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  • Print publication year: 2010
  • Online publication date: July 2011

10 - Applying artificial neural networks to the study of prey colouration

from Part III - Artificial neural networks as models of perceptual processing in ecology and evolutionary biology



In this chapter I will examine the use of artificial neural networks in the study of prey colouration as an adaptation against predation. Prey colouration provides numerous spectacular examples of adaptation (e.g. Cott, 1940; Edmunds, 1974; Ruxton et al., 2004). These include prey colour patterns used to disguise and make their bearers difficult to detect as well as brilliant colourations and patterns that prey may use to deter a predator. As a consequence, prey colouration has been a source of inspiration for biologists since the earliest days of evolutionary biology (e.g. Wallace, 1889).

The anti-predation function of prey colouration is evidently a consequence of natural selection imposed by predation. More specifically, it is the predators' way of processing visual information that determines the best possible appearance of the colouration of a prey for a given anti-predation function and under given conditions. Because predators' ability to process visual information has such a central role in the study of prey colouration, it follows that we need models that enable us to capture the essential features of such information processing.

An artificial neural network can be described as a data processing system consisting of a large number of simple, highly interconnected processing elements (artificial neurons) in an architecture inspired by biological nerve systems (Tsoukalas & Uhrig, 1997). Artificial neural networks provide a technique that has been applied in various disciplines of science and engineering for tasks such as pattern recognition, categorisation and decision making, as well as a modelling tool in neural biology (e.g. Bishop, 1995; Haykin, 1999).

Arak, A. & Enquist, M. 1993. Hidden preferences and the evolution of signals. Phil Trans R. SocB 265, 1059–1064.
Bain, R. S., Rashed, A., Cowper, V. J., Gilbert, F. S. & Sherratt, T. N. 2007. The key mimetic features of hoverflies through avian eyes. Proc R Soc B 274, 1949–1954.
Bishop, C. M. 1995. Neural Networks for Pattern Recognition. Oxford University Press.
Borst, A. 2007. Correlation versus gradient type motion detectors: the pros and cons. Phil Trans R Soc B 362, 369–374.
Cott, H. B. 1940. Adaptive Coloration in Animals. Methuen.
Cuthill, I. C., Stevens, M., Sheppard, al. 2005. Disruptive coloration and background pattern matching. Nature 434, 72–74.
Demuth, H. & Beale, M. 2000. Neural Network Toolbox for Use with Matlab, Version 4. The MathWorks Inc.
Dimitrova, M., Stobbe, N., Schaefer, H. M. & Merilaita, S. 2009. Concealed by conspicuousness: distractive prey markings and backgrounds. Proc R Soc B 276, 1905–1910.
Dittrich, W., Gilbert, F., Green, P., McGregor, P. & Grewcock, D. 1993. Imperfect mimicry: a pigeon's perspective. Proc R Soc B 251, 195–200.
Dukas, R. 1998. Constraints on information processing and their effects on behavior. In Cognitive Ecology: the Evolutionary Ecology of Information Processing and Decision Making (ed. Dukas, R.), pp. 89–128. University of Chicago Press.
Edmunds, M. 1974. Defence in Animals. Longman.
Endler, J. A. 1978. A predator's view of animal color patterns. Evol Biol 11, 319–364.
Endler, J. A. 1984. Progressive background matching in moths, and a quantitative measure of crypsis. Biol J Linn Soc 22, 187–231.
Endler, J. A. 1992. Signals, signal conditions and the direction of evolution. Am Nat 139, S125–S153.
Enquist, M. & Arak, A. 1993. Selection of exaggerated male traits by female aesthetic senses. Nature 361, 446–448.
Enquist, M. & Arak, A. 1994. Symmetry, beauty and evolution. Nature 372, 169–172.
Enquist, M. & Arak, A. 1998. Neural representation and the evolution of signal form. In Cognitive Ecology: The Evolutionary Ecology of Information Processing and Decision making (ed. Dukas, R.), pp. 21–87. University of Chicago Press.
Enquist, M., Arak, A., Ghirlanda, S. & Wachtmeister, C.-A. 2002. Spectacular phenomena and limits to rationality in genetic and cultural evolution. Phil Trans R SocB 357, 1585–1594.
Farmer, E. W. & Taylor, R. M. 1980. Visual search through color displays: effects of target-background similarity and background uniformity. Percept Psychophys 27, 267–272.
Fraser, S., Callahan, A., Klassem, D. & Sherratt, T. N. 2007. Empirical tests of the role of disruptive coloration in reducing detectability. Proc R SocB 274, 1325–1331.
Gendron, R. P. 1986. Searching for cryptic prey: evidence for optimal search rates and the formation of search images in quail. Anim Behav 34, 898–912.
Gendron, R. P. & Staddon, J. E. R. 1983. Searching for cryptic prey: the effect of search rate. Am Nat 121, 172–186.
Ghirlanda, S. & Enquist, M. 1998. Artificial neural networks as models of stimulus control. Anim Behav 56, 1383–1389.
Guilford, T. 1992. Predator psychology and the evolution of prey coloration. In Natural Enemies: The Population Biology of Predators, Parasites and Diseases (ed. Crawley, M. J.), pp. 377–394. Blackwell.
Gordon, I. E. 1968. Interactions between items in visual search. J Exp Psychol 76, 248–355.
Haykin, S. 1999. Neural Networks: A Comprehensive Foundation. 2nd edn. Prentice-Hall.
Holmgren, N. M. A. & Enquist, M. 1999. Dynamics of mimicry evolution. Biol J Linn Soc 66, 145–158.
Houston, A. I., Stevens, M. & Cuthill, I. C. 2007. Animal camouflage: compromise or specialize in a 2 patch-type environment. Behav Ecol 18, 769–775.
Kenward, B., Wachtmeister, C.-A., Ghirlanda, S. & Enquist, M. 2004. Spots and stripes: the evolution of repetition in visual signal form. J Theor Biol 230, 407–419.
Lindström, L., Alatalo, R. V., Lyytinen, A. & Mappes, J. 2001. Strong antiapostatic selection against novel rare aposematic prey. Proc Natl Acad SciUSA 98, 9181–9184.
Merilaita, S. 1998. Crypsis through disruptive coloration in an isopod. Proc R SocB 265, 1059–1064.
Merilaita, S. 2003. Visual background complexity facilitates the evolution of camouflage. Evolution 57, 1248–1254.
Merilaita, S. & Lind, J. 2005. Background-matching and disruptive coloration, and the evolution of cryptic coloration. Proc R SocB 272, 665–670.
Merilaita, S., Lyytinen, A. & Mappes, J. 2001. Selection for cryptic coloration in a visually heterogeneous habitat. Proc R SocB 268, 1925–1929.
Merilaita, S. & Ruxton, G. D. 2007. Aposematic signals and the relationship between conspicuousness and distinctiveness. J Theor Biol 245, 268–277.
Merilaita, S. & Tullberg, B. S. 2005. Constrained camouflage facilitates the evolution of conspicuous warning coloration. Evolution 59, 38–45.
Merilaita, S., Tuomi, J. & Jormalainen, V. 1999. Optimisation of cryptic coloration in heterogeneous habitats. Biol J Linn Soc 67, 151–161.
Mitchell, M. 1996. An Introduction to Genetic Algorithms. MIT Press.
Peck, S. L. 2004. Simulation as experiment: a philosophical reassessment for biological modeling. Trends Ecol Evol 19, 530–534.
Rowland, H. M., Speed, M. P., Ruxton, G. al. 2007a. Countershading enhances cryptic protection: an experiment with wild birds and artificial prey. Anim Behav 74, 1249–1258.
Rowland, H. M., Ihalainen, E., Lindström, L., Mappes, J. & Speed, M. P. 2007b. Co-mimics have a mutualistic relationship despite unequal defence levels. Nature 448, 64–66.
Ruxton, G. D., Sherratt, T. M. & Speed, M. P. 2004. Avoiding Attack: The Evolutionary Ecology of Crypsis, Warning Signals and Mimicry. Oxford University Press.
Sherratt, T. N. & Beatty, C. D. 2003. The evolution of warning signals as reliable indicators of prey defense. Am Nat 162, 377–389.
Skelhorn, J. & Rowe, C. 2005. Tasting the difference: do multiple defence chemicals interact in Müllerian mimicry?Proc R SocB 272, 339–345.
Stevens, M. 2007. Predator perception and the interrelation between different forms of protective coloration. Proc R Soc B 274, 1457–1464.
Stevens, M. & Cuthill, I. C. 2006. Disruptive coloration, crypsis and edge detection in early visual processing. Proc R SocB 273, 2141–2147.
Stevens, M., Hardman, C. J. & Stubbins, C. L. 2008. Conspicuousness, not eye mimicry, makes “eyespots” effective antipredator signals. Behav Ecol 19, 525–531.
Stevens, M. & Merilaita, S. 2009. Defining disruptive coloration and distinguishing its functions. Phil Trans R Soc B364, 423–427.
Tosh, C. R., Jackson, A. L. & Ruxton, G. D. 2007. Individuals from different-looking animal species may group together to confuse shared predators: simulations with artificial neural networks. Proc R Soc B 274, 827–832.
Thayer, G. H. 1909 Concealing Coloration in the Animal Kingdom. Macmillan.
Theodoridis, S. & Koutroumbas, K. 1999. Pattern Recognition. Academic Press.
Tsoukalas, L. H. & Uhrig, R. E. 1997. Fuzzy and Neural Approaches in Engineering. Wiley.
Tullberg, B. S., Merilaita, S. & Wiklund, C. 2005. Aposematic and crypsis combined as a result of distance dependence: functional versatility of the colour pattern in the swallowtail butterfly larva. Proc R SocB 272, 1315–1321.
Vallin, A., Jakobsson, S., Lind, J. & Wiklund, C. 2005. Prey survival by predator intimidation: an experimental study of peacock butterfly defence against blue tits. Proc R SocB 272, 1203–1207.
Wallace, A. R. 1889. Darwinism. Macmillan.
Wourms, M. K. & Wasserman, F. E. 1985. Butterfly wing markings are more advantageous during handling than during initial strike of an avian predator. Evolution 39, 845–851.