Wing interference patterns (WIPs) are stable structural colours displayed on insect wings which are only visible at specific viewing geometries and against certain backgrounds. These patterns are widespread among flies and wasps, and growing evidence suggests that they may function as species- and sex-specific mating cues in a range of taxa. As such, it is expected that WIPs should differ between species and show clear sexual dimorphisms. However, the true extent to which WIPs vary between species, sexes, and individuals is currently unclear, as previous studies have only taken a qualitative approach, without considering how WIPs might be perceived by the insect. Using multispectral digital imaging and a tentative model of blowfly colour vision, we provide the first quantitative dataset of inter- and intra-specific variation in WIPs across seven Australian species of the blowfly genus Chrysomya. These data suggest that WIPs have diversified substantially in blowflies as a result of either sexual or ecological selection.

Wings were mounted with transparent UHU glue onto a custom rotating stage and positioned at a 45° angle to maximise WIP visibility. Photos were taken of both the left and right wing of each fly with a MZ16A stereomicroscope mounted with a Leica DFC295 digital microscope colour camera. All photos were taken at the same magnification, under standardised and uniformly diffuse lighting provided by a Leica LED5000 HDI illuminator.

Images were processed using the Multispectral Image Analysis and Calibration Toolbox for ImageJ (MICA toolbox). This produces linearized, calibrated images which allow for the measurement of relative reflectances. We calibrated our images against a 3% reflectance standard from an X-rite colour checker passport, which was placed 5 mm below the wing in the background of each photo. This resulted in a total of 231 multispectral images (visible spectrum only) of left and right wings across the seven Chrysomya species.   

From these multispectral images, we made measurements of the average values of red, green, and blue (RGB) channels (hereafter referred to as mean ‘colour’) and the standard deviation in RGB (hereafter referred to as ‘colour contrast’) across five individual wing cells (a1, b1, c1, d1, and e1) as well as a measurement of the entire wing (wing1). In addition to this viewer-independent analysis, we used a cone-mapping approach to convert the multispectral images into two viewer-subjective formats; the CIELab model of human colour sensation, and a receptor-based model of ‘blowfly vision’ based on the visual phenotype of Calliphora. 

CIELab is a perceptually uniform model of human vision, whereby ‘L’ represents lightness, ‘a’ represents values on a green-red axis, and ‘b’ represents values on a blue-yellow axis. We measured the average L, a, and b pixel values (hereafter referred to as human ‘colour’) and standard deviation in L, a, and b pixel values (hereafter referred to as human ‘colour contrast’).

For the blowfly visual model, we were unable to measure UV reflectance due to the limitations of our digital microscope camera. Importantly however, UV and visible scattering are correlated in simple thin films, so even though we cannot measure UV directly, we are still capturing most of the variation in WIP appearance. As such, we created a simple receptor-based model of blowfly colour vision, based on the long-wavelength sensitivities of Calliphora as there are no published receptor sensitivities for Chrysomya species. We assumed involvement of the R8p (Rh5 opsin) and R8y (Rh6 opsin) receptors, which partly mediate colour vision as well as the R1-6 receptors (Rh1 opsin) which are sensitive around both 360nm and 490nm and contribute to both colour and luminance vision in flies. We estimated the mean quantum catch of Rh5, Rh6 and Rh1 (hereafter blowfly ‘colour’) as well as their standard deviation (hereafter blowfly ‘colour contrast’) across each of five individual wing cells, as well as the entire wing. This blowfly model was formatted as per the Drosophila cone catch data provided by MICA Toolbox.

Missing values represent wing cells which were not measured due to one or two damaged cells. Severely damaged wings (damage to several wing cells) were not included in the dataset.