A pipeline for the rapid collection of color data from photographs

Abstract

PremiseThere 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.MethodsWe 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.ResultsFlower 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.DiscussionThis 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.

Spatially resolved detection of small molecules from press‐dried plant tissue using MALDI imaging

“…However, our method offers the advantages of significantly simplified and relatively rapid sample preparation at no apparent cost to spatial resolution for very large sample areas, without the need for advanced instrumentation or sample preparation techniques or equipment. This technique can be used in conjunction with automated methodology for obtaining color information from photos (Luong et al, 2023 ), providing a molecular‐level map of flower colors. The issues we encountered with molecule identification and mass resolution can be improved by using more advanced instrumentation.…”

Advances in plant imaging across scales

“…A fourth paper using field‐captured photographs focuses on the analysis of color using images available on iNaturalist. To allow the rapid generation of color data, Luong et al ( 2023 ) present a computational pipeline developed using R scripts and showcasing the utility of R shiny apps for enhancing iNaturalist collections and aiding users, including students, in natural history research. As an example, the authors analyze variation in Erysimum capitatum , a native North American species that exhibits a wide range of flower colors.…”