A pipeline for the rapid collection of color data from photographs.

Premise: There are relatively few studies of flower color at landscape scales that can address the relative importance of competing mechanisms (e.g., biotic: pollinators; abiotic: ultraviolet radiation, drought stress) at landscape scales.

Methods: We developed an R shiny pipeline to sample color from images that were automatically downloaded using query results from a search using iNaturalist or the Global Biodiversity Information Facility (GBIF). The pipeline was used to sample ca. 4800 North American wallflower (, Brassicaceae) images from iNaturalist. We tested whether flower color was distributed non-randomly across the landscape and whether spatial patterns were correlated with climate. We also used images including ColorCheckers to compare analyses of raw images to color-calibrated images.

Results: Flower color was strongly non-randomly distributed spatially, but did not correlate strongly with climate, with most of the variation explained instead by spatial autocorrelation. However, finer-scale patterns including local correlations between elevation and color were observed. Analyses using color-calibrated and raw images revealed similar results.

Discussion: This pipeline provides users the ability to rapidly capture color data from iNaturalist images and can be a useful tool in detecting spatial or temporal changes in color using citizen science data.