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Quantitative description and discrimination of butterfly wing patterns using moment invariant analysis

Published online by Cambridge University Press:  09 March 2007

R.J. White*
Affiliation:
School of Biological Sciences, University of Southampton, Southampton, SO16 7PX, UK
L. Winokur
Affiliation:
School of Biological Sciences, University of Southampton, Southampton, SO16 7PX, UK
*
*Fax: +44 23 8059 4269 E-mail: [email protected]

Abstract

Studies examining and using pattern variation in insects for identification and characterization of individuals and populations have been limited by the methods available for quantifying wing patterns objectively. In this paper, differences in wing pattern are demonstrated statistically using moment invariant data sets generated automatically from digitized images of the speckled wood butterfly, Pararge aegeria (Linnaeus). Studies with other biological subjects have already shown moment invariants to work well with outline shapes and silhouettes. A pilot study with replicated monochrome photographs of a single butterfly showed the method could detect pattern differences between wing surfaces, even in the presence of simulated wing fading and damage. In a further study of the wings of 228 specimens, multivariate analyses of variance using the moment data reliably detected differences between groups of butterflies according to sex, geographical origin and culture history. Potential applications and future improvements of the moment methodology are considered.

Type
Review Article
Copyright
Copyright © Cambridge University Press 2003

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References

Brakefield, P.M. & Liebert, T.G. (1985) Studies of colour polymorphism in some marginal populations of the aposematic Jersey tiger moth Callimorpha quadripunctaria. Biological Journal of the Linnean Society 26, 225241.CrossRefGoogle Scholar
Brakefield, P.M. & Shreeve, T.G. (1992) Diversity within populations. pp. 178216in Dennis, R.L.H. (Ed.) The ecology of butterflies in Britain. Oxford, Oxford University Press.CrossRefGoogle Scholar
Chandran, V., Carswell, B., Boashash, B. & Elgar, S. (1997) Pattern recognition using invariants defined from higher order spectra: 2-D image inputs. IEEE Transactions on Image Processing 6, 703712.CrossRefGoogle ScholarPubMed
Chesmore, D. & Monkman, G. (1994) Automated analysis of variation in Lepidoptera. Entomologist 113, 171182.Google Scholar
Dudani, S.A., Breeding, K.J. & McGhee, R.B. (1977) Aircraft identification by moment invariants. IEEE Transactions on Computers C-26, 3945.CrossRefGoogle Scholar
Hu, M.K. (1962) Visual pattern recognition by moment invariants. IEEE Transactions on Information Theory 8, 179187.Google Scholar
Jarvinen, O. & Vepsäläinen, K. (1979) Morphological variation in Diachrysia chrysitis (Lepidoptera, Noctuidae): a statistical analysis of the wing pattern. Notulae Entomologicae 59, 1926.Google Scholar
Kuhl, F.P. & Giardina, C.R. (1982) Elliptic Fourier features of a closed contour. Computer Graphics and Image Processing 18, 236258.CrossRefGoogle Scholar
Liao, S.X. & Pawluk, M. (1996) On image analysis by moments. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 254266.CrossRefGoogle Scholar
Meacham, C.A. (1993) MorphoSys: an interactive machine vision program for acquisition of morphometric data. pp. 393402in Fortuner, R. (Ed.) Advances in computer methods for systematic biology: artificial intelligence, databases, computer vision. Baltimore, Johns Hopkins University Press.Google Scholar
Neil, G. & Curtis, K.M. (1997) Shape recognition using fractal geometry. Pattern Recognition 30, 19571969.CrossRefGoogle Scholar
Nixon, M.S. & Aguado, A.S. (2002) Feature extraction and image processing. 350 pp. Oxford, Newnes.Google Scholar
O'Neill, M.A., Gauld, I.D., Gaston, K.J. & Weeks, P.J.D. (1997) DAISY: an automated invertebrate identification system using holistic vision techniques, pp. 13–22 in Proceedings of the Inaugural Meeting of the BioNET-International group for Computer-aided Taxonomy (BIGCAT), Cardiff, 2–3 July 1997. BioNET International, Egham, Surrey (http://www.bionet-intl.org/html/outputs/publications/Bigcat.pdf).Google Scholar
Palmer, A.R. & Strobeck, C. (1986) Fluctuating asymmetry: measurement, analysis, patterns. Annual Review of Ecology and Systematics 17, 391421.CrossRefGoogle Scholar
Prokop, R.J. & Reeves, A.P. (1992) A survey of moment-based techniques for unoccluded object representation and recognition. CVGIP: Graphical Models and Image Processing 54, 438460.Google Scholar
Robertson, T.S. (1980) Seasonal variation in Pararge aegeria (Linnaeus) (Lepidoptera: Satyridae): a biometrical study. Entomologist's Gazette 31, 151156.Google Scholar
Rohlf, F.J. (1990) Morphometrics. Annual Review of Ecology and Systematics 21, 299316.CrossRefGoogle Scholar
Rohlf, F.J. & Archie, J.W. (1984) A comparison of Fourier methods for the description of wing shape in mosquitoes (Diptera: Culicidae). Systematic Zoology 33, 302317.CrossRefGoogle Scholar
Rohlf, F.J. & Bookstein, F.L. (1990) Proceedings of the Michigan Morphometrics Workshop. Special Publication Number 2. Ann Arbor, University of Michigan Museum of Zoology.Google Scholar
Shreeve, T.G. (1985) The population biology of the speckled wood butterfly Pararge aegeria (L.) (Lepidoptera: Satyridae). PhD thesis: Oxford Brookes University.Google Scholar
Sokal, R.R. & Rohlf, F.J. (1981) Biometry. 2nd edn. 859 pp. New York, Freeman.Google Scholar
Soulé, M. (1967) Phenetics of natural populations II. Asymmetry and evolution in a lizard. American Naturalist 101, 141160.CrossRefGoogle Scholar
SPSS, Inc. (1997) SPSS Base 7.5 for Windows user's guide. 628 pp. New Jersey, Prentice Hall.Google Scholar
Strachan, N.J.C., Nesvadba, P. & Allen, A.R. (1990) Fish species recognition by shape analysis of images. Pattern Recognition 23, 539544.CrossRefGoogle Scholar
Tang, X., Stewart, W.K., Vincent, L., Huang, H., Marra, M., Gallager, S.M. & Davis, C.S. (1998) Automatic plankton image recognition. Artificial Intelligence Review 12, 177199.CrossRefGoogle Scholar
Tolman, T. & Lewington, R. (1997) Butterflies of Britain and Europe. 320 pp. London, Harper Collins.Google Scholar
Vines, G. (1998) Hidden inheritance. New Scientist 160 (2162), 2630.Google Scholar
Weeks, P.J.D., Gauld, I.D., Gaston, K.J. & O'Neill, M.A. (1997) Automating the identification of insects: a new solution to an old problem. Bulletin of Entomological Research 87, 203211.CrossRefGoogle Scholar
White, R.J. (1974) Local evolution in the scarlet tiger moth. PhD thesis, University of Liverpool.Google Scholar
White, R.J. & Prentice, H.C. (1988) Comparison of shape description methods for biological outlines. pp. 395402in Bock, H.H. (Ed.) Classification and related methods of data analysis. Amsterdam, Elsevier (North-Holland).Google Scholar
White, R.J., Prentice, H.C. & Verwijst, T. (1988) Automated image acquisition and morphometric description. Canadian Journal of Botany 66, 450459.CrossRefGoogle Scholar
Windig, J.J. & Nylin, S. (1999) Adaptive wing asymmetry in males of the speckled wood butterfly (Pararge aegeria). Proceedings of the Royal Society of London B 266, 14131418.CrossRefGoogle Scholar
Winokur, L. (1989) Developmental and evolutionary implications of cold shock effects in the speckled wood butterfly. PhD thesis, University of Southampton.Google Scholar
Winokur, L. (1992) Stable changes in voltinism strategy and their implications. Nota Lepidopterologica supplement 4, 3656.Google Scholar
Winokur, L. (1995) Understanding size and pattern variation in mainland Britain speckled wood (Pararge aegeria) (Lepidoptera: Satyridae). British Journal of Entomology and Natural History 8, 102112 & Plate 1.Google Scholar
Winokur, L. (1996) Wing homoeosis in Pararge aegeria L. (Lepidoptera: Satyridae). British Journal of Entomology and Natural History 9, 193195.Google Scholar
Wolpert, L. (1998) Principles of development. 439 pp. Oxford, Oxford University Press.Google Scholar
Yu, D.S., Kokko, E.G, Barron, J.R., Schaalje, G.B. & Gowen, B.E. (1992) Identification of ichneumonid wasps using image analysis of wings. Systematic Entomology 17, 389395.CrossRefGoogle Scholar
Zion, B., Shklyar, A. & Karplus, I. (1999) Sorting fish by computer vision. Computers and Electronics in Agriculture 23, 175187.CrossRefGoogle Scholar