Abstract
The radiation of angiosperms is marked by a phenomenal diversity of floral size, shape, color, scent, and reward.(1)(,)(2)(,)(3)(,)(4) The multi-dimensional response to selection to optimize pollination has generated correlated suites of these floral traits across distantly related species, known as "pollination syndromes."(5)(,)(6)(,)(7)(,)(8)(,)(9) The ability to test the broad utility of pollination syndromes and expand upon the generalities of these syndromes is constrained by limited trait data, creating a need for new approaches that can integrate vast, unstructured records from community-science platforms. Here, we compile the largest North American flower color dataset to date, using GPT-4 with Vision to classify color in over 11,000 species across more than 1.6 million iNaturalist observations. We discover that red- and orange-flowering species (classic "hummingbird pollination" colors) bloom later in eastern North America compared with other colors, corresponding to the arrival of migratory hummingbirds. Our findings reveal how seasonal flowering phenology, in addition to floral color and morphology, can contribute to the hummingbird pollination syndrome in regions where these pollinators are migratory. Our results highlight phenology as an underappreciated dimension of pollination syndromes and underscore the utility of integrating artificial intelligence with community-science data. The potential breadth of analysis offered by community-science datasets, combined with emerging data extraction techniques, could accelerate discoveries about the evolutionary and ecological drivers of biological diversity.