Rapid body color brightening is associated with exposure to a stressor in an Anolis lizard

Effective camouflage renders a target indistinguishable from irrelevant background objects. Two interrelated but logically distinct mechanisms for this are background pattern matching (crypsis) and disruptive coloration: in the former, the animal's colours are a random sample of the background; in the latter, bold contrasting colours on the animal's periphery break up its outline. The latter has long been proposed as an explanation for some apparently conspicuous coloration in animals, and is standard textbook material. Surprisingly, only one quantitative test of the theory exists, and one experimental test of its effectiveness against non-human predators. Here we test two key predictions: that patterns on the body's outline should be particularly effective in promoting concealment and that highly contrasting colours should enhance this disruptive effect. Artificial moth-like targets were exposed to bird predation in the field, with the experimental colour patterns on the 'wings' and a dead mealworm as the edible 'body'. Survival analysis supported the predictions, indicating that disruptive coloration is an effective means of camouflage, above and beyond background pattern matching.

Summary Quantitative measurements of colour, pattern and morphology are vital to a growing range of disciplines. Digital cameras are readily available and already widely used for making these measurements, having numerous advantages over other techniques, such as spectrometry. However, off‐the‐shelf consumer cameras are designed to produce images for human viewing, meaning that their uncalibrated photographs cannot be used for making reliable, quantitative measurements. Many studies still fail to appreciate this, and of those scientists who are aware of such issues, many are hindered by a lack of usable tools for making objective measurements from photographs. We have developed an image processing toolbox that generates images that are linear with respect to radiance from the RAW files of numerous camera brands and can combine image channels from multispectral cameras, including additional ultraviolet photographs. Images are then normalised using one or more grey standards to control for lighting conditions. This enables objective measures of reflectance and colour using a wide range of consumer cameras. Furthermore, if the camera's spectral sensitivities are known, the software can convert images to correspond to the visual system (cone‐catch values) of a wide range of animals, enabling human and non‐human visual systems to be modelled. The toolbox also provides image analysis tools that can extract luminance (lightness), colour and pattern information. Furthermore, all processing is performed on 32‐bit floating point images rather than commonly used 8‐bit images. This increases precision and reduces the likelihood of data loss through rounding error or saturation of pixels, while also facilitating the measurement of objects with shiny or fluorescent properties. All cameras tested using this software were found to demonstrate a linear response within each image and across a range of exposure times. Cone‐catch mapping functions were highly robust, converting images to several animal visual systems and yielding data that agreed closely with spectrometer‐based estimates. Our imaging toolbox is freely available as an addition to the open source ImageJ software. We believe that it will considerably enhance the appropriate use of digital cameras across multiple areas of biology, in particular researchers aiming to quantify animal and plant visual signals.