Journal of Comparative Physiology A

, Volume 199, Issue 3, pp 211–225

Quantification of cuttlefish (Sepiaofficinalis) camouflage: a study of color and luminance using in situ spectrometry

  • Derya Akkaynak
  • Justine J. Allen
  • Lydia M. Mäthger
  • Chuan-Chin Chiao
  • Roger T. Hanlon
Original Paper

Abstract

Cephalopods are renowned for their ability to adaptively camouflage on diverse backgrounds. Sepia officinalis camouflage body patterns have been characterized spectrally in the laboratory but not in the field due to the challenges of dynamic natural light fields and the difficulty of using spectrophotometric instruments underwater. To assess cuttlefish color match in their natural habitats, we studied the spectral properties of S. officinalis and their backgrounds on the Aegean coast of Turkey using point-by-point in situ spectrometry. Fifteen spectrometry datasets were collected from seven cuttlefish; radiance spectra from animal body components and surrounding substrates were measured at depths shallower than 5 m. We quantified luminance and color contrast of cuttlefish components and background substrates in the eyes of hypothetical di- and trichromatic fish predators. Additionally, we converted radiance spectra to sRGB color space to simulate their in situ appearance to a human observer. Within the range of natural colors at our study site, cuttlefish closely matched the substrate spectra in a variety of body patterns. Theoretical calculations showed that this effect might be more pronounced at greater depths. We also showed that a non-biological method (“Spectral Angle Mapper”), commonly used for spectral shape similarity assessment in the field of remote sensing, shows moderate correlation to biological measures of color contrast. This performance is comparable to that of a traditional measure of spectral shape similarity, hue and chroma. This study is among the first to quantify color matching of camouflaged cuttlefish in the wild.

Keywords

Animal coloration Spectral angle Color match Body pattern Fish predator 

Abbreviations

B

Brightness

C

Chroma

D

Euclidean distance between hue, chroma and brightness of two spectra

H

Hue

JND

Just noticeable difference

SAM

Spectral Angle Mapper

\( \Updelta L \)

Luminance contrast

\( \Updelta S \)

Color contrast

Supplementary material

359_2012_785_MOESM1_ESM.tif (13.7 mb)
Online Resource 1: Traditionally, hue and chroma values have been used as rough estimates of spectral shape. In this example we demonstrate how SAM scores compare to hue and chroma by computing color differences between every pair of color patches in the Macbeth ColorChecker, shown in (a). (b) The reflectance spectra of each color patch. (c) Normalized intensity of the CIE D65 illuminant. D65 is commonly used as an approximation to noon daylight. Here it is used to compute the radiance spectra of each color patch through multiplication with their reflectance spectra. (d) The similarity scores between color patches computed by SAM. Chroma and hue differences between each color patch, (e) and (f), respectively. Chroma and hue values are calculated according to (Endler 1990) and the similarity scores were found by \( {{\Updelta}}C = \sqrt {(C_{1} - C_{2} )^{2} } \) and \( {{\Updelta}}H = \sqrt {(H_{1} - H_{2} )^{2} } \). (g) Chroma and hue are both attributes of spectral shape, and therefore it is more meaningful to compare their combination with SAM, rather than individually. Euclidean distance between the chroma and hue of two colors is found as follows: \( D1 = \sqrt {{{\Updelta}}C^{2} + {{\Updelta}}H^{2} } \). Note that C and H are independent of brightness. D1 similarity matrix is dominated by chroma, whose values are an order of magnitude larger than the values of hue. Chroma and hue values and, therefore, D1, are derived based on color opponency mechanisms. SAM on the other hand, calculates spectral shape similarity (alignment of two vectors) without any assumptions about an observer or a certain visual system. (h) Another objective way to calculate how similar two spectra are, is simply by computing the Euclidean distance between them. This is likely to result in errors if the spectra have large brightness differences, therefore each spectra should be multiplied by a constant so that they have the same overall brightness. Following this the similarity, or distance, can be found as follows: \( D2 = \sqrt {\mathop \sum \nolimits [R_{1} \left( \lambda \right) - R_{2} (\lambda )]^{2} } \) where \( R(\lambda ) \) represents radiance spectra (see (Endler 1990) for details). The correlation values between each method is as follows: \( \rho_{SAM,chroma} = 0.3379, \rho_{SAM,hue} = 0.5609, \)\( \rho_{SAM,D1} = 0.3461 \) and \( \rho_{SAM,D2} = 0.7597. \)
359_2012_785_MOESM2_ESM.tiff (5.9 mb)
Online Resource 2: For better visualization of cuttlefish body pattern and the surrounding ubstrate, unlabeled, high-resolution photographs corresponding to Fig. 2a-g are presented

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Derya Akkaynak
    • 1
    • 2
    • 3
  • Justine J. Allen
    • 3
    • 4
  • Lydia M. Mäthger
    • 3
  • Chuan-Chin Chiao
    • 3
    • 5
  • Roger T. Hanlon
    • 3
    • 6
  1. 1.Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Department of Applied Ocean Physics and EngineeringWoods Hole Oceanographic InstitutionWoods HoleUSA
  3. 3.Marine Biological LaboratoryMarine Resources CenterWoods HoleUSA
  4. 4.Department of NeuroscienceBrown UniversityProvidenceUSA
  5. 5.Department of Life ScienceNational Tsing Hua UniversityHsinchuTaiwan
  6. 6.Department of Ecology and Evolutionary BiologyBrown UniversityProvidenceUSA

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