文章基本信息

标题：Computer vision for assessing species color pattern variation from web-based community science images
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作者：Maggie M. Hantak ; Robert P. Guralnick ; Alina Zare 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2022
卷号：25
期号：8
页码：1-14
DOI：10.1016/j.isci.2022.104784
语种：English
出版社：Elsevier
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.