Spectral data processing
Spectrophotometric data were processed to reduce noise, remove technical artifacts, and analyze reflectance spectra using the package pavo(Maia et al., 2013) in R v.3.6.1 (R Development Core Team, 2011). Raw spectra were smoothed using the procspec function, which applies the LOESS (Locally Weighted Scatterplot Smoothing) method with a quadratic regression and a Gaussian distribution. To normalize spectra and correct for negative values, we set the minimum value to zero and scaled up other values accordingly using the procspec function. We tested for repeatability of measurements taken at the same position on the dewlap using the R package rptR (Stoffel et al., 2017). Because all repeatability estimates were above 80%, we averaged repeated measurements using the aggspec function in pavo . We extracted four colorimetric variables: total brightness, cut-on wavelength (i.e., hue), UV reflectance, and chroma. Total brightness was calculated as the area under the “uncorrected” spectral curve from 300 to 700 nm. To determine the UV reflectance and cut-on wavelength (i.e., the midpoint between baseline and maximum reflectance; Cummings, 2007), we corrected the spectra for brightness by making the area under each curve equal to 1.0 (Endler, 1990). This correction allows for the identification of differences in spectral shape that are unrelated to brightness (Ng et al., 2013a).
Dewlap color composition and pattern
Each extended dewlap was photographed using a Nikon D3300 (24.2 MP) digital camera on a white background under standardized room lighting conditions in the lab. We included a color standard (i.e., X-rite Mini ColorChecker® Classic) and a ruler for scale. Images were calibrated using the colorChecker function in the R packagepatternize (Van Belleghem et al., 2018). This function calculates a second order polynomial regression between the observed and expected RGB (red, green, and blue) values and performs the calibration of the image.
Because some dewlaps are a mixture of colors and the spectral data only represent three points on the dewlap, we also wanted to quantify the proportion of each color present in each dewlap. We determined the RGB values of the colors present in each pixel (size ≈ 0.007 mm) of the dewlap image using Color Inspector 3D (Barthel, 2006), a plugin for ImageJ (Rasband, 2012). This plugin displays the distribution of colors of an image within a 3D color space. We extracted the RGB values and their frequency and imported them to R. We obtained a list of known colors with their respective RGB values using the base R functionscolors and col2rgb . Then, we classified dewlap RGB values for each pixel to color categories using Euclidean distance, which determines the nearest known color in RGB-space. This quantitative measure of color composition was calculated as the percent of red, orange, and yellow present in each dewlap.
Brown anole dewlaps vary from a single color to some combination of red, orange, and yellow-colored patches. A previous study of dewlap variation in the native range of the brown anole categorized dewlap patterns into two types (Driessens et al., 2017). ‘Solid’ dewlaps are uniformly colored and may contain a distinct marginal color, such as a reddish color covering most of the dewlap with a yellowish color along the outer margin (Figure S2b). ‘Spotted’ dewlaps have yellowish spots scattered across the reddish center and may also contain a yellow outer margin (Figure S2c). We scored dewlaps from non-native populations in our study using these same two categories to facilitate comparison to the native range analysis in Driessens et al. (2017).