Animal coloration patterns, from zebra stripes to bird egg speckles, are remarkably varied. With research on the perception, function, and evolution of animal patterns growing rapidly, we require a convenient framework for quantifying their diversity, particularly in the contexts of camouflage, mimicry, mate choice, and individual recognition. Ideally, patterns should be defined by their locations in a low-dimensional pattern space that represents their appearance to their natural receivers, much as color is represented by color spaces. This synthesis explores the extent to which animal patterns, like colors, can be described by a few perceptual dimensions in a pattern space. We begin by reviewing biological spatial vision, focusing on early stages during which neurons act as spatial filters or detect simple features such as edges. We show how two methods from computational vision—spatial filtering and feature detection—offer qualitatively distinct measures of animal coloration patterns. Spatial filters provide a measure of the image statistics, captured by the spatial frequency power spectrum. Image statistics give a robust but incomplete representation of the appearance of patterns, whereas feature detectors are essential for sensing and recognizing physical objects, such as distinctive markings and animal bodies. Finally, we discuss how pattern space analyses can lead to new insights into signal design and macroevolution of animal phenotypes. Overall, pattern spaces open up new possibilities for exploring how receiver vision may shape the evolution of animal pattern signals.
All Science Journal Classification (ASJC) codes
- Ecology, Evolution, Behavior and Systematics
- Animal coloration patterns
- Animal spatial vision
- Fourier transform
- Sensory ecology