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bioRxiv · 10.1101/2022.02.11.480114

Computer vision for assessing species color pattern variation from web-based community science images

Hantak, M. M.·Guralnick, R. P.·Zare, A.·Stucky, B. J.
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Abstract

Openly 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.

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Hantak, M. M., Guralnick, R. P., Zare, A., Stucky, B. J.. 2022-02-14. Computer vision for assessing species color pattern variation from web-based community science images. https://doi.org/10.1101/2022.02.11.480114

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