Hantak MM, Guralnick RP, Zare A, Stucky BJ. Computer vision for assessing species color pattern variation from web-based community science images.
iScience 2022;
25:104784. [PMID:
35982791 PMCID:
PMC9379571 DOI:
10.1016/j.isci.2022.104784]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/16/2022] [Accepted: 07/13/2022] [Indexed: 11/30/2022] Open
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.
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 scale
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