A practical framework to analyze variation in animal colors using visual models

Abstract

To study biologically relevant variation in visual signals, these need to be assessed in relation to the sensory abilities of receivers. For the study of colors, reflectance spectrometry has been the method of choice, but analyses of reflectance spectra present challenges that hamper our understanding of color variation. Among these are computing meaningful color variables and interpreting their biological relevance. Here, we suggest how to overcome the limitations of commonly used approaches. We describe how to use psychophysical visual models to assess chromatic variation in the visual space of animals. This approach consists of 1) obtaining cone quantum catches from reflectance spectra, 2) transforming these into visual space coordinates where Euclidean distances reflect perceptual distances, 3) summarizing variation in visual space using principal component analysis (PCA) maintaining original perceptual units, and 4) interpreting the axes of chromatic variation (PC) based on their loadings and relative and absolute levels of chromatic variation. We illustrate this approach by comparing it to traditional color indices (hue and saturation) and PCA computed directly on reflectance spectra, using 2 examples: 1) determining the biological relevance of correlations between bill coloration and male quality in mallards and 2) assessing the success of experimental color manipulations in blue tits. In both cases, re-analyzing the data suggests different interpretations. This approach provides a simple way of objectively summarizing chromatic variation and interpreting the magnitude of biologically relevant effects. We provide R scripts to carry out computations and recommendations on how to report results to make data comparable between studies.