摘要：SummaryOpenly available community science digital vouchers provide a wealth of data to study phenotypic change across space and time. However, extracting phenotypic data from these resources requires significant human effort. Here, we demonstrate a workflow and computer vision model for automatically categorizing species color pattern from community science images. Our work is focused on documenting the striped/unstriped color polymorphism in the Eastern Red-backed Salamander (Plethodon cinereus). We used an ensemble convolutional neural network model to analyze this polymorphism in 20,318 iNaturalist images. Our model was highly accurate (∼98%) despite image heterogeneity. We used the resulting annotations to document extensive niche overlap between morphs, but wider niche breadth for striped morphs at the range-wide scale. Our work showcases key design principles for using machine learning with heterogeneous community science image data to address questions at an unprecedented scale.Graphical abstractDisplay OmittedHighlights•We built a deep learning model to group color morphs from community science images•Our model achieved 98% accuracy for classifying striped and unstriped salamanders•We used our model to classify >20,000 images and built morph-specific niche models•We then determined if Red-backed salamanders niche partition at a range-wide scaleComputer science; Ecology; Evolutionary biology.
