Cargando…
Quantifying camouflage: how to predict detectability from appearance
BACKGROUND: Quantifying the conspicuousness of objects against particular backgrounds is key to understanding the evolution and adaptive value of animal coloration, and in designing effective camouflage. Quantifying detectability can reveal how colour patterns affect survival, how animals’ appearanc...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217226/ https://www.ncbi.nlm.nih.gov/pubmed/28056761 http://dx.doi.org/10.1186/s12862-016-0854-2 |
_version_ | 1782492066420359168 |
---|---|
author | Troscianko, Jolyon Skelhorn, John Stevens, Martin |
author_facet | Troscianko, Jolyon Skelhorn, John Stevens, Martin |
author_sort | Troscianko, Jolyon |
collection | PubMed |
description | BACKGROUND: Quantifying the conspicuousness of objects against particular backgrounds is key to understanding the evolution and adaptive value of animal coloration, and in designing effective camouflage. Quantifying detectability can reveal how colour patterns affect survival, how animals’ appearances influence habitat preferences, and how receiver visual systems work. Advances in calibrated digital imaging are enabling the capture of objective visual information, but it remains unclear which methods are best for measuring detectability. Numerous descriptions and models of appearance have been used to infer the detectability of animals, but these models are rarely empirically validated or directly compared to one another. We compared the performance of human ‘predators’ to a bank of contemporary methods for quantifying the appearance of camouflaged prey. Background matching was assessed using several established methods, including sophisticated feature-based pattern analysis, granularity approaches and a range of luminance and contrast difference measures. Disruptive coloration is a further camouflage strategy where high contrast patterns disrupt they prey’s tell-tale outline, making it more difficult to detect. Disruptive camouflage has been studied intensely over the past decade, yet defining and measuring it have proven far more problematic. We assessed how well existing disruptive coloration measures predicted capture times. Additionally, we developed a new method for measuring edge disruption based on an understanding of sensory processing and the way in which false edges are thought to interfere with animal outlines. RESULTS: Our novel measure of disruptive coloration was the best predictor of capture times overall, highlighting the importance of false edges in concealment over and above pattern or luminance matching. CONCLUSIONS: The efficacy of our new method for measuring disruptive camouflage together with its biological plausibility and computational efficiency represents a substantial advance in our understanding of the measurement, mechanism and definition of disruptive camouflage. Our study also provides the first test of the efficacy of many established methods for quantifying how conspicuous animals are against particular backgrounds. The validation of these methods opens up new lines of investigation surrounding the form and function of different types of camouflage, and may apply more broadly to the evolution of any visual signal. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12862-016-0854-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5217226 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-52172262017-01-09 Quantifying camouflage: how to predict detectability from appearance Troscianko, Jolyon Skelhorn, John Stevens, Martin BMC Evol Biol Research Article BACKGROUND: Quantifying the conspicuousness of objects against particular backgrounds is key to understanding the evolution and adaptive value of animal coloration, and in designing effective camouflage. Quantifying detectability can reveal how colour patterns affect survival, how animals’ appearances influence habitat preferences, and how receiver visual systems work. Advances in calibrated digital imaging are enabling the capture of objective visual information, but it remains unclear which methods are best for measuring detectability. Numerous descriptions and models of appearance have been used to infer the detectability of animals, but these models are rarely empirically validated or directly compared to one another. We compared the performance of human ‘predators’ to a bank of contemporary methods for quantifying the appearance of camouflaged prey. Background matching was assessed using several established methods, including sophisticated feature-based pattern analysis, granularity approaches and a range of luminance and contrast difference measures. Disruptive coloration is a further camouflage strategy where high contrast patterns disrupt they prey’s tell-tale outline, making it more difficult to detect. Disruptive camouflage has been studied intensely over the past decade, yet defining and measuring it have proven far more problematic. We assessed how well existing disruptive coloration measures predicted capture times. Additionally, we developed a new method for measuring edge disruption based on an understanding of sensory processing and the way in which false edges are thought to interfere with animal outlines. RESULTS: Our novel measure of disruptive coloration was the best predictor of capture times overall, highlighting the importance of false edges in concealment over and above pattern or luminance matching. CONCLUSIONS: The efficacy of our new method for measuring disruptive camouflage together with its biological plausibility and computational efficiency represents a substantial advance in our understanding of the measurement, mechanism and definition of disruptive camouflage. Our study also provides the first test of the efficacy of many established methods for quantifying how conspicuous animals are against particular backgrounds. The validation of these methods opens up new lines of investigation surrounding the form and function of different types of camouflage, and may apply more broadly to the evolution of any visual signal. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12862-016-0854-2) contains supplementary material, which is available to authorized users. BioMed Central 2017-01-06 /pmc/articles/PMC5217226/ /pubmed/28056761 http://dx.doi.org/10.1186/s12862-016-0854-2 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Troscianko, Jolyon Skelhorn, John Stevens, Martin Quantifying camouflage: how to predict detectability from appearance |
title | Quantifying camouflage: how to predict detectability from appearance |
title_full | Quantifying camouflage: how to predict detectability from appearance |
title_fullStr | Quantifying camouflage: how to predict detectability from appearance |
title_full_unstemmed | Quantifying camouflage: how to predict detectability from appearance |
title_short | Quantifying camouflage: how to predict detectability from appearance |
title_sort | quantifying camouflage: how to predict detectability from appearance |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217226/ https://www.ncbi.nlm.nih.gov/pubmed/28056761 http://dx.doi.org/10.1186/s12862-016-0854-2 |
work_keys_str_mv | AT trosciankojolyon quantifyingcamouflagehowtopredictdetectabilityfromappearance AT skelhornjohn quantifyingcamouflagehowtopredictdetectabilityfromappearance AT stevensmartin quantifyingcamouflagehowtopredictdetectabilityfromappearance |