A computational neuroscience framework for quantifying warning signals

Database construction The novel database of lepidopteran patterns of aposematic and non-aposematic species consists of a representative set made of 125 species of Lepidoptera across 12 families (96 aposematic and 29 non-aposematic species, with a total of 676 hyperspectral images; see paper’s Supplementary Material 1 for details). Samples of each species were located in museum collections (the Natural History Museum (BMNH), London, UK, the Manchester Museum (MMUE), Manchester, UK, and the American National Museum (AMNH), New York, USA). Their dorsal and ventral sides were photographed using an ultraviolet hyperspectral camera (Resonon Pika NUV, Resonon Inc., MT USA) covering the 350 nm – 800 nm spectral range, with a spectral resolution of 1 nm. The camera was fitted with a near ultraviolet 17 mm focal length objective lens. To maximize the homogeneity of the light field, the specimens were illuminated by four blue-enhanced halogen lamps (SoLux, 35W, 12V-MR16 GU5.3 4700K, EiKO Global, KS USA) placed 22 cm apart on a squared fixture light and oriented vertically toward the horizontal scanning plane. See the paper’s Supplementary Methods 1 for details on the spatial and spectral calibration of the imaging system. The database is freely accessible at https://arts.st-andrews.ac.uk/lepidoptera/index.html Image analysis – neural model of predator vision – computation of metrics (summary statistics) The neural model of a predator visual system and the computation of the metrics of the modelled neural activity were coded in Matlab (MATLAB and Statistics Toolbox Release 2019b, 9.7.0.1190202 (R2019b). Natick, Massachusetts, The MathWorks Inc.). Please see details in accompanying the README.md file and Supplementary Method 2 and 3. Statistical analysis The statistical analysis was done in R (R Development Core Team 2020) using generalized linear models (function glm) for the logistic regressions and the function glmer in the package lme4 (Bates et al. 2014) for fitting generalized linear mixed models. See README.md and Supplementary Method 4 for details.