Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-8448b6f56d-jr42d Total loading time: 0 Render date: 2024-04-23T09:21:18.181Z Has data issue: false hasContentIssue false

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

Published online by Cambridge University Press:  05 July 2011

By
Sami Merilaita
Edited by
Colin R. Tosh  and
Graeme D. Ruxton
Sami Merilaita
Affiliation:
Åbo Akademi University
Colin R. Tosh
Affiliation:
University of Leeds
Graeme D. Ruxton
Affiliation:
University of Glasgow
Get access

Summary

Introduction

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).

Type
Chapter
Information
Modelling Perception with Artificial Neural Networks , pp. 215 - 235
Publisher: Cambridge University Press
Print publication year: 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Arak, A. & Enquist, M. 1993. Hidden preferences and the evolution of signals. Phil Trans R. SocB 265, 1059–1064.Google Scholar
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.CrossRefGoogle ScholarPubMed
Bishop, C. M. 1995. Neural Networks for Pattern Recognition. Oxford University Press.Google Scholar
Borst, A. 2007. Correlation versus gradient type motion detectors: the pros and cons. Phil Trans R Soc B 362, 369–374.CrossRefGoogle ScholarPubMed
Cott, H. B. 1940. Adaptive Coloration in Animals. Methuen.Google Scholar
Cuthill, I. C., Stevens, M., Sheppard, J.et al. 2005. Disruptive coloration and background pattern matching. Nature 434, 72–74.CrossRefGoogle ScholarPubMed
Demuth, H. & Beale, M. 2000. Neural Network Toolbox for Use with Matlab, Version 4. The MathWorks Inc.Google Scholar
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.CrossRefGoogle ScholarPubMed
Dittrich, W., Gilbert, F., Green, P., McGregor, P. & Grewcock, D. 1993. Imperfect mimicry: a pigeon's perspective. Proc R Soc B 251, 195–200.CrossRefGoogle Scholar
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.Google Scholar
Edmunds, M. 1974. Defence in Animals. Longman.Google Scholar
Endler, J. A. 1978. A predator's view of animal color patterns. Evol Biol 11, 319–364.Google Scholar
Endler, J. A. 1984. Progressive background matching in moths, and a quantitative measure of crypsis. Biol J Linn Soc 22, 187–231.CrossRefGoogle Scholar
Endler, J. A. 1992. Signals, signal conditions and the direction of evolution. Am Nat 139, S125–S153.CrossRefGoogle Scholar
Enquist, M. & Arak, A. 1993. Selection of exaggerated male traits by female aesthetic senses. Nature 361, 446–448.CrossRefGoogle ScholarPubMed
Enquist, M. & Arak, A. 1994. Symmetry, beauty and evolution. Nature 372, 169–172.CrossRefGoogle ScholarPubMed
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.Google Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Gendron, R. P. & Staddon, J. E. R. 1983. Searching for cryptic prey: the effect of search rate. Am Nat 121, 172–186.CrossRefGoogle Scholar
Ghirlanda, S. & Enquist, M. 1998. Artificial neural networks as models of stimulus control. Anim Behav 56, 1383–1389.CrossRefGoogle Scholar
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.Google Scholar
Gordon, I. E. 1968. Interactions between items in visual search. J Exp Psychol 76, 248–355.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Merilaita, S. 1998. Crypsis through disruptive coloration in an isopod. Proc R SocB 265, 1059–1064.CrossRefGoogle Scholar
Merilaita, S. 2003. Visual background complexity facilitates the evolution of camouflage. Evolution 57, 1248–1254.CrossRefGoogle ScholarPubMed
Merilaita, S. & Lind, J. 2005. Background-matching and disruptive coloration, and the evolution of cryptic coloration. Proc R SocB 272, 665–670.CrossRefGoogle ScholarPubMed
Merilaita, S., Lyytinen, A. & Mappes, J. 2001. Selection for cryptic coloration in a visually heterogeneous habitat. Proc R SocB 268, 1925–1929.CrossRefGoogle Scholar
Merilaita, S. & Ruxton, G. D. 2007. Aposematic signals and the relationship between conspicuousness and distinctiveness. J Theor Biol 245, 268–277.CrossRefGoogle ScholarPubMed
Merilaita, S. & Tullberg, B. S. 2005. Constrained camouflage facilitates the evolution of conspicuous warning coloration. Evolution 59, 38–45.CrossRefGoogle ScholarPubMed
Merilaita, S., Tuomi, J. & Jormalainen, V. 1999. Optimisation of cryptic coloration in heterogeneous habitats. Biol J Linn Soc 67, 151–161.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Rowland, H. M., Speed, M. P., Ruxton, G. D.et al. 2007a. Countershading enhances cryptic protection: an experiment with wild birds and artificial prey. Anim Behav 74, 1249–1258.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Ruxton, G. D., Sherratt, T. M. & Speed, M. P. 2004. Avoiding Attack: The Evolutionary Ecology of Crypsis, Warning Signals and Mimicry. Oxford University Press.CrossRefGoogle Scholar
Sherratt, T. N. & Beatty, C. D. 2003. The evolution of warning signals as reliable indicators of prey defense. Am Nat 162, 377–389.CrossRefGoogle ScholarPubMed
Skelhorn, J. & Rowe, C. 2005. Tasting the difference: do multiple defence chemicals interact in Müllerian mimicry?Proc R SocB 272, 339–345.CrossRefGoogle ScholarPubMed
Stevens, M. 2007. Predator perception and the interrelation between different forms of protective coloration. Proc R Soc B 274, 1457–1464.CrossRefGoogle ScholarPubMed
Stevens, M. & Cuthill, I. C. 2006. Disruptive coloration, crypsis and edge detection in early visual processing. Proc R SocB 273, 2141–2147.CrossRefGoogle ScholarPubMed
Stevens, M., Hardman, C. J. & Stubbins, C. L. 2008. Conspicuousness, not eye mimicry, makes “eyespots” effective antipredator signals. Behav Ecol 19, 525–531.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Thayer, G. H. 1909 Concealing Coloration in the Animal Kingdom. Macmillan.Google Scholar
Theodoridis, S. & Koutroumbas, K. 1999. Pattern Recognition. Academic Press.Google Scholar
Tsoukalas, L. H. & Uhrig, R. E. 1997. Fuzzy and Neural Approaches in Engineering. Wiley.Google Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Wallace, A. R. 1889. Darwinism. Macmillan.Google Scholar
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.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×