys (results were very similar for the tamarins). Normal trichromats, with the broadest spread of pigment peaks, were better at distinguishing target fruit under all conditions, but especially so under dim light. Anomalous trichromats were surprisingly good under bright light but less so under dim light, and were clearly better than dichromats. Dichromats performed better if they had longer-wavelength pigments, but under no conditions did they outperform the trichromats.

Figure 3: Color model of food detection by squirrel monkey visual phenotypes. (A) Measured spectral curves for the background (B) and target fruit (T). Photoreceptor quantum catches by receivers are estimated from color models combining responses for each receptor type, plus noise, for the target and background. (B) The target and background stimuli are located in a Cartesian space whose axes are given by the responses of S, M, and L cones. For T and B spectra, the estimated cone excitations locate the centers of ellipsoids whose dimensions are given by the standard deviation of noise in each cone mechanism. (C) The ellipsoids are projected onto a two-dimensional chromatic surface to estimate discriminability, the difference, d, between targets and various backgrounds (B1–B3). Only differences in hue and saturation (but not brightness) are incorporated in the model. (D) Pigment wavelength peaks for the six phenotypes. (E) The percentages of fruit items that are distinguishable (greater than threshold) against the background for the main phenotypes under bright illumination and dim illumination. (After Osorio et al. 2004).

Several studies have compared the foods taken by species with different color vision systems. Lucas et al. (2003) collected the fruit and leaves eaten by five catarrhine species and three platyrrhine species, measured their color and nutritive value, and modeled the hue contrast for these items relative to the background. Routine trichromats ingested leaves that were red-shifted compared to background foliage more frequently than allelic trichromats. They did not find any differences in fruit color between the two groups, and argued that the consumption of young leaves at the optimal nutritional state seemed to be the primary visual task selecting for routine trichromacy. Leonhardt et al. (2009) compared fruit colors and foraging performance for four lemur species and found a moderate shift toward redder fruit in ruffed lemurs which have allelic trichromancy, but also found that dichromatic collared lemurs were very efficient at retrieving red and green food items under camouflage conditions. Stoner et al. (2005) compared the color of fruits taken by sympatric howler and spider monkeys, and found that spider monkeys actually took more red fruit, while howlers took more green fruit (Figure 4). They concluded that dichromats were not at any particular disadvantage when searching for colored fruit, and that howler monkeys may have evolved routine trichromacy to detect young leaves better.

Figure 4: Primate fruit color selection. Chromaticities of dietary fruit items taken by spider monkeys Ateles geoffroyi (n = 25 fruit species) and howler monkeys Allouata palliata (n = 12 fruit species). The y-axis represents computed chromaticity on the yellow–blue channel (higher values are more blue) and the x-axis represents the red–green channel (higher values are more red), modeled from the color-sensitive perspective of each species separately. The sampled fruits comprise 70% of the total fruit diet for both monkey species. The fruit species taken by both monkey species are included and indicated by purple circles. Circle size represents the percentage of each fruit species in the diet of each primate (the bigger the circle, the greater the percentage it represents). The dashed rectangle represents the chromaticities of a mature-leaf background. (After Stoner et al. 2005.)

Studies on captive primates (and humans) given various types of discrimination tasks find that while trichromats generally perform better with colored objects against more or less contrasting backgrounds, trichromats can actually be confused when presented with cryptic stimuli or with colored objects on a colorful background (Morgan et al. 1992; Caine and Mundy 2000; Caine et al. 2003; Smith et al. 2003a; Saito et al. 2005a, b; Rowe and Jacobs 2007; Prado et al. 2008; Caine et al. 2010). Contrary to Orsorio et al. (2004), dichromats were found to perform better under low light conditions. These results suggest that dichromats rely more on achromatic contrasts.

With the ability to genotype individual monkeys in the field using blood or feces, it is now possible to determine whether there are any fitness or foraging strategy differences based on visual phenotype in allelic trichromatic species. Field studies can address the question of how the polymorphism of dichromats and trichromats remains stable in these species. Several hypotheses have been proposed. One hypothesis is heterozygote advantage, whereby heterozygous trichromatic females maintain the polymorphism via some fitness advantage. In this case, some type of balancing selection would operate to keep the number of L/M cone opsin alleles stable at three (Riba-Hernandez et al. 2004). Assuming Hardy-Weinberg equilibrium, the percentage of heterozygous females is 50, 67, and 75 with two, three, and four alleles, respectively. But the number of useful alleles may be limited by the available “color space” in the 535 to 565 nm range. Three alleles may represent a compromise that maximizes the distance between spectral tuning peaks (creating a useful color-opponent system) while maximizing the number of alleles to yield the greatest possible frequency of heterozygotes (Cropp et al. 2002). Real populations show a reasonable fit to these expectations, with most species having three alleles but a few having two and one unusual species (titi monkeys, Callicebus molloch) having 5 (Surridge and Mundy 2002; Surridge et al. 2005a; Jacobs and Deegan 2005; Hiramatsu et al. 2005; Jacobs 2007; Hiwatashi et al. 2010). In tri-allelic species, the three alleles are often not equal in frequency in the population, implying some selection on different types of heterozygotes or possibly inbreeding. For example, in tamarins (Saguinus labiatus), the middle-wavelength allele is rare, and field studies suggest that females with the two more extreme-wavelength alleles have greater longevity and prefer mates that would give their offspring the widest divergence in opsin phenotypes (Surridge et al. 2005b).

A second hypothesis is frequency dependence, whereby dichromats have an advantage over trichromats under some conditions. Human dichromats can detect patterns based on lightness that are indistinguishable to trichromats (Morgan et al. 1992), and, as mentioned above, experimental studies show that primate dichromats are able to select the correct visual stimulus under camouflaged conditions better than trichromats. Recent field studies designed to assess whether dichromats and trichromats differ in foraging strategies have yielded mixed results. A series of studies on white-faced capuchins (Cebus capucinus) have found a number of differences: trichromats spend more time foraging visually, select more red fig and high-quality fruits, and preferentially feed on colonial insects that require extraction (ants, wasps); dichromats rely more on taste and smell, spend more overall time foraging (possibly because they are not able to select the highest quality fruit), and are more successful in finding exposed but cryptic insects (Melin et al. 2007, 2009, 2010; Vogel et al. 2007). A study of black-handed spider monkeys (Ateles geoffroyi) found no differences in foraging efficiency and suggested that brightness contrast was the most important fruit identification cue, not hue (Hiramatsu et al. 2008). Finally, it has been suggested that the social nature of group foraging in these primates places no disadvantage on dichromats if their trichromatic group mates can assist them in finding food. However, one field study of tamarins found no tendency for trichromatic females to lead the progression of foraging groups (Smith et al. 2003b). The two hypotheses for opsin gene polymorphism maintenance in platyrrhine monkeys—heterozygote advantage and frequency dependence—are not mutually exclusive, so both processes may be operating simultaneously.

The unequal spacing of the spectral tuning peaks in all trichromatic mammals, compared to the more even distribution in other trichromatic and tetrachromatic taxa, may be an adaptive solution for making certain types of hue discriminations. The primate spectral tuning appears to be optimized for detecting red and orange fruit and leaves against a background of mature leaves, but is not well optimized for discriminating degrees of fruit ripeness, for which a greater spread of pigment peaks would be better (Sumner and Mollon 2000b, c; Osorio and Vorobyev 2005). This differs somewhat from the case of the marsupial honey possum mentioned earlier, where the pigment spacing it best tuned for disciminating degrees of flower maturity rather than contrast of flowers against the vegetation (Sumner et al. 2000). One possible cost of this uneven distribution, however, may be less effective color constancy, the ability to adapt to strongly colored ambient light. Models have shown that this ability is better when there are a greater number of narrowly tuned curves that are evenly spaced (Osorio et al. 1997). Certainly any human can attest to the frustrating difficulty of matching colors under different qualities of ambient light!

Further reading

Ahnelt, P. K. and H. Kolb. 2000. The mammalian photoreceptor mosaic-adaptive design. Progress in Retinal and Eye Research 19: 711–777.

Bowmaker, J. K. and D. M. Hunt. 2006. Evolution of vertebrate visual pigments. Current Biology 16: R484–R489.

Bowmaker, J. K. 2008. Evolution of vertebrate visual pigments. Vision Research 48: 2022–2041.

Collin, S. P., M. A. Knight, W. L. Davies, I. C. Potter, D. M. Hunt, and A. E. O. Trezise. 2003. Ancient colour vision: multiple opsin genes in the ancestral vertebrates. Current Biology 13: R864–R865.

Hunt, D. M., L. S. Carvalho, J. A. Cowing, and W. L. Davies. 2009. Evolution and spectral tuning of visual pigments in birds and mammals. Philosophical Transactions of the Royal Society B-Biological Sciences 364: 2941–2955.

Jacobs, G. H. 2007. New world monkeys and color. International Journal of Primatology 28: 729–759.

Jacobs, G. H. 2008. Primate color vision: A comparative perspective. Visual Neuroscience 25: 619–633.

Jacobs, G. H. 2010. Recent progress in understanding mammalian color vision. Ophthalmic and Physiological Optics 30: 422–434.

 Surridge, A. K., D. Osorio, and N. I. Mundy. 2003. Evolution and selection of trichromatic vision in primates. Trends in Ecology and Evolution 18: 198–205.

Yokoyama, S. 2002. Molecular evolution of color vision in vertebrates. Gene 300: 69–78.

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