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 ( Erysimum , 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.
(© 2023 The Authors. Applications in Plant Sciences published by Wiley Periodicals LLC on behalf of Botanical Society of America.)